Reservoir irrigation area water and soil resource allocation method considering supply and demand coordination

Abstract

The invention discloses a reservoir irrigation area water and soil resource allocation method considering supply and demand coordination in the field of agricultural water and soil resource optimal allocation. The method comprises the steps of collecting irrigation area related data and constructing a database; establishing a reservoir irrigation area water and soil resource optimal allocation model considering supply and demand coordination, taking the water supply amount of a reservoir in each time period and the planting area of each crop in the irrigation area as decision variables, and taking the minimum water supply and demand deviation of the irrigation area, the maximum economic benefit of the irrigation area and the minimum carbon emission of the irrigation area as objective functions; taking a reservoir operation criterion, grain safety, cultivated land utilization and decision variable nonnegative as constraint conditions; model parameters are extracted through a database, and the model is solved through a multi-target discrete dynamic programming coupling algorithm; and finally, selecting an optimal configuration scheme through a variable weight method. Water resource waste can be effectively reduced, the income of the irrigation area is improved, carbon emission of the irrigation area is reduced, and technical support is provided for fine management and scientific decision making of the reservoir irrigation area.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural water and soil resource optimal allocation, in particular to a water reservoir irrigation area water and soil resource allocation method. BACKGROUND

[0002] In recent years, with the intensification of global climate change and human activities, water resource shortage and low utilization efficiency of water and soil resources have become increasingly prominent, especially in the management of water resources in irrigation areas, the contradiction between supply and demand has become a key factor restricting the sustainable development of agriculture. Traditional water and soil resource allocation methods are mainly based on the supply side, emphasizing the optimal scheduling and distribution of water resources, while ignoring the dynamic changes and coordination mechanisms of the demand side, making it difficult to fully adapt to complex and variable hydrological conditions and diversified water demand. Therefore, there is an urgent need for a water reservoir irrigation area water and soil resource allocation method that considers supply and demand coordination, taking into account the benefits and carbon emissions of the irrigation area, to provide scientific basis and technical support for water resource management in the irrigation area. SUMMARY

[0003] In view of the deficiencies in the prior art, the present application provides a water reservoir irrigation area water and soil resource allocation method considering supply and demand coordination to solve the problem of imbalance between supply and demand in the traditional water and soil resource allocation process; by comprehensively considering the irrigation area water supply and demand deviation, economic benefits and carbon emissions of the irrigation area, a scientific and reasonable water and soil resource allocation model is constructed to realize efficient utilization and optimal allocation of water and soil resources; the present application can effectively reduce water waste, improve irrigation area benefits and reduce carbon emissions in the irrigation area, providing technical support for fine management and scientific decision-making of water reservoir irrigation areas.

[0004] The purpose of the present application is achieved by a water reservoir irrigation area water and soil resource allocation method considering supply and demand coordination, comprising the following steps:

[0005] Step 1) Database construction: collect daily weather data, annual economic data, annual carbon emission data, annual cultivation data and infrastructure data of the irrigation area, convert them into weather subarray A, economic subarray B, carbon emission subarray C, cultivation subarray D and infrastructure subarray E according to data characteristics and calculation requirements, combine all subarrays into a partition index array and store it in the PostgreSQL database;

[0006] Step 2) Model construction: establish a water reservoir irrigation area water and soil resource optimal allocation model considering supply and demand coordination, the model takes the water supply of the reservoir at each time period X i and the planting area of each crop in the irrigation area Y j as decision variables, takes the minimum irrigation area water supply and demand deviation F1, the maximum irrigation area economic benefit F2 and the minimum irrigation area carbon emission F3 as the objective function, takes the reservoir operation criteria, food security, farmland utilization and non-negative decision variables as the constraint conditions, i is the order number of the reservoir operation period, j is the order number of the planted crop type;

[0007] Step 3) Model parameter extraction: collect the meteorological data of the first month of the scheduling year, and convert it into a meteorological comparison array A ’ and compare it with the meteorological sub-array A in the PostgreSQL database. The upper bound distance is minimized, and the whole year daily meteorological data is converted and calculated to fill in the optimization model. Read the economic sub-array B and convert it into the annual average present value to fill in the optimization model. Read the carbon emission sub-array C and the cultivation sub-array D, take the average of the same data in different years, and fill them into the optimization model. Read the infrastructure sub-array E and fill it into the optimization model.

[0008] Step 4) Model solving: use the multi-objective discrete dynamic programming coupling algorithm MDDP to solve the model. The algorithm randomly generates multiple sets of crop planting proportions Y j in the irrigation area, and then calculates the optimal reservoir water supply quantity X i in each time period corresponding to each set of planting proportions. Through comparison between the multi-objective function values, the optimal decision variables are retained, the sub-optimal decision variables are updated, and the optimal decision variables and their multi-objective function value solution set are automatically iteratively optimized.

[0009] Step 5) Scheme development: select the final configuration scheme by the variable weight method, set a larger weight for the economic benefit target weight of the irrigation area, select the candidate point with the largest comprehensive optimization amplitude, read the corresponding crop planting proportions and reservoir water supply quantity in each time period, and generate the irrigation area water and soil resource optimization configuration scheme.

[0010] Further, the daily meteorological data in step 1) includes: daily maximum temperature T max , °C; daily minimum temperature T min , °C; daily average temperature T ave , °C; daily dew point temperature T dew , °C; daily average wind speed u at 2 meters above ground, m / s; daily net radiation flux R n , MJ / m 2 ; daily rainfall P, mm; store the above data as row data in the meteorological sub-array A, and the sub-array size is θ×7, θ is the number of collected meteorological data dates.

[0011] Further, the annual economic data in step 1) includes: annual production cost CP j of each crop, CNY / ha; regional annual import price IP j of each crop, CNY / ton; regional annual export price LP j of each crop, CNY / ton; store the above data as row data in the economic sub-array B, and the sub-array size is μ×3N, μ is the number of collected data years, and N is the number of planted crop types.

[0012] Furthermore, the annual carbon emission data mentioned in step 1) includes: annual emissions of CFU per unit area for each crop. j , kg CO2-eg / ha; store the above data as row data in the carbon emission subarray C, with a subarray size of μ×N.

[0013] Furthermore, the annual cultivation data mentioned in step 1) includes: annual yield P of each crop. j ton / ha; annual cultivated area S for each crop j ,ha; Store the above data as row data in the seed array D, with a subarray size of μ×2N.

[0014] Furthermore, the infrastructure data mentioned in step 1) includes: the MD requirement for each crop region. j ton; irrigation water utilization rate η; minimum reservoir capacity V at different times i,min m 3 Maximum reservoir capacity V at different times i,max m 3 ; Irrigation area of ​​cultivated land S total ,ha; Store the above data as row data in the infrastructure subarray E, the subarray size is 1×N+2T+2, and T is the number of reservoir operation segments.

[0015] Furthermore, the objective function described in step 2), which aims to minimize the water supply-demand deviation in the irrigation district, is:

[0016]

[0017] The goal of maximizing the economic benefits of the irrigation district is:

[0018]

[0019] The minimum carbon emission target for the irrigation district is:

[0020]

[0021] In the formula, WF Blue,ij Let be the water requirement per unit area for the j-th crop in the i-th time period, in mm;

[0022] The constraints described in step 2) and the reservoir operation criteria are as follows:

[0023]

[0024]

[0025]

[0026] The food security constraint is:

[0027]

[0028] The cultivated land utilization constraint is:

[0029]

[0030] The non-negative constraint of the decision variable is:

[0031]

[0032] In the formula, V i is the reservoir capacity in the i th period, m 3 ; LS i is the reservoir inflow in the i th period, m 3 ; EF i is the reservoir loss in the i th period, m 3 ; PS i is the reservoir spill in the i th period, m 3 .

[0033] Further, the data reading based on the supremum distance minimum in step 3) is as follows:

[0034]

[0035] In the formula, k is the row number of any row of the meteorological array A, k = 1, 2, …, θ-364, all k need to be calculated in the calculation process, and the minimum d and the corresponding k are selected ’ , and the k ’ to k ’ +364 rows in A are read to generate a meteorological reading subarray F, the subarray size is 365x7, and θ is the number of collected meteorological data dates;

[0036] The meteorological data conversion calculation in step 3) is as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] In the formula, ET ijm EP represents the evapotranspiration of the j-th crop in the i-th time period on the m-th day, in mm. im Let be the effective rainfall in the i-th time period on the m-th day, in mm; ∆ be the slope of the saturated vapor pressure curve, in e. s The saturated vapor pressure is kPa; e a The actual water vapor pressure is given in kPa; the calculated WF Blue,ij Substitute into Formula 1;

[0044] The annual economic value described in step 3) is converted into the annual average present value, and its calculation formula is as follows:

[0045]

[0046] In the formula, B ’ 1,l Let l be the annual average present value of the l-th economic data, where l = 1, 2, ... 3N; t is the year number for calculation, t=1,2,…,μ; generate the economic transformation subarray B. ’ The size of the subarray is 1×3N. Substitute the transformed data into formula 2.

[0047] Furthermore, the calculation process of the multi-objective discrete dynamic programming algorithm described in step 4) is as follows:

[0048] Step 4-1) Initialize the candidate solution size T+N, the number of candidate solutions n, the calculation factor 1 α1, and the calculation factor 2 α2. Randomly generate the planting area N in the candidate solution T+N in the form of real number encoding, and generate a candidate solution set based on the number of candidate solutions according to the crop rainy and dry seasons to satisfy the farmland use constraints.

[0049] Step 4-2) Select two sets of candidate solutions based on the probability of factor 1 α1, and swap the two values ​​of the same ordinal number in different candidate solutions; select one set of candidate solutions based on the probability of factor 2 α2, and regenerate one of the values; merge the processed candidate solutions into the candidate solution set.

[0050] Step 4-3) Based on the generated planting area N, construct recursive equation formula 18 to solve for the objective of minimizing the water supply and demand deviation in the irrigation area. Recursive equation formula 18 and state transition equation formula 19 are as follows:

[0051]

[0052]

[0053] In the formula, g i (λ i ) is the cumulative value of Formula 1 from stage 1 to stage i, i=1,…,T; λ i (m 3 X is the sum of water supply from stage 1 to stage i; X is the water supply from the reservoir at each time period. i In the range [0, min(λ)] i , Discretize the contents sequentially and substitute them into Formula 18; during this process, calculate and store the reservoir capacity of the i-th stage according to Formulas 4-6, and find the minimum g. T (λ T Based on Formula 19, the water supply X of the reservoir at different times is derived. i The values ​​of the three objective functions can be determined based on the provisional water supply X from the reservoir at different time periods. i Y, the planting area of ​​various crops in the irrigation area j Perform calculations; use the penalty function method to handle the situation where the farmland use constraints are not met due to step 4-2;

[0054] Step 4-4) Classify and sort the candidate solutions according to the three objective function values ​​corresponding to each candidate solution. Non-dominated candidate solutions of the same level are grouped together, and the distance between all candidate solutions in each group is calculated using the following formula:

[0055]

[0056] In the formula, d p is the distance between the p-th solutions of the groups; s is the number of objective functions, s=1, 2, 3; F s p+1 and F s p-1 These are the values ​​of the (p+1)th and (p-1)th solutions on the given frontier in the s-th objective function, respectively; F s max and F s min These are the maximum and minimum values ​​of the s-th objective function between groups, respectively. If F s p = Fs max or F s min , then d p =∞;

[0057] Steps 4-5) Candidate solutions with high dominance levels and high distances are retained in the candidate solution set. For the remaining candidate solutions, proceed to step 4-3, and repeat steps 4-2 to 4-4 iteratively until the loop count limit is reached. Then, output all candidate solutions with the highest dominance level as the optimal candidate solution set, and save the corresponding reservoir water supply X for each time period. i Y, the planting area of ​​various crops in the irrigation area j .

[0058] Furthermore, the target weights for the water supply and demand deviation of the irrigation area (F1) are set to 2 / 9, the target weights for the economic benefits of the irrigation area (F2) are set to 5 / 9, and the target weights for carbon emissions of the irrigation area (F3) are set to 2 / 9. The calculation formula for the variable weight method described in step 5) is as follows:

[0059]

[0060] In the formula, O represents the comprehensive optimization magnitude of the three objectives, and F1 P F2 P F3 P For the three objective function values ​​corresponding to the P-th candidate solution in the optimal candidate solution set, F1 A F2 A F3 A These are the three objective function values ​​corresponding to the current state of the irrigation district.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] (1) This invention establishes a water and soil resource optimization model for reservoir irrigation areas that considers supply and demand coordination. It can achieve efficient utilization of water and land resources on the basis of supply and demand coordination and improve resource allocation efficiency. Taking reservoir water supply and crop planting area as decision variables, combined with reservoir operation criteria, food security and arable land utilization constraints, it provides a comprehensive decision basis for irrigation area management. By optimizing the allocation of water and soil resources in irrigation areas, carbon emissions can be effectively reduced, food security can be guaranteed, and green and sustainable development of agricultural production can be promoted.

[0063] (2) This invention proposes a multi-objective discrete dynamic programming coupled algorithm (MDDP), which overcomes the low efficiency of existing heuristic algorithms in solving reservoir operation criterion constraints and the difficulty of function optimization methods in handling multi-objective problems, and provides an efficient algorithm for solving relevant reservoir irrigation area models. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0065] Figure 1 This is a schematic diagram of the reservoir irrigation area and its decision variables in this invention.

[0066] Figure 2 This is a flowchart illustrating the present invention.

[0067] Figure 3 This is a flowchart illustrating the multi-objective discrete dynamic programming coupled algorithm MDDP of the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] A method for water and soil resource allocation in reservoir irrigation areas that considers supply and demand coordination is proposed, taking a reservoir irrigation area in Jiangsu Province as an example. A schematic diagram of the reservoir irrigation area and decision variables is shown below. Figure 1 As shown. This irrigation district grows six crops, j=6. The rainy season crops are rice, cotton, peanuts, and corn, while the dry season crops are rapeseed and wheat. The reservoir operates in ten-day periods, with an annual operating cycle of 36 cycles, i=36. Figure 2 The flowchart shown below illustrates the allocation of water and soil resources. The specific implementation steps are as follows:

[0070] Step 1) Database Construction: Collect 1460 daily meteorological data points from the irrigation area, including: daily maximum temperature T max °C; Daily minimum temperature T min °C; Daily average temperature T ave °C; Daily dew point temperature T dew °C; daily average wind speed u at 2 meters above ground, m / s; daily net radiation flux R n MJ / m 2 Daily rainfall P, mm; Store the above data as row data in a meteorological subarray A, with a subarray size of 1460×7; Collect four annual economic data points for the irrigation district, including: annual production cost CP for each crop. j CNY / ha; Annual import price (IP) for each crop region jCNY / ton; Annual export price per crop region (LP) j CNY / ton; Store the above data as row data in the economic subarray B, with a subarray size of 4×18; Collect four annual carbon emission data for the irrigation district, including: annual emissions per unit area of ​​each crop (CNY / ton). j kg CO2-eg / ha; store the above data as row data in a carbon emission subarray C, with a subarray size of 4×6; collect four annual cultivation data for the irrigation district, including: annual yield P of each crop. j ton / ha; annual cultivated area S for each crop j , ha; Store the above data as row data in the seed array D, with a subarray size of 4×12; Collect one infrastructure data point for the irrigation district, including: the regional demand MD for each crop. j ton; irrigation water utilization rate η; minimum reservoir capacity V at different times i,min m 3 Maximum reservoir capacity V at different times i,max m 3 ; Irrigation area of ​​cultivated land S total ,ha; Store the above data as row data in the infrastructure subarray E, with a subarray size of 1×80; Create indexes for all subarrays according to their types, merge them into a partition index array, and store it in the PostgreSQL database.

[0071] Step 2) Model Construction: Establish a water and soil resource optimization allocation model for reservoir irrigation areas that considers supply and demand coordination. The model uses the water supply volume of the reservoir at different time periods as X i Y, the planting area of ​​various crops in the irrigation area j The decision variables are: minimizing the water supply-demand deviation F1, maximizing the economic benefits F2, and minimizing the carbon emissions F3 of the irrigation area. The constraints are reservoir operation criteria, food security, farmland utilization, and the non-negativity of the decision variables. Let i be the order number of the reservoir operation period, and j be the order number of the crop types planted. The objective of minimizing the water supply-demand deviation of the irrigation area is:

[0072]

[0073] The goal of maximizing the economic benefits of the irrigation district is:

[0074]

[0075] The minimum carbon emission target for the irrigation district is:

[0076]

[0077] In the formula, WF Blue,ijLet be the water requirement per unit area for the j-th crop in the i-th time period, in mm;

[0078] The reservoir operation criteria are constrained as follows:

[0079]

[0080]

[0081]

[0082] Food security constraints are:

[0083]

[0084] Farmland use constraints are:

[0085]

[0086] The nonnegativity constraint for the decision variables is:

[0087]

[0088] In the formula, V i Let m be the reservoir capacity during the i-th time period. 3 ;LS i Let m be the inflow of water into the reservoir during the i-th time period. 3 ;EF i Let m be the water loss from the reservoir during the i-th time period. 3 PS i Let m be the amount of water discharged from the reservoir during the i-th time period. 3 .

[0089] Step 3) Model parameter extraction: Collect meteorological data for the first month of the scheduling year and convert it into a meteorological comparison array A. ’ It is then compared with the meteorological subarray A in the PostgreSQL database, using the annual daily meteorological data with the smallest supremacy distance. The specific calculation formula is as follows:

[0090]

[0091] In the formula, k is the row number of any row in the meteorological array A (k=1,2,…, (1096) During the calculation of d, all k need to be calculated, and the smallest d and its corresponding k are selected. ’ And read k from A ’ To k ’Line +364 generates a weather reading subarray F, with a size of 365×7. This subarray is then converted and calculated using the following formula:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] In the formula, ET ijm EP represents the evapotranspiration of the j-th crop in the i-th time period on the m-th day, in mm. im Let be the effective rainfall in the i-th time period on the m-th day, in mm; ∆ be the slope of the saturated vapor pressure curve, in e. s The saturated vapor pressure is kPa; e a The actual water vapor pressure is given in kPa; the calculated WF Blue,ij Substitute into Formula 1;

[0099] Read the economic subarray B and convert it into the annual average present value. The calculation formula is as follows:

[0100]

[0101] In the formula, B ’ 1,l Let l be the annual average present value of the l-th economic data, where l = 1, 2, ... 18; t is the year number for calculation, t=1,2,…,4; Generate the economic transformation subarray B. ’ The size of the subarray is 1×18. Substitute the transformed data into formula 2.

[0102] Read the carbon emissions subarray C, average the same data from different years, and substitute it into formula 3; read the seed arables array D, average the same data from different years, and substitute it into formula 2; read the infrastructure subarray E and substitute it into formulas 4, 6, 7, and 8.

[0103] Step 4) Model Solving: According to Figure 3The flowchart of the multi-objective discrete dynamic programming coupled algorithm MDDP shown is used to solve the model. Step 4-1) Initialize the candidate solution size 42, the number of candidate solutions 50, the calculation factor 1 0.6, and the calculation factor 2 0.2. Randomly generate the planting area N in the candidate solution T+N in the form of real number encoding, and satisfy the farmland use constraints according to the crop rainy and dry seasons respectively. Generate a candidate solution set based on the number of candidate solutions.

[0104] Step 4-2) Select two sets of candidate solutions based on the probability of factor 1 = 0.6, and swap the two values ​​of the same ordinal number in the different candidate solutions; select one set of candidate solutions based on the probability of factor 2 = 0.2, and regenerate one of the values; merge the processed candidate solutions into the new candidate solution set.

[0105] Step 4-3) Based on the generated planting area 6, construct recursive equation formula 18 to solve for the objective of minimizing the water supply and demand deviation in the irrigation area. Recursive equation formula 18 and state transition equation formula 19 are as follows:

[0106]

[0107]

[0108] In the formula, g i (λ i ) is the cumulative value of Formula 1 from stage 1 to stage i, i=1,…,T; λ i (m 3 X is the sum of water supply from stage 1 to stage i; X is the water supply from the reservoir at each time period. i In the range [0, min(λ)] i , Discretize the contents sequentially and substitute them into Formula 18; during this process, calculate and store the reservoir capacity of the i-th stage according to Formulas 4-6, and find the minimum g. T (λ T Based on Formula 19, the water supply X of the reservoir at different times is derived. i The values ​​of the three objective functions can be determined based on the provisional water supply X from the reservoir at different time periods. i Y, the planting area of ​​various crops in the irrigation area j Perform calculations; use the penalty function method to handle the situation where the farmland use constraints are not met due to step 4-2;

[0109] Step 4-4) Classify and sort the candidate solutions according to the three objective function values ​​corresponding to each candidate solution. Non-dominated candidate solutions of the same level are grouped together, and the distance between all candidate solutions in each group is calculated using the following formula:

[0110]

[0111] In the formula, d p is the distance between the p-th solutions of the groups; s is the number of objective functions, s=1, 2, 3; F s p+1 and F s p-1 These are the values ​​of the (p+1)th and (p-1)th solutions on the given frontier in the s-th objective function, respectively; F s max and F s min These are the maximum and minimum values ​​of the s-th objective function between groups, respectively. If F s p = F s max or F s min , then d p =∞;

[0112] Steps 4-5) Candidate solutions with high dominance levels and high distances are retained in the candidate solution set. For the remaining candidate solutions, proceed to step 4-3. Repeat steps 4-2 to 4-4 iteratively until the loop count limit is reached. Then, output all candidate solutions with the highest dominance level as the optimal candidate solution set. The optimal candidate solution set is:

[0113]

[0114] Save the corresponding water supply X of the reservoir at each time period. i for:

[0115]

[0116] Save the planting area Y of each crop in the corresponding irrigation district. j for:

[0117]

[0118] Step 5) Scheme Formulation: The final configuration scheme is selected using the variable weighting method. The target weights for the irrigation district water supply and demand deviation F1 are set to 2 / 9, the target weights for the irrigation district economic benefits F2 are set to 5 / 9, and the target weights for the irrigation district carbon emissions F3 are set to 2 / 9. The calculation formula for the variable weighting method is as follows:

[0119]

[0120] In the formula, O represents the comprehensive optimization magnitude of the three objectives, and F1 P F2 P F3 PFor the three objective function values ​​corresponding to the P-th candidate solution in the optimal candidate solution set, F1 A F2 A F3 A These are the three objective function values ​​corresponding to the current state of the irrigation area. The 31st candidate point, with the largest comprehensive optimization magnitude, is selected to generate an optimal allocation scheme for water and soil resources in the irrigation area. The corresponding crop planting ratios are then retrieved.

[0121]

[0122] The corresponding water supply from the reservoir at different times is as follows:

[0123]

[0124] The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, characterized in that, Includes the following steps: Step 1) Database construction: Collect daily meteorological data, annual economic data, annual carbon emission data, annual cropping data, and infrastructure data of the irrigation area. Based on the data characteristics and computing needs, convert them into meteorological subarray A, economic subarray B, carbon emission subarray C, cropping subarray D, and infrastructure subarray E. Merge all subarrays into a partitioned index array and store it in the PostgreSQL database. Step 2) Model Construction: Establish a water and soil resource optimization allocation model for reservoir irrigation areas that considers supply and demand coordination. The model uses the water supply volume of the reservoir at different time periods as X i Y, the planting area of ​​various crops in the irrigation area j The decision variables are: minimizing the water supply and demand deviation F1, maximizing the economic benefits F2, and minimizing the carbon emissions F3 of the irrigation area. The constraints are: reservoir operation criteria, food security, arable land use, and non-negativity of decision variables. i is the order number of the reservoir operation period, and j is the order number of the types of crops planted. Step 3) Model parameter extraction: Collect meteorological data for the first month of the scheduling year and convert it into a meteorological comparison array A. ’ The model is compared with the meteorological subarray A in the PostgreSQL database. The annual daily meteorological data with the smallest supremacy distance is used to convert and calculate the data and fill it into the optimization model. The economic subarray B is read and converted into the annual average present value and filled into the optimization model. The carbon emission subarray C and the seed array D are read, and the same data from different years is averaged and filled into the optimization model. The infrastructure subarray E is read and filled into the optimization model. Step 4) Model Solving: The multi-objective discrete dynamic programming coupled algorithm MDDP is used to solve the model. The algorithm randomly generates multiple sets of planting ratios Y for each crop in the irrigation area. j Then, the optimal water supply X from the reservoir at each time period corresponding to each planting ratio is calculated. i By comparing the values ​​of multiple objective functions, the better decision variables are retained, the second-best decision variables are updated, and the optimal decision variables and their multi-objective function value solution set are automatically generated through iterative optimization. Step 5) Scheme Formulation: Select the final configuration scheme by using the variable weight method, set a larger weight for the economic benefit target of the irrigation area, select the candidate point with the largest comprehensive optimization range, read the corresponding crop planting ratio and the water supply of the reservoir at each time period, and generate the irrigation area water and soil resource optimization configuration scheme.

2. The method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination as described in claim 1, characterized in that, The daily meteorological data mentioned in step 1) includes: the daily maximum temperature T max °C; Daily minimum temperature T min °C; Daily average temperature T ave °C; Daily dew point temperature T dew °C; daily average wind speed u at 2 meters above ground, m / s; daily net radiation flux R n MJ / m 2 Daily rainfall P, mm; Store the above data as row data in a meteorological subarray A, with a subarray size of θ×7, where θ is the number of days the meteorological data was collected.

3. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, as described in claim 2, is characterized in that... The annual economic data mentioned in step 1) includes: the annual production cost (CP) of each crop. j CNY / ha; Annual import price (IP) for each crop region j CNY / ton; Annual export price per crop region (LP) j , CNY / ton; Store the above data as row data in the economic subarray B, the subarray size is μ×3N, μ is the number of years of data collection, and N is the number of crop types planted.

4. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, as described in claim 3, is characterized in that... The annual carbon emission data mentioned in step 1) includes: annual emissions of CFU per unit area for each crop. j , kg CO2-eg / ha; store the above data as row data in the carbon emission subarray C, with a subarray size of μ×N.

5. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, as described in claim 4, is characterized in that... The annual cultivation data mentioned in step 1) includes: annual yield P of each crop. j ton / ha; annual cultivated area S for each crop j ,ha; Store the above data as row data in the seed array D, with a subarray size of μ×2N.

6. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, as described in claim 5, is characterized in that... The infrastructure data mentioned in step 1) includes: the MD requirement for each crop region. j ton; irrigation water utilization rate η; minimum reservoir capacity V at different times i,min m 3 Maximum reservoir capacity V at different times i,max m 3 ; Irrigation area of ​​cultivated land S total ,ha; Store the above data as row data in the infrastructure subarray E, the subarray size is 1×N+2T+2, and T is the number of reservoir operation segments.

7. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, as described in claim 6, is characterized in that... The objective function described in step 2), which aims to minimize the water supply-demand deviation in the irrigation district, is: ； The goal of maximizing the economic benefits of the irrigation district is: ； The minimum carbon emission target for the irrigation district is: ； In the formula, WF Blue,ij Let be the water requirement per unit area for the j-th crop in the i-th time period, in mm; The constraints described in step 2) and the reservoir operation criteria are as follows: ； ； ； Food security constraints are: ； Farmland use constraints are: ； The nonnegativity constraint for the decision variables is: ； In the formula, V i Let m be the reservoir capacity during the i-th time period. 3 ; LS i Let m be the inflow of water into the reservoir during the i-th time period. 3 ; EF i Let m be the water loss from the reservoir during the i-th time period. 3 ; PS i Let m be the amount of water discharged from the reservoir during the i-th time period. 3 .

8. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, as described in claim 7, is characterized in that... The data reading based on the minimum supremacy distance described in step 3) uses the following formula for calculating the supremacy distance: ； In the formula, k is the row number of any row in the meteorological array A, k=1,2,…,θ-364. During the calculation of d, all k need to be calculated, and the smallest d and its corresponding k are selected. ’ And read k from A ’ To k ’ +364 lines generate a meteorological data subarray F, with a size of 365×7, where θ is the number of days for which meteorological data was collected; The meteorological data conversion and calculation described in step 3) uses the following formula: ； ； ； ； ； ； In the formula, ET ijm Let be the evapotranspiration of the j-th crop in the i-th time period on the m-th day, in mm; EP im Let be the effective rainfall in the i-th time period on the m-th day, in mm; ∆ be the slope of the saturated vapor pressure curve, in e. s The saturated vapor pressure is kPa; e a The actual water vapor pressure is given in kPa; the calculated WF Blue,ij Substitute into Formula 1; The annual economic value described in step 3) is converted into the annual average present value, and its calculation formula is as follows: ； In the formula, B ’ 1,l Let be the annual average present value of the l-th economic data, l = 1, 2, ..., 3N; t is the year index for calculation, t = 1, 2, ..., μ; generate the economic transformation subarray B. ’ The subarray size is 1×3N. Substitute the transformed data into formula 2.

9. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, as described in claim 8, is characterized in that... The calculation process of the multi-objective discrete dynamic programming algorithm described in step 4) is as follows: Step 4-1) Initialize the candidate solution size T+N, the number of candidate solutions n, the calculation factor 1 α1, and the calculation factor 2 α2. Randomly generate the planting area N in the candidate solution T+N in the form of real number encoding, and generate a candidate solution set based on the number of candidate solutions according to the crop rainy and dry seasons to satisfy the farmland use constraints. Step 4-2) Select two sets of candidate solutions based on the probability of factor 1 α1, and swap the two values ​​of the same ordinal number in different candidate solutions; select one set of candidate solutions based on the probability of factor 2 α2, and regenerate one of the values; merge the processed candidate solutions into the candidate solution set. Step 4-3) Based on the generated planting area N, construct recursive equation formula 18 to solve for the objective of minimizing the water supply and demand deviation in the irrigation area. Recursive equation formula 18 and state transition equation formula 19 are as follows: ； ； In the formula, g i (λ i ) is the cumulative value of Formula 1 from stage 1 to stage i, i=1,…,T; λ i (m 3 X is the sum of water supply from stage 1 to stage i; X is the water supply from the reservoir at each time period. i In the range [0, min(λ)] i , Discretize the contents sequentially and substitute them into Formula 18; during this process, calculate and store the reservoir capacity of the i-th stage according to Formulas 4-6, and find the minimum g. T (λ T Based on Formula 19, the water supply X of the reservoir at different times is derived. i The values ​​of the three objective functions can be determined based on the provisional water supply X from the reservoir at different time periods. i Y, the planting area of ​​various crops in the irrigation area j Perform calculations; use the penalty function method to handle the situation where the farmland use constraints are not met due to step 4-2; Step 4-4) Classify and sort the candidate solutions according to the three objective function values ​​corresponding to each candidate solution. Non-dominated candidate solutions of the same level are grouped together, and the distance between all candidate solutions in each group is calculated using the following formula: ； In the formula, d p is the distance between the p-th solutions of the groups; s is the number of objective functions, s=1, 2, 3; F s p+1 and F s p-1 These are the values ​​of the (p+1)th and (p-1)th solutions on the given frontier in the s-th objective function, respectively; F s max and F s min These are the maximum and minimum values ​​of the s-th objective function between groups, respectively. If F s p = F s max or F s min , then d p =∞; Steps 4-5) Candidate solutions with high dominance levels and high distances are retained in the candidate solution set. For the remaining candidate solutions, proceed to step 4-3, and repeat steps 4-2 to 4-4 iteratively until the loop count limit is reached. Then, output all candidate solutions with the highest dominance level as the optimal candidate solution set, and save the corresponding reservoir water supply X for each time period. i Y, the planting area of ​​various crops in the irrigation area j .

10. A method for allocating water and soil resources in reservoir irrigation areas considering supply and demand coordination, as described in claim 9, is characterized in that... The target weights for the water supply and demand deviation of the irrigation area (F1) are set to 2 / 9, the target weights for the economic benefits of the irrigation area (F2) are set to 5 / 9, and the target weights for carbon emissions of the irrigation area (F3) are set to 2 / 9. The calculation formula for the variable weight method described in step 5) is as follows: ； In the formula, O represents the comprehensive optimization magnitude of the three objectives, and F1 P F2 P F3 P For the three objective function values ​​corresponding to the P-th candidate solution in the optimal candidate solution set, F1 A F2 A F3 A These are the three objective function values ​​corresponding to the current state of the irrigation district.

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