Irrigation area water volume scheduling optimization method based on index triangular optimization algorithm

Abstract

The invention discloses an irrigation area water volume scheduling optimization method based on an index triangular optimization algorithm, and relates to the field of water conservancy informatization. By constructing an optimization model with the branch channel water supply amount as a decision variable and combining an index triangular optimization algorithm, efficient distribution and refined regulation and control of the water amount of the irrigation area are achieved. The water demand of each irrigation piece, a branch channel and different crops in each irrigation period in an irrigation area is integrated, constraint conditions such as the maximum water supply amount of a water source, the water delivery capacity of the channels and the irrigation pieces and the like are introduced, and a multi-objective function system for minimizing the overall water shortage rate and maximizing the crop yield is constructed. Through an index triangular perturbation mechanism, the global exploration capability is enhanced at the initial stage of search, the local search precision is optimized at the later stage, the stability and convergence of solutions are ensured, compared with a traditional method, the water utilization efficiency and the crop yield are improved, the stability of multi-target optimization in a complex environment is enhanced, and the method is suitable for large-scale popularization and application. And a reliable technical support is provided for the construction of an intelligent irrigation area water volume scheduling system.

Description

Technical Field

[0001] This invention relates to the field of water conservancy information technology, and in particular to an optimization method for irrigation district water allocation based on the exponential triangular optimization algorithm. Background Technology

[0002] Irrigation district water allocation is a crucial link in agricultural production, and its technological development level directly affects food security and water utilization efficiency. Currently, irrigation district water allocation faces many challenges, especially in the management of multiple water sources and complex hydraulic structures.

[0003] Currently, water allocation in most irrigation districts relies primarily on manual experience for decision-making, lacking the ability to dynamically coordinate the allocation of multiple water sources and hydraulic structures. This experience-based allocation model cannot adapt to real-time changes in hydrological and meteorological conditions, resulting in low water allocation efficiency and inaccurate resource utilization. Furthermore, the hydraulic infrastructure within irrigation districts is complex, comprising multiple main canals, branch canals, and hydraulic structures. Existing allocation systems are slow to react and lack sufficient control precision when faced with such a complex hydraulic network.

[0004] In actual operation, irrigation district scheduling systems generally suffer from insufficient intelligence, especially when facing complex situations such as overlapping water usage periods for different crops, high irrigation timeliness, and uneven water supply and demand. Existing systems struggle to achieve efficient and precise scheduling. Traditional scheduling methods rely heavily on fixed rules or manual experience, failing to dynamically adjust scheduling strategies based on real-time changes in water demand and supply conditions. This easily leads to uneven water supply, resource waste, and negatively impacts the overall irrigation efficiency and crop yield of the irrigation district.

[0005] To overcome the limitations of traditional manual decision-making, optimization algorithms (such as genetic algorithms and particle swarm optimization) have been gradually adopted in recent years to improve irrigation water allocation. However, existing optimization algorithms have certain limitations in practical applications, mainly in the following aspects: First, physical constraints are ignored. Existing optimization algorithms fail to fully consider the physical constraints of canal systems and hydraulic structures within the irrigation area, making it difficult to effectively implement the allocation plan and even resulting in resource waste. Second, multi-objective optimization is difficult. When facing multiple objectives such as agricultural irrigation and rural water supply, existing systems fail to achieve optimal resource allocation and struggle to balance the needs of all parties.

[0006] These technical deficiencies not only restrict the overall benefits of irrigation water use but also affect the achievement of irrigation design goals. Therefore, it is urgent to develop an intelligent irrigation water allocation technology to overcome key technical challenges such as precise control of complex canal systems and hydraulic structures, and multi-objective collaborative optimization, in order to improve the utilization efficiency of irrigation water and ensure the stability of agricultural production and township water supply. Summary of the Invention

[0007] To address the aforementioned problems, this invention provides an irrigation district water allocation optimization method based on the exponential triangular optimization algorithm. By combining the exponential triangular optimization algorithm, the water allocation in the irrigation district is optimized, improving water utilization efficiency and crop yield. It can comprehensively consider the actual water demand of each irrigation area, branch canal, and different crops in each irrigation period, fully reflecting the spatial and temporal differences in water use, and improving the scientificity and accuracy of the scheduling plan.

[0008] A method for optimizing irrigation district water allocation based on the exponential triangular optimization algorithm includes the following steps:

[0009] Step S1: Collect irrigation district data and calculate the water conveyance capacity of each canal and the water requirements of crops.

[0010] Extract channel cross-sectional parameters and design standards from irrigation district data to determine the water conveyance capacity of each branch canal; extract the number and location of branch canals and classify them according to irrigation season and irrigation area;

[0011] The water requirements of crops in the irrigation area are extracted, and a hierarchical calculation method of overall decomposition-local accounting-local summarization is adopted. The total water requirements of different crops in each branch canal and each irrigation period are obtained by accumulating according to the irrigation period and location.

[0012] Step S2, determine the optimization objective and constraints:

[0013] The decision variables to be optimized are set as the water supply of the branch canals under each irrigation area during different irrigation periods. Constraints including water source-branch canal-irrigation area triple constraints are constructed, as well as a dual objective function of minimizing the overall water shortage rate of the irrigation area and maximizing crop yield. The parameters required for solving are clearly defined.

[0014] Step S3, optimize irrigation district water allocation:

[0015] The decision variables, constraints, and parameters required for the objective function are input into the model. The exponential triangular optimization algorithm generates an initial population matrix based on the decision variable dimensions and boundaries, and initializes the optimal solution. After parameter setting, a perturbation factor is generated to dynamically update the individual positions. The solutions beyond the boundary are forcibly absorbed. After iteration, individuals are sorted by fitness to update the optimal solution, resulting in the optimized irrigation district water allocation scheme and objective function value.

[0016] Furthermore, in step S1, the hierarchical calculation method of overall decomposition-partial accounting-partial summarization is adopted, and the total water demand of different crops in each irrigation canal during each irrigation period is obtained by accumulating according to irrigation period and location, including:

[0017] The calculation involves determining the water requirements of different crops within the irrigation district during each irrigation period. The calculation unit is further subdivided according to the spatial hierarchy of irrigation district, irrigation area, and branch canal. The water requirements of crops within the irrigation district are broken down to the scale of irrigation areas and branch canals, and the water requirements of different crops within each irrigation area and branch canal are calculated separately for each irrigation period. At the branch canal level, within the same irrigation period, the water requirements of different crops are summed to obtain the total water requirements of the branch canal for the corresponding irrigation period. This represents the sum of crop water requirements, calculated using the following formula:

[0018]

[0019] In the formula Representing the One stenosis, the first In each branch canal, crops In the Water demand for each irrigation period.

[0020] Furthermore, in step 2, the constraint is that the water supply of each branch canal must not exceed the maximum water supply at the water source during the current irrigation period. The calculation formula is as follows:

[0021]

[0022] in, Representing the One stenosis, the first The total water supply for the t-th irrigation period from each branch canal, in m³. 3 ; For the first The maximum water supply at the water source during the irrigation season;

[0023] Furthermore, the sum of the water supply from all branch canals must not exceed the maximum water supply from the water source during the current irrigation season, calculated using the following formula:

[0024]

[0025] The maximum water supply of each branch canal during all irrigation periods must not exceed the upper limit of the water conveyance capacity of that branch canal. The calculation formula is as follows:

[0026]

[0027] in, Representing the One stenosis, the first The upper limit of the water conveyance capacity of each branch canal, where t is the irrigation period. This represents the total number of irrigation periods;

[0028] Furthermore, belonging to the first The maximum sum of the water supply volumes of all branch canals within an irrigation area during the same irrigation period shall not exceed the upper limit of the irrigation area's water conveyance capacity, as shown in the following formula:

[0029]

[0030] In the formula, For the total irrigation period, For film filling The total number of channels within the country For the first One stenosis, the first The first branch canal in the... Water supply for each irrigation period For the first One stenosis, the first The upper limit of the water conveyance capacity of each branch canal. For the first The maximum water supply from the water source during the irrigation season. For the first The upper limit of water conveyance capacity of each irrigation area.

[0031] Furthermore, in step 2, the overall water shortage rate of the irrigation area is minimized, and the calculation formula is as follows:

[0032]

[0033] The formula for maximizing crop yield is as follows:

[0034]

[0035] In the formula, Representing the One stenosis, the first Crops in each ditches during the irrigation period The average sensitivity index, For the first One stenosis, the first The first branch canal in the... Water supply for each irrigation period Representing the One stenosis, the first The crop planting area of ​​each branch canal, per mu. Representing the One stenosis, the first Crop yield per canal, tons mu; Representing the One stenosis, the first The first branch canal in the... The sum of water requirements for crops during each irrigation period.

[0036] Furthermore, in step S2, the objective function is optimized through a weighted summation method to achieve dual-objective collaborative optimization. The collaborative objective function is defined as follows:

[0037]

[0038] in, For water shortage rate weight, As a weight for crop yield, satisfying Adjustments will be made dynamically based on the actual needs of the irrigation district. The theoretical maximum value, The theoretical maximum value is obtained by normalization to eliminate dimensional differences and ensure fair coordination between the two objectives.

[0039] Furthermore, step S3 also includes steps to improve search efficiency:

[0040] 1) Calculate key iteration points during the parameter setting stage. The calculation formula is as follows:

[0041]

[0042] Algorithm search based on the current iteration number Divided into the global exploration phase ( ) and partial development phase ( This enables dynamic adjustment of search strategies.

[0043] 2) During the iteration process, when the critical iteration point is reached... At that time, a contraction interval is constructed based on the current optimal solution X* and the suboptimal solution X**. The formula for calculating the boundary of the contraction interval is as follows:

[0044]

[0045]

[0046] Used for the next stage of population initialization;

[0047] 3) Individual ranking updates the optimal solution. After each iteration, all individuals are ranked according to their fitness function values ​​to determine the optimal and suboptimal solutions, record the optimal target value, and update the convergence curve. The ranking results are fed back to the subsequent perturbation factor generation and interval adjustment process to dynamically optimize the search strategy.

[0048] Furthermore, in step S3, generating the initial population matrix and initializing the optimal solution based on the decision variable dimensions and boundaries includes:

[0049] Initializing the population and determining the initial optimal solution and optimal objective function are based on the dimensionality of the decision variables. and its upper and lower boundaries Randomly and uniformly generate within the decision space containing Initial population matrix of individuals :

[0050]

[0051] Each individual This represents a complete water supply scheduling scheme, i.e., the allocation of irrigation water to all channels; utilizing a multi-objective function. This includes calculating the overall water shortage rate and crop yield, determining the objective function value for each individual crop, and identifying the initial optimal solution. and the optimal objective function value The calculation formula is as follows:

[0052]

[0053] And record the changes in the optimal objective function value for each generation.

[0054] Furthermore, in step S3, the forced absorption process for the superboundary solution includes:

[0055] Boundary and constraint handling; if the optimal solution is updated after iteration. Exceeding boundary conditions During the first iteration Then, forced absorption to the boundary is performed, and the calculation formula is as follows:

[0056]

[0057] If the updated optimal solution does not meet the inequality constraints, an absorption strategy is adopted, that is, values ​​that exceed the boundary are set as boundary values.

[0058] Furthermore, in step S3, the step of updating the optimal solution by sorting individuals by fitness after iteration includes:

[0059] If the number of iterations has not reached the set maximum number of iterations If the number of iterations reaches a certain threshold, the perturbation factor is regenerated and the individual position is dynamically updated; if the number of iterations reaches a certain threshold, the perturbation factor is regenerated and the individual position is dynamically updated. When the time is up, the algorithm terminates and outputs the final optimal scheduling scheme. Corresponding objective function value This satisfies multiple constraints and completes the optimized scheduling of water volume in the irrigation area.

[0060] Furthermore, in step S3, the formula for generating the dynamic disturbance factor is as follows:

[0061]

[0062] Disturbance coefficient The calculation formula is as follows:

[0063]

[0064] in, A random number between 0 and 1.

[0065] The beneficial effects of this invention are: This invention provides an irrigation district water allocation optimization method based on the exponential triangular optimization algorithm. Under multi-period irrigation and multi-canal systems, it constructs water supply at different spatial scales (such as irrigation patches and branch canals) and time scales (each irrigation period) as decision variables, thereby realizing refined modeling and optimized control of the irrigation district water allocation process.

[0066] The optimization algorithm is designed based on the exponential trigonometric perturbation strategy. In the early stage of the search, the perturbation direction and intensity are constructed by combining trigonometric functions and exponential functions to enhance the global search capability of the algorithm. In the later stage of the search, hyperbolic tangent and exponential functions are introduced to perform local fine search, which improves the stability and optimality of the solution and has strong convergence performance and global optimization capability.

[0067] In terms of optimization objectives, a multi-objective function system was constructed that includes minimizing the overall water shortage rate of the irrigation area and maximizing crop yield. This system was then uniformly transformed into a function with the water supply volume of the branch canals as the optimization decision variable, which facilitates unified modeling and solving, and improves the coordination of water allocation and the economy of crop production.

[0068] In terms of constraint control, boundary constraints such as the maximum water supply capacity of the water source, the water conveyance capacity of the channel, and the water conveyance capacity of the irrigation area are fully introduced. The feasibility of the scheduling results and the engineering adaptability are ensured through boundary absorption, interval contraction and dynamic update mechanisms.

[0069] This invention unifies and collaboratively optimizes scheduling elements such as canal water supply, crop water demand, and irrigation period, so that the scheduling results can balance water use efficiency and agricultural output benefits while meeting physical constraints. It also improves the reliability of decision variables in complex environments by relying on the exponential triangular optimization algorithm. This invention supports the construction of intelligent water scheduling systems and enhances the scheduling adaptability and system operation stability in normal operating scenarios.

[0070] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0071] Figure 1 This is a framework diagram of the irrigation district water allocation optimization method based on the exponential triangular optimization algorithm of the present invention.

[0072] Figure 2 This is a graph showing the changes of several key variables in the exponential triangular optimization algorithm of this invention under different iteration numbers. Detailed Implementation

[0073] Example 1:

[0074] This embodiment presents an irrigation district water allocation optimization method based on the exponential triangular optimization algorithm, such as... Figure 1As shown, it includes the following steps:

[0075] In agricultural irrigation systems, irrigation districts → irrigation patches → branch canals form a hierarchical relationship from top to bottom. An irrigation district is an irrigation area that relies on a unified water source, backbone water conveyance, and irrigation and drainage facilities, and is the highest-level administrative and engineering unit. Irrigation patches are secondary areas of irrigation districts divided according to topography, administrative divisions, etc. Branch canals are extensions of the backbone canals of irrigation districts, conveying water to the farmland of irrigation patches.

[0076] Step S1: Collect irrigation district data and calculate the water conveyance capacity of each canal and the water requirements of crops.

[0077] Extract channel cross-sectional parameters and design standards from irrigation district data to determine the water conveyance capacity of each branch canal; extract the number and location of branch canals and classify them according to irrigation period and irrigation area; extract the water requirements, types and areas of crops in the irrigation district, and use a hierarchical calculation method of overall decomposition-partial accounting-partial summarization to accumulate according to irrigation period and location to obtain the total water requirements of different crops in each branch canal and each irrigation period;

[0078] S101. Based on the irrigation district's planning data, calculate the total number of irrigation areas (I), the total number of canals in the canal system (J), and their locations; obtain the maximum water supply at the water source, canal cross-sectional parameters, and calculate the upper limit of the water conveyance capacity of each branch canal.

[0079] S102 employs a hierarchical calculation logic of overall decomposition—partial accounting—partial summarization to calculate the total water demand of different crops in each branch canal and irrigation period. First, calculate the water requirements of different crops within the entire irrigation area during each irrigation period; then, subdivide the calculation units according to the spatial hierarchy of "irrigation area → irrigation patch → branch canal", breaking down the water requirements of crops in the entire irrigation area to the scale of irrigation patch and branch canal, and calculate the water requirements of different crops in each irrigation patch and branch canal separately during each irrigation period; finally, at the branch canal level and within the same irrigation period, sum up the water requirements of different crops to obtain the total water requirements of the branch canal during the corresponding irrigation period. (This represents the sum of crop water requirements), and the calculation formula is as follows:

[0080]

[0081] In the formula, Representing the One stenosis, the first In each branch canal, crops In the Water demand for each irrigation period.

[0082] Step S2, determine the optimization objective and constraints:

[0083] Define the decision variables to be optimized, and based on the data obtained in step S1, construct the constraints and objective function of the model, and clarify the parameters required to solve the constraints and calculate the objective function.

[0084] S201, the optimization decision variable is the water supply of each irrigation area's sub-canals during different irrigation periods.

[0085] S202, the constraint is the water supply volume of each branch canal. All water supplies must not exceed the maximum water supply from the water source during the current irrigation season. The calculation formula is as follows:

[0086]

[0087] in, Representing the One stenosis, the first The total water supply for the t-th irrigation period from each branch canal, in m³. 3 ;

[0088] Water supply from all branch canals The sum must not exceed the maximum water supply from the water source during the current irrigation period. The calculation formula is as follows:

[0089]

[0090] in, For the first The maximum water supply at the water source during the irrigation season;

[0091] Water supply of each branch canal During all irrigation periods

[0092] ( The maximum value shall not exceed the upper limit of the water conveyance capacity of the branch canal. The calculation formula is as follows:

[0093]

[0094] in, For the first One stenosis, the first The upper limit of water conveyance capacity of each branch canal, where i is the irrigation area number, j is the branch canal number, and t is the irrigation period. This represents the total number of irrigation periods;

[0095] Belonging to the The maximum sum of the water supply volumes of all branch canals within an irrigation area during the same irrigation period must not exceed the upper limit of the irrigation area's water conveyance capacity. The calculation formula is as follows:

[0096]

[0097] In the formula, Total irrigation period ( ), Total number of tablets ( ), For film filling Total number of channels within ( ), For the first One stenosis, the first The first branch canal in the... Water supply for each irrigation period For the first The upper limit of water conveyance capacity of each irrigation area.

[0098] S203, the first objective function is the overall water shortage rate of the irrigation district, and the objective is to minimize the water shortage rate. The calculation formula is as follows:

[0099]

[0100] The second objective function is crop yield, and the goal is to maximize crop yield. The calculation formula is as follows:

[0101]

[0102] In the formula, Representing the One stenosis, the first Crops in each ditches during the irrigation period The average sensitivity index, Representing the One stenosis, the first Crop planting area (mu) of each branch canal. Representing the One stenosis, the first Crop yield per branch canal (tons) mu).

[0103] S204, The parameters required to calculate the constraints and objective function include: maximum water supply. Upper limit of water conveyance capacity of the branch canals Upper limit of water conveyance capacity of irrigation patches Crop water requirements Crop planting area Crop yield Average sensitivity index .

[0104] In the above text, The unit is m 3 The flow rate is calculated based on the duration of the irrigation period. The units are all in m 3 The conversion method is similar.

[0105] Step S3, optimize irrigation district water allocation:

[0106] The decision variables, constraints, and parameters required for the objective function are input into the exponential triangular optimization algorithm to obtain the optimized irrigation district water allocation scheme (optimized decision variables) and the objective function value.

[0107] S301, Initialize the population and determine the initial optimal solution and optimal objective function, based on the dimensions of the decision variables. and its upper and lower boundaries Randomly and uniformly generate within the decision space containing Initial population matrix of individuals :

[0108]

[0109] Each individual This represents a complete water supply scheduling scheme, i.e., the allocation of irrigation water to all channels. It utilizes a multi-objective function. (Including overall water shortage rate and crop yield) Calculate the objective function value for each individual, and then determine the initial optimal solution. and the optimal objective function value As shown below:

[0110]

[0111] And begin recording the changes in the optimal objective function value for each generation.

[0112] S302, Parameter settings and search phase division, setting the maximum number of iterations. Introducing adjustment parameters Calculate key iteration points As shown below:

[0113]

[0114] Algorithm search based on the current iteration number Divided into the global exploration phase ( ) and partial development phase ( This enables dynamic adjustment of search strategies.

[0115] S303, Generate dynamic perturbation factor and position update, based on the current iteration number. ,individual In each dimension Calculate the disturbance factor :

[0116]

[0117] Define the perturbation coefficient :

[0118]

[0119] in, A random number between 0 and 1.

[0120] S3031, Global Search Phase ( The location of ) is updated if The updated solution As shown below:

[0121]

[0122] like The updated solution As shown below:

[0123]

[0124] in, For the first During the first iteration The optimal solution for the dimension. For the first Group, No. Dimensions The solutions obtained during rounds of iteration are both scalars.

[0125] S3032, Local search phase ( Position update, weight factor The calculation is as follows:

[0126]

[0127] like The updated solution As shown below:

[0128]

[0129] like The updated solution As shown below:

[0130]

[0131] S304, Boundary and Constraint Handling: If the updated solution exceeds the boundary conditions... (during the first iteration) If ), then it will be forcibly absorbed to the boundary:

[0132]

[0133] If the updated solution does not meet the inequality constraints, an absorption strategy is adopted, that is, values ​​that exceed the boundary are set as boundary values.

[0134] S305, Search interval shrinkage, in the number of iterations At that time, a shrinking interval is constructed based on the current optimal and second-best individual positions. The shrinking interval is used for initialization in the next stage to improve search efficiency, as follows:

[0135]

[0136]

[0137] in, For the first During the first iteration A suboptimal solution for the dimension. After completing the initial boundary contraction, The value is also updated accordingly, and used in subsequent iterations to shrink the interval multiple times to approximate the optimal solution. The update formula is as follows:

[0138]

[0139] S306, Individual Ranking and Optimal Solution Update: After each iteration, all individuals are ranked according to their fitness function values ​​to determine the optimal and second-best solutions. The optimal objective value is recorded, and the convergence curve is updated. The ranking results are fed back to the perturbation and interval adjustment process to ensure dynamic optimization of the search strategy.

[0140] S307, Termination Decision and Output: If the number of iterations has not been reached... If the number of iterations reaches a certain threshold, then return to step S303; When the time is up, the algorithm terminates and outputs the final optimal scheduling scheme. Corresponding objective function value This satisfies multiple constraints and completes the optimized scheduling of water volume in the irrigation area.

[0141] Example 2:

[0142] This embodiment is a preferred implementation of the optimization method described in Embodiment 1, such as... Figure 1 As shown, it includes the following steps:

[0143] Step S1: Collect irrigation district data and calculate the water conveyance capacity of each canal and the water requirements of crops.

[0144] Extract channel cross-sectional parameters and design standards from irrigation district data to determine the water conveyance capacity of each branch canal; extract the number and location of branch canals and classify them according to irrigation period and irrigation area; extract the water requirements, types and areas of crops in the irrigation district, and use a hierarchical calculation method of overall decomposition-partial accounting-partial summarization to accumulate according to irrigation period and location to obtain the total water requirements of different crops in each branch canal and each irrigation period;

[0145] S101, based on the irrigation district planning data, count the total number of irrigation areas I (preferably 3 in this embodiment), the total number of channels in the canal system, and their locations (preferably the total number of branch channels J in this embodiment is 10, and the total number of branch channels for each irrigation area is the same); obtain the maximum water supply at the water source, and the channel cross-sectional parameters (preferably trapezoidal open channel, Manning roughness coefficient n, cross-sectional area A, hydraulic radius R, and hydraulic gradient S in this embodiment), and calculate the upper limit of the water conveyance capacity of each branch channel using the Manning formula, as follows:

[0146]

[0147] in, For flow rate (m) 3 / s), The cross-sectional area of ​​the water passage (m²) 2 ), The hydraulic radius is (m). Hydraulic gradient, is the Manning roughness coefficient.

[0148] S102 employs a hierarchical calculation logic of overall decomposition—partial accounting—partial summarization to calculate the total water demand of different crops in each branch canal and irrigation period. First, calculate the water requirements of different crops in each irrigation period within the entire irrigation area (in this embodiment, the total number of irrigation periods T is preferably 12). Then, subdivide the calculation units according to the spatial hierarchy of "irrigation area → irrigation patch → branch canal", and break down the water requirements of crops in the entire irrigation area to the scale of irrigation patch and branch canal, and calculate the water requirements of different crops in each irrigation patch and branch canal in each irrigation period separately. Finally, at the branch canal level and within the same irrigation period, add up the water requirements of different crops (in this embodiment, the number of crop types is preferably 3, and the crops in each irrigation patch and branch canal are the same) to obtain the total water requirements of the branch canal in the corresponding irrigation period. (This represents the sum of crop water requirements), and the calculation formula is as follows:

[0149]

[0150] In the formula, Representing the One stenosis, the first In each branch canal, crops In the Water demand for each irrigation period.

[0151] Step S2, determine the optimization objective and constraints:

[0152] Define the decision variables to be optimized, and based on the data obtained in step S1, construct the constraints and objective function of the model, and clarify the parameters required to solve the constraints and calculate the objective function.

[0153] S201, the optimization decision variable is the water supply of each irrigation area's sub-canals during different irrigation periods.

[0154] S202, the constraint is the water supply volume of each branch canal. All water supplies must not exceed the maximum water supply from the water source during the current irrigation season. (In this embodiment, the preferred number of water sources is 1), and the calculation formula is as follows:

[0155]

[0156] in, Representing the One stenosis, the first The total water supply for the t-th irrigation period from each branch canal, in m³. 3 Since it is an optimization decision variable, no unit conversion is required.

[0157] Water supply from all branch canals The sum must not exceed the maximum water supply from the water source during the current irrigation period. The calculation formula is as follows:

[0158]

[0159] Water supply of each branch canal During all irrigation periods ( The maximum value shall not exceed the upper limit of the water conveyance capacity of the branch canal. The calculation formula is as follows:

[0160]

[0161] Where i is the irrigation area number, j is the branch canal number, and t is the irrigation period. This represents the total number of irrigation periods;

[0162] Belonging to the The maximum sum of the water supply volumes of all branch canals within an irrigation area during the same irrigation period must not exceed the upper limit of the irrigation area's water conveyance capacity. The calculation formula is as follows:

[0163]

[0164] In the formula, Total irrigation period ( ), For the first One stenosis, the first The first branch canal in the... Water supply for each irrigation period For the first The upper limit of water conveyance capacity of each irrigation area.

[0165] S203, the first objective function is the overall water shortage rate of the irrigation district, and the objective is to minimize the water shortage rate. The calculation formula is as follows:

[0166]

[0167] The second objective function is crop yield, and the goal is to maximize crop yield. The calculation formula is as follows:

[0168]

[0169] In the formula, Representing the One stenosis, the first Crops in each ditches during the irrigation period The average sensitivity index, Representing the One stenosis, the first Crop planting area (mu) of each branch canal. Representing the One stenosis, the first Crop yield per branch canal (tons) mu).

[0170] The weighted summation method is used to achieve dual-objective collaborative optimization. The collaborative objective function is defined as follows:

[0171]

[0172] in, (Water shortage rate weight) (Crop yield weight) satisfies Adjustments will be made dynamically based on the actual needs of the irrigation district. The theoretical maximum value, The theoretical maximum value is obtained by normalization to eliminate dimensional differences and ensure fair coordination between the two objectives.

[0173] S204, The parameters required to calculate the constraints and objective function include: maximum water supply. Upper limit of water conveyance capacity of the branch canals Upper limit of water conveyance capacity of irrigation patches Crop water requirements Crop planting area Crop yield Average sensitivity index .

[0174] In the above text, The unit is m 3 The flow rate is calculated based on the duration of the irrigation period. The units are all in m 3The conversion method is similar.

[0175] Step S3, optimize irrigation district water allocation:

[0176] The decision variables, constraints, and parameters required for the objective function are input into the exponential triangular optimization algorithm to obtain the optimized irrigation district water allocation scheme (optimized decision variables) and the objective function value.

[0177] S301, Initialize the population and determine the initial optimal solution and optimal objective function, based on the dimensions of the decision variables. (Its value is determined by the product of the total number of irrigation plots, the total number of branch canals, and the total number of irrigation periods); According to the above, the total number of irrigation plots I=3, the total number of branch canals J=10, and the total number of irrigation periods T=12, then: and its upper and lower boundaries Randomly and uniformly generate within the decision space containing Group of individuals (in this embodiment) The initial population matrix is ​​preferably 600. :

[0178]

[0179] Each individual This represents a complete water supply scheduling scheme, with the dimension being... That is, the allocation of irrigation water to all channels (which can be...) The column vectors are rearranged as (To enhance the readability of the results). Utilizing multi-objective functions. (Including overall water shortage rate and crop yield) Calculate the objective function value for each individual, and then determine the initial optimal solution. and the optimal objective function value The calculation formula is as follows:

[0180]

[0181] And begin recording the changes in the optimal objective function value for each generation.

[0182] S302, Parameter settings and search phase division, this embodiment preferably selects the maximum number of iterations. Preferred adjustment parameters Calculate key iteration points The calculation formula is as follows:

[0183]

[0184] Algorithm search based on the current iteration number Divided into global search phase ( ) and local search phase ( This enables dynamic search strategy adjustment; that is, the first 445 iterations are the global search phase, and after the number of iterations exceeds 446, it enters the local search phase.

[0185] S303, Generate dynamic perturbation factor and position update, based on the current iteration number. ,individual In each dimension Calculate the disturbance factor The calculation formula is as follows:

[0186]

[0187] The amplitude of the perturbation factor varies with the number of iterations. Increase and decrease, such as Figure 2 As shown in (a), the perturbation coefficient is defined. The calculation formula is as follows:

[0188]

[0189] in, A random number between 0 and 1.

[0190] S3031, Global Search Phase ( The location of ) is updated if The updated solution The calculation formula is as follows:

[0191]

[0192] like The updated solution The calculation formula is as follows:

[0193]

[0194] in, For the first During the first iteration The optimal solution for the dimension. For the first Group, No. Dimensions The solutions obtained during rounds of iteration are both scalars.

[0195] S3032, Local search phase ( Position update, weight factor The calculation formula is as follows:

[0196]

[0197] like The updated solution The calculation formula is as follows:

[0198]

[0199] like The updated solution The calculation formula is as follows:

[0200]

[0201] S304, Boundary and Constraint Handling: If the updated solution exceeds the boundary conditions... (during the first iteration) If the boundary is reached, then forced absorption is performed, and the calculation formula is as follows:

[0202]

[0203] If the updated solution does not meet the inequality constraints, an absorption strategy is adopted, that is, values ​​that exceed the boundary are set as boundary values.

[0204] S305, Search interval shrinkage, in the number of iterations At this time, a shrinking interval is constructed based on the current optimal and second-best individual positions. The shrinking interval is used for initialization in the next stage to improve search efficiency. The specific calculation formula is as follows:

[0205]

[0206]

[0207] in, For the first During the first iteration A suboptimal solution for the dimension. After completing the initial boundary contraction, The value is also updated accordingly, and used in subsequent iterations to shrink the interval multiple times to approximate the optimal solution. The update calculation formula is as follows:

[0208]

[0209] As can be seen from the above, the first contraction occurred in At that time; subsequently, interval shrinkage is performed every two iterations, such as Figure 2 As shown in (b).

[0210] S306, Individual Ranking and Optimal Solution Update: After each iteration, all individuals are ranked according to their fitness function values ​​to determine the optimal and second-best solutions. The optimal objective value is recorded, and the convergence curve is updated. The ranking results are fed back to the perturbation and interval adjustment process to ensure dynamic optimization of the search strategy.

[0211] S307, Termination Decision and Output: If the number of iterations has not reached 1000, return to step S303; if the number of iterations reaches 1000, the algorithm terminates and outputs the final optimal scheduling scheme. Corresponding objective function value This satisfies multiple constraints and completes the optimized scheduling of water volume in the irrigation area.

[0212] The above description is only used to illustrate the technical solutions of the present invention and is not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention (such as the application of various formulas, the order of steps, etc.) without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing irrigation district water allocation based on the exponential triangular optimization algorithm, characterized in that, Includes the following steps: Step S1: Collect irrigation district data and calculate the water conveyance capacity of each canal and the water requirements of crops. Extract channel cross-sectional parameters and design standards from irrigation district data to determine the water conveyance capacity of each branch canal; extract the number and location of branch canals and classify them according to irrigation season and irrigation area; The water requirements of crops in the irrigation area are extracted, and a hierarchical calculation method of overall decomposition-local accounting-local summarization is adopted. The total water requirements of different crops in each branch canal and each irrigation period are obtained by accumulating according to the irrigation period and location. Step S2, determine the optimization objective and constraints: The decision variables to be optimized are set as the water supply of the branch canals under each irrigation area during different irrigation periods. Constraints including water source-branch canal-irrigation area triple constraints are constructed, as well as a dual objective function of minimizing the overall water shortage rate of the irrigation area and maximizing crop yield. The parameters required for solving are clearly defined. Step S3, optimize irrigation district water allocation: The decision variables, constraints, and parameters required for the objective function are input into the model. The exponential triangular optimization algorithm generates an initial population matrix based on the decision variable dimensions and boundaries, and initializes the optimal solution. After parameter setting, a perturbation factor is generated to dynamically update the individual positions. The solutions beyond the boundary are forcibly absorbed. After iteration, individuals are sorted by fitness to update the optimal solution, resulting in the optimized irrigation district water allocation scheme and objective function value.

2. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, In step S1, the hierarchical calculation method of overall decomposition-partial accounting-partial summarization is adopted, and the total water demand of different crops in each irrigation canal during each irrigation period is obtained by accumulating according to irrigation period and location, including: The calculation involves determining the water requirements of different crops within the irrigation district during each irrigation period. The calculation unit is further subdivided according to the spatial hierarchy of irrigation district, irrigation area, and branch canal. The water requirements of crops within the irrigation district are broken down to the scale of irrigation areas and branch canals, and the water requirements of different crops within each irrigation area and branch canal are calculated separately for each irrigation period. At the branch canal level, within the same irrigation period, the water requirements of different crops are summed to obtain the total water requirements of the branch canal for the corresponding irrigation period. This represents the sum of crop water requirements, calculated using the following formula: ； In the formula Representing the One stenosis, the first In each branch canal, crops In the Water demand for each irrigation period.

3. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, In step 2, the constraint is that the water supply of each branch canal must not exceed the maximum water supply at the water source during the current irrigation period. The calculation formula is as follows: ； in, Representing the One stenosis, the first The total water supply for the t-th irrigation period from each branch canal, in m³. 3 ; For the first The maximum water supply at the water source during the irrigation season; Furthermore, the sum of the water supply from all branch canals must not exceed the maximum water supply from the water source during the current irrigation season, calculated using the following formula: ； The maximum water supply of each branch canal during all irrigation periods must not exceed the upper limit of the water conveyance capacity of that branch canal. The calculation formula is as follows: ； in, Representing the One stenosis, the first The upper limit of the water conveyance capacity of each branch canal, where t is the irrigation period. This represents the total number of irrigation periods; Furthermore, belonging to the first The maximum sum of the water supply volumes of all branch canals within an irrigation area during the same irrigation period shall not exceed the upper limit of the irrigation area's water conveyance capacity, as shown in the following formula: ； In the formula, For the total irrigation period, For film filling The total number of channels within the country For the first One stenosis, the first The first branch canal in the... Water supply for each irrigation period For the first One stenosis, the first The upper limit of the water conveyance capacity of each branch canal. For the first The maximum water supply from the water source during the irrigation season. For the first The upper limit of water conveyance capacity of each irrigation area.

4. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, In step 2, the overall water shortage rate of the irrigation area is minimized, and the calculation formula is as follows: ； The formula for maximizing crop yield is as follows: ； In the formula, Representing the One stenosis, the first Crops in each ditches during the irrigation period The average sensitivity index, For the first One stenosis, the first The first branch canal in the... Water supply for each irrigation period Representing the One stenosis, the first The crop planting area of ​​each branch canal, per mu. Representing the One stenosis, the first Crop yield per canal, tons mu; Representing the One stenosis, the first The first branch canal in the... The sum of water requirements for crops during each irrigation period.

5. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, In step S2, the objective function is optimized through a weighted summation method to achieve dual-objective collaborative optimization. The collaborative objective function is defined as follows: ； in, For water shortage rate weight, As a weight for crop yield, satisfying Adjustments will be made dynamically based on the actual needs of the irrigation district. The theoretical maximum value, The theoretical maximum value is obtained by normalization to eliminate dimensional differences and ensure fair coordination between the two objectives.

6. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, Step S3 also includes steps to improve search efficiency: 1) Calculate key iteration points during the parameter setting stage. The calculation formula is as follows: ； Algorithm search based on the current iteration number Divided into the global exploration phase ( ) and partial development phase ( This enables dynamic adjustment of search strategies. 2) During the iteration process, when the critical iteration point is reached... At that time, a contraction interval is constructed based on the current optimal solution X* and the suboptimal solution X**. The formula for calculating the boundary of the contraction interval is as follows: ； ； Used for the next stage of population initialization; 3) Individual ranking updates the optimal solution. After each iteration, all individuals are ranked according to their fitness function values ​​to determine the optimal and suboptimal solutions, record the optimal target value, and update the convergence curve. The ranking results are fed back to the subsequent perturbation factor generation and interval adjustment process to dynamically optimize the search strategy.

7. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, In step S3, generating the initial population matrix and initializing the optimal solution based on the decision variable dimensions and boundaries includes: Initializing the population and determining the initial optimal solution and optimal objective function are based on the dimensionality of the decision variables. and its upper and lower boundaries Randomly and uniformly generate within the decision space containing Initial population matrix of individuals : ； Each individual This represents a complete water supply scheduling scheme, i.e., the allocation of irrigation water to all channels; utilizing a multi-objective function. This includes calculating the overall water shortage rate and crop yield, determining the objective function value for each individual crop, and identifying the initial optimal solution. and the optimal objective function value The calculation formula is as follows: ； And record the changes in the optimal objective function value for each generation.

8. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, In step S3, the forced absorption process for the superboundary solution includes: Boundary and constraint handling; if the optimal solution is updated after iteration. Exceeding boundary conditions During the first iteration Then, forced absorption to the boundary is performed, and the calculation formula is as follows: ； If the updated optimal solution does not meet the inequality constraints, an absorption strategy is adopted, that is, values ​​that exceed the boundary are set as boundary values.

9. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, In step S3, the process of updating the optimal solution by sorting individuals by fitness after iteration includes: If the number of iterations has not reached the set maximum number of iterations If the number of iterations reaches a certain threshold, the perturbation factor is regenerated and the individual position is dynamically updated; if the number of iterations reaches a certain threshold, the perturbation factor is regenerated and the individual position is dynamically updated. When the time is up, the algorithm terminates and outputs the final optimal scheduling scheme. Corresponding objective function value This satisfies multiple constraints and completes the optimized scheduling of water volume in the irrigation area.

10. The irrigation district water allocation optimization method based on the exponential triangular optimization algorithm according to claim 1, characterized in that, In step S3, the formula for generating the dynamic disturbance factor is as follows: ； Disturbance coefficient The calculation formula is as follows: ； in, A random number between 0 and 1.

Images