A water resource optimal allocation method based on growth period

Abstract

The application discloses a water resource optimal allocation method based on growth periods, and relates to the technical field of intelligent agriculture. The method comprises the following steps: collecting the start and end dates of each growth period stage of different crops to construct an ordered key date set; taking the ordered key date set as a benchmark, the natural month is segmented to generate multiple sub-periods; a mapping relationship between each growth period stage of different crops and the sub-periods is established; a mapping relationship between the water consumption of each sub-period and the water consumption of the natural month is established; a water resource optimal allocation model with the dual objectives of maximizing economic net benefits and minimizing carbon emissions is constructed; and the water resource optimal allocation model is optimized and solved to generate a water resource optimal allocation scheme. The application can ensure that the water resource allocation scheme can accurately meet the actual water demand of different growth stages of crops, realize the accurate allocation of water resources in irrigation areas, and provide a solution for the efficient and sustainable use of agricultural water resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart agriculture, and particularly relates to a water resource optimal allocation method based on growth periods. BACKGROUND

[0002] With the continuous intensification of global climate change, agricultural production is facing the dual challenges of resource shortage and environmental constraints. As a core element of agricultural production, the efficient use and scientific allocation of water resources are directly related to food security, sustainable agricultural development and ecological system balance. Currently, agricultural water use accounts for more than 70% of the total global water use, but its utilization efficiency is generally low, and the imbalance in allocation among different crops is increasingly prominent, which not only limits the full play of crop yield potential, but also causes water waste and aggravates the burden on the agricultural ecosystem. Therefore, it is of great significance to build an efficient water resource optimal allocation model, scientifically adjust the agricultural planting structure, and reasonably allocate irrigation water resources to ensure food security and achieve ecological sustainable development.

[0003] In the research of agricultural water resource optimal allocation, the whole year is usually divided into 12 months, and water resource allocation decisions are made based on the supply-demand relationship of the month scale. This method divides the natural month, mechanically follows the start and end of the month, and does not conform to the actual growth cycle and growth law of crops. When there are different growth stages in the same month, the month scale model cannot reflect the differences in water demand characteristics of crops in different growth stages, and the decision result may have large deviation.

[0004] The water production function quantifies the crop water demand law and yield response mechanism. In the water resource optimal allocation model, the growth period of all crops is actually simplified to be consistent with the natural month. However, the division stage, start and end time and duration of the growth period of different crops are significantly different from the natural month. Therefore, the calculation of the water production function of the traditional natural month scale model is not accurate, and deviates from the actual water demand law, which greatly affects the efficiency of water resource allocation.

[0005] The water resource allocation model based on the growth period scale can better utilize the water production function, and the calculation result is more in line with the actual situation. However, the total amount of water resources, the constraint of arable land resources, and the irrigation plan are formulated based on the year or month scale, and the annual resource allocation based on the growth period of crops may cause the superposition of crop water demand exceeding the regional water supply capacity, or the conflict of arable land resource allocation, which may lead to the non-uniformity of the optimization scale or the water resource optimal allocation scheme being difficult to apply to the water resource supply constraint conditions and losing practical guiding value.

[0006] Therefore, the present application proposes a water resource optimal allocation method based on growth periods to solve the problems existing in the prior art, which is a problem urgently needed to be solved by the person skilled in the art. SUMMARY

[0007] In view of this, the present invention provides a water resource optimization allocation method based on the growth period, which can ensure that the water resource allocation scheme can accurately match the actual water demand of plants at different growth stages, realize the precise allocation of irrigation area water resources, and provide a solution for the efficient and sustainable use of agricultural water resources.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for optimizing water resource allocation based on reproductive period includes:

[0010] S1. Collect the start and end dates of each growth stage of different crops and construct an ordered set of key dates;

[0011] S2. Based on an ordered set of key dates, divide the natural month into multiple sub-time periods;

[0012] S3. Establish the mapping relationship between each growth stage and sub-period of different crops;

[0013] S4. Establish the mapping relationship between water consumption in each sub-period and water consumption in a natural month;

[0014] S5. Construct a water resource optimization allocation model with the dual objectives of maximizing net economic benefits and minimizing carbon emissions;

[0015] S6. Based on the GAMultiObj algorithm, optimize and solve the water resource optimization allocation model to generate a water resource optimization allocation scheme.

[0016] Optionally, in the above method, in S1, the start and end dates of each growth stage of different crops are collected to construct an ordered set of key dates, specifically as follows:

[0017] S101. Let the crop number be j (j=1, 2, 3, ..., J), the growth stage number be n (n=1, 2, 3, ..., N), and the month number be m (m=1, 2, 3, ..., 12).

[0018] S102. For any month m, summarize the start and end dates of all crop growth stages in that month to form the original key date set;

[0019] S103. Perform deduplication on the original key date set, and sort the deduplicated key date set in chronological order to obtain an ordered key date set.

[0020] ,

[0021] Where K is the total number of key dates within month m.

[0022] Optionally, in the above method, S2, the natural month is divided into multiple sub-time periods based on an ordered set of key dates, specifically as follows:

[0023] For a natural month m, the first sub-period is t. m，1 =[1, d m，1 -1];

[0024] For the i-th key date d in natural month m m，i If the key date d m，i The current sub-period does not end on any particular crop growth stage; its start and end dates are from the day after the previous critical date to the day before the current critical date. If the key date is d m，i If the date is the end date of a certain crop growth stage, then that date will be the end date of the current sub-period.

[0025] For the last key date d of calendar month m m，K The corresponding sub-time period t m，K+1 The end date is the total number of days in month m. , ;

[0026] All the sub-time periods of natural month m constitute the set T of sub-time periods of natural month m. m .

[0027] Optionally, in S3 of the above method, a mapping relationship between each growth stage and sub-time period of different crops is established, specifically as follows:

[0028] Establish the growth stage n and sub-time period of crop j Mapping relationship:

[0029] ;

[0030] In the formula, , These represent the start and end dates of growth stage n for crop j, respectively. , These represent the start and end dates of sub-period t, respectively; map j，n，t =1 indicates that sub-time period t belongs to the growth stage n of crop j, map j，n，t =0 indicates that the sub-period t does not belong to the growth stage n of crop j.

[0031] Optionally, in the above method, in S4, a mapping relationship is established between the water consumption of each sub-period and the water consumption of a natural month, specifically as follows:

[0032] Establish the relationship between water consumption in sub-periods and monthly water consumption:

[0033] ;

[0034] In the formula, W m The total irrigation water volume for month m, in cubic meters. 3 ; This represents the irrigation water volume per unit area of ​​crop j within sub-period t, in m³. 3 / hm 2 ;a j The planting area of ​​crop j is expressed in hectares (hm²). 2 .

[0035] Optionally, in S5, a water resource optimization allocation model is constructed using the above method, specifically as follows:

[0036] With the goals of maximizing net economic benefits and minimizing carbon emissions, and with constraints such as available water in the irrigation area, crop irrigation water volume, crop planting area, and grain yield, and with the irrigation water volume and planting area of ​​crops in different sub-periods as decision variables, a model is constructed.

[0037] With the goal of maximizing net economic benefit, the objective function is as follows:

[0038] ;

[0039] In the formula, It is a crop type. J This represents the total number of crop types. It is a crop The price is in yuan / kg; It is a crop The cost; It is a crop The planting area, in hm², is a decision variable. It is a crop Total output, expressed in kg / hm²;

[0040] Calculated using the moisture production function:

[0041] ;

[0042] In the formula, It is a crop Maximum yield under conditions of sufficient water supply, expressed in kg / hm²; N j For crops j The total number of reproductive stages, yes Crop growth stage n Moisture sensitivity index; It is a crop During the reproductive stage nThe water demand is expressed in m³ / hm². It is a crop The actual transpiration rate of n during the reproductive stage is expressed in m³ / hm².

[0043] crop Actual evapotranspiration during the reproductive stage n This is obtained by summing the evaporation amounts of the associated sub-periods:

[0044] ;

[0045] In the formula, ET j,t For crops Sub-period t The transpiration rate is determined by the amount of irrigation water. and effective rainfall constitute;

[0046] With the goal of minimizing carbon emissions from agricultural production, the objective function is as follows:

[0047] ;

[0048] In the formula, It is a crop The carbon emission factor, in units of kg / hm² 2 ;

[0049] Water supply constraints in the irrigation area:

[0050] ;

[0051] In the formula, For months m The upper limit of water conveyance capacity, in meters (m). 3 ;

[0052] Crop irrigation water constraints:

[0053] ;

[0054] In the formula, It is a crop During the reproductive stage n The lower limit of irrigation water volume, in m³ / hm²; It is a crop During the reproductive stage n The upper limit of irrigation water volume, in m³ / hm²;

[0055] Crop planting area constraints:

[0056] ;

[0057] In the formula, This is the total cultivated land area of ​​the irrigation district, expressed in hm².

[0058] Grain production constraints:

[0059] ;

[0060] In the formula, It is the minimum guarantee for grain production, and the unit is kg;

[0061] Non-negativity constraint: All variables in the model must satisfy the non-negativity constraint.

[0062] Optionally, in step S6 of the above method, the water resource optimization allocation model is optimized and solved to generate a water resource optimization allocation scheme, specifically as follows:

[0063] Based on the traditional GAMultiObj algorithm, the mapping relationship between crop growth stages and sub-periods is used to identify irrigation water decision variables corresponding to non-critical growth periods;

[0064] By setting the water volume for crop rotation during non-critical growth periods to 0 for individuals in the initial population, non-critical variables are removed from the optimization process.

[0065] By iteratively improving candidate solutions through fitness calculation, genetic operations, and Pareto dominance evaluation, a frontier solution set is output.

[0066] The decision variables and objective function values ​​corresponding to each solution are dimensionless to construct a decision variable matrix. ,in, Let the k-th option be the e-th decision variable;

[0067] Target value matrix , This represents the quantified value of the net economic benefit of the k-th scheme. This represents the quantified carbon emission value for the k-th scheme.

[0068] Based on current production conditions, set target weights: ,satisfy ;

[0069] Calculate the overall score for each decision option. Based on the overall score The highest score will be used to determine the final decision:

[0070] .

[0071] As can be seen from the above technical solution, compared with the prior art, the present invention provides a water resource optimization allocation method based on the growth period, which has the following beneficial effects: The present invention uses the key dates of the crop growth period as the benchmark, divides the natural month into multiple closely connected sub-periods of varying lengths, each sub-period corresponding to a specific crop growth stage, and constructs an optimization allocation model that can effectively match the crop water demand pattern and agricultural production resource constraints. It can capture the differences in water demand at different crop growth stages, match the differences in crop water demand and water supply scales, and take into account the accuracy of yield calculation during the growth period. This allows the water production function to accurately reflect the crop water demand-yield response, significantly improves the accuracy of yield calculation, and solves the problem of traditional monthly scale models being inadequate for certain crops. This paper addresses the problem of coarsely representing water demand patterns. It introduces a two-layer mapping mechanism to precisely map the quantitative analysis results of crop water production functions and growth indicators calculated based on the growth period scale to the corresponding sub-periods. It establishes a mapping relationship between sub-periods and months, linking irrigation water volume data for each sub-period with monthly water supply capacity data. This coordinates the spatiotemporal matching relationship between field water demand and regional water supply capacity in the time dimension, effectively solving the data gap problem between different calculation scales and achieving efficient coordination between micro-level crop water demand and macro-level water resource supply. It effectively avoids the risk of water supply capacity overload caused by the overlapping growth periods of multiple crops, and has significant practical value for promoting the scientific and precise decision-making of water-saving irrigation in the context of smart agriculture. Attached Figure Description

[0072] 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.

[0073] Figure 1 A flowchart of a water resource optimization allocation method based on reproductive period provided by the present invention;

[0074] Figure 2 This is a schematic diagram illustrating the unit water allocation for crops in different sub-periods in a specific embodiment of a water resource optimization allocation method based on the growth period provided by the present invention. Detailed Implementation

[0075] 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.

[0076] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0077] Reference Figure 1 As shown, this invention discloses a method for optimal allocation of water resources based on the reproductive period, comprising:

[0078] S1. Collect the start and end dates of each growth stage of different crops and construct an ordered set of key dates;

[0079] S2. Based on an ordered set of key dates, divide the natural month into multiple sub-time periods;

[0080] S3. Establish the mapping relationship between each growth stage and sub-period of different crops;

[0081] S4. Establish the mapping relationship between water consumption in each sub-period and water consumption in a natural month;

[0082] S5. Construct a water resource optimization allocation model with the dual objectives of maximizing net economic benefits and minimizing carbon emissions;

[0083] S6. Based on the GAMultiObj algorithm, optimize and solve the water resource optimization allocation model to generate a water resource optimization allocation scheme.

[0084] Furthermore, in S1, the start and end dates of each growth stage of different crops are collected to construct an ordered set of key dates, specifically:

[0085] S101. Let the crop number be j (j=1, 2, 3, ..., J), the growth stage number be n (n=1, 2, 3, ..., N), and the month number be m (m=1, 2, 3, ..., 12).

[0086] S102. For any month m, summarize the start and end dates of all crop growth stages in that month to form the original key date set;

[0087] S103. Perform deduplication on the original key date set, and sort the deduplicated key date set in chronological order to obtain an ordered key date set.

[0088] ,

[0089] Where K is the total number of key dates within month m.

[0090] Furthermore, in S2, based on an ordered set of key dates, the natural month is divided into multiple sub-time periods, specifically:

[0091] For a natural month m, the first sub-period is t. m，1 =[1, d m，1 -1];

[0092] For the i-th key date d in natural month m m，i If the key date d m，i The current sub-period does not end on any particular crop growth stage; its start and end dates are from the day after the previous critical date to the day before the current critical date. If the key date is d m，i If the date is the end date of a certain crop growth stage, then that date will be the end date of the current sub-period.

[0093] For the last key date d of calendar month m m，K The corresponding sub-time period t m，K+1 The end date is the total number of days in month m. (Values ​​range from 28 to 31, depending on the month). ;

[0094] All the sub-time periods of natural month m constitute the set T of sub-time periods of natural month m. m ;

[0095] By following the steps above, a natural month can be subdivided into sub-periods, providing a more refined time scale for subsequent resource allocation.

[0096] Furthermore, considering that the water production function is a key tool for measuring the relationship between crop yield and water supply, it is generally calculated using the Jensen function, which is usually calculated on a time scale of each growth stage of the crop; while the water supply capacity calculation generally uses an annual or monthly scale, which is inconsistent with the sub-period scale obtained by the time period division strategy of this invention. Therefore, this invention constructs a time series mapping between sub-periods and growth stages, and between sub-periods and months. It uses the physiological water demand pattern of the growth stage to refine it to sub-periods, and then matches the refined water demand data with the water supply capacity data through the mapping relationship between sub-periods and months.

[0097] Furthermore, in S3, a mapping relationship is established between each growth stage and sub-time period for different crops, specifically as follows:

[0098] Establish the growth stage n and sub-time period of crop j Mapping relationship:

[0099] ;

[0100] In the formula, , These represent the start and end dates of growth stage n for crop j, respectively. , These represent the start and end dates of sub-period t, respectively; map j，n，t =1 indicates that sub-time period t belongs to the growth stage n of crop j, map j，n，t =0 indicates that the sub-period t does not belong to the growth stage n of crop j;

[0101] This mapping relationship provides a foundation for quantitative analysis at the fertility stage scale. When calculating crop yield based on the Jensen function, the water sensitivity coefficient at the fertility stage scale can be coupled by accumulating data from sub-periods.

[0102] Furthermore, in S4, a mapping relationship is established between water consumption in each sub-period and water consumption in a natural month, specifically as follows:

[0103] Considering the seasonal fluctuations of natural water sources and the limitations of irrigation facilities' water delivery capacity, the relationship between sub-period water consumption and monthly water consumption is established:

[0104] ;

[0105] In the formula, W m The total irrigation water volume for month m, in cubic meters. 3 ; This represents the irrigation water volume per unit area of ​​crop j within sub-period t, in m³. 3 / hm 2 ;a j The planting area of ​​crop j is expressed in hectares (hm²). 2 The mapping relationship was constructed, providing quantitative support for the refined optimization of the irrigation system.

[0106] Furthermore, in S5, a water resource optimization allocation model is constructed, specifically as follows:

[0107] With the goals of maximizing net economic benefits and minimizing carbon emissions, and with constraints such as available water in the irrigation area, crop irrigation water volume, crop planting area, and grain yield, and with the irrigation water volume and planting area of ​​crops in different sub-periods as decision variables, a model is constructed.

[0108] With the goal of maximizing net economic benefit, the objective function is as follows:

[0109] ;

[0110] In the formula, It is a crop type. J This represents the total number of crop types. It is a crop The price is in yuan / kg; It is a crop The cost; It is a crop The planting area, in hm², is a decision variable. It is a crop Total output, expressed in kg / hm²;

[0111] Calculated using the moisture production function:

[0112] ;

[0113] In the formula, It is a crop Maximum yield under conditions of sufficient water supply, expressed in kg / hm²; N j For crops j The total number of reproductive stages, yes Crop growth stage n Moisture sensitivity index; It is a crop During the reproductive stage n The water demand is expressed in m³ / hm². It is a crop The actual transpiration rate of n during the reproductive stage is expressed in m³ / hm².

[0114] crop Actual evapotranspiration during the reproductive stage n This is obtained by summing the evaporation amounts of the associated sub-periods:

[0115] ;

[0116] In the formula, ET j,t For crops Sub-period t The transpiration rate is determined by the amount of irrigation water. and effective rainfall Composition; the outer layer sums over all 12 months of the year (to cover the fertility period across months), the inner layer... Select and accumulate sub-time periods belonging to the target reproductive stage;

[0117] With the goal of minimizing carbon emissions from agricultural production, the objective function is as follows:

[0118] ;

[0119] In the formula, It is a crop The carbon emission factor, in units of kg / hm² 2 ;

[0120] Water supply constraints in the irrigation area:

[0121] ;

[0122] In the formula, For months m The upper limit of water conveyance capacity, in meters (m). 3 ;

[0123] Crop irrigation water constraints:

[0124] ;

[0125] In the formula, It is a crop During the reproductive stage n The lower limit of irrigation water volume, in m³ / hm²; It is a crop During the reproductive stage n The upper limit of irrigation water volume, in m³ / hm²;

[0126] Crop planting area constraints:

[0127] ;

[0128] In the formula, This is the total cultivated land area of ​​the irrigation district, expressed in hm².

[0129] Grain production constraints:

[0130] ;

[0131] In the formula, It is the minimum guarantee for grain production, and the unit is kg;

[0132] Non-negativity constraint: All variables in the model must satisfy the non-negativity constraint.

[0133] Furthermore, in S6, the water resource optimization allocation model is optimized and solved to generate an optimal water resource allocation scheme, specifically as follows:

[0134] The GAMultiObj algorithm continuously improves candidate solutions during the iteration process, which can effectively reduce the risk of getting trapped in local optima. It is suitable for handling nonlinear optimization problems with complex constraints. Based on the traditional GAMultiObj algorithm, it uses the mapping relationship between crop growth stages and sub-periods to identify irrigation water decision variables corresponding to non-critical growth periods.

[0135] By setting the water volume for crop rotation during non-critical growth periods to 0 for individuals in the initial population, non-critical variables are removed from the optimization process.

[0136] By iteratively improving candidate solutions through fitness calculation, genetic operations, and Pareto dominance evaluation, a frontier solution set is output.

[0137] The decision variables and objective function values ​​corresponding to each solution are dimensionless to construct a decision variable matrix. ,in, Let the k-th option be the e-th decision variable;

[0138] Target value matrix , This represents the quantified value of the net economic benefit of the k-th scheme. This represents the quantified carbon emission value for the k-th scheme.

[0139] Based on current production conditions, set target weights: ,satisfy ;

[0140] Calculate the overall score for each decision option. Based on the overall score The highest score will be used to determine the final decision:

[0141] .

[0142] In one specific embodiment, refer to Figure 2 As shown, to verify the effectiveness of the present invention, three crops were selected and empirical analysis was conducted by setting key parameters. Using the monthly scale division strategy based on the growth period proposed in this invention, the final configuration results show that the irrigation water allocation for the key stages of the crops is highly matched with the water demand. The present invention achieves precise water allocation for each stage through sub-time period division, avoiding water shortage in the key period or waste in the non-key period caused by "average" water supply.

[0143] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0144] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimal allocation of water resources based on the reproductive period, characterized in that, include: S1. Collect the start and end dates of each growth stage of different crops and construct an ordered set of key dates. Specifically: S101, Let the crop number be... j , j =1, 2, 3, ..., J, the reproductive stage number is n , n =1, 2, 3, ..., N, with month numbers as m , m =1, 2, 3, ..., 12; S102, For any month m The start and end dates of all crop growth stages in the current month are summarized to form the original set of key dates; S103. Deduplicate the original set of key dates, and sort the deduplicated set of key dates in chronological order to obtain an ordered set of key dates: ， Where K represents the month m The total number of key dates; S2. Based on an ordered set of key dates, the natural month is divided into multiple sub-time periods, specifically: For the natural month m The first sub-period is t m，1 =[1, d m，1 -1]; For the natural month m The i-th key date d m，i If key dates d m，i The current sub-period does not end on any particular crop growth stage; its start and end dates are from the previous critical date to the day before the current critical date. If the key date d m，i If the date is the end date of a certain crop growth stage, then that date will be the end date of the current sub-period. For the natural month m Last key date d m，K The corresponding sub-time period t m，K+1 The end date is the month m Total number of days , ; All calendar months m The sub-periods constitute the natural month m The sub-time period set T m ; S3. Establish the mapping relationship between each growth stage and sub-time period of different crops, specifically as follows: Establish crops j reproductive stage n sub-period Mapping relationship: ; In the formula , Crops j reproductive stage n The start and end dates; , Sub-time periods t The start and end dates; Indicates sub-time period t Crops j reproductive stage n , Indicates sub-time period t Not a crop j reproductive stage n ; S4. Establish the mapping relationship between water consumption in each sub-period and water consumption in a natural month; S5. Construct a water resource optimization allocation model with the dual objectives of maximizing net economic benefits and minimizing carbon emissions; S6. Based on the GAMultiObj algorithm, optimize and solve the water resource optimization allocation model to generate a water resource optimization allocation scheme.

2. The water resource optimization allocation method based on the reproductive period according to claim 1, characterized in that, In S4, a mapping relationship is established between water consumption in each sub-period and water consumption in a natural month, specifically as follows: Establish the relationship between water consumption in sub-periods and monthly water consumption: ； In the formula, month m Total irrigation water volume, in m³ 3 ; Sub-period t Inland crops j The amount of irrigation water per unit area, expressed in m³. 3 / hm 2 ; For crops j The planting area, in hectares. 2 .

3. The method for optimal allocation of water resources based on reproductive period according to claim 2, characterized in that, In S5, a water resource optimization allocation model is constructed, specifically as follows: With the goals of maximizing net economic benefits and minimizing carbon emissions, and with constraints such as available water in the irrigation area, crop irrigation water volume, crop planting area, and grain yield, and with the irrigation water volume and planting area of ​​crops in different sub-periods as decision variables, a model is constructed. With the goal of maximizing net economic benefit, the objective function is as follows: ； In the formula, It is a crop type. J This represents the total number of crop types. It is a crop The price is in yuan / kg; It is a crop The cost; It is a crop The planting area, in hm², is a decision variable. It is a crop Total output, expressed in kg / hm²; Calculated using the moisture production function: ； In the formula, It is a crop The maximum yield under conditions of sufficient water supply is expressed in kg / hm²; N j For crops j The total number of reproductive stages, yes Crop growth stage n Moisture sensitivity index; It is a crop During the reproductive stage n The water demand is expressed in m³ / hm². It is a crop The actual transpiration rate of n during the reproductive stage is expressed in m³ / hm². crop Actual evapotranspiration during the reproductive stage n This is obtained by summing the evaporation amounts of the associated sub-periods: ; In the formula, ET j,t For crops Sub-period t The transpiration rate is determined by the amount of irrigation water. and effective rainfall constitute; With the goal of minimizing carbon emissions from agricultural production, the objective function is as follows: ； In the formula, It is a crop The carbon emission factor, in units of kg / hm² 2 ; Water supply constraints in the irrigation area: ； In the formula, For months m The upper limit of water conveyance capacity, in meters (m). 3 ; Crop irrigation water constraints: ； In the formula, It is a crop During the reproductive stage n The lower limit of irrigation water volume, in m³ / hm²; It is a crop During the reproductive stage n The upper limit of irrigation water volume, in m³ / hm²; Crop planting area constraints: ； In the formula, This is the total cultivated land area of ​​the irrigation district, expressed in hm². Grain production constraints: ； In the formula, It is the minimum guarantee for grain production, and the unit is kg; Non-negativity constraint: All variables in the model must satisfy the non-negativity constraint.

4. The method for optimal allocation of water resources based on reproductive period according to claim 3, characterized in that, In S6, the water resource optimization allocation model is optimized and solved to generate an optimal water resource allocation scheme, specifically as follows: Based on the traditional GAMultiObj algorithm, the mapping relationship between crop growth stages and sub-periods is used to identify irrigation water decision variables corresponding to non-critical growth periods; By setting the water volume for crop rotation during non-critical growth periods to 0 for individuals in the initial population, non-critical variables are removed from the optimization process. By iteratively improving candidate solutions through fitness calculation, genetic operations, and Pareto dominance evaluation, a frontier solution set is output. The decision variables and objective function values ​​corresponding to each solution are dimensionless to construct a decision variable matrix. ,in, Let the e-th decision variable be the k-th option; Target value matrix , This represents the quantified net economic benefit of the k-th scheme. This represents the quantified carbon emission value for the k-th scheme. Based on current production conditions, set target weights: ,satisfy ; Calculate the overall score for each decision option. Based on the overall score The highest score will be used to determine the final decision: 。

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