Water resource optimal configuration method and system

Abstract

The invention provides a water resource optimal allocation method and system, and relates to the technical field of water resource optimal allocation, and the method comprises the steps: firstly obtaining the hydrological historical data of a research area, and then inputting the hydrological historical data into a preset river runoff prediction model, outputting a monthly runoff sequence in a specific year region by the river runoff prediction model; on the basis of the monthly runoff sequence, a multi-condition constraint water resource optimal configuration model based on user distribution of the research area under the extreme climate condition is established; and finally, solving the multi-condition constraint water resource optimal configuration model to obtain a solving result, and carrying out water resource optimal configuration on users in the research area according to the solving result. According to the method, accurate and reasonable optimal allocation of water resources of users under the multi-target constraint condition is effectively realized.

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 mankind, 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 tremendously 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 has become increasingly prominent around the world. Especially today, with the accelerated urbanization process, urban water resources management faces unprecedented challenges.

[0003] In order to effectively respond to this challenge, water resource optimization allocation technology has emerged and has become an important way to alleviate the contradiction between water resource supply and demand and promote the efficient use of water resources. This technology aims to achieve the reasonable allocation and efficient use of water resources through scientific methods and water resource optimization allocation models, comprehensively considering the needs and restrictions of the three dimensions of economy, society and environment. Water resource optimization allocation is not only related to the direct management of water resources, but also has a profound impact on urban development planning, ecological environment 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 allocation method based on genetic algorithm. The scheme normalizes economic goals, social goals and ecological goals, so that each goal is weighted under the same dimension. 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 facing the water resource environment in complex areas, it is impossible to make more conditional constraints under the economy, society and ecology, 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 prior art has a single target constraint, resulting in inaccurate and unreasonable optimal allocation of water resources to users, the present invention proposes a water resource optimal 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 solution of the present invention is as follows:

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

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

[0008] S2. Inputting the hydrological historical data into a preset river runoff prediction model, the river runoff prediction model outputs a monthly runoff sequence in a specific annual region;

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

[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 according to 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. Obtaining actual runoff data in the historical year region, dividing the actual runoff data in the historical year region into a warm-up period data set, a rate period data set, a verification period data set, and a simulation period data set;

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

[0015] Preferably, 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.

[0016] Preferably, the multi-condition constrained water resources optimization allocation 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 for socio-economic units, water balance constraints for reservoir nodes, water balance constraints for river and canal nodes, water balance constraints for ecological units, water transfer capacity constraints and non-negative constraints.

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

[0018]

[0019] Where f1(x) is the economic benefit function, k is the sub-area number, k = 1, 2, ..., n, n is the total number of sub-areas, i is the water source number in each sub-area, i = 1, ..., m k , m is the total number of water sources, j is the serial number of water demand departments in each sub-area, j(k) is the number of productive water-using departments in each sub-area, t is the planning period, T is the sum of t short-term planning periods, is the benefit coefficient of water supply to water demanding departments in each sub-area during the planning period t, is the cost coefficient of supplying water to water-demanding departments during the planning period of each sub-area water source t, is the water intake of the j-th water-using department in the k-th sub-area from water source i during planning period t, is the weight coefficient of water supply to different water demand departments in the planning period of each sub-area water source t, 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] Where j = 1, ..., q, q is the total number of water-using departments, is the available water volume of the ith water source in the kth sub-area;

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

[0024]

[0025] in, is the maximum water demand of the jth department in the kth sub-area, is the minimum water demand of the jth department in the kth sub-area;

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

[0027]

[0028] Among them, m k are the number of water sources in the kth sub-area, respectively, and W is the amount of water resources under the most stringent water resources management indicator in the study area;

[0029] The calculation expression of the water balance constraint of the socio-economic unit is:

[0030]

[0031] is the water consumption of the jth department in the kth sub-area during the tth planning period, is the surface water supply in the jth sector of the kth sub-region during the tth planning period; is the groundwater supply in the jth sector of the kth sub-region during the tth planning period; is the inter-basin water supply of the jth sector in the kth sub-region during the tth planning period; The supply of unconventional water sources in the jth sector of the kth sub-region during the tth planning period;

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

[0033] QR(t+1)=QR(t)+Q rin (t+1)+Q rin0 (t+1)-Q rko (t+1)-Q rfo (t+1)

[0034] Among them, QR(t+1) is the reservoir water storage at the end of the t+1 planning period, QR(t) is the reservoir water storage at the end of the t planning period, and Q rin (t+1) is the water inflow at the end of the t+1 planning period, Q rin0 (t+1) is the amount of water released from the upstream reservoir at the end of the t+1 planning period, Q rko (t+1) is the amount of water supplied by the reservoir to each department at the end of the t+1 planning period, Q rfo (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] Among them, rep v+1,t is the runoff of the v+1 river section during the planning period t calculated by the river runoff prediction model; rep v,t The runoff of river section v during planning period t calculated by the river runoff prediction model; is the rainfall in river section v of sub-area k during planning period t; is the water discharge from the reservoir at the end of the planning period of river section t in sub-area k; λ is the water resource 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] de v,t ≥10% rep v,t

[0040] Among them, v,tis the monthly ecological water demand of river section v during planning period t, rep v,t The runoff of river section v during planning period t calculated by the river runoff prediction model;

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

[0042]

[0043] in is the minimum capacity of the inter-basin water pipeline, is the maximum capacity of the inter-basin water pipeline; the calculation expression of the non-negative constraint is:

[0044]

[0045] Preferably, a rule algorithm or an optimization algorithm is used to solve the water resource optimization allocation 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 optimally allocated to users in the study area based on the solution.

[0046] The present invention also proposes a water resource optimization configuration system, comprising:

[0047] The acquisition module is used to obtain the hydrological historical data of the study area;

[0048] A runoff prediction module, used for inputting the hydrological historical data into a preset river runoff prediction model, and the river runoff prediction model outputs a monthly runoff sequence in a specific annual region;

[0049] 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;

[0050] 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.

[0051] The present invention also proposes 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;

[0052] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the operation of the water resource optimization configuration method as described above.

[0053] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0054] The present invention proposes a method and system for optimizing the allocation of water resources. First, the hydrological historical data of the study area is obtained, and the monthly runoff sequence in the specific annual area is predicted by 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 allocation 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flowchart showing a method for optimizing water resource allocation proposed in an embodiment of the present invention is shown;

[0056] Figure 2 A schematic diagram showing the principle of water resource optimization configuration proposed in an embodiment of the present invention;

[0057] Figure 3 A block diagram of a water resources optimization configuration system proposed in an embodiment of the present invention is shown;

[0058] Figure 4 A structural block diagram of a computer device proposed in an embodiment of the present invention is shown.

[0059] 401. Processor; 402. Memory; 403. Communication interface; 404. Communication bus; 405. Executable instructions. DETAILED DESCRIPTION

[0060] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0061] It is understandable to those skilled in the art that some well-known contents may be omitted in the drawings;

[0062] In order to facilitate understanding of this embodiment, first, the prior art information of this embodiment is introduced as follows:

[0063] Water shortage caused by uneven spatial distribution of water resources has become a key global issue that threatens water security and restricts sustainable social and economic development. At the same time, this problem has also seriously affected the coordinated development of regional socio-economic and environmental systems. In order to achieve water security and sustainable development, it is essential to implement water resource allocation within the region. In addition, with the rapid growth of population and economy, the demand for water resources will continue to increase under the condition of limited water supply. Water shortage caused by uneven distribution of water resources will further aggravate water conflicts and make water problems more complicated. Therefore, considering a reasonable and effective water resource allocation strategy for the future is a necessary condition to help regional water managers alleviate these problems.

[0064] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0065] Example 1

[0066] like Figure 1 and Figure 2 As shown, this embodiment proposes a method for optimizing water resource allocation, which includes the following steps:

[0067] S1. Obtain historical hydrological data of the study area;

[0068] 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.

[0069] S2. Inputting the hydrological historical data into a preset river runoff prediction model, the river runoff prediction model outputs a monthly runoff sequence in a specific annual region;

[0070] 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, to calibrate and verify the model parameters on a monthly scale, and conduct forecast simulation from 2019 to 2022.

[0071] The river runoff prediction model is the SWAT model, and the training process of the SWAT model is as follows:

[0072] S201. Obtain the actual runoff data in the region from 1982 to 2022, and divide the actual runoff data in the region from 1982 to 2022 into a warm-up period data set, a rate period data set, a verification period data set, and a simulation period data set; the warm-up period data set is the actual runoff data in the region from 1982 to 1991, the rate period data set is the actual runoff data in the region from 1991 to 2011, and the verification period data set is the actual runoff data in the region from 2011 to 2018.

[0073] S202. Use the warm-up period data set to initialize the network parameters of the SWAT model, use the calibration period data set to calibrate the initialized network parameters of the SWAT model to obtain a calibrated SWAT model, and use the validation period data set to verify the calibrated SWAT model to obtain a trained SWAT model.

[0074] In S202, the SUFI-2 algorithm of the widely used SWAT-CUP calibration tool is used to perform SWAT model calibration verification. The P-factor and R-factor are mainly used to measure the uncertainty calculation results of the evaluation model. 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 prediction is, that is, the more reliable the prediction result of the SWAT model is; the R-factor is an indicator to measure the difference between the SWAT model prediction value and the actual observation value, which is usually 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 prediction value and the actual observation value is, that is, the higher the prediction accuracy of the SWAT model is.

[0075] 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:

[0076]

[0077] n represents the total number of summations, O i represents the simulated value of the SWAT model, P i represents the actual observed value, O ave The average value of the simulation value of the SWAT model, the coefficient of determination R 2 The calculation formula is:

[0078]

[0079] P ave represents the average of the actual observed values;

[0080] The simulation time is set from January 1, 1982 to December 31, 2022, and the hydrological historical data in the region from 1982 to 2022 is set as a historical sequence with monthly precision and input into the river runoff prediction model. The river runoff prediction model uses the variable storage coefficient method to analyze the historical sequence with monthly precision and output the monthly runoff sequence. This study overturned the stereotype that there is no water shortage in the Guangdong-Hong Kong-Macao Greater Bay Area due to the large number of rivers. In fact, the Greater Bay Area has seasonal water shortages, water quality shortages, and engineering water shortages, and is accompanied by saltwater tides, floods, high water consumption rates, and a single water supply structure, resulting in prominent supply and demand contradictions.

[0081] S3. Based on the monthly runoff series, a multi-condition constraint water resource optimization allocation model based on user allocation under extreme climate conditions in the study area is established;

[0082] In S3, the multi-condition constrained water resources optimization allocation 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 for socio-economic units, water balance constraints for reservoir nodes, water balance constraints for river and canal nodes, water balance constraints for ecological units, water transfer capacity constraints and non-negative constraints.

[0083] The calculation expression of the objective function is:

[0084]

[0085] Where f1(x) is the economic benefit function, k is the sub-area number, k = 1, 2, ..., n, n is the total number of sub-areas, i is the water source number in each sub-area, i = 1, ..., m k , m is the total number of water sources, j is the serial number of water demand departments in each sub-area, j(k) is the number of productive water-using departments in each sub-area, t is the planning period, T is the sum of t 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, is the cost coefficient of water supply to water demanding departments during the planning period of each sub-area, 10,000 yuan / m3, is the water intake of the j-th water-using department in the k-th sub-area from water source i during planning period t, is the weight coefficient of water supply to different water demand departments in the planning period of each sub-area water source t, It is the weight coefficient of water supply priority of water-demanding departments in each sub-area, 10,000 yuan / m3.

[0086] The water resource carrying capacity constraint means that the water supply from any water source i to all industries j in any period t must be within the range of available water and not exceed the utilization potential of water resources. The calculation expression of the water resource carrying capacity constraint is:

[0087]

[0088] Where j = 1, ..., q, q is the total number of water-using departments, is the available water volume of the ith water source in the kth sub-area;

[0089] The upper and lower limits of water demand 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 demand of the water user; the calculation expression of the upper and lower limits of water demand constraints is:

[0090]

[0091] in, is the maximum water demand of the jth department in the kth sub-area, is the minimum water demand of the jth department in the kth sub-area; the constraint of the minimum water demand is mainly to ensure the balance of water consumption among departments, to avoid excessive concentration of water in departments with higher water efficiency, and to avoid departments with lower water efficiency being allocated very little water or even no water.

[0092] The total water consumption constraint means that the available water volume of the water source must be the water resource volume under the most stringent water resource management index promulgated by the government of the study area; the calculation expression of the total water consumption constraint is:

[0093]

[0094] Among them, m k are the number of water sources in the kth sub-area, respectively, and W is the amount of water resources under the most stringent water resources management indicator in the study area;

[0095] The calculation expression of the water balance constraint of the socio-economic unit is:

[0096]

[0097] is the water consumption of the jth department in the kth sub-area during the tth planning period, is the surface water supply in the jth sector of the kth sub-region during the tth planning period; is the groundwater supply in the jth sector of the kth sub-region during the tth planning period; is the inter-basin water supply of the jth sector in the kth sub-region during the tth planning period; The supply of unconventional water sources in the jth sector of the kth sub-region during the tth planning period;

[0098] The calculation expression of the water balance constraint of the reservoir node is:

[0099] QR(t+1)=QR(t)+Q rin (t+1)+Q rin0 (t+1)-Q rko (t+1)-Q rfo (t+1)

[0100] Among them, QR(t+1) is the reservoir water storage at the end of the t+1 planning period, QR(t) is the reservoir water storage at the end of the t planning period, and Q rin (t+1) is the water inflow at the end of the t+1 planning period, Q rin0 (t+1) is the amount of water released from the upstream reservoir at the end of the t+1 planning period, Q rko (t+1) is the amount of water supplied by the reservoir to each department at the end of the t+1 planning period, Q rfo (t+1) is the amount of water discharged from the reservoir at the end of the planning period t+1;

[0101] The calculation expression of the water balance constraint of the river channel node is:

[0102]

[0103] Among them, rep v+1,t is the runoff of the v+1 river section during the planning period t calculated by the river runoff prediction model; rep v,t The runoff of river section v during planning period t calculated by the river runoff prediction model; is the rainfall in the k-sub-area v-river section t during the planning period; is the water discharge from the reservoir at the end of the planning period of river section t in sub-area k; λ is the water resource 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;

[0104] 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:

[0105] de v,t ≥10% rep v,t

[0106] Among them, v,t is the monthly ecological water demand of river section v during planning period t, rep v,t The runoff of river section v during planning period t calculated by the river runoff prediction model;

[0107] 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:

[0108]

[0109] in is the minimum capacity of the inter-basin water pipeline, is the maximum capacity of the inter-basin water pipeline; the calculation expression of the non-negative constraint is:

[0110]

[0111] 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 according to the solution result.

[0112] In S4, the water resource optimization allocation model is solved by 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 optimally allocated to users in the study area according to the solution.

[0113] 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; in combination 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, and the goal of maximizing economic benefits is included. This embodiment takes into account 40 social and economic water use units, 23 river ecological flow control sections and 30 reservoirs; with reference 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 social and economic units, water balance constraints of reservoir nodes, water balance constraints of river channels 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 sequence in the specific annual area is predicted by 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 allocation 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.

[0114] Example 2

[0115] This embodiment proposes a water resource optimization configuration system, including:

[0116] The acquisition module is used to obtain the hydrological historical data of the study area;

[0117] A runoff prediction module, used for inputting the hydrological historical data into a preset river runoff prediction model, and the river runoff prediction model outputs a monthly runoff sequence in a specific annual region;

[0118] 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;

[0119] 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.

[0120] In this embodiment, firstly, the historical hydrological data of the study area is obtained, and the monthly runoff sequence in the area of ​​a specific year is predicted by 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 water resource allocation to users and ensuring the sustainable use of water resources.

[0121] Example 3

[0122] This embodiment also provides 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 through the communication bus 404;

[0123] 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 processors of different types, such as one or more CPUs and one or more ASICs.

[0124] 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.

[0125] The executable instruction 405 may be specifically called by the processor 401 to cause the computer device to perform the following operations:

[0126] S1. Obtain historical hydrological data of the study area;

[0127] S2. Inputting the hydrological historical data into a preset river runoff prediction model, the river runoff prediction model outputs a monthly runoff sequence in a specific annual region;

[0128] S3. Based on the monthly runoff series, a multi-condition constraint water resource optimization allocation model based on user allocation under extreme climate conditions in the study area is established;

[0129] 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 according to the solution result.

[0130] In this embodiment, firstly, the historical hydrological data of the study area is obtained, and the monthly runoff sequence in the area of ​​a specific year is predicted by 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 water resource allocation to users and ensuring the sustainable use of water resources.

[0131] Obviously, the above embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the implementation methods of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope 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. Inputting the hydrological historical data into a preset river runoff prediction model, the river runoff prediction model outputs a monthly runoff sequence in a specific annual region; S3. Based on the monthly runoff series, a multi-condition constraint water resource optimization allocation model based on user allocation under extreme climate conditions in the study area is established; 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 according to the solution result.

2. The water resources optimization allocation method according to claim 1, characterized in that: The hydrological historical data include 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 method for optimizing water resource allocation 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. Obtaining actual runoff data in the historical year region, dividing the actual runoff data in the historical year region into a warm-up period data set, a rate period data set, a verification period data set, and a simulation period data set; S202. Use the warm-up period data set to initialize the network parameters of the SWAT model, use the calibration period data set to calibrate the initialized network parameters of the SWAT model to obtain a calibrated SWAT model, and use the validation period data set to verify the calibrated SWAT model to obtain a trained SWAT model.

4. The method for optimizing water resource allocation 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 method for optimizing water resource allocation according to claim 1, characterized in that: The multi-conditional constraint water resource optimization allocation model includes an objective function and multi-conditional constraints. The objective function takes maximizing economic benefits as the optimization goal. The multi-conditional constraints include water resource carrying capacity constraints, upper and lower limit constraints on water demand, total water use constraints, water balance constraints for social and economic units, water balance constraints for reservoir nodes, water balance constraints for river and canal nodes, water balance constraints for ecological units, water transfer capacity constraints and non-negative constraints.

6. The method for optimizing water resource allocation according to claim 5, characterized in that: The calculation expression of the objective function is: Where f1(x) is the economic benefit function, k is the sub-area number, k = 1, 2, ..., n, n is the total number of sub-areas, i is the water source number in each sub-area, i = 1, ..., m k , m is the total number of water sources, j is the serial number of water demand departments in each sub-area, j(k) is the number of productive water-using departments in each sub-area, t is the planning period, T is the sum of t short-term planning periods, is the benefit coefficient of water supply to water demanding departments in each sub-area during the planning period t, is the cost coefficient of supplying water to water-demanding departments during the planning period of each sub-area water source t, is the water intake of the j-th water-using department in the k-th sub-area from water source i during planning period t, is the weight coefficient of water supply to different water demand departments in the planning period of each sub-area water source t, It is the weight coefficient of water supply priority of water demand departments in each sub-area.

7. The method for optimizing water resource allocation according to claim 6, characterized in that: The calculation expression of the water resources carrying capacity constraint is: Where j = 1, ..., q, q is the total number of water-using departments, is the available water volume of the ith water source in the kth sub-area; The calculation expression of the upper and lower limit constraints of water demand is: in, is the maximum water demand of the jth department in the kth sub-area, is the minimum water demand of the jth department in the kth sub-area; The calculation expression of the total water consumption constraint is: Among them, m k are the number of water sources in the kth sub-area, respectively, and W is the amount of water resources under the most stringent water resources management indicator in the study area; The calculation expression of the water balance constraint of the socio-economic unit is: is the water consumption of the jth department in the kth sub-area during the tth planning period, is the surface water supply in the jth sector of the kth sub-region during the tth planning period; is the groundwater supply in the jth sector of the kth sub-region during the tth planning period; is the inter-basin water supply of the jth sector in the kth sub-region during the tth planning period; The supply of unconventional water sources in the jth sector of the kth sub-region during the tth planning period; The calculation expression of the water balance constraint of the reservoir node is: QR(t+1)=QR(t)+Q rin (t+1)+Q rin0 (t+1)-Q rko (t+1)-Q rfo (t+1) where QR(t+1) is the reservoir water storage at the end of the t+1 planning period, QR(t) is the reservoir water storage at the end of the t planning period, Q rin (t+1) is the water inflow at the end of the t+1 planning period, Q rin0 (t+1) is the amount of water released from the upstream reservoir at the end of the t+1 planning period, Q rko (t+1) is the amount of water supplied by the reservoir to each department at the end of the t+1 planning period, Q rfo (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: Among them, rep v+1,t is the runoff of the v+1 river section during the planning period t calculated by the river runoff prediction model; rep v,t The runoff of river section v during planning period t calculated by the river runoff prediction model; is the rainfall in the k-sub-area v-river section t during the planning period; is the water discharge from the reservoir at the end of the planning period of river section t in sub-area k; λ is the water resource 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: of v,t ≥10 reps v,t Among them, v,t is the monthly ecological water demand of river section v during planning period t, rep v,t The runoff of river section v during planning period t calculated by the river runoff prediction model; 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 inter-basin water pipelines; The calculation expression of the non-negative constraint is:

8. The method for optimizing water resource allocation according to claim 7, characterized in that: The water resource optimization allocation model is solved by 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 optimally allocated to users in the study area according to the solution.

9. A water resource optimization allocation system, characterized in that: include: The acquisition module is used to obtain the hydrological historical data of the study area; A runoff prediction module, used for inputting the hydrological historical data into a preset river runoff prediction model, and the river runoff prediction model outputs a monthly runoff sequence in 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.

10. 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 resources optimization allocation method as described in any one of claims 1-8.

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