Regional water resource multi-objective optimization configuration decision-making method and system and storage medium

Abstract

The invention belongs to the field of water resource optimal configuration methods, and particularly provides a regional water resource multi-objective optimal configuration decision-making method, which comprises the following steps of: acquiring regional water conservancy project, water system communication, regional hydrology and water resources, social economy and ecological environment data; a regional water resource bearing capacity comprehensive evaluation system is constructed, and the coupling coordination level among the water resource subsystem, the social economy subsystem and the ecological environment subsystem is determined; identifying a main influence factor of a subsystem coupling coordination level by adopting an optimal parameter geographic detector; constructing a multi-target water resource optimal configuration model; a Pareto frontier is obtained for the water resource allocation model under the typical water inflow situation and the specific water consumption level year through a multi-target strategy search algorithm; and sorting by using a cumulative foreground theory to obtain a recommended configuration scheme under an excellent comprehensive foreground value. According to the method, the coupling coordination effect of the water resource bearing capacity subsystem is considered, and an important and high-operability reference basis is provided for optimal configuration of water resources.

Description

Technical Field

[0001] The present invention belongs to the field of water resource optimization configuration methods, and specifically relates to a regional water resource multi-objective optimization configuration decision-making method, system and storage medium. Background Art

[0002] Optimal allocation of water resources is a core strategy for achieving sustainable water use. It is a key measure for promoting a water-saving society and reducing water pollution risks. Furthermore, through mechanisms such as inter-basin water transfers and water rights trading, optimal allocation can narrow regional water resource disparities and promote coordinated regional development. Furthermore, optimal allocation can significantly improve water resource utilization efficiency, alleviate the imbalance between water supply and demand, ensure ecological water needs, maintain the healthy functioning of ecosystems such as rivers and wetlands, and promote biodiversity conservation.

[0003] Despite significant progress in existing water resource allocation research, some key issues remain. One of the most significant shortcomings is the neglect of water resource carrying capacity. Water resource carrying capacity refers to the population size, economic development level, and ecosystem health that water resources can support over the long term under specific hydrological, ecological, and socioeconomic conditions. Currently, water resource allocation research focuses on developing new algorithms to optimize and solve models. Water resource carrying capacity research focuses on carrying capacity evaluation and analysis, with less attention paid to the coupling and coordination between water resource carrying capacity subsystems. Furthermore, no research has considered this coupling and coordination in water resource optimization. Conventional water resource optimization sets the objective function as maximizing economic benefits and minimizing pollution emissions. The lack of consideration of the coupling and coordination of water resource carrying capacity in models may result in poor performance of the output water resource allocation plan in this regard. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, system and storage medium for regional water resources multi-objective optimization configuration decision-making, taking into account the coupling and coordination effect of water resources carrying capacity subsystems, and providing an important and highly operational reference basis for the optimal configuration of water resources.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: a method for multi-objective optimization allocation decision-making of regional water resources, comprising the following steps: Step 1: Collect data on regional water conservancy projects, water system connectivity, regional hydrological and water resources, social economy, and ecological environment; Step 2: Build a comprehensive evaluation system for regional water resources carrying capacity to determine the level of coupling and coordination among the water resources subsystem, the socio-economic subsystem, and the ecological and environmental subsystem; Step 3: Use the optimal parameter geo-detector to identify the main influencing factors of the subsystem coupling coordination level; Step 4: Construct a multi-objective water resource optimization allocation model. This model systematically considers various physical constraints and water supply and use constraints. The optimization objectives include maximizing the economic benefits of regional water resource allocation, minimizing pollution emissions, and optimizing the coupling and coordination level of water resource carrying capacity subsystems. Step 5: Obtain the Pareto frontier of the water resources allocation model under typical water inflow scenarios and specific water use level years through a multi-objective strategy search algorithm; Step 6: Use the cumulative prospect theory to sort the Pareto frontiers obtained to obtain the recommended configuration plan with excellent comprehensive prospect value.

[0006] In a preferred solution, the implementation of step 2 is as follows: Step 2.1: Consider the water resources subsystem, socio-economic subsystem, and ecological environment subsystem as subsystems of water resources carrying capacity, forming the criterion layer for water resources carrying capacity targets. Based on the nature and connotation of the problems described in the criterion layer, select indicators to construct a comprehensive evaluation system for regional water resources carrying capacity. Step 2.2: Obtain the subjective weight of the indicator based on the genetic hierarchical analysis method, obtain the objective weight based on the IDOCRIW method, and use the game theory method to fuse the subjective and objective weights of the indicator to obtain the comprehensive weight; Step 2.3: Normalize each indicator in the comprehensive evaluation system for water resource carrying capacity, mapping the original value to the interval [0, 1]. At the same time, for each subsystem of water resource carrying capacity, amplify the weight of the internal indicators of each subsystem to a sum of 1, and calculate the carrying capacity level of each subsystem: Assume that a subsystem contains k The normalized value of each indicator is x i ( i =1, 2, …, k ), the indicator weights are w i ( i =1, 2, …, k ), then the subsystem carrying capacity level S for: ; Step 2.4: Calculate the water resource subsystem level based on step 2.3 S 1. Socioeconomic subsystem level S 2 and ecological environment subsystem level S 3. The coupling coordination degree is calculated using a double-layer improved coupling coordination degree calculation method.

[0007] In the preferred solution, in step 2.2, the subjective weight vector and the distribution coefficient are u 1 and α1, the objective weight vector and distribution coefficient are u 2 and α 2. Set the fusion weight to u , the calculation formula is: ; Based on the theoretical ideas of game theory, the subjective and objective weights are integrated to determine the distribution coefficient, which satisfies the following formula: ; According to the above formula, the distribution coefficient is calculated as α 1 and α 2. Obtain the final indicator fusion weight.

[0008] In a preferred solution, in step 2.4, the coupling coordination degree is calculated using a double-layer improved coupling coordination degree calculation method, and the calculation method is: Corrected coupling CD The formula is: ; Where, U is the level of each subsystem; n is the number of subsystems; The contribution coefficient is calculated using the following formula: ; Modified comprehensive coordination index T The formula is: ; Obtain improved coupling coordination based on the modified coupling degree and comprehensive coordination index: .

[0009] In a preferred embodiment, step 3 includes the following sub-steps: Step 3.1: Using the indicators in the comprehensive evaluation system of water resources carrying capacity as driving factors and the coupling coordination degree of water resources carrying capacity in each sub-region as the explained variable, construct a data set for each sub-region and each year; Step 3.2: Use the optimal parameter geographic detector to identify the main influencing factors of the water resources carrying capacity system coupling coordination level in each sub-region and each year; Step 3.3: Based on the results of step 3.2, select the indicator with the highest impact on the entire region from each subsystem; Step 3.4: Based on the results of step 3.3, calculate the coupling coordination degree based on the selected representative indicators.

[0010] In a preferred embodiment, step 4 includes the following sub-steps: Step 4.1: Deconstruct water resource zones based on the four-level water resource zones of the study area and the county-level administrative divisions. Determine the scheduling procedures for each water conservancy project in the study area, determine the water supply parties and water supply rules, determine the hydraulic connections based on the upstream and downstream and left and right bank water flow directions, and clarify the water demand of each zone. Step 4.2: Various physical constraints and water supply and use constraints include zone water balance constraints, reservoir water balance constraints, reservoir storage capacity constraints, reservoir discharge constraints, water supply capacity constraints, and water demand constraints; Step 4.3: Determine the model objective function, including maximizing the economic benefits of regional water resources allocation and minimizing pollution emissions, as well as the coupling coordination degree calculated in step 3.4.

[0011] In a preferred embodiment, the step 5 comprises the following steps: Step 5.1: Set the typical water inflow scenario and the water demand scenario for a specific water use level year for the water resources allocation model; Step 5.2: Use the Kepler optimization algorithm to perform multi-objective optimization on the water resources allocation model and output the Pareto frontier.

[0012] In a preferred embodiment, step 6 includes the following sub-steps: Step 6.1: Based on the Pareto front obtained in step 5, establish an eigenvalue decision matrix, normalize the matrix, and determine the positive and negative ideal solutions; Step 6.2: Based on the value function of the cumulative prospect theory, construct a comprehensive prospect value model and rank the water resource allocation options based on the comprehensive prospect value.

[0013] The present invention also provides a regional water resources multi-objective optimization configuration decision-making system for executing the above-mentioned regional water resources multi-objective optimization configuration decision-making method, comprising: Data integration module, used to collect data on regional water conservancy projects, water system connectivity, regional hydrology and water resources, social economy and ecological environment; Water resources carrying capacity and coupling coordination assessment module, used to assess the water resources carrying capacity level, subsystem carrying capacity level and coupling coordination level of each region and year; The influencing factor identification module uses the optimal parameter geographic detector to determine the main influencing factors of the coupling coordination degree of regional water resources carrying capacity; The water resources optimization allocation model establishment module determines the optimization target of regional water resources optimization allocation, sets constraints, and establishes the optimization allocation model; The model solving and solution recommendation module uses the Kepler optimization algorithm to find the optimal solution and finally recommends the water resource allocation plan based on the cumulative prospect theory.

[0014] The present invention also provides a computer-readable storage medium, which stores program code. When the program code is executed by a processor, it implements the steps of the above-mentioned method for multi-objective optimization configuration decision-making of regional water resources.

[0015] The present invention provides a method, system, and storage medium for regional water resources multi-objective optimization allocation decision-making, which have the following beneficial effects: 1. The main controlling factors of the coupling coordination degree of regional water resources carrying capacity were determined: The present invention constructs a comprehensive water resources carrying capacity evaluation index system, improves the calculation method of coupling coordination degree, and uses the optimal parameter geographic detector to discover the main influencing factors, providing a basis for the coordinated and sustainable development of regional water resources.

[0016] 2. It can provide important and highly operational reference for water resources allocation: Conventional water resource optimization and allocation mainly consider economic benefits and pollutant emissions, but insufficiently consider the coupling and coordination level of the water resource carrying capacity system. The recommended configuration scheme is difficult to meet the coupling and coordinated development between regions and systems. The present invention takes the above factors into consideration in detail and can be further promoted and applied in regional water resource optimization and allocation. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0018] Figure 1 Detailed flow chart of the method of the present invention; Figure 2 This is a comparison chart of the original coupling coordination degree and the improved coupling coordination degree; Figure 3 This is a schematic diagram of the optimal parameter geographic detector process; Figure 4 This is a generalized diagram of the water system; Figure 5 Optimize algorithm flow chart for Kepler; Figure 6 is the objective function value corresponding to the selected solution set; Figure 7 Allocate water to each area corresponding to the selected solution set. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] Example 1: A method for regional water resources multi-objective optimization allocation decision-making, such as Figure 1 As shown, the following steps are included: Step 1: Collect data on regional water conservancy projects, water system connectivity, regional hydrological and water resources, social economy and ecological environment.

[0022] Collect data on the characteristics of water supply projects such as storage, diversion, lifting and regulation in the study area, as well as water resource endowment, water resource development and utilization, social and economic development, ecological and environmental protection of each sub-region, and water demand of each water-using department in a specified level year.

[0023] Step 2: Build a comprehensive evaluation system for regional water resources carrying capacity to determine the level of coupling and coordination among the water resources subsystem, the socio-economic subsystem, and the ecological and environmental subsystem. This includes the following steps: Step 2.1: The water resources subsystem, socio-economic subsystem, and ecological environment subsystem are considered as subsystems of water resources carrying capacity and serve as the criterion layer for the water resources carrying capacity target. Based on the nature and connotation of the problems described in the criterion layer, 16 indicators are selected to construct a comprehensive evaluation system for regional water resources carrying capacity (see Table 1), which reflects the characteristics of water resources carrying capacity as comprehensively as possible.

[0024]

[0025] Step 2.2: Obtain the subjective weight of the indicator based on the genetic hierarchical analysis method, obtain the objective weight based on the IDOCRIW method, and use the game theory method to fuse the subjective weight and objective weight of the indicator to obtain the comprehensive weight.

[0026] The subjective weight vector and distribution coefficient are u1 and α 1, the objective weight vector and distribution coefficient are u 2 and α 2. Set the fusion weight to u , the calculation formula is: ; Based on the theoretical ideas of game theory, the subjective and objective weights are integrated to determine the distribution coefficient, which satisfies the following formula: ; According to the above formula, the distribution coefficient is calculated as α 1 and α 2. Obtain the final indicator fusion weight.

[0027] Step 2.3: Normalize each indicator in the comprehensive evaluation system for water resource carrying capacity, mapping the original value to the interval [0, 1]. At the same time, for each subsystem of water resource carrying capacity, amplify the weight of the internal indicators of each subsystem to a sum of 1, and calculate the carrying capacity level of each subsystem: Assume that a subsystem contains k The normalized value of each indicator is x i ( i =1, 2, …, k ), the indicator weights are w i ( i =1, 2, …, k ), then the subsystem carrying capacity level S for: ; Step 2.4: Calculate the water resource subsystem level based on step 2.3 S 1. Socioeconomic subsystem level S 2 and ecological environment subsystem level S 3. The coupling coordination degree is calculated using the double-layer improved coupling coordination degree calculation method. The calculation method is: Corrected coupling CD The formula is: ; Where, U is the level of each subsystem; n represents the number of subsystems.

[0028] The coupling distribution effect of the modified model and the original model output is shown in Figure 2 ,It can be seen that after correction, the distribution of each divided interval on the plane is more uniform, which can effectively improve the reliability of differentiation and better adhere to the principle of uniform distribution.

[0029] First, sort the load levels of each subsystem. For example, the sorting result is S 1< S 2< S 3. According to the synergetics theory, relatively higher weights are assigned to underdeveloped systems, and their corresponding contribution coefficients are ranked α>β>γ.

[0030] The contribution coefficient is calculated using the following formula: ; Modified comprehensive coordination index T The formula is: ; Obtain improved coupling coordination based on the modified coupling degree and comprehensive coordination index: .

[0031] Step 3: Use the optimal parameter geodetector to identify the main influencing factors of the subsystem coupling coordination level, including the following substeps: Step 3.1: Using the indicators in the comprehensive evaluation system of water resources carrying capacity as driving factors and the coupling coordination degree of water resources carrying capacity of each sub-region as the explained variable, construct a data set for each sub-region and each year.

[0032] Step 3.2: Use the optimal parameter geographic detector to identify the main influencing factors of the coupling coordination level of the water resources carrying capacity system in each district and year, such as Figure 3 shown.

[0033] The optimal parameter geographic detector is an existing technology and can be implemented by using relevant technologies in a tourist flow data analysis method based on GPS big data disclosed in CN117454319A.

[0034] Step 3.3: Based on the results of step 3.2, select the indicator with the highest impact on the entire region from each subsystem.

[0035] Step 3.4: Based on the results of step 3.3, calculate the coupling coordination degree based on the selected representative indicators.

[0036] Step 4: Construct a multi-objective water resources optimization allocation model. The multi-objective water resources optimization allocation model systematically considers various physical constraints and water supply and use constraints. The optimization objectives include maximizing the economic benefits of regional water resources allocation, minimizing pollution emissions, and optimizing the coupling and coordination level of water resources carrying capacity subsystems.

[0037] The following sub-steps are included: Step 4.1: Deconstruct the water resource zoning according to the four-level water resource zoning of the study area and the county-level administrative divisions, determine the scheduling procedures of each water conservancy project in the study area, determine the water supply parties and water supply rules, determine the hydraulic connection based on the upstream and downstream and left and right bank water flow directions, and clarify the water demand of each division.

[0038] Step 4.2: Various physical constraints and water supply and use constraints include zone water balance constraints, reservoir water balance constraints, reservoir capacity constraints, reservoir discharge constraints, water supply capacity constraints, and water demand constraints.

[0039] Step 4.3: Determine the model objective function, including maximizing the economic benefits of regional water resources allocation and minimizing pollution emissions, as well as the coupling coordination degree calculated in step 3.4.

[0040] Taking the middle and lower reaches of the Hanjiang River in Hubei Province as an example, the river system is generalized as shown in Figure 4 .

[0041] Taking the maximum economic benefit, the minimum sum of pollutant emissions, and the maximum coupling coordination degree of water resource carrying capacity subsystems as the objective function, a regional water resources optimization allocation model is established, and its objective function expression is: (a) Maximum economic benefits: ; Where: NER i,j For the i Calculate the partition j Water use efficiency coefficient of water-using departments, in yuan / m 3 , x i,j,t is the decision variable, t Moment i Calculate the partition j Water allocation by water-using departments, T is the calculation cycle, I To calculate the number of partitions, J The number of water-using departments.

[0042] (b) The sum of pollutant emissions is minimized ; Where: d i,j For the i Calculate the partition j The content of important pollutants in the unit wastewater discharge of water-using departments (mg / L), p i,j For the i Calculate the partition j Wastewater discharge coefficient of water-using sectors.

[0043] (c) The coupling coordination degree of the water resources carrying capacity subsystem is the largest ; ; ; In the above formula: is the coupling coordination degree, which represents the coupling coordination degree between subsystems. C is the coupling degree, T is the comprehensive coordination index, X n is the corresponding evaluation index, is the weight of each evaluation indicator.

[0044] The following constraints are considered in this implementation: (a) Regional water balance constraints ; Where: W i,t For the t Moment i Calculate the flow of the partition, W n,t For the i Calculation partitions are hydraulically connected n The flow rate of the upstream interval; n Is the upstream partition consistent with the i The calculation partition is related to α n,i The value is 0 or 1; R i,t For the i Calculate the total natural water supply in the sub-district; O k,t For the k outflow from the reservoir; β k,i For the i Compute partitions and k The hydraulic connection between the reservoirs is based on the i Compute partitions and k The water diversion coefficient in the reservoir is determined, 0≤ β k,i ≤1; cc i,j,t It is i Calculate the partition j Regression water coefficient of water-using sector, 0≤ cc i,j,t ≤1; β k,i and cc i,j,t All provided by the Yangtze River Water Conservancy Commission.β k,i It is constant between the same reservoir and the same calculation partition. cc i,j,t The same water-using sector in the same calculation zone of the model is also a constant. L i,t Water loss (including evaporation loss, leakage loss and water transmission loss); TW i,t Water transfer outside the basin.

[0045] (b) Reservoir water balance constraints ; Where: V k,t For the t Moment k Reservoir capacity, V k,t+1 For the t+ 1st moment k Reservoir capacity; I k,t For the t Moment k Reservoir inflow; O k,t For the t Moment k Reservoir outflow; EV k,t For the t Moment k Reservoir evaporation losses.

[0046] (c) Reservoir capacity constraints The operation of the reservoir must comply with actual management requirements, and its storage capacity should be between the minimum storage capacity and the maximum storage capacity.

[0047] ; Where: V min,k,t For the t Moment k Minimum reservoir capacity, V max,k,t For the t Moment k The maximum storage capacity of the reservoir.

[0048] (d) Reservoir discharge constraints ; Where: For the t Time delivery Corresponding to the reservoir discharge capacity.

[0049] (e) Water supply capacity constraints ; Where: AW i,t For the t Moment i Water availability by zone.

[0050] (f) Water demand constraints ; Where: wd i,j,t For the t Moment i Division No. Water demand by water-using sectors.

[0051] Step 5: Use a multi-objective strategy search algorithm to obtain the Pareto frontier of the water resources allocation model under typical water inflow scenarios and specific water use level years.

[0052] The following steps are involved: Step 5.1: Set the typical water inflow scenario and the water demand scenario for a specific water use level year for the water resources allocation model; Step 5.2: Use the Kepler optimization algorithm to perform multi-objective optimization on the water resources allocation model and output the Pareto frontier, such as Figure 5 shown.

[0053] The Kepler optimization algorithm is an existing technology, and can be specifically implemented by using the relevant technologies in the distributed photovoltaic maximum power point tracking method, device and electronic equipment disclosed in CN119088166A.

[0054] Step 6: Use the cumulative prospect theory to sort the Pareto frontiers obtained to obtain the recommended configuration plan with excellent comprehensive prospect value.

[0055] The following sub-steps are included: Step 6.1: Based on the Pareto front obtained in step 5, establish an eigenvalue decision matrix, normalize the matrix, and determine the positive and negative ideal solutions.

[0056] The specific implementation steps are: 1) Establish the eigenvalue decision matrix.

[0057] The total number of water resources optimization allocation schemes is n , recorded as Each plan has m evaluation index, denoted as The weight of the evaluation index is , No. i The first j The attribute value of the indicator is , the evaluation eigenvalue decision matrix is .

[0058] 2) Construct a normalized evaluation matrix.

[0059] According to the max-min method, the benefit-type indicators and cost-type indicators are normalized respectively. The normalized dimensionless decision matrix is recorded as .

[0060] 3) Determine the positive and negative ideal solutions.

[0061] by A + and A - Represent the positive and negative ideal solutions respectively, and the calculation formula is as follows: ; ; Where: J + and J - The subscript sets representing benefit-type and cost-type indicators respectively; u j + and u j - Represents the positive and negative ideal solutions respectively j An indicator value.

[0062] 4) Construct the grey correlation coefficient matrix of positive and negative ideal solutions. Take the positive and negative ideal solutions as reference series, compare other solutions with them, and construct the grey correlation coefficient matrix R + and R - as follows: ; ; Where: r i,j + and r i,j - Represent the positive and negative grey correlation coefficients respectively, and are calculated as follows: ; Where: ρ It represents the resolution coefficient, which is usually taken as 0.5.

[0063] Step 6.2: Based on the value function of the cumulative prospect theory, construct a comprehensive prospect value model and rank the water resource allocation options based on the comprehensive prospect value. The operation is as follows: 1) Constructing a positive and negative prospect value matrix based on the value function of cumulative prospect theory V + and V - as follows: ; ; Where: v i,j + and v i,j - Represents positive and negative prospect values respectively, and is calculated as follows: ; Where: θ , α and β They represent the loss aversion coefficient, risk preference coefficient and risk aversion coefficient respectively, and their values are θ =2.25, α = β =0.88.

[0064] 2) Build a comprehensive prospect value model.

[0065] The comprehensive prospect value of the plan is determined by the decision maker's subjective value, that is, the positive and negative prospect value matrix V + and V - , and the prospect weight function of the decision maker facing gains and losses π + ( ω j )and π - ( ω j ) two aspects. The greater the comprehensive prospect value of the plan, the higher the recognition of the corresponding plan. Therefore, an optimization model can be established: ; Where: ω j Indicates the j The weight coefficient of each indicator; ω j min and ω j max Respectively ω j Upper and lower limits; π + ( ω j)and π - ( ω j ) represent the prospect weight functions of gains and losses, respectively, and are calculated as follows: ; Where: γ + and γ - Represent the degree of concavity and convexity of the foreground weight function, usually γ + =0.61, γ - =0.69. To avoid the one-sidedness of a single weight, after obtaining the subjective and objective weights separately, they are usually effectively integrated to obtain a comprehensive weight. In this embodiment, the range given by the subjective and objective weights is defined as the search space for the comprehensive weight, and the weight information is effectively integrated within this space.

[0066] 3) Use genetic algorithm to solve the comprehensive prospect value model constructed in step 2) to obtain the optimal weight coefficient , and calculate the optimal comprehensive prospect value of each solution: ; Where: V i * Indicates the i The optimal comprehensive prospect value of the scheme.

[0067] The optimal comprehensive prospect value of each scheme is evaluated and ranked, and the scheme with the largest optimal comprehensive prospect value is selected as the preferred scheme.

[0068] Figure 6 is the objective function value corresponding to the selected solution set, Figure 7 Allocate water to each area corresponding to the selected solution set, and thus generate the optimal water resource allocation plan.

[0069] In summary, the present invention takes into account the coupling coordination problem between water resource carrying capacity subsystems in water resource allocation, constructs a comprehensive evaluation index system for water resource carrying capacity, improves the calculation method of coupling coordination, identifies the main influencing factors with the optimal parameter geographic detector, and incorporates them into the water resource multi-objective optimization allocation model. This model can consider economic benefits and pollution emissions on the basis of traditional optimization models, and can also take into account the coupling coordination level of regional water resource carrying capacity. The recommended water resource optimization allocation scheme can provide an important and highly operational reference basis for water resource management and sustainable utilization.

[0070] Example 2: This embodiment provides a regional water resources multi-objective optimization configuration decision-making system, which is used to execute the regional water resources multi-objective optimization configuration decision-making method described in Example 1, including: Data integration module, used to collect data on regional water conservancy projects, water system connectivity, regional hydrology and water resources, social economy and ecological environment; Water resources carrying capacity and coupling coordination assessment module, used to assess the water resources carrying capacity level, subsystem carrying capacity level and coupling coordination level of each region and year; The influencing factor identification module uses the optimal parameter geographic detector to determine the main influencing factors of the coupling coordination degree of regional water resources carrying capacity; The water resources optimization allocation model establishment module determines the optimization target of regional water resources optimization allocation, sets constraints, and establishes the optimization allocation model; The model solving and solution recommendation module uses the Kepler optimization algorithm to find the optimal solution and finally recommends the water resource allocation plan based on the cumulative prospect theory.

[0071] Example 3: This embodiment provides a computer-readable storage medium, which stores program code. When the program code is executed by a processor, it implements the steps of a regional water resources multi-objective optimization configuration decision-making method as described in Example 1.

[0072] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for multi-objective optimization of regional water resources allocation decision-making, characterized by: The following steps are involved: Step 1: Collect data on regional water conservancy projects, water system connectivity, regional hydrological and water resources, social economy, and ecological environment; Step 2: Build a comprehensive evaluation system for regional water resources carrying capacity to determine the level of coupling and coordination among the water resources subsystem, the socio-economic subsystem, and the ecological and environmental subsystem; Step 3: Use the optimal parameter geo-detector to identify the main influencing factors of the subsystem coupling coordination level; Step 4: Construct a multi-objective water resource optimization allocation model. This model systematically considers various physical constraints and water supply and use constraints. The optimization objectives include maximizing the economic benefits of regional water resource allocation, minimizing pollution emissions, and optimizing the coupling and coordination level of water resource carrying capacity subsystems. Step 5: Obtain the Pareto frontier of the water resources allocation model under typical water inflow scenarios and specific water use level years through a multi-objective strategy search algorithm; Step 6: Use the cumulative prospect theory to sort the Pareto frontiers obtained to obtain the recommended configuration plan with excellent comprehensive prospect value.

2. A method for regional water resources multi-objective optimization allocation decision-making according to claim 1, characterized in that: The implementation of step 2 is as follows: Step 2.1: Consider the water resources subsystem, socio-economic subsystem, and ecological environment subsystem as subsystems of water resources carrying capacity, forming the criterion layer for water resources carrying capacity targets. Based on the nature and connotation of the problems described in the criterion layer, select indicators to construct a comprehensive evaluation system for regional water resources carrying capacity. Step 2.2: Obtain the subjective weight of the indicator based on the genetic hierarchical analysis method, obtain the objective weight based on the IDOCRIW method, and use the game theory method to fuse the subjective and objective weights of the indicator to obtain the comprehensive weight; Step 2.3: Normalize each indicator in the comprehensive evaluation system for water resource carrying capacity, mapping the original value to the interval [0, 1]. At the same time, for each subsystem of water resource carrying capacity, amplify the weight of the internal indicators of each subsystem to a sum of 1, and calculate the carrying capacity level of each subsystem: Assume that a subsystem contains k The normalized value of each indicator is x i ( i =1, 2, …, k ), the indicator weights are w i ( i =1, 2, …, k ), then the subsystem carrying capacity level S for: ； Step 2.4: Calculate the water resource subsystem level based on step 2.3 S 1. Socioeconomic subsystem level S 2 and ecological environment subsystem level S 3. The coupling coordination degree is calculated using a double-layer improved coupling coordination degree calculation method.

3. A method for regional water resources multi-objective optimization allocation decision-making according to claim 2, characterized in that: In step 2.2, the subjective weight vector and the distribution coefficient are u 1 and α 1, the objective weight vector and distribution coefficient are u 2 and α 2. Set the fusion weight to u , the calculation formula is: ； Based on the theoretical ideas of game theory, the subjective and objective weights are integrated to determine the distribution coefficient, which satisfies the following formula: ； According to the above formula, the distribution coefficient is calculated as α 1 and α 2. Obtain the final indicator fusion weight.

4. A method for regional water resources multi-objective optimization allocation decision-making according to claim 2, characterized in that: In step 2.4, the coupling coordination degree is calculated using a double-layer improved coupling coordination degree calculation method, and the calculation method is: Corrected coupling CD The formula is: ； Where, U is the level of each subsystem; n is the number of subsystems; The contribution coefficient is calculated using the following formula: ； Modified comprehensive coordination index T The formula is: ； Obtain improved coupling coordination based on the modified coupling degree and comprehensive coordination index: 。 5. The method for regional water resources multi-objective optimization allocation decision-making according to claim 1 is characterized in that: Step 3 includes the following sub-steps: Step 3.1: Using the indicators in the comprehensive evaluation system of water resources carrying capacity as driving factors and the coupling coordination degree of water resources carrying capacity in each sub-region as the explained variable, construct a data set for each sub-region and each year; Step 3.2: Use the optimal parameter geographic detector to identify the main influencing factors of the water resources carrying capacity system coupling coordination level in each sub-region and each year; Step 3.3: Based on the results of step 3.2, select the indicator with the highest impact on the entire region from each subsystem; Step 3.4: Based on the results of step 3.3, calculate the coupling coordination degree based on the selected representative indicators.

6. A method for regional water resources multi-objective optimization allocation decision-making according to claim 5, characterized in that: The step 4 includes the following sub-steps: Step 4.1: Deconstruct water resource zones based on the four-level water resource zones of the study area and the county-level administrative divisions. Determine the scheduling procedures for each water conservancy project in the study area, determine the water supply parties and water supply rules, determine the hydraulic connections based on the upstream and downstream and left and right bank water flow directions, and clarify the water demand of each zone. Step 4.2: Various physical constraints and water supply and use constraints include zone water balance constraints, reservoir water balance constraints, reservoir storage capacity constraints, reservoir discharge constraints, water supply capacity constraints, and water demand constraints; Step 4.3: Determine the model objective function, including maximizing the economic benefits of regional water resources allocation and minimizing pollution emissions, as well as the coupling coordination degree calculated in step 3.

4.

7. A method for regional water resources multi-objective optimization allocation decision-making according to claim 1, characterized in that: The step 5 comprises the following steps: Step 5.1: Set the typical water inflow scenario and the water demand scenario for a specific water use level year for the water resources allocation model; Step 5.2: Use the Kepler optimization algorithm to perform multi-objective optimization on the water resources allocation model and output the Pareto frontier.

8. A method for regional water resources multi-objective optimization allocation decision-making according to claim 1, characterized in that: Step 6 includes the following sub-steps: Step 6.1: Based on the Pareto front obtained in step 5, establish an eigenvalue decision matrix, normalize the matrix, and determine the positive and negative ideal solutions; Step 6.2: Based on the value function of the cumulative prospect theory, construct a comprehensive prospect value model and rank the water resource allocation options based on the comprehensive prospect value.

9. A regional water resources multi-objective optimization configuration decision-making system, characterized by: A method for implementing a regional water resources multi-objective optimization allocation decision-making method according to any one of claims 1 to 8, comprising: Data integration module, used to collect data on regional water conservancy projects, water system connectivity, regional hydrology and water resources, social economy and ecological environment; Water resources carrying capacity and coupling coordination assessment module, used to assess the water resources carrying capacity level, subsystem carrying capacity level and coupling coordination level of each region and year; The influencing factor identification module uses the optimal parameter geographic detector to determine the main influencing factors of the coupling coordination degree of regional water resources carrying capacity; The water resources optimization allocation model establishment module determines the optimization target of regional water resources optimization allocation, sets constraints, and establishes the optimization allocation model; The model solving and solution recommendation module uses the Kepler optimization algorithm to find the optimal solution and finally recommends the water resource allocation plan based on the cumulative prospect theory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, and when the program code is executed by a processor, the steps of the method for multi-objective optimization configuration decision-making of regional water resources as described in any one of claims 1 to 8 are implemented.

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