A method and system for optimizing water resource allocation

Abstract

This invention proposes a method and system for optimizing water resource allocation, which relates to the technical field of water resource optimization. The method includes first obtaining historical hydrological data for a study area, then inputting the data into a preset river runoff prediction model, which then outputs a monthly runoff sequence for a specific year within the region. Based on the monthly runoff sequence, a multi-conditional constrained water resource optimization allocation model based on user allocation is established for the study area under extreme climate conditions. Finally, the multi-conditional constrained water resource optimization allocation model is solved to obtain a solution result, and water resource optimization is performed for users in the study area based on the solution result. This invention effectively achieves accurate and reasonable optimal allocation of water resources to users under multi-objective constraints.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource optimization configuration, and in particular to a water resource optimization configuration method and system. Background Art

[0002] The sustainable use of water resources is related to the coordinated development of humanity, society, resources and the environment. With the growth of population, the development of social economy and the improvement of people's living standards, the demand for water resources has increased dramatically in both quantity and quality. However, the total amount of available water resources in nature is limited and unevenly distributed. This contradiction between supply and demand is becoming increasingly prominent around the world. Especially with the accelerated urbanization process today, urban water resources management faces unprecedented challenges.

[0003] To effectively address this challenge, water resource optimization technology has emerged as an important way to alleviate the contradiction between water supply and demand and promote its efficient use. This technology aims to achieve the rational allocation and efficient use of water resources through scientific methods and water resource optimization models, comprehensively considering the needs and constraints of the three dimensions of economy, society, and environment. Water resource optimization is not only related to the direct management of water resources, but also has a profound impact on urban development planning, ecological and environmental protection, economic and social stability, and other aspects. It is of great significance to the coordinated and sustainable development of society, economy, and environment. The prior art discloses a water resource optimization method based on genetic algorithm. The scheme normalizes economic goals, social goals, and ecological goals, so that each goal is weighted under the same dimensional conditions. After normalization, the weight of each goal is more practical. However, the constraints under the economic goals, social goals, and ecological goals in this method are all single constraints, and user allocation is not taken into consideration. When faced with the water resource environment in a complex area, it is impossible to impose more constraints under the economic, social, and ecological conditions, resulting in unreasonable and inaccurate water resource optimization allocation for users. Summary of the Invention

[0004] In order to solve the problem that the above-mentioned existing technology has a single target constraint, resulting in inaccurate and unreasonable optimal allocation of water resources to users, the present invention proposes a water resource optimization allocation method and system, which effectively realizes the accurate and reasonable optimal allocation of water resources to users under multi-target constraints.

[0005] In order to achieve the above technical effects, the technical solutions of the present invention are as follows:

[0006] A method for optimizing water resource allocation, comprising the following steps:

[0007] S1. Obtain historical hydrological data of the study area;

[0008] S2. The hydrological historical data is input into a preset river runoff prediction model, and the river runoff prediction model outputs a monthly runoff sequence within a specific annual region;

[0009] S3. Based on the monthly runoff series, establish a multi-conditional water resource optimization model based on user allocation under extreme climate conditions in the study area;

[0010] S4. Solve the multi-condition constrained water resource optimization configuration model to obtain a solution result, and optimize water resource configuration for users in the study area based on the solution result.

[0011] Preferably, the hydrological historical data includes DEM elevation data, land use data, soil property data, rainfall, relative humidity, solar radiation, wind speed, meteorological observation data, hydrological station observation data, and non-observation point runoff data.

[0012] Preferably, the river runoff prediction model is a SWAT model, and the training process of the SWAT model is as follows:

[0013] S201. Obtain actual runoff data within the historical year region, and divide the actual runoff data within the historical year region into a warm-up period data set, a rate period data set, a validation period data set, and a simulation period data set;

[0014] S202. Initialize the network parameters of the SWAT model using the warm-up period data set, calibrate the initialized network parameters of the SWAT model using the calibration period data set to obtain a calibrated SWAT model, and verify the calibrated SWAT model using the validation period data set to obtain a trained SWAT model.

[0015] Preferably, the hydrological historical data is set as a historical sequence with monthly accuracy and input into the river runoff prediction model. The river runoff prediction model uses a variable storage coefficient method to analyze the historical sequence with monthly accuracy and output the monthly runoff sequence.

[0016] Preferably, the multi-condition constrained water resources optimization configuration model includes an objective function and multi-condition constraints. The objective function takes maximizing economic benefits as the optimization goal. The multi-condition constraints include water resources carrying capacity constraints, upper and lower limit constraints on water demand, total water use constraints, water balance constraints of socio-economic units, water balance constraints of reservoir nodes, water balance constraints of river and canal nodes, water balance constraints of ecological units, water transfer capacity constraints and non-negative constraints.

[0017] Preferably, the calculation expression of the objective function is:

[0018]

[0019] in, is the economic benefit function, k is the sequence number of each sub-area, , n is the total number of sub-areas, i is the sequence number of water sources in each sub-area, , m is the total number of water sources, j is the serial number of the water demand department in each sub-area, j (k ) is the number of productive water-using sectors in each sub-area, t For the planning period, T for t The sum of the short-term planning periods, Water sources for each sub-area t The benefit coefficient of water supply to water-demanding departments during the planning period, Water sources for each sub-district t The cost coefficient of supplying water to water-demanding departments during the planning period, For the k Sub-district j During the planning period, water use departments should provide water resources i The amount of water taken, Water source for each sub-district t The weight coefficients of water supply to different water demand sectors during the planning period, It is the weight coefficient of water supply priority of water demand departments in each sub-area.

[0020] Preferably, the calculation expression of the water resources carrying capacity constraint is:

[0021]

[0022] in, , q is the total number of water-using departments, is the water supply of the i-th water source in the k-th sub-area;

[0023] The calculation expression of the upper and lower limit constraints of water demand is:

[0024]

[0025] in, For the k Sub-district j The department's maximum water demand, For the k Sub-district j Minimum water requirements for the department;

[0026] The calculation expression of the total water consumption constraint is:

[0027]

[0028] in,m k They are k Number of water sources in the sub-area, W is the amount of water resources under the most stringent water resources management indicators in the study area;

[0029] The calculation expression of the water balance constraint of the socioeconomic unit is:

[0030]

[0031] For the k Sub-district j Department t's water consumption during the planning period, For the k Sub-district j department t Surface water availability during the planning period; For the k Sub-district j department t Groundwater availability during the planning period; For the k Sub-district j department t Inter-basin water supply during the planning period; No. k Sub-district j department t The amount of water available from unconventional sources during the planning period;

[0032] The calculation expression of the water balance constraint of the reservoir node is:

[0033]

[0034] in, is the reservoir storage capacity at the end of the planning period t+1, is the reservoir storage capacity at the end of planning period t, is the water inflow at the end of the planning period t+1, is the amount of water released from the upstream reservoir at the end of the planning period t+1, is the amount of water supplied by the reservoir to various departments at the end of the planning period t+1, is the amount of water discharged from the reservoir at the end of the planning period t+1;

[0035] The calculation expression of the water balance constraint of the river channel node is:

[0036]

[0037] in, Calculated by the river runoff prediction model v+1Runoff volume of river section t during the planning period; Calculated by the river runoff prediction model v Runoff volume of river section t during the planning period; is the rainfall in the k-sub-area v river section during the planning period t; k subregions v The amount of water discharged from the reservoir at the end of the planning period for river segment t; is the water resources regression coefficient; is the total amount of water supplied to the agricultural and rural sectors by the water source of sub-area k during planning period t;

[0038] The calculation expression of the water balance constraint of the ecological unit is:

[0039]

[0040] in, for v Monthly ecological water demand of river section t during the planning period, Calculated by the river runoff prediction model v Runoff volume of river section t during the planning period;

[0041] The calculation expression of the water delivery capacity constraint is:

[0042]

[0043] in is the minimum capacity of the inter-basin water pipeline, The maximum capacity of the inter-basin water transmission pipeline;

[0044] The calculation expression of the non-negative constraint is:

[0045] .

[0046] Preferably, a rule algorithm or an optimization algorithm is used to solve the water resource optimization configuration model until a solution is found that satisfies the multiple condition constraints and maximizes the economic benefits of the objective function, and water resources are optimized for users in the study area based on the solution.

[0047] The present invention also proposes a water resource optimization configuration system, comprising:

[0048] Acquisition module, used to obtain hydrological historical data of the study area;

[0049] A runoff prediction module, configured to input the hydrological historical data into a preset river runoff prediction model, and have the river runoff prediction model output a monthly runoff sequence within a specific annual region;

[0050] A water resources optimization allocation model establishment module is used to establish a water resources optimization allocation model based on user allocation in the study area under extreme climate conditions according to the monthly runoff sequence;

[0051] The solution module is used to solve the water resource optimization configuration model to obtain a solution result, and perform water resource optimization configuration for users in the study area according to the solution result.

[0052] The present invention also provides a computer device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0053] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations of the water resource optimization configuration method.

[0054] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0055] The present invention proposes a method and system for optimizing water resource allocation. First, the hydrological historical data of the study area is obtained, and a preset river runoff prediction model is used to predict the monthly runoff sequence in a specific annual area. Then, under extreme climatic conditions, a multi-condition constrained water resource optimization allocation model is established based on user allocation. By solving the multi-condition constrained water resource optimization allocation model, it is possible to achieve accurate and reasonable optimization of water resources for users in the study area under multiple condition constraints, thereby improving the efficiency and fairness of water resource allocation to users and ensuring the sustainable use of water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flow chart showing a method for optimizing water resource allocation proposed in an embodiment of the present invention;

[0057] Figure 2 A schematic diagram showing the principle of water resource optimization configuration proposed in an embodiment of the present invention;

[0058] Figure 3 A block diagram of a water resource optimization configuration system proposed in an embodiment of the present invention is shown;

[0059] Figure 4 A structural block diagram of a computer device proposed in an embodiment of the present invention is shown.

[0060] 401. Processor; 402. Memory; 403. Communication interface; 404. Communication bus; 405. Executable instructions. DETAILED DESCRIPTION

[0061] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0062] It is understandable to those skilled in the art that some well-known contents may be omitted in the drawings;

[0063] To facilitate understanding of this embodiment, first, the prior art information of this embodiment is introduced as follows:

[0064] Water scarcity, caused by the uneven spatial distribution of water resources, has become a critical global issue that threatens water security and constrains sustainable social and economic development. Furthermore, this issue severely impacts the coordinated development of regional socioeconomic and environmental systems. To achieve water security and sustainable development, implementing regional water resource allocation is crucial. Furthermore, with rapid population and economic growth, water demand will continue to increase within a limited water supply. Water scarcity caused by uneven water distribution will further exacerbate water conflicts and complicate water issues. Therefore, considering rational and effective future-oriented water resource allocation strategies is essential to help regional water managers mitigate these challenges.

[0065] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0066] Example 1

[0067] like Figure 1 and Figure 2 As shown, this embodiment proposes a method for optimizing water resource allocation, which includes the following steps:

[0068] S1. Obtain historical hydrological data of the study area;

[0069] In S1, the study area was selected as the Guangdong-Hong Kong-Macao Greater Bay Area, and the hydrological historical data included DEM elevation data with a spatial resolution of 90m, land use data, soil property data, rainfall, relative humidity, solar radiation, wind speed, meteorological observation data, hydrological station observation data, and non-observation point runoff data.

[0070] S2. The hydrological historical data is input into a preset river runoff prediction model, and the river runoff prediction model outputs a monthly runoff sequence within a specific annual region;

[0071] In S2, the river runoff prediction model outputs the monthly runoff series in the three river basins of Xijiang, Dongjiang and Beijiang in the Guangdong-Hong Kong-Macao Greater Bay Area from 2019 to 2022. This study selected the actual runoff data from 1982 to 2022 as the time series research scope, with 1982 to 1991 as the model warm-up period, 1991 to 2011 as the calibration period, and 2011 to 2018 as the verification period. The model parameters were calibrated and verified on a monthly scale, and forecast simulations were carried out from 2019 to 2022.

[0072] The river runoff prediction model is the SWAT model, and the training process of the SWAT model is as follows:

[0073] S201. Obtain the actual runoff data for the region from 1982 to 2022, and divide the actual runoff data for the region from 1982 to 2022 into a warm-up period dataset, a rate period dataset, a validation period dataset, and a simulation period dataset; the warm-up period dataset is the actual runoff data for the region from 1982 to 1991, the rate period dataset is the actual runoff data for the region from 1991 to 2011, and the validation period dataset is the actual runoff data for the region from 2011 to 2018.

[0074] S202. Initialize the network parameters of the SWAT model using the warm-up period data set, calibrate the initialized network parameters of the SWAT model using the calibration period data set to obtain a calibrated SWAT model, and verify the calibrated SWAT model using the validation period data set to obtain a trained SWAT model.

[0075] In S202, the SWAT model calibration and verification is performed using the SUFI-2 algorithm, part of the widely used SWAT-CUP calibration tool. The P-factor and R-factor are primarily used to evaluate the model's uncertainty calculation results. Multiple parameters can be calibrated. The P-factor represents the percentage of monitoring data within the 95% prediction uncertainty. The closer the P-factor value is to 1, the smaller the uncertainty of the SWAT model's predictions, meaning the SWAT model's predictions are more reliable. The R-factor measures the difference between the SWAT model's predictions and the actual observed values, and is typically related to the standard deviation of the data. The closer the R-factor value is to 0, the smaller the difference between the SWAT model's predictions and the actual observed values, meaning the SWAT model's prediction accuracy is higher.

[0076] The Nash-Sutcliffe efficiency coefficient Ens and determination coefficient R are used as the accuracy evaluation criteria for SWAT model research. 2 , the calculation formula of Nash-Sutcliffe efficiency coefficient Ens is:

[0077]

[0078] n represents the total number of sums, represents the simulated value of the SWAT model, represents the actual observed value, The average value of the simulation value of the SWAT model, the determination coefficient R 2 The calculation formula is:

[0079]

[0080] represents the average of the actual observations;

[0081] The simulation period was set from January 1, 1982, to December 31, 2022. The regional hydrological historical data from 1982 to 2022 was set as a monthly historical sequence and input into the river runoff prediction model. The river runoff prediction model used a variable storage coefficient method to analyze the monthly historical sequence and output the monthly runoff series. This study overturned the stereotype that the Guangdong-Hong Kong-Macao Greater Bay Area, with its numerous rivers, is not short of water. In fact, the Greater Bay Area suffers from seasonal water shortages, water quality shortages, and engineering water shortages, accompanied by problems such as saltwater tides, flooding, high water consumption rates, and a single water supply structure, leading to prominent supply and demand contradictions.

[0082] S3. Based on the monthly runoff series, establish a multi-conditional water resource optimization model based on user allocation under extreme climate conditions in the study area;

[0083] In S3, the multi-condition constrained water resources optimization configuration model includes an objective function and multi-condition constraints. The objective function takes maximizing economic benefits as the optimization goal. The multi-condition constraints include water resources carrying capacity constraints, upper and lower limit constraints on water demand, total water use constraints, water balance constraints of socio-economic units, water balance constraints of reservoir nodes, water balance constraints of river and canal nodes, water balance constraints of ecological units, water transfer capacity constraints and non-negative constraints.

[0084] The calculation expression of the objective function is:

[0085]

[0086] in, is the economic benefit function, k is the sequence number of each sub-area, , n is the total number of sub-areas, i is the sequence number of water sources in each sub-area, , m is the total number of water sources, j is the serial number of the water demand department in each sub-area, j (k) is the number of productive water-using sectors in each sub-area, t For the planning period, T for t The sum of the short-term planning periods, is the benefit coefficient of water supply to water-demanding departments during the planning period of each sub-area, 10,000 yuan / m3, Water sources for each sub-district t Cost coefficient of supplying water to water-demanding departments during the planning period, 10,000 yuan / m3, For the k Sub-district j During the planning period, water use departments should provide water resources i The amount of water taken, Water source for each sub-district t The weight coefficients of water supply to different water demand sectors during the planning period, It is the weight coefficient of water supply priority of water-demanding departments in each sub-area, 10,000 yuan / m3.

[0087] The water resource carrying capacity constraint indicates that any water source i , any period t For all industries j The water supply must be within the range of available water and not exceed the potential utilization of water resources. The calculation expression of the water resource carrying capacity constraint is:

[0088]

[0089] in, , q is the total number of water-using departments, is the water supply of the i-th water source in the k-th sub-area;

[0090] The water demand upper and lower limit constraints indicate that the water demand of any zone k, any period t, and any industry j must be between the upper and lower limits of the water user's water demand. The calculation expression of the water demand upper and lower limit constraints is:

[0091]

[0092] in, For the k Sub-district j The department's maximum water demand, For the k Sub-district j The minimum water demand of each department; the main purpose of setting the minimum water demand constraint is to ensure the balance of water consumption among various departments, to avoid excessive concentration of water in departments with higher water efficiency, while departments with lower water efficiency are allocated very little water or even no water.

[0093] The total water consumption constraint means that the available water volume of the water source must be within the most stringent water resource management indicators issued by the government of the study area. The calculation expression of the total water consumption constraint is:

[0094]

[0095] in, m k They are k Number of water sources in the sub-area, W is the amount of water resources under the most stringent water resources management indicators in the study area;

[0096] The calculation expression of the water balance constraint of the socioeconomic unit is:

[0097]

[0098] For the k Sub-district j Department t's water consumption during the planning period, For the k Sub-district j department t Surface water availability during the planning period; For the k Sub-district j department t Groundwater availability during the planning period; For the k Sub-district j department t Inter-basin water supply during the planning period; No. k Sub-district j department t The amount of water available from unconventional sources during the planning period;

[0099] The calculation expression of the water balance constraint of the reservoir node is:

[0100]

[0101] in, is the reservoir storage capacity at the end of the planning period t+1, is the reservoir storage capacity at the end of planning period t, is the water inflow at the end of the planning period t+1, is the amount of water released from the upstream reservoir at the end of the planning period t+1, is the amount of water supplied by the reservoir to various departments at the end of the planning period t+1, is the amount of water discharged from the reservoir at the end of the planning period t+1;

[0102] The calculation expression of the water balance constraint of the river channel node is:

[0103]

[0104] in, Calculated by the river runoff prediction model v+1 Runoff volume of river section t during the planning period; Calculated by the river runoff prediction model v Runoff volume of river section t during the planning period; is the rainfall in the k-sub-area v river section during the planning period t; k subregions v The amount of water discharged from the reservoir at the end of the planning period for river segment t; is the water resources regression coefficient; is the total amount of water supplied to the agricultural and rural sectors by the water source of sub-area k during planning period t;

[0105] The water balance constraint of the ecological unit represents the ecological water demand for each cross section, with 10% (dry season), 30% (non-flood season), or 40% (flood season) of the multi-year average runoff as the appropriate ecological water demand. The calculation expression of the water balance constraint of the ecological unit is:

[0106]

[0107] in, for v Monthly ecological water demand of river section t during the planning period, Calculated by the river runoff prediction model v Runoff volume of river section t during the planning period;

[0108] The water transfer capacity constraint indicates that the amount of water transferred from the source to the destination of the inter-basin water transfer should be between the maximum and minimum water transfer capacities. The calculation expression of the water transfer capacity constraint is:

[0109]

[0110] in is the minimum capacity of the inter-basin water pipeline, The maximum capacity of the inter-basin water transmission pipeline;

[0111] The calculation expression of the non-negative constraint is:

[0112] .

[0113] S4. Solve the multi-condition constrained water resource optimization configuration model to obtain a solution result, and optimize water resource configuration for users in the study area based on the solution result.

[0114] In S4, the water resource optimization configuration model is solved using a rule algorithm or an optimization algorithm until a solution is found that satisfies the multiple condition constraints and maximizes the economic benefits of the objective function, and water resources are optimized for users in the study area based on the solution.

[0115] In this embodiment, a multi-condition constrained water resources optimization allocation model for the Guangdong-Hong Kong-Macao Greater Bay Area is proposed, including simulating the river runoff in the study area based on the hydrological data of the Guangdong-Hong Kong-Macao Greater Bay Area using the SWAT model; combining with the government's "Water Resources Bulletin" document, a multi-condition constrained water resources optimization allocation model based on user allocation under extreme climatic conditions in the Greater Bay Area is established, incorporating the goal of maximizing economic benefits. This embodiment takes into account 40 socioeconomic water use units, 23 river ecological flow control sections and 30 reservoirs; referring to the ecological red line issued by the Guangdong Provincial Water Resources Bureau, based on the degree of water resources development and utilization, combined with the water conservancy projects in the planning year to analyze the water supply capacity in the planning year, select constraints and calculate the global optimal operation; this application adopts a rule algorithm or an optimization algorithm to solve the optimization goal of maximizing economic benefits; an optimization model with water resources carrying capacity constraints, upper and lower limits of water demand constraints, total water use constraints, water balance constraints of socioeconomic units, water balance constraints of reservoir nodes, water balance constraints of river canal nodes, water balance constraints of ecological units, water transfer capacity constraints and non-negative constraints as constraints. In addition, the present invention proposes a method for optimizing the allocation of water resources. First, the hydrological historical data of the study area is obtained, and the monthly runoff series in the specific annual area is predicted using a preset river runoff prediction model. Then, under extreme climatic conditions, a multi-condition constrained water resource optimization allocation model is established based on user allocation. By solving the multi-condition constrained water resource optimization allocation model, it is possible to achieve accurate and reasonable optimization of the water resources of users in the study area under multiple condition constraints, thereby improving the efficiency and fairness of user water resource allocation and ensuring the sustainable use of water resources.

[0116] Example 2

[0117] This embodiment proposes a water resource optimization configuration system, including:

[0118] Acquisition module, used to obtain hydrological historical data of the study area;

[0119] A runoff prediction module, configured to input the hydrological historical data into a preset river runoff prediction model, and have the river runoff prediction model output a monthly runoff sequence within a specific annual region;

[0120] A water resources optimization allocation model establishment module is used to establish a water resources optimization allocation model based on user allocation in the study area under extreme climate conditions according to the monthly runoff sequence;

[0121] The solution module is used to solve the water resource optimization configuration model to obtain a solution result, and perform water resource optimization configuration for users in the study area according to the solution result.

[0122] In this embodiment, first, the hydrological historical data of the study area is obtained, and the monthly runoff series in the specific annual area is predicted using a preset river runoff prediction model. Then, under extreme climatic conditions, a multi-condition constrained water resource optimization configuration model is established based on user allocation. By solving the multi-condition constrained water resource optimization configuration model, it is possible to achieve accurate and reasonable optimization of water resources for users in the study area under multiple condition constraints, thereby improving the efficiency and fairness of user water resource allocation and ensuring the sustainable use of water resources.

[0123] Example 3

[0124] This embodiment also proposes a computer device, see Figure 4 , comprising: a processor 401, a memory 402, a communication interface 403 and a communication bus 404, wherein the processor 401, the memory 402 and the communication interface 403 communicate with each other via the communication bus 404;

[0125] Wherein: the processor 401, the memory 402 and the communication interface 403 communicate with each other through the communication bus 404. The communication interface 403 is used for network communication with other devices such as a client or other servers. The processor 401 is used to execute executable instructions 405, which can specifically execute the operations of the water resource optimization configuration system. Specifically, the executable instructions 405 may include program codes. The processor 401 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement an embodiment of the present invention. The computer device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or different types of processors, such as one or more CPUs and one or more ASICs.

[0126] The memory 402 is used to store executable instructions 405. The memory 402 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0127] The executable instructions 405 may be specifically invoked by the processor 401 to cause the computer device to perform the following operations:

[0128] S1. Obtain historical hydrological data of the study area;

[0129] S2. The hydrological historical data is input into a preset river runoff prediction model, and the river runoff prediction model outputs a monthly runoff sequence within a specific annual region;

[0130] S3. Based on the monthly runoff series, establish a multi-conditional water resource optimization model based on user allocation under extreme climate conditions in the study area;

[0131] S4. Solve the multi-condition constrained water resource optimization configuration model to obtain a solution result, and optimize water resource configuration for users in the study area based on the solution result.

[0132] In this embodiment, first, the hydrological historical data of the study area is obtained, and the monthly runoff series in the specific annual area is predicted using a preset river runoff prediction model. Then, under extreme climatic conditions, a multi-condition constrained water resource optimization configuration model is established based on user allocation. By solving the multi-condition constrained water resource optimization configuration model, it is possible to achieve accurate and reasonable optimization of water resources for users in the study area under multiple condition constraints, thereby improving the efficiency and fairness of user water resource allocation and ensuring the sustainable use of water resources.

[0133] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. A person skilled in the art would be able to make other variations or modifications based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing water resource allocation, characterized in that: The method comprises the following steps: S1. Obtain historical hydrological data of the study area; S2. The hydrological historical data is input into a preset river runoff prediction model, and the river runoff prediction model outputs a monthly runoff sequence within a specific annual region; S3. Based on the monthly runoff series, establish a multi-conditional water resource optimization model based on user allocation under extreme climate conditions in the study area; S4. Solving the multi-condition constrained water resource optimization allocation model to obtain a solution result, and optimizing water resource allocation for users in the study area based on the solution result; The multi-condition constrained water resource optimization allocation model includes an objective function and multi-condition constraints, wherein the objective function takes maximizing economic benefits as the optimization goal, and the multi-condition constraints include water resource carrying capacity constraints, upper and lower limit constraints on water demand, total water consumption constraints, water balance constraints of social and economic units, water balance constraints of reservoir nodes, water balance constraints of river and canal nodes, water balance constraints of ecological units, water transfer capacity constraints and non-negative constraints; The calculation expression of the objective function is: in, is the economic benefit function, k is the sequence number of each sub-area, , n is the total number of sub-areas, i is the sequence number of water sources in each sub-area, , m is the total number of water sources, j is the serial number of the water demand department in each sub-area, j(k ) is the number of productive water-using sectors in each sub-area, t For the planning period, T for t The sum of the short-term planning periods, Water sources for each sub-district t The benefit coefficient of water supply to water-demanding departments during the planning period, Water sources for each sub-district t The cost coefficient of supplying water to water-demanding departments during the planning period, For the k Sub-district j During the planning period, water use departments should provide water resources i The amount of water taken, Water source for each sub-district t The weight coefficients of water supply to different water demand sectors during the planning period, The weight coefficient for the water supply priority of each sub-area water demand department; The calculation expression of the water resources carrying capacity constraint is: in, , q is the total number of water-using departments, is the water supply of the i-th water source in the k-th sub-area; The calculation expression of the upper and lower limit constraints of water demand is: in, For the k Sub-district j The department's maximum water demand, For the k Sub-district j Minimum water requirements for the department; The calculation expression of the total water consumption constraint is: in, m k They are k Number of water sources in the sub-area, W is the amount of water resources under the most stringent water resources management indicators in the study area; The calculation expression of the water balance constraint of the socioeconomic unit is: For the k Sub-district j Department t's water consumption during the planning period, For the k Sub-district j department t Surface water availability during the planning period; For the k Sub-district j department t Groundwater availability during the planning period; For the k Sub-district j department t Inter-basin water supply during the planning period; No. k Sub-district j department t The amount of water available from unconventional sources during the planning period; The calculation expression of the water balance constraint of the reservoir node is: in, is the reservoir storage capacity at the end of the planning period t+1, is the reservoir storage capacity at the end of planning period t, is the water inflow at the end of the planning period t+1, is the amount of water released from the upstream reservoir at the end of the planning period t+1, is the amount of water supplied by the reservoir to various departments at the end of the planning period t+1, is the amount of water discharged from the reservoir at the end of the planning period t+1; The calculation expression of the water balance constraint of the river channel node is: in, Calculated by the river runoff prediction model v+1 Runoff volume of river section t during the planning period; Calculated by the river runoff prediction model v Runoff volume of river section t during the planning period; is the rainfall in the k-sub-area v river section during the planning period t; k subregions v The amount of water discharged from the reservoir at the end of the planning period for river segment t; is the water resources regression coefficient; is the total amount of water supplied to the agricultural and rural sectors by the water source of sub-area k during planning period t; The calculation expression of the water balance constraint of the ecological unit is: in, for v Monthly ecological water demand of river section t during the planning period, Calculated by the river runoff prediction model v Runoff volume of river section t during the planning period; The calculation expression of the water delivery capacity constraint is: in is the minimum capacity of the inter-basin water pipeline, The maximum capacity of the inter-basin water transmission pipeline; The calculation expression of the non-negative constraint is: 。 2. The water resources optimization allocation method according to claim 1, characterized in that: The hydrological historical data includes DEM elevation data, land use data, soil property data, rainfall, relative humidity, solar radiation, wind speed, meteorological observation data, hydrological station observation data, and non-observation point runoff data.

3. The water resource optimization allocation method according to claim 1, characterized in that: The river runoff prediction model is the SWAT model, and the training process of the SWAT model is as follows: S201. Obtain actual runoff data within the historical year region, and divide the actual runoff data within the historical year region into a warm-up period data set, a rate period data set, a validation period data set, and a simulation period data set; S202. Initialize the network parameters of the SWAT model using the warm-up period data set, calibrate the initialized network parameters of the SWAT model using the calibration period data set to obtain a calibrated SWAT model, and verify the calibrated SWAT model using the validation period data set to obtain a trained SWAT model.

4. The water resource optimization allocation method according to claim 1, characterized in that: The hydrological historical data is set as a historical sequence with monthly precision and input into the river runoff prediction model. The river runoff prediction model uses a variable storage coefficient method to analyze the historical sequence with monthly precision and output the monthly runoff sequence.

5. The water resource optimization allocation method according to claim 1, characterized in that: The water resource optimization allocation model is solved using a rule algorithm or an optimization algorithm until a solution is found that satisfies the multiple condition constraints and maximizes the economic benefits of the objective function. Water resources are optimized for users in the study area based on the solution.

6. A water resource optimization configuration system, the system being implemented based on the water resource optimization configuration method according to any one of claims 1 to 5, characterized in that: include: Acquisition module, used to obtain hydrological historical data of the study area; A runoff prediction module, configured to input the hydrological historical data into a preset river runoff prediction model, and have the river runoff prediction model output a monthly runoff sequence within a specific annual region; A water resources optimization allocation model establishment module is used to establish a water resources optimization allocation model based on user allocation in the study area under extreme climate conditions according to the monthly runoff sequence; The solution module is used to solve the water resource optimization configuration model to obtain a solution result, and perform water resource optimization configuration for users in the study area according to the solution result.

7. A computer device, characterized in that: include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation of the water resource optimization configuration method according to any one of claims 1 to 5.

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