A method for constructing a canal system optimal water distribution model for constant power pump units

Abstract

The present invention discloses a method for constructing a canal system optimized water distribution model for constant-power pump units, comprising: collecting data from a study area, establishing a canal system optimized water distribution model, and solving the model. The canal system optimized water distribution model in the present invention uses smooth water flow transition and minimizing water abandonment as its objective functions, and imposes corresponding flow constraints on the gross flow of the main canal water distribution based on a linear combination of the pump unit power. The model uses an annealing algorithm as its solution algorithm, and rationally allocates irrigation water to the irrigation area based on the irrigation water demand of each branch canal control area obtained by the solution. This invention can address the problem that existing water distribution models do not conform to the actual water distribution process of some pumping stations.

Description

Technical Field

[0001] The present invention belongs to the technical field of canal water distribution scheduling, and in particular relates to a canal water distribution optimization model for constant power water pump units. Background Art

[0002] Irrigation water is the largest consumer of freshwater resources worldwide, accounting for approximately 72% of total water use. It utilizes approximately 20% of the world's arable land and produces approximately 40% of global grain production. Efficient use of irrigation water is crucial for conserving water resources and increasing grain production. Monitoring irrigated area is a crucial component of irrigation district management and a crucial source of irrigation information. The canal system optimization water allocation model optimizes water flow and distribution timing through rational methods to optimize water resource allocation, providing data support for decision-making by irrigation district management departments.

[0003] In existing water distribution models, the allocation of main canal flow is determined by the flow of branch canals. That is, the flow of the main canal is equal to the sum of the gross flow of the branch canals, and is constrained only by the design flow. In this case, if the sum of the gross flow of the branch canals varies significantly over time, the amount of water allocated to the main canal will also vary accordingly. However, in actual water distribution, many pumping stations use a combination of different constant power pumps. The output flow is a linear combination of these constant powers, resulting in a stable flow output. Therefore, using existing water distribution models to allocate main canal flow during water distribution is not suitable for the actual water distribution process of some pumping stations. Summary of the Invention

[0004] In order to overcome the problems of the existing technology, the present invention proposes a canal system optimization water distribution model for constant power water pump units, with smooth water flow transition and minimum water abandonment as the objective function, and the main canal water distribution flow is subject to corresponding flow constraints according to the power of the water pump unit. The annealing algorithm is used as the solution algorithm, and the irrigation water volume is reasonably allocated to the irrigation area based on the irrigation water demand of each branch canal control area to solve the problem that the existing water distribution model does not conform to the actual water distribution process of some pumping stations.

[0005] The object of the present invention is achieved like this:

[0006] A method for constructing a canal system optimal water distribution model for a constant power water pump unit includes the following steps:

[0007] Step 1: Collect the data of the study area: including the flow corresponding to the constant power of the irrigation area water pump unit under different scenarios [C1, C2, ..., C n ],m 3 / s, the number of water pumps used in different scenarios is different; the planned rotation irrigation cycle, d; the design flow rate of the main canal, m3 / s; Design flow of each branch canal, m 3 / s; minimum flow reduction coefficient for main and branch canals, increased flow coefficient for main and branch canals; leakage reduction coefficient for main and branch canals after anti-leakage measures are taken; soil permeability coefficient for main and branch canal beds; soil permeability index for main and branch canal beds; main canal length, km; length of each branch canal, km; irrigation water demand for the area controlled by each branch canal, m 3 ; The present invention takes into account the problem of unstable flow connection caused by excessive gate operation times when selecting data to be collected in the study area.

[0008] Step 2: Establish a canal system optimized water distribution model, including:

[0009] Determine the objective function 1, the goal is to ensure smooth transition of water flow, and the function formula is as follows:

[0010]

[0011] Where S is the standard deviation of the water flow rate of the upper channel over time; t is the tth day of the irrigation cycle; T is the rotation irrigation cycle, d; q I ' t is the gross water flow of the main canal, m 3 / s;q It is the net water flow of the main canal, m 3 / s; j represents the branch channel number, and J is the total number of branch channel numbers; is the average water flow rate of the main canal during the entire period, m 3 / s;Q' j , Q j is the gross flow and net flow of the branch canal, m 3 / s; f(t) is a 0-1 variable, reflecting the water distribution status of each branch canal within the time period, 1 means water is being distributed within the time period, and 0 means no water is being distributed within the time period; L I , l j are the lengths of the main and branch canals, km; β I , β j A is the reduction coefficient of water leakage after anti-leakage measures are taken for the main and branch canals; I , A j are the soil permeability coefficients of the main and branch canals respectively; m I , m j are the soil permeability indexes of the main and branch canals respectively; t′ j , t″ j are the start and end irrigation time of the branch canal, d;

[0012] The significance of determining objective function 1 is to minimize the fluctuation of the output flow corresponding to the constant power of the irrigation area water pump unit under different water pump usage scenarios, so that the water flow transition is smooth. At the same time, it can also reduce the frequency of changes in the overall power of the water pump unit and simplify the operation of irrigation area personnel.

[0013] Determine objective function 2, the goal is to minimize the amount of abandoned water: the function formula is as follows (7):

[0014]

[0015] Among them, P is the amount of water discarded in the irrigation area after the irrigation area replenishes the irrigation water demand; R j is the irrigation water demand of crops in the control area of ​​branch canal j, m 3 ;

[0016] The significance of determining objective function 2 is to minimize the amount of water abandoned in the irrigation area after the irrigation area allocates water to supplement the irrigation water demand.

[0017] Determine constraint 1, flow constraint:

[0018]

[0019] q I ' t = [C1, C2, ..., C n ] (9)

[0020]

[0021] Among them, Q dj is the design flow of the branch canal, m 3 / s; α dj is the minimum flow reduction coefficient of the branch canal; α uj is the flow coefficient of the branch canal, C1, C2, ..., Cn are the flow rates corresponding to the constant power of the irrigation area water pump unit under different scenarios, m 3 / s, n is a natural number not less than 1, indicating the number of different scenarios; q dI Design flow rate of the main canal, m 3 / s; α dI is the minimum flow reduction coefficient of the main canal, α uI Increase the flow coefficient for the main canal;

[0022] Determine constraint 2, time constraint:

[0023] 0≤t′ j ≤t″ j ≤T (11)

[0024] Determine constraint 3, water quantity constraint:

[0025]

[0026] Q j (t″ j -t′ j )≥R j (13)

[0027]

[0028] Where: W max is the allowable water supply, m 3 ;

[0029] In step 3, the simulated annealing algorithm is used to solve the canal system optimal water distribution model. During the iteration process, the flow of each branch canal is adjusted to optimize the water distribution between the main canal and the branch canals, so that the water flow is smooth, the amount of water discarded is minimized, and the flow and time constraints are met.

[0030] Further optimization, in step 3, Step 1 initializes a sufficiently large temperature T0>0, the number of cooling times k=0, and with the initialization x (0) , so that x (i) =x (0) , and calculate its energy value E(x (0) ); specifically comprising the following steps:

[0031] Step 1: Initialization

[0032] Initialization temperature: First, set a sufficiently large initial temperature T0>0. This value is automatically adjusted through experimentation and there is no unified standard. In this study, the initial temperature is 100.

[0033] Initialization solution: set an initial solution x (0) The solution is the water distribution status of the main canal and its branch canals in the current irrigation system, i.e., the flow time profiles of the different branch canals and the main canal, i.e., the distribution flow and distribution time of the main and branch canals. This can be obtained through simple heuristic methods or directly from experimental data.

[0034] Calculate energy: Calculate the initial solution x (0) The energy value E(x (0) ), where the energy function E(x) is calculated based on multiple factors, including objective function 1 (smooth flow transition) and objective function 2 (minimizing water abandonment). Constraints are also considered in the energy function, which appropriately weights the objective function and uses the constraints as penalty terms.

[0035] In order to ensure smooth water flow, the first term of the energy function is defined as:

[0036]

[0037] The minimum amount of water discarded is the second term of the energy function:

[0038]

[0039] Since the constraints must be satisfied, energy penalties are imposed when the constraints are violated. A penalty term is added to each constraint. Specifically:

[0040] Traffic Constraints: For traffic constraints, if the constraints are not met, a penalty term is added:

[0041]

[0042] Time constraint: For time constraints, if the constraints are not met, a penalty term is added:

[0043]

[0044] Water Constraint: For water constraints, if the constraints are not met, a penalty term is added:

[0045]

[0046] The final energy function E(x) is the weighted sum of the above terms:

[0047] E(x)=w1·E1(x)+w2·E2(x)+w3·E flow (x)+w4·E time (x)+w5·E water (x) (22)

[0048] Among them, w1, w2, w3, w4, and w5 are weight coefficients used to balance the influence of different objectives and constraints.

[0049] Step 2: Determine whether the loop termination condition is met

[0050] The maximum number of iterations or the minimum temperature are set to determine whether the simulated annealing algorithm has terminated. If the termination condition is met, proceed to Step 3; otherwise, continue to update the solution.

[0051] Step 3: Randomly select a new solution and calculate the energy difference

[0052] In the domain N(x (i) ) randomly selects a new solution x (j) , and calculate the energy difference between the two:

[0053] ΔE ij = E(x (j) ) - E(x (i) ) (15)

[0054] If ΔE ij ≤0, that is, the energy of the new solution is less than or equal to the energy of the current solution, then directly accept the new solution, and let x (i) =x (j) ;

[0055] If ΔE ij >0, that is, the energy of the new solution is greater than the energy of the current solution, then it is accepted according to the probability p, and the calculation is

[0056]

[0057] If p>η, where η is a random number uniformly distributed between (0,1), then let x (i) =x (j) , otherwise keep the current solution x (i) , repeat Step 2; T k represents the temperature at the kth iteration. The temperature gradually decreases over time, so that the system gradually transitions from a higher energy state (worse solution) to a lower energy state (better solution).

[0058] Step 4: Cool down and update the temperature

[0059] According to the cooling formula T k+1 =d(T k ) updates the temperature, where d(T k ) is the cooling function, which is generally in the form of T k+1 =αd(T k ), α is a constant less than 1.

[0060] Increase the number of iterations to k = k + 1 and determine whether the Metropolis criterion is satisfied. If so, the calculation ends and the result is output; otherwise, return to Step 2 and continue iterating.

[0061] Through the optimization solution of the simulated annealing algorithm, the flow of each branch canal will be continuously adjusted during each iteration, and the water distribution between the main canal and branch canals will be optimized to ensure smooth water flow, minimize water abandonment, and meet all flow and time constraints.

[0062] As a further solution, the main canal length in step 2 is determined based on the location of the water supply branch canal in the main canal to the pumping station, where the main canal length data in the model input is different under different scenarios.

[0063] Assume that the main canal supplies water to n branch canals on the same side, and the distances from the branch canals to the pumping station are ranked 1, 2, ... n from near to far. The specific length of the main canal is:

[0064] If the branch canal supplies water to branch canal n on a certain day, the length of the main canal is from the pumping station to branch canal n;

[0065] …

[0066] If the branch canal supplies water to branch canal 2 on a certain day, the length of the main canal is from the pumping station to branch canal 2;

[0067] If the branch canal supplies water to branch canal 1 on a certain day, the length of the main canal is from the pumping station to branch canal 1.

[0068] For further optimization, the irrigation water requirement in step 1 is calculated based on the irrigation area using remote sensing evapotranspiration combined with effective precipitation. The calculation formula is as follows:

[0069] R=ET-P (23)

[0070] Where: R is irrigation water requirement; ET is evapotranspiration; P is effective precipitation.

[0071] The advantages and beneficial effects of the present invention are:

[0072] The canal system optimization water distribution model for constant-power pump units described in this paper takes into account the fact that pumps in pumping stations often operate at constant power. This model establishes a canal system optimization water distribution model by limiting the main canal water flow rate to the same level as the pumping station's output power. This model can be used to simulate actual water distribution processes in some irrigation areas where pumps operate at constant power. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The present invention will be further described below with reference to the accompanying drawings and examples.

[0074] Figure 1 This is a time diagram of the main canal flow in the Fuliu Irrigation District simulated by the model in Example 1;

[0075] Figure 2 This is a flow time diagram of the four branch canals in the Fuliu Irrigation District simulated by the model in Example 1. DETAILED DESCRIPTION

[0076] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] Example 1

[0078] This example uses the Fuliu Irrigation District in Weinan City, Shaanxi Province as the research area. The Fuliu Irrigation District includes one main canal and four branch canals. The optimized water distribution simulation includes the following steps:

[0079] Step 1, data collection in the study area:

[0080] The pump units corresponding to the main canal flow in the Fuliu Irrigation District are used in different numbers of pumps in this simulation (the power of the small unit is 1.6m 3 / s, large unit power 2.2m 3 / s, power of large and small units running: 3.8m3 / s) The flow rate corresponding to the constant power is [C1=1.6、C2=2.2、C3=3.8], m 3 / s; the planned rotation irrigation cycle is 30 days; the irrigation water volume of each branch canal control area is calculated based on the irrigation area using remote sensing evapotranspiration combined with effective precipitation.

[0081] Among them, the minimum flow reduction coefficient α of the main and branch channels dI , α dj Both are 0.4;

[0082] The reduction coefficient of water leakage after anti-leakage measures are taken in the main and branch canals β I , β j Both are 0.1;

[0083] The flow increase coefficient of the main canal is shown in the table below

[0084] Design flow <1 1-5 5-10 10-30 >30 Increase coefficient 1.325 1.275 1.225 1.175 1.125

[0085] Increased flow coefficient of branch canals α u1 =α u2 =α u3 =1.30α u4 =1.32;

[0086] Soil permeability coefficient A of main and branch canal beds I 、A j Both are 3.4;

[0087] Soil permeability index of main and branch canals m I 、m j Both are 0.5;

[0088] Step 2: Establish a canal system optimized water distribution model:

[0089] Determine the objective function 1, the goal is to ensure smooth transition of water flow, and the function formula is as follows:

[0090]

[0091] Where S is the standard deviation of the gross flow rate of the upper channel over time; t is the tth day of the irrigation cycle; T is the rotation irrigation cycle, d; q I ' t is the gross water flow of the main canal, m 3 / s;q It is the net water flow of the main canal, m 3 / s; j represents the branch channel number, and J is the total number of branch channel numbers; is the average water flow rate of the main canal during the entire period, m 3 / s;Q′ j , Qj is the gross flow and net flow of the branch canal, m 3 / s; f(t) is a 0-1 variable, reflecting the water distribution status of each branch canal within the time period, 1 means water is being distributed within the time period, and 0 means no water is being distributed within the time period; L I , l j are the lengths of the main and branch canals, km; β I , β j A is the reduction coefficient of water leakage after anti-leakage measures are taken for the main and branch canals; I , A j are the soil permeability coefficients of the main and branch canals respectively; m I , m j are the soil permeability indexes of the main and branch canals respectively; t′ j , t″ j are the start and end irrigation time of each branch canal, d;

[0092] Determine objective function 2, the goal is to minimize the amount of abandoned water: the function formula is as follows (7):

[0093]

[0094] Among them, P is the amount of water discarded in the irrigation area after the irrigation area replenishes the irrigation water demand; R j is the irrigation water demand of crops in the control area of ​​branch canal j, m 3 ;

[0095] Determine constraint 1, flow constraint:

[0096] α dj Q dj ≤Q′ j ≤α uj Q dj (8)

[0097] q′ It =[C1, C2, ..., C n ] (9)

[0098] α dI q dI ≤q′ It ≤α uI q dI (10)

[0099] Among them, Q dj is the design flow of the branch canal, m 3 / s; α dj is the minimum flow reduction coefficient of the branch canal; α uj is the flow coefficient of the branch canal, C1, C2, ..., Cn are the flow rates corresponding to the constant power of the irrigation area water pump unit under different scenarios, m3 / s, n is a natural number not less than 1, indicating the number of different scenarios; q dI Design flow rate of the main canal, m 3 / s; α dI is the minimum flow reduction coefficient of the main canal, α uI Increase the flow coefficient for the main canal;

[0100] Determine constraint 2, time constraint:

[0101] 0≤t′ j ≤t″ j ≤T (11)

[0102] Determine constraint 3, flow constraint:

[0103]

[0104] Q j (t″ j -t′ j )≥R j (13)

[0105]

[0106] Where: W max is the allowable water supply, m 3 ;

[0107] The main canal length L mentioned in step 2 is different in different scenarios. The main canal length data in the model input is different in different scenarios. The scenario of Fuliu Irrigation District is: the main canal supplies water to four branch canals on the same side, and the distances from the branch canal outlets to the pump station are ranked 1, 2, 3, and 4 from near to far. The specific main canal length is:

[0108] If the branch canal supplies water to branch canal 4 on a certain day, the length of the main canal is from the pumping station to branch canal 4;

[0109] If the branch canal supplies water to branch canal 3 on a certain day, the length of the main canal is from the pumping station to branch canal 3;

[0110] If the branch canal supplies water to branch canal 2 on a certain day, the length of the main canal is from the pumping station to branch canal 2;

[0111] If the branch canal supplies water to branch canal 1 on a certain day, the length of the main canal is from the pumping station to branch canal 1.

[0112] Step 3: Use the simulated annealing algorithm to solve the canal system optimal water distribution model:

[0113] Step 1: Initialization

[0114] Initialization temperature: First, set a sufficiently large initial temperature T0>0. In this embodiment, T0=100

[0115] Initialization solution: set an initial solution x (0) The solution can be the water distribution status of the main canal and each branch canal in the current irrigation system. That is, the water distribution flow and water distribution time of the main and branch canals can be obtained through simple heuristic methods or directly from experimental data.

[0116] Calculate energy: Calculate the initial solution x (0) The energy value E(x (0) ), where the energy function E(x) is calculated based on multiple factors, including objective function 1 (smooth water flow transition) and objective function 2 (minimizing water abandonment). Constraints are also factors considered in the energy function. The energy function appropriately weights the objective function and uses the constraints as penalty terms. This is shown in formulas (17) to (22) above.

[0117] Step 2: Determine whether the loop termination condition is met

[0118] The maximum number of iterations or the minimum temperature are set to determine whether the simulated annealing algorithm has terminated. If the termination condition is met, proceed to Step 3; otherwise, continue to update the solution.

[0119] Step 3: Randomly select a new solution and calculate the energy difference

[0120] In the domain N(x (i) ) randomly selects a new solution x (j) , and calculate the energy difference between the two:

[0121] ΔE ij = E(x (j) ) - E(x (i) ) (15)

[0122] If ΔE ij ≤0, that is, the energy of the new solution is less than or equal to the energy of the current solution, then directly accept the new solution, let x (i) =x (j) ;

[0123] If ΔE ij >0, that is, the energy of the new solution is greater than the energy of the current solution, then it is accepted according to the probability p, and the calculation is

[0124]

[0125] If p>η, where η is a random number uniformly distributed between (0,1), then let x (i) =x (j) , otherwise keep the current solution x (i) , repeat Step 2;

[0126] Step 4: Cool down and update the temperature

[0127] According to the cooling formula T k+1 =d(T k ) updates the temperature, where d(T k ) is the cooling function, which is generally in the form of T k+1 =αd(T k ), α is a constant less than 1.

[0128] Increase the number of iterations to k = k + 1 and determine whether the Metropolis criterion is satisfied. If so, the calculation ends and the result is output; otherwise, return to Step 2 and continue iterating.

[0129] Through the optimization solution of the simulated annealing algorithm, the flow of each branch canal will be continuously adjusted during each iteration, and the water distribution between the main canal and the branch canals will be optimized to ensure smooth water flow, minimize water abandonment, and meet all flow, water volume and time constraints. The final configuration result is as follows Figure 1 and Figure 2 ,Depend on Figure 1 It can answer the question of when and how large the units should be set for the optimal irrigation system under multiple objectives and constraints. Figure 2 It shows the real-time status of the water distribution flow of each branch canal.

[0130] Comparative Example 1:

[0131] Regarding the algorithm selection in step 3 of Example 1, the study also used a multi-objective particle swarm algorithm to solve it. However, in most cases, the solution of the model did not converge when using the multi-objective particle swarm algorithm, and no feasible solution was found. Therefore, the simulated annealing algorithm in the embodiment was used to solve it.

[0132] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention and is not limiting. Although the present invention is described in detail with reference to the preferred arrangement scheme, ordinary technicians in this field should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for constructing a canal system optimized water distribution model for a constant power water pump unit, characterized in that: The following steps are involved: Step 1: Collect the data of the study area: including the flow corresponding to the constant power of the irrigation area water pump unit under different scenarios [C1, C2, ..., C n ],m 3 / s, the number of water pumps used in different scenarios is different; the planned rotation irrigation cycle, d; Design flow of main canal, m 3 / s; Design flow of each branch canal, m 3 / s; minimum flow reduction coefficient for main and branch canals, increased flow coefficient for main and branch canals; leakage reduction coefficient for main and branch canals after anti-leakage measures are taken; soil permeability coefficient for main and branch canal beds; soil permeability index for main and branch canal beds; main canal length, km; length of each branch canal, km; irrigation water demand for the area controlled by each branch canal, m 3 ; Step 2: Establish a canal system optimized water distribution model, including: Determine the objective function 1, the goal is to ensure smooth transition of water flow, and the function formula is as follows: Where S is the standard deviation of the gross flow rate of the upper channel over time; t is the tth day of the irrigation cycle; T is the rotation irrigation cycle, d; q I ' t is the gross water flow of the main canal, m 3 / s;q It is the net water flow of the main canal, m 3 / s; j represents the branch channel number, and J is the total number of branch channel numbers; is the average water flow rate of the main canal during the entire period, m 3 / s;Q j ′, Q j is the gross flow and net flow of the branch canal, m 3 / s; f(t) is a 0-1 variable, reflecting the water distribution status of each branch canal within the time period, 1 means water is being distributed within the time period, and 0 means no water is being distributed within the time period; L I , l j are the lengths of the main and branch canals, km; β I , β j A is the reduction coefficient of water leakage after anti-leakage measures are taken for the main and branch canals; I , A j are the soil permeability coefficients of the main and branch canals respectively; m I , m j are the soil permeability indexes of the main and branch canals, respectively; t j ′, t j ″ are the start and end irrigation time of each branch canal, d; Determine objective function 2, the goal is to minimize the amount of abandoned water: the function formula is as follows (7): Among them, P is the amount of water discarded in the irrigation area after the irrigation area replenishes the irrigation water demand; R j is the irrigation water demand of crops in the control area of ​​branch canal j, m 3 ; Determine constraint 1, flow constraint: α dj Q dj ≤Q′ j ≤α uj Q dj (8) q′ It =[C1、C2、…、C n ] (9) α dI q dI ≤q′ It ≤α uI q dI (10) Among them, Q dj is the design flow of the branch canal, m 3 / s; α dj is the minimum flow reduction coefficient of the branch canal; α uj is the flow coefficient of the branch canal, C1, C2, ..., Cn are the flow rates corresponding to the constant power of the irrigation area water pump unit under different scenarios, m 3 / s, n is a natural number not less than 1, indicating the number of different scenarios; q dI Design flow rate of the main canal, m 3 / s; α dI is the minimum flow reduction coefficient of the main canal, α uI Increase the flow coefficient for the main canal; Determine constraint 2, time constraint: 0≤t′ j ≤t″ j ≤T (11) Determine constraint 3, water quantity constraint: Q j (t″ j -t′ j )≥R j (13) Where: W max is the allowable water supply, m 3 ; In step 3, the simulated annealing algorithm is used to solve the canal system optimal water distribution model. During the iteration process, the flow of each branch canal is adjusted to optimize the water distribution between the main canal and the branch canals, so that the water flow is smooth, the amount of water discarded is minimized, and the flow, water volume and time constraints are met.

2. The method for constructing a canal system optimized water distribution model according to claim 1, characterized in that: Step 3 includes the following steps: Step 1: Initialization, including: Initialization temperature: set the initial temperature T0>0; Initialization solution: set an initial solution x (0) , the solution is the water distribution status of the main canal or each branch canal in the current irrigation system; that is, the water distribution flow and water distribution time of the main canal and branch canal; Calculate energy: Calculate the initial solution x (0) The energy value E(x (0) ); Step 2: Determine whether the loop termination condition is met: The maximum number of iterations or the minimum temperature is set to determine whether the simulated annealing algorithm is terminated; if the termination condition is met, go to Step 3; otherwise, continue to update the solution; Step 3: Randomly select a new solution and calculate the energy difference: In the domain N(x (i) ) randomly selects a new solution x (j) , and calculate the energy difference between the two solutions: ΔE ij = E(x (j) ) - E(x (i) ) (15) If ΔE ij ≤0, then let x (i) =x (j) ; If ΔE ij >0, then accept it with probability p, If p>η, where η is a random number uniformly distributed between (0,1), then let x (i) =x (j) , otherwise keep the current solution x (i) , repeat Step 2; T k represents the temperature at the kth iteration; Step 4: Cool down and update the temperature: According to the cooling formula T k+1 =d(T k )Update the temperature, Increase the number of iterations k = k + 1 and determine whether the Metropolis criterion is satisfied. If so, the calculation ends and the result is output. Otherwise, return to Step 2 and continue iterating.

3. The method for constructing a canal system optimized water distribution model according to claim 2, characterized in that: Step 3: The calculation process of energy value E(x): In order to ensure smooth water flow, the first term of the energy function is defined as: The minimum amount of water discarded is the second term of the energy function: Traffic Constraints: For traffic constraints, if the constraints are not met, a penalty term is added: Time constraint: For time constraints, if the constraints are not met, a penalty term is added: Water Constraint: For water constraints, if the constraints are not met, a penalty term is added: The final energy function E(x) is the weighted sum of the above terms: E(x)=w1·E1(x)+w2·E2(x)+w3·E flow (x)+w4·E time (x)+w5·E water (x) (22)Wherein, w1, w2, w3, w4, and w5 are weight coefficients used to balance the influence of different objectives and constraints.

4. The method for constructing a canal system optimized water distribution model according to claim 1, characterized in that: The irrigation water requirement in step 1 is calculated based on the irrigation area using remote sensing evapotranspiration combined with effective precipitation. The calculation formula is as follows: R=ET-P (23) Where: R is irrigation water requirement; ET is evapotranspiration; P is effective precipitation.

5. The method for constructing a canal system optimized water distribution model according to claim 1, characterized in that: The main canal length described in step 2 is different in different scenarios. The main canal length data in the model input is different and is determined according to the location of the water supply branch canal in the main canal to the pumping station.

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