Water resource optimization control method for long-tomato water network irrigation system

Abstract

The application discloses a water resource optimal regulation and control method of a long vine snake gourd water network irrigation system, and comprises the following steps: (1) establishing a generalization and network topology relationship of a long vine snake gourd water network system water supply end, water taking end and water conveying and distributing end; (2) designing a three-layer hierarchical control structure of "general regulation-subordinate regulation-self regulation" according to the concept of "multi-level hierarchical control structure", wherein, the "general regulation layer" is a backbone water source regulation, water is pumped to key nodes of each backbone branch canal through a canal head pump station; the "subordinate regulation layer" regulates water quantity to branch canal water distribution points; the "self regulation layer" forms a point-to-point relationship of many water distribution points of the trunk and branch canals, local ponds, i.e. low vine snake gourds and water users, so as to redistribute water quantity; (3) constructing a long vine snake gourd reservoir and canal combined multi-objective water resource optimal regulation and control model and solving by using NSGA-II, wherein, the model comprises: determining a decision variable, establishing an objective function and constructing a constraint condition; and the application provides a scientific basis for water resource optimal regulation and control of the long vine snake gourd water network irrigation system.

Description

Technical Field

[0001] This invention relates to the field of water resource management and regulation technology, specifically to a method for optimizing and regulating water resources in a long-vine-and-melon-growing irrigation network system. Background Technology

[0002] The "long vine bearing melons" irrigation system is a common water resource management model in the hilly areas of southern China, possessing advantages such as extensive water source development, unified resource allocation, expanded irrigation benefits, and reduced operating costs of main irrigation canals. Through the headworks of the main irrigation system, the uneven spatial distribution of water resources can be effectively addressed, while small and medium-sized water source projects within the irrigation district regulate the uneven temporal distribution through the reservoir's storage mechanism, operating on a "filling during off-peak seasons and irrigating during peak seasons" model. Precise joint scheduling of reservoir and canal water resources is of great significance in ensuring urban and rural water security, coordinating water and soil resources, and promoting high-quality development of irrigation districts.

[0003] Unlike general water resource scheduling research, the multi-source optimization and allocation in reservoir-canal interconnected irrigation districts faces unique complexities, uncertainties, and peculiarities. The complexity lies in the fact that the problem spans multiple fields and tasks, involving runoff forecasting, irrigation demand prediction, reservoir scheduling, water resource allocation, and water conveyance scheduling. The system needs to balance irrigation, water supply, power generation, and ecological needs, and the "lifting-regulation-storage-distribution" process in each stage increases the difficulty. Uncertainties arise from the diverse forms of reservoirs and ponds, the complex topology of the water conveyance system, the dispersed and diverse water supply targets, and the challenges of water resource supply and demand forecasting due to climate change and socio-economic development. The peculiarity lies in the fact that this problem is a phased, dynamically linked, ultra-high-dimensional nonlinear optimization problem involving numerous scheduling and control nodes. Treating it in stages and blocks makes it difficult to solve the overall problem, while global solutions easily lead to the curse of dimensionality or local optima. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method for optimizing and regulating water resources in a long-vine-and-melon-growing irrigation network system, thereby solving the problems of complex water resource regulation and low utilization efficiency in existing long-vine-and-melon-growing irrigation network systems.

[0005] Technical solution: The present invention provides a method for optimizing and regulating water resources in a long-vine, gourd-bearing irrigation network system, comprising the following steps:

[0006] (1) Establish the generalized and network topology relationships of the water supply end, water intake end and water transmission and distribution end of the long vine and melon water network system;

[0007] (2) Based on the concept of “multi-level hierarchical control structure”, a three-level hierarchical control structure of “general dispatch-sub-distribution dispatch-self-distribution” is designed. Among them, the “general dispatch layer” is the dispatch of backbone water sources, which lifts water to key nodes of each backbone branch canal through the head pump station; the “sub-distribution layer” dispatches water to the water distribution points of each branch canal; the “self-distribution layer” establishes a point-to-point relationship between the many water distribution points of the main and branch canals, local reservoirs and ponds, and water users, thereby redistributing the water.

[0008] (3) Construct a multi-objective water resources optimization and regulation model for the combined reservoir and canal system and solve it using NSGA-II. The model includes: determining decision variables, establishing objective functions, and constructing constraints.

[0009] Furthermore, step (1) includes the following steps:

[0010] (11) The water supply end is generalized into several nodes, each node representing a different water source type and location; for river water supply systems, the generalized nodes represent the main river inlet locations; for reservoir water supply systems, the nodes represent the reservoir intake locations; and for groundwater water supply systems, the nodes represent the main water intake wells; each water supply node is classified and connected according to its geographical location, water supply capacity, and the relationship between them.

[0011] (12) The water intake end is generalized according to functional requirements. Different water intake end nodes represent different user groups or regions. Each water intake node is numbered according to the actual water demand, its hydraulic connection relationship with the water supply end is described, and a transmission channel with the water supply end is set up.

[0012] (13) The water transmission and distribution ends are connected by water channels and pipelines, simplifying the complex water transmission and distribution system into several main water transmission trunk lines and branch pipelines; the flow rate, capacity and hydraulic conditions of each water transmission path are modeled according to the terrain and engineering conditions and managed in layers.

[0013] (14) Generalization of water replenishment logic: First, the water supply volume of the water supply node and the water demand of the water intake node are monitored in real time to generate a supply and demand balance state; at the same time, the regional regulation priority is set according to the supply and demand situation, and the transportation path is adjusted in real time through a dynamic feedback mechanism to optimize the efficiency of water resource allocation.

[0014] (15) By generalizing the water supply end, water intake end and water transmission and distribution end, the nodes are connected to form a complete water network system; the network topology is constructed using graph theory, each node represents the water supply, water intake or water transmission and distribution end, and the edge represents the path of water flow; each edge is supplemented with flow rate and pressure loss parameters.

[0015] Furthermore, step (2) includes the following steps:

[0016] (21) Main control layer, i.e., backbone water source control: At this level, the water source control of the headworks pumping station is achieved by controlling the water volume Q. 总调 (t) The key node formula for supplying the main and branch canals is as follows:

[0017] Q 总调 (t)=f1(t,Q 输入 ,X 渠首泵站 (1.1)

[0018] In the formula, t is a time variable, representing the time-varying nature of the system; Q 输入 It is the water input flow rate, X 渠首泵站 It refers to the status of the headworks pumping station (pumping station on / off status, pumping station capacity, etc.).

[0019] (22) Branch canal scheduling: At this level, the water volume scheduling of the branch canals is controlled down to each water distribution point Q. 分调 (t), this water volume is provided by the output of the main regulating layer, as shown in the following formula:

[0020] Q 分调 (t)=f2(t,Q 总调 (t),X 支渠 (1.2)

[0021] In the formula, Q 总调 (t) represents the amount of water input from the previous layer; X 支渠 These are the state variables of the branch canals (including the canal's flow capacity, leakage, flow distribution, etc.).

[0022] (23) Self-regulating layer, i.e., point-to-point scheduling: The self-regulating layer is the redistribution of water volume between the water distribution point, local reservoir or pond, and water users. The formula is as follows:

[0023] Q 自调 (t)=f3(t,Q 分调 (t),X 库塘 ,X 用水户 (1.3)

[0024] In the formula, Q 分调 (t) is the water input from the sub-stratum; X 库塘 X is the state variable of a local reservoir or pond; 用水户 It refers to the needs and status of water users (including user water consumption, water intake point, etc.).

[0025] (24) Constructing a multi-level hierarchical control structure for the entire system: The water allocation of the entire system can be represented as a multi-layered feedback control system through hierarchical control theory: The overall mathematical expression of the system is described by a nested control equation:

[0026] Q(t) = f3(t, f2(t, f1(t, Q) 输入,X 渠首泵站 ),X 支渠 ),X 库塘 ,X 用水户 (1.4)

[0027] Furthermore, in step (3), the decision variables are as follows: the hydrological year is used as the scheduling cycle, and the scheduling period is T;

[0028] Using the optimized reservoir water supply W, optimized reservoir discharge Q, water filling C from the canal to the low-lying reservoir, and direct irrigation B from the canal to the farmland as decision variables, after introducing the control period, the total number of decision variables for this multi-objective optimization problem is (W+Q+C+B)×T.

[0029] Furthermore, in step (3), the objective function is as follows: The minimum sum of squares of regional time-period water shortage rates is taken as the water supply objective, in order to coordinate the competition between the water supply objective and other objectives. The mathematical expression is:

[0030]

[0031] In the formula: D k,t Let Q be the water demand of the k-th user in time period t; i,k,t Let α be the water supply from the i-th water source to the k-th user in the t-th time period. j,k This is the importance coefficient of the k-th water user relative to other water users in receiving priority water supply.

[0032] The mathematical expression for economic goals is:

[0033]

[0034] In the formula: T1 represents a specific peak water inflow period 1, and T2 represents a specific peak water inflow period 2; β represents the weight of the pumping station's water lifting cost in the time allocation, β1 (water replenishment for period 1) < β2 (water replenishment for period 2); M represents the number of pumping stations, m = 1, 2, ..., M; N represents the number of units, n = 1, 2, ..., N; ρ represents the density of water; q t,m,n H represents the pumping flow rate of the nth unit at the mth pumping station during time period t. t,m η is the head of the m-th pump station in time period t; t,m,n The efficiency of the nth unit of the mth pumping station in time period t; Δt is the time interval; c t Let be the electricity price for time period t.

[0035] Furthermore, in step (3), the constraints include:

[0036] Reservoir water balance constraints

[0037] V t -V t-1 =(WRt +B t -W t -Q t -C t )Δt (1.7)

[0038] In the formula: V t V t-1 WR represents the reservoir storage at the end and beginning of time period t. t For the Quanmutang Reservoir, the average inflow during time period t is the inflow rate, which needs to be subtracted from the water pumped from the dam from the natural runoff; B t C represents the amount of water supplied to the Digua Reservoir by the canal during time period t (if any); t Let Q be the amount of water discharged from Gaogua Reservoir into the canal during time period t (if any); t W represents the outflow rate from the reservoir during time period t. t Let t be the water supply from the reservoir during the t-th time period;

[0039] Reservoir water supply constraints

[0040] W t ≤W kg (1.8)

[0041] In the formula: W kg Let t be the available water volume of the reservoir in the design year during the t-th period.

[0042] Reservoir outflow restrictions

[0043] Q t,min ≤Q t ≤Q t,max (1.9)

[0044] In the formula: Q t,min Q is the minimum allowable outflow during time period t, set as the ecological base flow downstream of the reservoir dam; t,max This represents the maximum allowable outflow from the reservoir during time period t, i.e., the maximum discharge from the reservoir.

[0045] Reservoir water level limit

[0046] Z t,min ≤Z t ≤Z t,max (1.10)

[0047]

[0048] Downflow amplitude constraint

[0049] |Q t -Q t-1 |≤Δ (1.12)

[0050] In the formula: Δ represents the maximum variation in reservoir outflow;

[0051] Water balance constraints at water diversion sections

[0052] O t =I t -W t (1.13)

[0053] In the formula: O t I t W t These represent the outflow, inflow, and diversion volume at the river cross-section during time period t, respectively.

[0054] Cross-sectional water balance constraints

[0055]

[0056] In the formula: Q i,t Q represents the average flow rate at the i-th cross-section during time period t; i-1,t-1 Q represents the average flow rate at the (i-1)th cross section during the time period t-1; i取,t Q i入,t Q i退,t Q i损,t These represent the water intake flow, interval inflow, outflow, and loss flow at the river cross-sections from the (i-1)th to the ithth cross-section within the time period t-1; τ i,t Let be the propagation time of the flow from the (i-1)th section to the ith section within time period t;

[0057] Cross-sectional ecological flow constraints

[0058] O t ≥Q t,e (1.15)

[0059] In the formula: O t Q represents the downstream discharge at the river cross-section during time period t; t,e The minimum or suitable ecological flow for the river cross-section;

[0060] Channel traffic balance constraints

[0061] Q j,t =Q j-1,t +Q l,t (1.16)

[0062] Q l,t =βALQ j-1,t 1-m / 100 (1.17)

[0063] In the formula: Q j,t Q represents the gross flow rate of the first section of the j-th segment of the water conveyance channel during time period t; j-1,t Q represents the net flow rate at the end of the j-th segment of the water conveyance channel (or the beginning of the (j-1)-th segment) during time period t; l,tLet L be the water conveyance loss of the j-th segment of the channel during time period t; estimated using an empirical formula, where L is the channel length (km); A is the permeability coefficient of the channel bed soil (light clay, A = 2.65); m is the permeability index of the channel bed soil (medium permeability, m = 0.45); and β is the reduction factor for seepage after implementing anti-seepage measures (concrete lining, β = 0.15).

[0064] Channel water distribution flow constraints

[0065] q min ≤q t ≤q max (1.18)

[0066] In the formula: q t q represents the channel water distribution flow rate during time period t; max q min These are the upper and lower limits of the water intake flow rate at each water intake point in the channel;

[0067] Channel water flow capacity constraints

[0068] Q j,t ≤Q j,cmax (1.19)

[0069] In the formula: Q j,t Q represents the flow rate at the j-th channel section during time period t; j,cmax This refers to the corresponding channel throughput capacity.

[0070] Pump station head constraints

[0071] H m,min ≤H m,t ≤H m,max (1.20)

[0072] In the formula: H m,min H m,max H represents the minimum and maximum head of the m-th pumping station; m,t Let m be the head of the m-th pump station during time period t;

[0073] Pump station flow capacity constraints and power constraints

[0074] q n,min ≤q n,t ≤q n,max (1.21)

[0075] ξ n,t ≤ξ n,max (1.22)

[0076] In the formula: q n,min q n,max These are the minimum and maximum pumping flow rates of the nth unit, respectively; q n,tξ is the pumping flow rate of the nth unit during time period t; n,max Let ξ be the maximum power of the nth unit. n,t Let n be the pumping power of the nth unit during time period t;

[0077] Water demand constraints

[0078]

[0079] Water supply guarantee rate constraint

[0080]

[0081] In the formula: p k Let be the water supply guarantee rate for the k-th water user; Let be the design guarantee rate for the k-th water user.

[0082] Furthermore, in step (3), the specific solution process for the model is as follows: Water resource optimization in the long vine and melon irrigation network system involves a multi-objective nonlinear problem. A global search method is used to avoid the "dead loop" problem of getting trapped in local minima. The overall process is expressed as follows:

[0083] P t+1 =Selection(P t )+Crossover(P t )+Mutation(P t (1.25)

[0084] Furthermore, a multi-objective genetic algorithm based on non-dominated sorting (NSGA-II) was chosen to solve the model, specifically as follows: First, the fitness function for non-dominated sorting was constructed:

[0085]

[0086] Where R(x) represents the non-dominated rank of individual x; the goal of NSGA-II is to find an approximate non-dominated solution set S using a fitness function. nd And it continuously approaches the true non-inferior solution set S.

[0087] Then, the projection pursuit optimization model is used to optimize the solution set S. nd The various schemes are evaluated and ranked; let the data in the high-dimensional space be X = (x1, x2, ..., x...). d If the projection direction is ω, then the projection value in the low-dimensional space is:

[0088]

[0089] In the formula, ω T It is the transpose of the projection vector, ω iIt is the weight along the projection direction, while x i These are eigenvalues ​​in high-dimensional data;

[0090] Finding the optimal projection vector ω during the iteration process can be represented as:

[0091] max w Q(ω,X) (1.28) where Q(ω,X) represents the objective function of projection pursuit, reflecting the quality of the projection result in the low-dimensional space; by maximizing Q(ω,X), we can find the optimal projection vector ω. * This ensures that the data structure in the low-dimensional space can most effectively reflect the characteristics of the high-dimensional data; the obtained optimal projection direction and corresponding projection values ​​can be used for subsequent analysis, visualization, and decision support, thereby realizing the solution set S nd Effective evaluation and ranking of the proposed solutions.

[0092] The present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program, when loaded onto the processor, implements a water resource optimization and control method for a long-vine-and-melon-growing irrigation network system as described in any one of the claims.

[0093] The present invention discloses a storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a water resource optimization and control method for a long vine gourd irrigation network system as described in any one of the claims.

[0094] Compared with existing technologies, this invention has the following beneficial effects: By generalizing and designing the network topology of the water supply, intake, and distribution ends of the Changtengjiegua irrigation network, this invention simplifies the complex water resource system structure and clarifies the relationships between its components. By adopting a multi-level canal hierarchical control structure, the complex water conservancy system is decomposed into multiple levels, achieving hierarchical optimal control and improving the efficiency and accuracy of water resource scheduling. The constructed reservoir-canal joint multi-objective water resource optimization and regulation model comprehensively considers multiple objectives such as water supply and economy, taking into account the needs of various water users and the operating costs of pumping stations, thus achieving optimal allocation of water resources. The application of advanced multi-objective genetic algorithms and multi-attribute decision-making methods improves the efficiency of model solving and the reliability of results, providing a scientific basis for the optimal regulation of water resources in the Changtengjiegua irrigation network. Attached Figure Description

[0095] Figure 1 This is a schematic diagram of the process of the present invention;

[0096] Figure 2 This is a schematic diagram of the region of the present invention;

[0097] Figures 3-1 to 3-2This is a simplified diagram of the water resources system and a water distribution diagram of the canal system of the present invention;

[0098] Figures 4-1 to 4-4 This is a multi-objective Pareto front plot for various incoming water frequencies according to the present invention;

[0099] Figure 5 These are the projection vectors of each target at each incoming water frequency according to the present invention;

[0100] Figures 6-1 to 6-3 This is a typical reservoir scheduling process line of the present invention. Detailed Implementation

[0101] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0102] like Figure 1 As shown in the figure, this invention provides a method for optimizing and regulating water resources in a long-vine, gourd-bearing irrigation network system, comprising the following steps:

[0103] S10. Simplification and network topology of the water supply, intake, and distribution ends of the long vine and melon-bearing water network system; including the following steps: S11. Simplification of the water supply end

[0104] Water supply sources include various types such as rivers, reservoirs, and groundwater. To simplify the complexity of the water network, the water supply end is generalized into several nodes, each representing a different water source type and location. For river water supply systems, the generalized nodes can represent the main river inlets; for reservoir water supply systems, the nodes represent the reservoir intakes; and for groundwater supply systems, the nodes represent the main intake wells. Each water supply node is classified and connected based on its geographical location, water supply capacity, and interrelationships.

[0105] S12, Generalization of water intake end

[0106] Water intake points are key nodes in water resource utilization, typically encompassing various demand types such as farmland irrigation, urban water supply, and industrial water use. For ease of management and scheduling, water intake points are generalized according to functional requirements, with different intake nodes representing different user groups or regions. Each intake node is numbered based on its actual water demand, its hydraulic connection to the water supply end is described, and a transmission channel to the water supply end is established.

[0107] S13, Generalization of Water Transmission and Distribution End

[0108] The water transmission and distribution end is the intermediate link between the water supply end and the water intake end, mainly including various water conveyance channels, storage tanks, and distribution pumping stations. The water transmission and distribution end is connected by water channels and pipelines, simplifying the complex water transmission and distribution system into several main water conveyance channels and branch pipelines. The flow rate, capacity, and hydraulic conditions of each water conveyance path are modeled according to the terrain and engineering conditions, and managed hierarchically to ensure that the water conveyance system at each level can effectively transfer water resources.

[0109] S14, Generalization of Water Replenishment Logic

[0110] The water replenishment logic generalization ensures a balanced water resource allocation across all nodes within the system through dynamic monitoring and control of the water supply, intake, and distribution ends. First, the system monitors the water supply volume at the supply nodes and the water demand at the intake nodes in real time, generating a supply-demand balance. When a water shortage occurs at an intake node, the system identifies available water nodes and generates an optimal water allocation path to efficiently deliver water to the node requiring replenishment. Simultaneously, regional control priorities are set based on supply and demand conditions to ensure that emergency areas receive priority water replenishment. A dynamic feedback mechanism adjusts the delivery path in real time, optimizing water allocation efficiency and ensuring the rational utilization and continuous balance of water resources within the system.

[0111] S15, Network Topology Construction

[0112] By generalizing the water supply, intake, and distribution ends, the nodes are connected to form a complete water network system. The network topology reflects the path and flow rate of water resources from the supply nodes to the intake nodes, as well as the mutual influences between the nodes. The network topology is constructed using graph theory, where each node represents a water supply, intake, or distribution end, and edges represent the paths of water flow. Parameters such as flow rate and pressure loss are appended to each edge for analysis and calculation in subsequent water resource optimization models.

[0113] S20. Design of a multi-level canal system hierarchical control structure for the long-vine-and-melon-growing irrigation network; including the following steps: S21. Overall regulation layer (backbone water source scheduling).

[0114] In this embodiment, the main control layer of the long-vine-and-melon irrigation network system achieves water source scheduling for key nodes of the main and branch canals through the headworks pumping station. Specifically, the water input flow rate (Q) 输入 The water is obtained from natural sources (such as rivers or reservoirs) and pumped to the main and branch canals via headworks pumping stations. The on / off status and flow control of the pumping stations are dynamically adjusted based on real-time water resource demand and system status.

[0115] The pump station's status parameters include its on / off status (operating or shut down), capacity (maximum liftable water volume), and operating time. The water allocation formula for this part is:

[0116] Q 总调 (t)=f1(t,Q 输入 ,X 渠首泵站 (2.1)

[0117] During implementation, the system will monitor the water level and flow changes of each key node in real time through sensors, and adjust the working status of the head pumping station according to the regional water demand and river flow fluctuations to ensure that the main and branch canals receive sufficient water supply.

[0118] S22, Sub-level Dispatch (Branch Canal Dispatch)

[0119] In this embodiment, the water volume scheduling of the branch canal system is allocated through the output water volume of the upper-level main scheduling layer.

[0120] Water flows into the branch canals from the main regulating point and is distributed to lower-level distribution canals and farmland irrigation areas through the branch canal's branch outlets. The state variables of the branch canals include their water conveyance capacity, leakage, and flow distribution ratio.

[0121] Each branch canal in the system is equipped with automated control devices (such as gates or flow meters) to monitor and regulate the water distribution at the branch points. The control formula for this process is:

[0122] Q 分调 (t)=f2(t,Q 总调 (t),X 支渠 (2.2)

[0123] The water allocation within the branch canals is dynamically adjusted based on the irrigation needs of farmland, and real-time monitoring and feedback are conducted using an information management platform to ensure that each branch can receive a reasonable water supply.

[0124] S23, Self-regulating layer (branch canal scheduling)

[0125] The self-regulating layer is the lowest level of water resource allocation in this embodiment, responsible for further distributing water from the branch canals and local reservoirs (or ponds) to water users. This layer adopts a point-to-point water resource allocation method to ensure that each water user receives an appropriate amount of water according to their actual needs.

[0126] In the self-regulation layer, the system achieves precise water allocation by monitoring the water level of local reservoirs and the needs of water users in real time. The water control formula for this layer is:

[0127] Q 自调 (t)=f3(t,Q 分调 (t),X 库塘 ,X 用水户 (2.3)

[0128] Among them, the state variables of the reservoir include factors such as the current water level, reservoir capacity, and evaporation rate; the state of water users includes the user's water consumption, crop type, and irrigation cycle.

[0129] S24, System-wide multi-level hierarchical control

[0130] The entire system adopts a hierarchical control structure, gradually distributing water from the main control layer to the self-regulating layer. The main control layer controls the water volume of the headworks pumping station based on the input of the main water source; the sub-control layer then delivers the water to the branch outlets of each branch canal; and the self-regulating layer delivers water resources from the branch canals to farmland and water users.

[0131] The system employs a hierarchical control and feedback mechanism to ensure that each level of the system can regulate water volume according to actual needs, and coordinates lower-level subsystems through higher-level systems to achieve overall optimization of the entire water network system. The overall mathematical model of the system is as follows:

[0132] Q(t) = f3(t, f2(t, f1(t, Q) 输入 ,X 渠首泵站 ),X 支渠 ),X 库塘 ,X 用水户 (2.4)

[0133] This hierarchical control structure enables dynamic optimization of water resource allocation in time and space, effectively improving the efficiency and water resource utilization rate of the long vine and melon irrigation network system.

[0134] Through the multi-level hierarchical control structure of this invention, the long vine and melon irrigation network system can effectively respond to changes in regional water resource demand, rationally allocate water supply, reduce water waste, and ensure the coordinated optimization of each level of the system, thereby improving the overall efficiency of water resource management.

[0135] S30. Construct a multi-objective water resource optimization and regulation model for the combined reservoir and canal system. Based on the multi-objective optimization model, efficient regulation of water resources within the system is achieved. The scheduling cycle of this method is based on the hydrological year, and the scheduling period is T. The method optimizes the reservoir water supply (W) for each period. t ), reservoir discharge (Q) t ), the amount of water supplied from the canal to the low-lying reservoir (C) t ) and the amount of water directly irrigated into farmland through canals (B t Multi-objective water resource allocation is carried out. This includes the following steps:

[0136] S31. Setting Decision Variables

[0137] In this invention, the decision variables include:

[0138] Reservoir water supply W t This refers to the water supply volume of each reservoir in the system at different times, ensuring that the needs of water users are met.

[0139] Reservoir discharge flow Q t The amount of water discharged from the reservoir into downstream rivers or other water bodies to ensure ecological flow requirements.

[0140] Water filling amount C t The water volume supplied to the Digua Reservoir through the canal meets the region's water resource allocation needs.

[0141] Direct irrigation volume B t The irrigation water directly supplied to farmland through canals meets the agricultural water demand.

[0142] The total number of decision variables is (W+Q+C+B)×T, and it is dynamically adjusted as the control period changes.

[0143] S32, Objective Function

[0144] To achieve multi-objective water resource optimization, this invention introduces two main objective functions:

[0145] (1) Water supply target

[0146] The optimization of water supply targets aims to ensure that the region's water demand is met to the greatest extent possible by minimizing the sum of squares of regional water shortage rates across different time periods. The mathematical expression for the water supply target is:

[0147]

[0148] In the formula: D k,t Let Q be the water demand of the k-th user in time period t; i,k,t Let α be the water supply from the i-th water source to the k-th user in the t-th time period. j,k This is the importance coefficient of the k-th water user relative to other water users in receiving priority water supply.

[0149] (2) Economic Objectives

[0150] To reduce the operating costs of pumping stations, this invention optimizes the operating time and electricity costs of pumping stations during peak water supply periods. The mathematical expression for the economic objective is:

[0151]

[0152] In the formula: T1 represents a specific peak water inflow period 1, and T2 represents a specific peak water inflow period 2; β represents the weight of the pumping station's water lifting cost in the time allocation, β1 (water replenishment for period 1) < β2 (water replenishment for period 2); M represents the number of pumping stations, m = 1, 2, ..., M; N represents the number of units, n = 1, 2, ..., N; ρ represents the density of water; q t,m,n H represents the pumping flow rate. t,m For the pump station head; η t,m,n Unit efficiency; Δt is the time interval; c t Let be the electricity price for time period t.

[0153] S33, Constraints

[0154] The multi-objective optimization scheduling model in this invention also needs to meet a series of constraints to ensure that the actual operation of the system meets the requirements. The water volume of the reservoir needs to maintain dynamic balance; the inflow, supply, discharge, and filling volumes should be reasonably matched to ensure stable reservoir storage. The reservoir supply should be less than the designed available water volume, and the outflow should vary between the minimum ecological flow and the maximum allowable discharge to avoid impacting the ecosystem and flood control safety. The reservoir water level should be maintained between the specified upper and lower limits, and the variation in outflow should not exceed the set value. The river cross-section needs to maintain water balance; the outflow should meet the ecological flow requirements, and the flow during inflow, intake, and outflow processes should remain balanced. Flow balance in the canal system is equally important; the flow in each section should operate within the design capacity, losses should be minimized during water conveyance, and the intake flow must meet the upper and lower limits. The pump station's head, flow rate, and power must operate within the equipment's design capacity to ensure safe and efficient system operation. Finally, it is necessary to ensure that the water needs of users are met, and the water supply guarantee rate should meet the design standards.

[0155] S34. Multi-objective intelligent optimization solution

[0156] This invention addresses the multi-objective water resource optimization problem in a long-vine, gourd-bearing irrigation network system, employing an intelligent optimization algorithm to ensure efficient system operation under complex conditions. The goal of multi-objective intelligent optimization is to simultaneously consider multiple factors such as water demand, economic costs, and ecological protection to find an optimal or near-optimal solution.

[0157] S35, Multi-attribute Decision Analysis

[0158] In the multi-objective intelligent optimization solution of this invention, multiple Pareto front solution sets are obtained, reflecting different water resource regulation schemes. To select the optimal scheme, this invention employs a multi-attribute decision analysis method based on a projection pursuit model, combining multiple attributes to evaluate and rank the schemes in the solution sets, thereby determining the regulation scheme that best meets actual needs.

[0159] In practical applications, multi-attribute decision analysis based on the projection pursuit model can not only help decision-makers select the optimal water resource regulation scheme, but also dynamically respond to changes in the system. When water demand, environmental conditions, or other factors change, the projection pursuit model can quickly re-evaluate the Pareto solution set and adjust the projection direction to ensure that the system always selects the best regulation scheme.

[0160] Example 2

[0161] Based on Example 1, this example illustrates the water resource optimization and regulation method of the long vine gourd irrigation network system of Example 1 using a specific instance.

[0162] This embodiment selects the Quanmutang Irrigation District as an implementation case, such as... Figure 2 As shown. The Quanmutang Reservoir irrigation area is located in the central part of the "Hengshao Arid Corridor" in Hunan Province, spanning the Zi River and Xiang River basins. The irrigation area covers four cities and eight counties (districts, cities): Shaoyang County, Shaodong City, Daxiang District, and Shuangqing District of Shaoyang City; Lengshuitan District and Qiyang County of Yongzhou City; Qidong County of Hengyang City; and Shuangfeng County of Loudi City, specifically between 111°10′ and 112°10′ east longitude and 26°30′ and 27°30′ north latitude. The irrigation area belongs to the subtropical monsoon climate zone, characterized by a warm, humid climate with abundant rainfall, distinct seasons, a short cold period, and a long frost-free period. May and June are the plum rain season, characterized by muggy weather and high humidity. From July to August, under the influence of the western Pacific subtropical high pressure, the extreme maximum temperature reaches approximately 40.0℃. The multi-year average temperature from 1979 to 2018 is 17.43℃. The flood season in the basin is from April to October. Rainfall is heaviest in late spring and early summer, with approximately 42% of the annual total occurring from April to June. Winter rainfall is the lightest, with only 14% occurring from December to February. The average annual precipitation is 1354.56 mm. There are 23 rivers and streams of varying sizes within the irrigation area and its surrounding region, with a total length of 996.02 km. The drainage area is over 200 km². 2 The above-mentioned rivers include the Tanjiang River, Shaoshui River, Chajiang River, Qishui River, Shuangjiangkou River, Guiyang River, Meishui River, and Si'anbu River, totaling eight rivers with a drainage area of ​​over 200 km². 2 The following rivers include 15 streams: Daba, Leijiasi, Louduodi, Buyunqiao, Songjiapai, Chebitang, Huihekou, Gaoqiao, Huangtupu, Yangjiazhou, Epotang, Dongxiashanshui, Shizijiang, Gantangpu, and Hongqihe.

[0163] Step S10, the generalization and network topology method of the water supply end, water intake end, and water transmission and distribution end of the long vine and melon water network system, is as follows:

[0164] S11, Generalized Elements of Water Supply End

[0165] The water sources within the study area are shown in the table below. The water sources involved in the optimized regulation model of this invention include medium-sized reservoirs and small Class I reservoirs, which are marked with * in the table. Other water bodies are used for runoff regulation calculations according to the principle of "use water whenever it is available".

[0166]

[0167]

[0168] S12, Generalized Elements of Water Intake End

[0169] The Quanmutang Irrigation District encompasses four urban areas: Shaodong City, Qidong County, Daxiang District, and Shuangqing District, as well as 26 townships. Urban and rural domestic water supply primarily comes from local rivers and medium-sized / small reservoirs. Some townships draw water from local rivers and wells, but due to their small scale, they are not generalized. When local water conservancy facilities are insufficient, water is considered to be pumped from Quanmutang to the reservoir before being supplied to the townships. The water intake situation for each urban area and township is shown in the table below. D represents the generalized urban and rural area number, totaling 17; W1 to W25 are the urban and rural water supply and demand nodes, totaling 25, where * indicates optimization decision-making targets.

[0170]

[0171]

[0172] Based on the locational relationship between the reservoir and the Changtengjiegua irrigation canal in the design year, medium-sized reservoirs, small reservoirs, ponds, and river dams supply water to local agriculture according to their respective irrigation areas. According to the layout of the Quanmutang irrigation district, water replenishment to the Changlinchong and Jinjitang reservoirs via the canal system can meet the water replenishment requirements for the Hongqi River. The replenishment water volume is the minimum ecological flow at the Hongqi River estuary. This study sets two ecological water replenishment nodes: the outflow from the Jinjitang and Changlinchong reservoirs, both of which are optimization decision variables.

[0173] S13, General elements of water transmission and distribution end

[0174] The irrigation district canal system includes one main canal and ten backbone branch canals, with a total length of 223.3 km. It includes the main canal Q0, Jiugongqiao branch canal Q1, Daxiang branch canal Q2, Xiakoushan branch canal Q3, Huangjiaba lifting canal Q4, Jiangkou branch canal Q5, Qiyang main canal Q6, Yangjiatai branch canal Q7, Qidong main canal Q8, Sandu lifting canal Q9, Shaofeng main canal Q10, canal water distribution nodes q1 to q78, and five generalized pumping station nodes.

[0175] S14, Generalization of Water Replenishment Logic

[0176] Water replenishment is carried out according to the coupling relationship between the main and branch canals and the logical order of the water distribution nodes of each subordinate canal. B represents the water replenishment node for the generalized canal to directly irrigate the irrigation area, totaling 13 nodes; C represents the water replenishment node for the connected Digua Reservoir, totaling 30 nodes; B and C are both optimization decision objects in this study; q is the generalized water distribution node proposed based on the canal system's field acreage distribution and canal system water distribution map.

[0177] S15, System Overview Diagram

[0178] A simplified diagram of the water resources system of the Quanmutang Irrigation District Project is shown below. Figure 3-1 The diagram shown is a water distribution diagram of the canal system. Figure 3-2 As shown.

[0179] Step S20, the design of the multi-level canal system hierarchical control structure for the long-vine-and-melon-growing irrigation network system, specifically involves:

[0180] S21, General Dispatch Layer (Key Water Source Dispatch)

[0181] The main control layer of the Quanmutang Longvine Gourd Water Network Irrigation System achieves water source scheduling for key nodes of the main and branch canals through the headworks pumping station. Water input flow rate (Q) 输入 The water was obtained from the Quanmutang Reservoir and pumped to the main branch canals via the headworks pumping station.

[0182] The design flow and increased flow results of the main nodes of the irrigation district's main canal are shown in the table below.

[0183]

[0184] S22, Sub-level Dispatch (Branch Canal Dispatch)

[0185] In this embodiment, the water allocation of the branch canal system is determined by the output water volume of the upper-level main control layer. Water flows into the branch canals from the main control point and is distributed to the lower-level distribution canals and farmland irrigation areas through the branch canal's branch outlets. The state variables of the branch canals include their water conveyance capacity, leakage rate, and flow distribution ratio. The allocation of some branch canals in the irrigation area is shown in the table below.

[0186]

[0187]

[0188] S23, Self-regulating layer (branch canal scheduling)

[0189] The self-regulating layer is the lowest level of water resource allocation in this embodiment, responsible for further distributing water from the branch canals and local reservoirs (or ponds) to water users. This layer adopts a point-to-point water resource allocation method to ensure that each water user receives an appropriate amount of water according to their actual needs.

[0190] Basic information on each irrigation section of the self-regulating small I-type reservoir.

[0191]

[0192]

[0193]

[0194] The basic information for bundling small Class II reservoirs is shown in the table below.

[0195]

[0196] Step S30 involves constructing a multi-objective water resources optimization and regulation model combining the reservoir and canal, specifically including:

[0197] S31, Decision Variables

[0198] The hydrological year is used as the scheduling cycle (starting in early April), and the scheduling period T is set as a ten-day period. The optimized reservoir water supply W (W1~W59), optimized reservoir discharge Q (Q1~Q43), water filling C (C1~C30) from the canal to the Digua Reservoir, and direct irrigation B (B1~B13) from the canal to farmland are used as decision variables. After introducing the regulation period, the total number of decision variables for this multi-objective optimization problem is 145×36=5220.

[0199] S32. Multi-objective optimization solution

[0200] Based on the above model and solution algorithm, with the hydrological year as the allocation cycle and ten-day periods as the control period, and combined with decision variables, the multi-objective allocation model of water resources in the Quanmutang irrigation area is solved based on the NSGA-II algorithm. The competitive game relationship between multiple objectives such as water supply and pump station operation in the Quanmutang irrigation area is comprehensively analyzed.

[0201] The population for model optimization was set to 1000, and the number of iterations was set to 2000. After solving the multi-objective optimization model, the multi-objective non-dominated solution set of the combined multi-objective optimization regulation model of the Changtengjiegua reservoir-canal system was obtained. The Pareto fronts for each inflow frequency (25%, 50%, 80%, 95%) are shown below. Figures 4-1 to 4-4 As shown.

[0202] As shown in the figure, the target values ​​for the non-dominated solution set of the sum of squares of water shortage rate at each water inflow frequency (25%, 50%, 80%, 95%) are 2.04–4.79 (average 2.32), 2.86–9.21 (average 3.99), 3.4–9.66 (average 7.82), and 5.81–28.6 (average 22.24), respectively. The target values ​​for the non-dominated solution set of pump station operating costs are 33.1244–38.0112 million yuan (average 37.2244 million yuan), 32.2428–40.8036 million yuan (average 38.1268 million yuan), 31.8048–40.0856 million yuan (average 33.1992 million yuan), and 33.9916–53.0728 million yuan (average 37.298 million yuan), as shown in the table below.

[0203]

[0204] The projections of the non-dominated frontier solutions onto the pump station operation-water supply aspect are relatively clustered. Analyzing the competitive relationship between objectives leads to the following conclusion: as pump station operating costs increase, the sum of squares of the water shortage rate in the Quanmutang irrigation district decreases. There is a significant competitive relationship between the pump station operation and water supply functions in the region. This competition arises primarily because the water supply objective aims to minimize the sum of squares of the watershed water shortage rate, with water volume distributed evenly based on demand. Conversely, the pump station operating cost objective aims to minimize regional pump station operating costs, favoring water users with lower operating costs. This inherent conflict between the two objectives results in a clear competitive relationship.

[0205] S33, Optimal Solution Decision

[0206] A multi-objective water resource regulation scheme optimization model for the Quanmutang irrigation area was constructed based on the projection pursuit method, and solved using a genetic algorithm to obtain the projected characteristic values ​​of the population in typical years, and the optimal regulation scheme was selected. The objective function values ​​of each objective function in the multi-objective water resource optimization model were selected as multi-objective evaluation indicators, namely, minimizing the sum of squares of water shortage rate and minimizing pump station operating cost; both indicators are negative. Combining the NSGA-II algorithm in the multi-objective optimization model with a population size of 100, the Pareto set of water supply and economic objectives for 100 populations was selected as the evaluation indicators and scheme set.

[0207] The multi-objective optimization control schemes for each typical year in the Quanmutang Irrigation District are shown in the table below. Scheme I is the optimal water supply target, Scheme II is the optimal system operation cost target, and Scheme III is the optimal comprehensive benefit for all targets.

[0208]

[0209] The results show that a lower water supply target matches a higher pumping station cost target, and vice versa. The unit projection vectors of the water supply target and pumping station cost target in wet years, normal years, dry years, and extremely dry years are 0.674 / 0.739, 0.705 / 0.709, 0.721 / 0.693, and 0.742 / 0.670, respectively, indicating that the economic target is a relatively important evaluation indicator in wet years, while the water supply target is a more important evaluation indicator under other water inflow frequencies. As the water inflow frequency increases, the unit projection vector of the water supply target increases accordingly, and the importance of the water supply target increases accordingly. Based on this, the optimal multi-objective regulation scheme of reservoir and canal joint operation in the Quanmutang irrigation area for wet years, normal years, dry years, and extremely dry years is obtained, such as... Figure 5 .

[0210] S34. Typical Year Water Resources Optimization and Regulation Scheme (Normal Water Year)

[0211] The following is a summary table of urban and rural water resource allocation, irrigation water resource allocation, and pumping volume of each scheme for a normal water year. The detailed allocation schemes (Scheme III) are as follows.

[0212] Summary of Urban and Rural Water Resource Allocation Plans for Normal Water Years (10,000 m³) 3 As shown in the table below.

[0213]

[0214]

[0215] Summary of Irrigation Water Resource Allocation Plans for Normal Water Years (10,000 m³) 3 As shown in the table below.

[0216]

[0217] S35, Typical Annual Reservoir Filling and Storage Dispatch Rules

[0218] Taking the optimal water resource optimization and regulation scheme III for each typical year as an example, the corresponding reservoir filling and storage protection scheduling process for each optimized reservoir (medium-sized reservoir and small type I bundled reservoir) in the Changtengjiegua irrigation network can be obtained. This includes the inflow process line of the reservoir, the filling line of Quanmutang Reservoir (net water volume at the target reservoir section), the water supply process line, the discharge process line, and the reservoir storage change process. The statistical results are shown in the table below. Taking Huangjiaba Reservoir, Longjiangqiao Reservoir, and Qunli Reservoir as typical reservoirs, their reservoir scheduling process lines are as follows: Figures 6-1 to 6-3 .

[0219]

[0220]

[0221] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the computer program is loaded onto the processor, it implements a water resource optimization and control method for a long vine gourd irrigation network system as described in any one of the embodiments.

[0222] This invention also provides a storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a water resource optimization and control method for a long-vine-and-melon-growing irrigation network system as described in any one of the claims.

Claims

1. A water resource optimization control method for a water network irrigation system of long vine loofah, characterized in that, Comprising the following steps: (1) Establishing the generalization and network topology relationship of the water supply end, water intake end and water distribution end of the water network system of long vine melon; (2) According to the concept of "multi-level hierarchical control structure", a three-level hierarchical control structure of "general control-sub-control-self-control" is designed; the "general control layer" is the scheduling of the main water source, which is pumped to the key nodes of each main branch channel through the headworks pump station; the "sub-control layer" schedules the water quantity to each branch channel; the "self-control layer" forms a point-to-point relationship between the many water distribution points of the main and branch channels, local reservoirs and ponds, i.e. low melons, and water users, so as to redistribute the water quantity; comprising the following steps: (21) The total adjustment layer, i.e. the backbone water source adjustment: In this layer, the water source adjustment of the head pump station is realized by controlling the water quantity supplied to the key nodes of the backbone branch canal (1.1) In the formula, is a time variable, representing the time-varying nature of the system; is the water source input flow, is the state of the canal head pump station, including the pump station on-off state, pump station capacity; (22) Subordinate regulation layer, i.e. branch canal regulation: at this level, the water quantity regulation of the branch canal is controlled to each sub-water point The water quantity is provided by the output of the total regulation layer, and the formula is as follows: (1.2) wherein is the water input from the upper layer; is the state variable of the branch canal including the over-flow capacity of the canal, leakage, flow distribution; (23) Self-control layer, i.e. point-to-point scheduling: the self-control layer is the water quantity redistribution between the water distribution point, local reservoir or dam and water users, and the formula is as follows: (1.3) wherein is the amount of water input from the distribution layer; is the state variable of the local reservoir or pond; is the demand and state of the water user including the amount of water used by the user, the water intake point; (24) Building a multi-level hierarchical control structure of the whole system: the water quantity scheduling of the whole system can be represented as a multi-layer feedback control system by the hierarchical control theory: the overall mathematical expression of the system is described by a nested control equation: (1.4)； (3) Building a long vine melon reservoir and canal combined multi-objective water resources optimization control model and solving it by NSGA-II, wherein the model includes: determining the decision variables, establishing the objective function, and building the constraint conditions; the decision variables are as follows: taking the hydrological year as the scheduling period, and the scheduling period T; taking the optimized reservoir water supply quantity W, the optimized reservoir discharge quantity Q, the channel water filling quantity C to the low melon reservoir and the channel direct irrigation water quantity B to the farmland as the decision variables, after introducing the control period, the total number of decision variables of this multi-objective optimization problem is (W+Q+C+B) x T; The objective function is as follows: taking the minimum of the square sum of the regional period water shortage rate as the water supply target, and coordinating the competition relationship between the water supply target and other targets, the mathematical expression is: (1.5) In the formula: For the t-th time period Water demand of each water user; For the ith water source in the t-th time period, water is supplied to the ith water source. Water supply to each water user; For the first The importance coefficient of each water user in receiving priority water supply relative to other water users; The mathematical expression of the economic target is: (1.6) In the formula: T1 is a specific water inflow peak period 1, T2 is a specific water inflow peak period 2; The weight of the pumping cost of the pumping station in time distribution, ; wherein, Indicates the water supplement of period 1; Indicates the water supplement of period 2; The number of pumping stations, m = 1, 2, …, The number of units, n = 1, 2, … The density of water; The pumping flow of the nth unit of the mth pumping station in the tth period; The lift of the mth pumping station in the tth period; The efficiency of the nth unit of the mth pumping station in the tth period; The time interval; The electricity price of the tth period.​​​ 2. The water resource optimization control method of the water network irrigation system for long vine cucumis sativus L. according to claim 1, characterized in that, Step (1) includes the following steps: (11) Generalize the water supply end into several nodes, each node representing different water source types and locations; for river water supply system, the generalized nodes represent the main river mouth locations; for reservoir water supply system, the nodes represent the water intake locations of the reservoir; the nodes of the groundwater water supply system represent the main water intake wells; each water supply node is classified and connected according to its geographical location, water supply capacity and relationship with each other; (12) Generalize the water intake end according to functional requirements, different water intake end nodes represent different user groups or regions; each water intake node is numbered according to the actual water demand, describes its hydraulic connection relationship with the water supply end, and sets the transmission channel with the water supply end; (13) The water distribution end is connected through water channels and pipelines, simplifying the complex water distribution system into several main water distribution channels and branch pipelines; the flow, capacity and hydraulic conditions of each water distribution path are modeled according to the terrain and engineering conditions, and are managed in layers; (14) The logic of water replenishment is first to monitor the water source quantity of the water supply node and the water demand quantity of the water intake node in real time, to generate the supply and demand balance state; at the same time, according to the supply and demand situation, set the regional control priority, and through the dynamic feedback mechanism, adjust the transmission path in real time, optimize the water resources allocation efficiency; (15) By generalizing the water supply end, water intake end and water distribution end, each node is connected to form a complete water network system; the network topology is constructed by using the graph theory method, each node represents the water supply, water intake or water distribution end, and the edge represents the path of water flow; each edge is attached with flow and pressure loss parameters.

3. The water resource optimization control method of the water network irrigation system for long vine cucumis sativus L. according to claim 1, characterized in that, In step (3), the constraint conditions include: Reservoir water balance constraint (1.7) In the formula: , is the water storage at the end of the first period; is the average inflow to the reservoir during the first period, for which the amount of water pumped over the dam needs to be subtracted from the natural runoff for the Gounmugong Reservoir; is the amount of water filled into the Gao'gua Reservoir through the channel during the first period; is the amount of water discharged from the Gao'gua Reservoir to the channel during the first period; is the amount of water discharged from the reservoir during the first period; is the amount of water supplied by the reservoir during the first period; Reservoir water supply constraint (1.8) In the formula: is the designed annual water level of the reservoir the water supply in the time period; Reservoir outflow limit (1.9) In the formula: is Period minimum allowable outflow, set as the ecological base flow downstream of the reservoir dam; is Period maximum allowable outflow, i.e. the maximum discharge of the reservoir; Reservoir water level limit (1.10) (1.11) Discharge amplitude constraint (1.12) In the formula: is the maximum amplitude of the reservoir outflow; Water balance constraint of diversion section (1.13) In the formula: , , The first section of the river channel The outflow, inflow, and diversion volume for each time period; Section water balance constraint (1.14) In the formula: for The average flow rate at the i-th cross-section during the time period; for -1 period Average flow rate; , , , They are respectively -1 period -1 to the Water intake flow, inflow rate, outflow rate, and loss flow rate at the river cross-section; for Within the time period -1 to the The propagation time of cross-sectional flow; Ecological flow constraint of section (1.15) wherein: is the river cross section is the discharge during the time period; is the minimum or appropriate ecological flow for the river cross section; Channel flow balance constraint (1.16) (1.17) In the formula: for The gross flow rate of the first section of the water conveyance channel in the j-th time period; for The net flow rate at the end of the j-th segment of the water conveyance channel or the beginning of the (j-1)-th segment of the water conveyance channel during the time period; for The water conveyance loss of the j-th segment of the channel during time period; estimated using an empirical formula, where, The length of the channel is in kilometers. The permeability coefficient of the canal bed soil is light clay. =2.65; The soil permeability index of the canal bed is moderate. =0.45; The reduction factor for water leakage in the canal bed after implementing anti-seepage measures is used; the canal is clad with concrete. =0.15; Channel water distribution flow constraint (1.18) In the formula: is the first period channel distribution flow; , is the upper and lower limits of the water intake flow of each water intake of the channel; Channel water carrying capacity constraint (1.19) wherein: is the period is the flow capacity of the channel cross-section; is the corresponding channel flow capacity; Pump station head constraint (1.20) In the formula: , is the minimum and maximum lift of the mth pump station; is the lift of the mth pump station at time t; Pump station flow capacity constraint and power constraint (1.21) (1.22) In the formula: , are the minimum and maximum pumping flow of the nth unit, respectively; is the pumping flow of the nth unit at time t; is the maximum power of the nth unit, is the pumping power of the nth unit at time t; Water demand constraint (1.23) Water supply guarantee rate constraint (1.24) In the formula: For the first Water supply guarantee rate for each water user; For the first Design guarantee rate for each water user.

4. The water resource optimization control method of the water web irrigation system for long vine snake melon according to claim 1, characterized in that, In step (3), the model solving process is as follows: The water resources optimization of the long-tomato water network irrigation system involves a multi-objective nonlinear problem, and a global search method is used to avoid the "dead loop" problem of falling into a local minimum value, and the overall process is represented as: (1.25) The non-dominated sorting genetic algorithm based on multi-objective (NSGA-II) is selected for model solving, and the specific process is as follows: First, the fitness function of non-dominated sorting is constructed: (1.26) wherein denotes the non-dominated rank of the individual . The objective of NSGA-II is to find an approximate non-dominated set of solutions by means of a fitness function and to keep approaching the true non-dominated set ; Then the projection pursuit optimization model is used to evaluate and rank each solution in the solution set ; let the data in the high-dimensional space be , the projection direction be , and the projection value in the low-dimensional space be: (1.27) wherein is the transpose of the projection vector, is the weight in the projection direction, and is the eigenvalue in the high-dimensional data; Finding the optimal projection vector in an iterative process The process is represented as: (1.28) wherein, represents the objective function of the projection pursuit, reflecting the quality of the projection result in the low-dimensional space; by maximizing , we can find the optimal projection vector , so as to ensure that the data structure in the low-dimensional space can most effectively reflect the characteristics of the high-dimensional data; the optimal projection direction and the corresponding projection value obtained can be used for subsequent analysis, visualization and decision support, thereby realizing the effective evaluation and ranking of the solutions in the scheme.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is loaded into the processor to realize the water resources optimization control method of the long-tomato water network irrigation system according to any one of claims 1-4.

6. A storage medium storing a computer program, characterized by The computer program is executed by the processor to realize the water resources optimization control method of the long-tomato water network irrigation system according to any one of claims 1-4.

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