Method for optimizing water distribution in multi-stage irrigation channel based on iterative fixed delay

Abstract

This invention provides a method for optimizing water distribution scheduling in multi-level irrigation channels based on iterative fixed delay, relating to the field of water conservancy engineering technology. The method involves calculating the flow rate of each channel in each time period based on the topology and initial water delivery delay of the multi-level irrigation channels, calculating the average flow velocity of each channel, and obtaining the target water delivery delay. Based on the initial and target water delivery delays, a relaxation update strategy is used to calculate the corrected water delivery delay. The method then determines whether the change between the corrected and initial water delivery delays satisfies the convergence condition. If not, the corrected water delivery delay is used as the initial water delivery delay to calculate the flow rate of each channel in each time period. If the convergence condition is met, the start / stop status and flow rate of each channel in each time period are calculated based on the corrected water delivery delay to obtain the optimized scheduling result. This invention solves the problem that existing irrigation water distribution schemes struggle to balance physical rationality and computational efficiency.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, and in particular to a method for optimizing the water allocation and scheduling of multi-level irrigation channels based on iterative fixed delay. Background Technology

[0002] Agricultural irrigation systems widely employ multi-stage, cascaded canal structures consisting of main canals, branch canals, and distribution canals. In such systems, a water delivery time delay is unavoidable as water flows from upstream to downstream. This delay is not only related to the canal length but also closely influenced by factors such as the hydraulic conditions of the canal cross-section, the flow rate, and the roughness coefficient.

[0003] Existing irrigation water distribution and scheduling studies typically simplify water delivery delay to a constant or only consider fixed time offsets related to channel sequence. Under these assumptions, linear or mixed-integer programming models can be easily constructed to optimize the start and stop times and flow rates of irrigation users. However, under actual open channel conditions, water flow velocity changes significantly with flow rate, resulting in a pronounced time-varying characteristic of water delivery delay.

[0004] For open channels, flow velocity is typically described by hydraulic relationships such as the Manning formula, which are nonlinear functions. Ignoring these nonlinear coupling relationships in the scheduling model can easily lead to inaccurate predictions of downstream water supply times, increased deviations in the execution of scheduling plans, and even water supply conflicts and resource waste. Therefore, how to reasonably incorporate the dynamic changes in water delivery delays into the irrigation water distribution scheduling model while ensuring model solvability has become a critical technical problem that urgently needs to be solved.

[0005] Problems with existing technologies: (1) Modeling the time-varying water delivery delay. In a multi-level irrigation canal system, the water delivery delay from upstream to downstream nodes varies with the flow rate. The traditional fixed delay assumption is difficult to accurately reflect the actual water delivery process. (2) The contradiction between nonlinear hydraulic relations and the solvability of the scheduling model. Hydraulic relations such as the Manning formula have significant nonlinearity, while irrigation scheduling models usually include binary variables of start and stop states, time window constraints, and multi-level series constraints. If nonlinear terms are directly introduced, the difficulty of solving the problem will be significantly increased. (3) The coordination between water supply efficiency and demand satisfaction. Under the premise of meeting the water demand of irrigation users, how to shorten the overall scheduling completion time as much as possible and reduce the amount of demand default is the core problem in irrigation water allocation optimization. (4) The stable solution problem under the delay-flow coupling. The water delivery delay depends on the flow rate, and the flow rate is affected by the delay constraint. The two form a fixed-point problem. If a reasonable iterative strategy is lacking, numerical oscillations or non-convergence are likely to occur. Summary of the Invention

[0006] To address the aforementioned shortcomings in existing technologies, this invention provides a multi-level irrigation channel water allocation scheduling optimization method based on iterative fixed delay, which solves the problem that existing irrigation water allocation schemes struggle to balance physical rationality and computational efficiency.

[0007] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a multi-level irrigation canal water allocation scheduling optimization method based on iterative fixed delay, comprising: S1: Based on the topology of the multi-level irrigation canals and the initial water delivery delay, the irrigation water allocation scheduling model with the inner layer mixed integer linear programming as the core is used to calculate the flow rate of each canal in each time period. S2: Based on the flow rate of each channel in each time period, calculate the average flow velocity of each channel, and calculate the target water delivery delay based on the average flow velocity; S3: Based on the initial water delivery delay and the target water delivery delay, the corrected water delivery delay is calculated using a relaxation update strategy. S4: Determine whether the change between the corrected water delivery delay and the initial water delivery delay meets the convergence condition; if not, return to step S1 with the corrected water delivery delay as the initial water delivery delay; if the convergence condition is met, calculate the start-stop status and flow of each channel in each time period based on the corrected water delivery delay using the irrigation water allocation scheduling model, obtain the scheduling optimization result, and complete the scheduling optimization of irrigation channel water allocation.

[0008] Further, S1 includes: S110: Based on the topology of multi-level irrigation channels and the initial water delivery delay, constraints including flow capacity constraints, demand satisfaction constraints, start-stop time logic constraints, upstream and downstream sequence constraints, and flow balance constraints are set to construct an irrigation water allocation scheduling model with inner-layer mixed integer linear programming as the core. S120: In the irrigation water allocation and scheduling model, a weighted sum of minimizing the total system completion time and water demand default penalty is established as the objective function; S130: Solve the irrigation water distribution scheduling model to obtain the flow rate of each channel in each time period that satisfies the objective function.

[0009] Furthermore, the expression for the constraint condition is: ; ; ; ; ; ; ; ; in, This represents the water intake flow rate of channel i at time t. This indicates the amount of water demand default. This represents a binary state variable; if channel i is in the running state at time t, it is 1; otherwise, it is 0. Indicates channel index, This represents the set of nodes in a multi-level irrigation canal. Indicates a time index. Represents the set of discrete time steps. Indicates the duration of a single time step. This indicates the amount of water demand defaulted by channel i. This represents the total water demand of channel i during the scheduling period. This indicates the actual start time of water delivery in channel i. This indicates the actual end time of water delivery in channel i. This represents a large positive constant used for the Big M method constraint. This indicates the earliest start time allowed for channel i. Indicates the latest allowed end time for channel i. This indicates the bottom slope of channel j. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This represents the buffer time between upstream channel j and downstream channel i. This indicates that water is transported from upstream channel j to downstream channel i. Represents the set of channel topology connections. This represents the discretization weight of the water conveyance delay. This represents the outflow volume of channel j at time t-1. This represents the outflow volume of channel i at time t'. This indicates the total completion time of the entire irrigation system.

[0010] Furthermore, the expression for the objective function is: ; in, This represents the function that takes the minimum value. Describe the objective function. The weight representing the total completion time. This indicates the total completion time of the entire irrigation system. The weighting of water demand defaults is indicated. This indicates the amount of water demand defaulted by channel i. Indicates channel index, This represents the set of nodes in a multi-level irrigation canal.

[0011] Further, S130 includes: Determine whether the initial water transfer delay is an integer time step; if not, discretize the initial water transfer delay into a linear combination of two adjacent integer time steps, and construct a delay weight based on the linear combination. The delay weights are input into the irrigation water distribution scheduling model. The delay weights are used to describe the impact of the outflow from the upstream channel at different times on the inflow from the downstream channel, and the flow rate of each channel in each time period is calculated.

[0012] Furthermore, the expression for the delay weight is: ; ; in, This represents the integer part of the water conveyance delay discretization. and This represents the fractional part of the water delivery delay discretization.

[0013] Furthermore, the expression for the corrected water delivery delay is: ; ; ; in, This represents the corrected water delivery delay calculated in the k-th iteration, which will be used in the next iteration. Denotes the relaxation factor, 0 < α ≤ 1. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This represents the target water delivery delay calculated based on the physical formula in the k-th iteration. This represents the water conveyance distance from upstream channel j to downstream channel i. This represents the average flow velocity of channel j in the k-th iteration. Indicates the duration of a single time step. This represents the average flow velocity of channel i in the k-th iteration. This represents the roughness coefficient of channel i. Indicates the hydraulic radius of channel i. The slope of channel i is represented by k, and the index of the outer iteration number is represented by k.

[0014] Furthermore, the expression for the convergence condition is: ; ; in, This indicates that the maximum value is taken among all upstream and downstream channel connections (j,i) belonging to set E. This represents the corrected water delivery delay calculated in the k-th iteration, which will be used in the next iteration. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This indicates the water conveyance delay convergence tolerance. This indicates that the maximum value is taken among all channels i belonging to set I and all time steps t belonging to set T. This represents the flow rate of channel i at time t, calculated using the irrigation water allocation scheduling model in the (k+1)th iteration. This represents the flow rate of channel i at time t, which serves as the benchmark, in the k-th iteration. This indicates the flow convergence tolerance.

[0015] The beneficial effects of this invention are as follows: This invention provides a multi-level irrigation channel water distribution scheduling optimization method based on iterative fixed delay. It adopts a two-layer optimization architecture of "inner layer mixed integer linear programming and outer layer iterative update of water delivery delay." In the inner layer calculation, the water delivery delay is treated as a fixed parameter, thus avoiding the solution difficulties caused by directly introducing nonlinear hydraulic relationships into the scheduling model. This approach allows the scheduling problem to be solved directly using a mature and efficient mixed integer linear programming (MILP) solver, ensuring computational stability and engineering applicability. Through the step of "calculating the average flow velocity based on the flow rate and then updating the target water delivery delay," the water delivery delay is no longer a static constant but a variable that can be dynamically adjusted according to changes in channel flow. This mechanism can more realistically reflect the actual propagation process of water flow in multi-level channels, effectively reducing downstream water supply timing deviations caused by inaccurate delay estimation. A relaxation update strategy is introduced in the delay update step. By weighted correction between the initial delay and the target delay, numerical oscillations that may be caused by the strong coupling relationship between "flow rate and delay" are effectively suppressed. This ensures that the algorithm can stably converge to a consistent solution under different channel parameters and initial conditions, thus improving the computational robustness of the method. Attached Figure Description

[0016] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is an exemplary flowchart of a multi-level irrigation canal water distribution scheduling optimization method based on iterative fixed delay, as shown in some embodiments of this specification. Figure 2 These are exemplary schematic diagrams illustrating the water diversion times of various water channels according to some embodiments of this specification; Figure 3This is an exemplary schematic diagram comparing water supply under different requirements according to some embodiments of this specification. Detailed Implementation

[0017] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0018] Example Figure 1 This is an exemplary flowchart illustrating an iterative fixed-delay-based multi-level irrigation canal water allocation scheduling optimization method according to some embodiments of this specification. Figure 1 As shown, the process includes the following steps. In some embodiments, the process may be executed by a processor.

[0019] S1: Based on the topology of the multi-level irrigation canals and the initial water delivery delay, the irrigation water allocation scheduling model with the inner layer mixed integer linear programming as the core is used to calculate the flow rate of each canal in each time period.

[0020] Multi-level irrigation canals refer to a hierarchical water conveyance network system composed of main canals, branch canals, distribution canals, and farm canals. In this embodiment, the system is abstracted as a directed graph structure composed of nodes (canal heads / branching points) and edges (canal segments).

[0021] In some embodiments, the processor can obtain multi-level irrigation channels by inputting field survey data or reading the channel connection relationships and hierarchical attributes from an irrigation district geographic information system (GIS) database.

[0022] Topology is a logical structure that describes the upstream and downstream connections and water flow direction among the channels in a multi-level irrigation canal, corresponding to a graph in a mathematical model.

[0023] In some embodiments, the processor can obtain the topology by reading pre-stored channel network connection matrix or adjacency list data in memory.

[0024] The initial water delivery delay is the time required for water to flow from the upstream channel to the downstream channel, which is preset in the first calculation of the iterative optimization.

[0025] In some embodiments, the processor may estimate the initial water delivery delay based on the quotient of channel length and design flow rate, or based on the average statistical value of historical operating data.

[0026] An irrigation water allocation and scheduling model refers to a mathematical programming model built into computer memory to describe the operating logic, constraints, and optimization objectives of an irrigation system. In this embodiment, it specifically refers to a computational model with inner-layer mixed integer linear programming (MILP) as its core.

[0027] In some embodiments, the processor can instantiate and construct the set of variables and constraint equations of the irrigation water allocation scheduling model in memory based on the channel parameters, water demand requirements and scheduling rules input by the user.

[0028] The flow rate of each channel in each time period refers to the decision variable, which represents the volumetric flow rate of water intake or transmission in a specific channel within a specific time step.

[0029] In some embodiments, the processor can solve the irrigation water allocation scheduling model by calling a mixed-integer linear programming solver (such as CPLEX or Gurobi) to obtain the flow rate of each channel in each time period.

[0030] In some embodiments, the processor may implement S1 based on the following steps.

[0031] S110: Based on the topology of multi-level irrigation channels and the initial water delivery delay, constraints including flow capacity constraints, demand satisfaction constraints, start-stop time logic constraints, upstream and downstream sequence constraints, and flow balance constraints are set to construct an irrigation water allocation scheduling model with inner-layer mixed integer linear programming as the core.

[0032] S120: In the irrigation water allocation and scheduling model, a weighted sum of the total system completion time and the water demand default penalty is established as the objective function.

[0033] S130: Solve the irrigation water distribution scheduling model to obtain the flow rate of each channel in each time period that satisfies the objective function.

[0034] Constraints refer to mathematical inequalities or sets of equations that limit the feasible solution space of a scheduling model, including but not limited to physical capacity constraints, hydraulic balance relationships, and operational logic constraints. For example, constraints may include flow and demand constraints, start-up and shutdown time logic, upstream and downstream coupling constraints, and system completion time definitions.

[0035] Flow and demand constraints include: Flow capacity constraints, which stipulate that channel flow cannot exceed its maximum design flow and are related to operational status; and demand satisfaction constraints, which require that the total water intake plus the amount of defaults within the scheduling cycle must meet the total water demand.

[0036] The start / stop time logic includes: time continuity means the start time must be earlier than or equal to the end time. Operating interval definition: if the channel is running at time t ( If ), then t must be Within the interval. If If the condition is met, this constraint will be automatically satisfied. Time window constraint: The actual start and stop time must be within the allowable range.

[0037] The upstream and downstream coupling constraints include: 1. Sequence constraint: the start time of the downstream channel must be later than the start time of the upstream channel plus the water conveyance delay and buffer time. 2. Flow balance: the inflow of the channel at time t is equal to the sum of its water intake flow and the delayed outflow of all upstream channels.

[0038] The system completion time is defined as the system completion time not being earlier than the end time of any channel.

[0039] In some embodiments, the processor can integrate all objective functions and constraints to obtain the optimal solution. ;in, This represents the corresponding optimal scheduling scheme. This represents the optimal water demand default amount. This represents the optimal termination time. After optimization using the above constraints and objective function, the optimal scheduling scheme under the current delay is obtained. .

[0040] In some embodiments, the processor can automatically generate constraints based on channel physical properties (such as maximum flow rate), user demand data, and hydraulic principles.

[0041] In some embodiments, the expression for the constraint is: ; ; ; ; ; ; ; ; in, This represents the water intake flow rate of channel i at time t. This indicates the amount of water demand default. This represents a binary state variable; if channel i is in the running state at time t, it is 1; otherwise, it is 0. Indicates channel index, This represents the set of nodes in a multi-level irrigation canal. Indicates a time index. Represents the set of discrete time steps. Indicates the duration of a single time step. This indicates the amount of water demand defaulted by channel i. This represents the total water demand of channel i during the scheduling period. This indicates the actual start time of water delivery in channel i. This indicates the actual end time of water delivery in channel i. This represents a large positive constant used for the Big M method constraint. This indicates the earliest start time allowed for channel i. Indicates the latest allowed end time for channel i. This indicates the bottom slope of channel j. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This represents the buffer time between upstream channel j and downstream channel i. This indicates that water is transported from upstream channel j to downstream channel i. Represents the set of channel topology connections. This represents the discretization weight of the water conveyance delay. This represents the outflow volume of channel j at time t-1. This represents the outflow volume of channel i at time t'. This indicates the total completion time of the entire irrigation system.

[0042] The objective function is a mathematical expression used to evaluate the merits of a scheduling scheme. In this embodiment, it is the weighted minimization of the total system completion time and the total water demand default.

[0043] In some embodiments, the processor can construct and generate an objective function based on a preset optimization strategy and weight parameters.

[0044] In some embodiments, the expression for the objective function is: ; in, This represents the function that takes the minimum value. Describe the objective function. The weight representing the total completion time. This indicates the total completion time of the entire irrigation system. The weighting of water demand defaults is indicated. This indicates the amount of water demand defaulted by channel i. Indicates channel index, This represents the set of nodes in a multi-level irrigation canal.

[0045] In some embodiments, the processor can determine whether the initial water delivery delay is an integer time step; if not, the initial water delivery delay is discretized into a linear combination of two adjacent integer time steps, and a delay weight is constructed based on the linear combination. The delay weights are input into the irrigation water distribution scheduling model. The delay weights are used to describe the impact of the outflow from the upstream channel at different times on the inflow from the downstream channel, and the flow rate of each channel in each time period is calculated.

[0046] Linear combination refers to the decomposition of water transfer delay into a weighted representation of two consecutive integer time points on the time axis when the delay is not an integer.

[0047] In some embodiments, the processor can construct weights to achieve a linear combination by calculating the fractional and integer parts of the latency value.

[0048] Delay weight is a coefficient used to quantify the contribution of upstream outflows at different historical moments to downstream inflows at the current moment.

[0049] In some embodiments, the expression for the delay weight is: ; ; in, This represents the integer part of the water conveyance delay discretization. and This represents the fractional part of the water delivery delay discretization.

[0050] S2: Based on the flow rate of each channel in each time period, calculate the average flow velocity of each channel, and calculate the target water delivery delay based on the average flow velocity.

[0051] Average flow velocity is the average water flow velocity across a channel under specific flow conditions.

[0052] In some embodiments, the processor can calculate the flow rate based on the solved flow rate data using the Manning formula.

[0053] The target water delivery delay is a theoretical water delivery time calculated based on the current average flow velocity and conforming to the current hydraulic conditions.

[0054] In some embodiments, the processor can obtain the target water delivery delay by calculating the ratio of the channel length to the current average flow rate.

[0055] S3: Based on the initial water delivery delay and the target water delivery delay, the corrected water delivery delay is calculated using a relaxation update strategy.

[0056] The relaxation update strategy is a numerical calculation method used to suppress iterative oscillations, which involves retaining a certain proportion of historical values ​​when updating parameters.

[0057] The corrected water delivery delay is the delay value calculated using the relaxation update strategy and used for the next iteration or final convergence determination.

[0058] In some embodiments, the expression for the modified water delivery delay is: ; ; ; in, This represents the corrected water delivery delay calculated in the k-th iteration, which will be used in the next iteration. Denotes the relaxation factor, 0 < α ≤ 1. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This represents the target water delivery delay calculated based on the physical formula in the k-th iteration. This represents the water conveyance distance from upstream channel j to downstream channel i. This represents the average flow velocity of channel j in the k-th iteration. Indicates the duration of a single time step. This represents the average flow velocity of channel i in the k-th iteration. This represents the roughness coefficient of channel i. Indicates the hydraulic radius of channel i. The slope of channel i is represented by k, and the index of the outer iteration number is represented by k.

[0059] S4: Determine whether the change between the corrected water delivery delay and the initial water delivery delay meets the convergence condition; if not, return to step S1 with the corrected water delivery delay as the initial water delivery delay; if the convergence condition is met, calculate the start-stop status and flow of each channel in each time period based on the corrected water delivery delay using the irrigation water allocation scheduling model, obtain the scheduling optimization result, and complete the scheduling optimization of irrigation channel water allocation.

[0060] Convergence criteria are mathematical standards for determining whether the iterative process has ended. In this embodiment, it includes both the change in delay and the change in flow rate being less than a preset tolerance.

[0061] In some embodiments, the processor can obtain the convergence condition by comparing the calculation results of two iterations and making a logical judgment with a threshold in memory.

[0062] In some embodiments, the convergence condition is expressed as: ; ; in, This indicates that the maximum value is taken among all upstream and downstream channel connections (j,i) belonging to set E. This represents the corrected water delivery delay calculated in the k-th iteration, which will be used in the next iteration. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This indicates the water conveyance delay convergence tolerance. This indicates that the maximum value is taken among all channels i belonging to set I and all time steps t belonging to set T. This represents the flow rate of channel i at time t, calculated using the irrigation water allocation scheduling model in the (k+1)th iteration. This represents the flow rate of channel i at time t, which serves as the benchmark, in the k-th iteration. This indicates the flow convergence tolerance.

[0063] Start-stop status refers to a binary variable that indicates whether a channel is in a working state of water supply at a certain moment.

[0064] The scheduling optimization result is the final water distribution scheme that satisfies the convergence condition and all constraints, including the start-up and shutdown schedules of each channel, the flow process line, and system performance indicators.

[0065] After iterative convergence, the processor formats and outputs the final model solution set to a display device or storage medium.

[0066] In some embodiments, the water diversion time of each canal is as follows: Figure 2 As shown. Water supply under different demands, for example... Figure 3 As shown, the method of the present invention has significant advantages in terms of balancing water adjustment time and water supply volume.

[0067] In some embodiments of this invention, a multi-stage irrigation canal water distribution scheduling optimization method based on iterative fixed delay is provided. This method employs a two-layer optimization architecture of "inner-layer mixed-integer linear programming and outer-layer iterative update of water delivery delay." In the inner-layer calculation, the water delivery delay is treated as a fixed parameter, thus avoiding the solution difficulties caused by directly introducing nonlinear hydraulic relationships into the scheduling model. This approach allows the scheduling problem to be solved directly using mature and efficient MILP solvers, ensuring computational stability and engineering applicability. By using the step of "calculating the average flow velocity based on the flow rate and then updating the target water delivery delay," the water delivery delay is no longer a static constant but a variable that can be dynamically adjusted according to changes in the canal flow rate. This mechanism can more realistically reflect the actual propagation process of water flow in multi-stage canals, effectively reducing downstream water supply timing deviations caused by inaccurate delay estimation. A relaxation update strategy is introduced in the delay update step. By weighted correction between the initial delay and the target delay, numerical oscillations that may be caused by the strong coupling relationship between "flow rate and delay" are effectively suppressed. This ensures that the algorithm can stably converge to a consistent solution under different canal parameters and initial conditions, improving the computational robustness of the method.

Claims

1. A method for optimizing the water allocation and scheduling of multi-level irrigation channels based on iterative fixed delay, characterized in that, Includes the following steps: S1: Based on the topology of the multi-level irrigation canals and the initial water delivery delay, the irrigation water allocation scheduling model with the inner layer mixed integer linear programming as the core is used to calculate the flow rate of each canal in each time period. S2: Based on the flow rate of each channel in each time period, calculate the average flow velocity of each channel, and calculate the target water delivery delay based on the average flow velocity; S3: Based on the initial water delivery delay and the target water delivery delay, the corrected water delivery delay is calculated using a relaxation update strategy. S4: Determine whether the change between the corrected water delivery delay and the initial water delivery delay satisfies the convergence condition; If the conditions are not met, the corrected water delivery delay is used as the initial water delivery delay and the process returns to step S1. If the convergence condition is met, the start-stop status and flow rate of each channel in each time period are calculated based on the corrected water delivery delay using the irrigation water allocation scheduling model. The scheduling optimization results are obtained, and the scheduling optimization of irrigation channel water allocation is completed.

2. The multi-level irrigation canal water allocation scheduling optimization method based on iterative fixed delay according to claim 1, characterized in that, S1 includes: S110: Based on the topology of multi-level irrigation channels and the initial water delivery delay, constraints including flow capacity constraints, demand satisfaction constraints, start-stop time logic constraints, upstream and downstream sequence constraints, and flow balance constraints are set to construct an irrigation water allocation scheduling model with inner-layer mixed integer linear programming as the core. S120: In the irrigation water allocation and scheduling model, a weighted sum of minimizing the total system completion time and water demand default penalty is established as the objective function; S130: Solve the irrigation water distribution scheduling model to obtain the flow rate of each channel in each time period that satisfies the objective function.

3. The multi-level irrigation canal water allocation scheduling optimization method based on iterative fixed delay according to claim 2, characterized in that, The expression for the constraint condition is: ； ； ； ； ； ； ； ； in, This represents the water intake flow rate of channel i at time t. This indicates the amount of water demand default. This represents a binary state variable; if channel i is in the running state at time t, it is 1; otherwise, it is 0. Indicates channel index, This represents the set of nodes in a multi-level irrigation canal. Indicates a time index. Represents the set of discrete time steps. Indicates the duration of a single time step. This indicates the amount of water demand defaulted by channel i. This represents the total water demand of channel i during the scheduling period. This indicates the actual start time of water delivery in channel i. This indicates the actual end time of water delivery in channel i. This represents a large positive constant used for the Big M method constraint. This indicates the earliest start time allowed for channel i. Indicates the latest allowed end time for channel i. This indicates the bottom slope of channel j. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This represents the buffer time between upstream channel j and downstream channel i. This indicates that water is transported from upstream channel j to downstream channel i. Represents the set of channel topology connections. This represents the discretization weight of the water conveyance delay. This represents the outflow volume of channel j at time t-1. This represents the outflow volume of channel i at time t'. This indicates the total completion time of the entire irrigation system.

4. The multi-level irrigation canal water allocation scheduling optimization method based on iterative fixed delay according to claim 2, characterized in that, The expression for the objective function is: ； in, This represents the function that takes the minimum value. Describe the objective function. The weight representing the total completion time. This indicates the total completion time of the entire irrigation system. The weighting of water demand defaults is indicated. This indicates the amount of water demand defaulted by channel i. Indicates channel index, This represents the set of nodes in a multi-level irrigation canal.

5. The multi-level irrigation canal water allocation scheduling optimization method based on iterative fixed delay according to claim 2, characterized in that, S130 includes: Determine whether the initial water transfer delay is an integer time step; if not, discretize the initial water transfer delay into a linear combination of two adjacent integer time steps, and construct a delay weight based on the linear combination. The delay weights are input into the irrigation water distribution scheduling model. The delay weights are used to describe the impact of the outflow from the upstream channel at different times on the inflow from the downstream channel, and the flow rate of each channel in each time period is calculated.

6. The multi-level irrigation canal water allocation scheduling optimization method based on iterative fixed delay according to claim 5, characterized in that, The expression for the delay weight is: ； ； in, This represents the integer part of the water conveyance delay discretization. and This represents the fractional part of the water delivery delay discretization.

7. The multi-level irrigation canal water allocation scheduling optimization method based on iterative fixed delay according to claim 1, characterized in that, The expression for the corrected water delivery delay is: ； ； ； in, This represents the corrected water delivery delay calculated in the k-th iteration, which will be used in the next iteration. Denotes the relaxation factor, 0 < α ≤ 1. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This represents the target water delivery delay calculated based on the physical formula in the k-th iteration. This represents the water conveyance distance from upstream channel j to downstream channel i. This represents the average flow velocity of channel j in the k-th iteration. Indicates the duration of a single time step. This represents the average flow velocity of channel i in the k-th iteration. This represents the roughness coefficient of channel i. Indicates the hydraulic radius of channel i. The slope of channel i is represented by k, and the index of the outer iteration number is represented by k.

8. The multi-level irrigation canal water allocation scheduling optimization method based on iterative fixed delay according to claim 1, characterized in that, The expression for the convergence condition is: ； ； in, This indicates that the maximum value is taken among all upstream and downstream channel connections (j,i) belonging to set E. This represents the corrected water delivery delay calculated in the k-th iteration, which will be used in the next iteration. This represents the initial water transport delay from upstream channel j to downstream channel i in the k-th iteration. This indicates the water conveyance delay convergence tolerance. This indicates that the maximum value is taken among all channels i belonging to set I and all time steps t belonging to set T. This represents the flow rate of channel i at time t, calculated using the irrigation water allocation scheduling model in the (k+1)th iteration. This represents the flow rate of channel i at time t, which serves as the benchmark, in the k-th iteration. This indicates the flow convergence tolerance.

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