Crop water distribution method, device, medium and equipment

Abstract

The invention discloses a crop water distribution method and device, a medium and equipment, and relates to the field of water resource management, and the method comprises the steps: constructing a multi-target simulation-optimization model; the multi-objective simulation-optimization model comprises an EPI crop growth model and an optimization model which takes maximized economic water productivity, maximized irrigation water productivity and maximized nutritive water productivity as objective functions and takes irrigation water quantity and salt accumulation in a crop growth period as constraints; inputting the acquired meteorological data, crop data, soil data, channel data and groundwater data into a simulation model of a multi-target simulation-optimization model, determining simulation results of actual crop evapotranspiration, final crop yield and straw quantity, taking the simulation results as input of an optimization model, and determining an irrigation water distribution scheme; constructing a water-salt migration model; and updating the simulation result through the water-salt migration model so as to optimize the irrigation water distribution scheme until a preset optimization target is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource management, and particularly relates to a crop water allocation method, device, medium and equipment. Background Art

[0002] Speeding up the development of efficient agricultural water use and improving agricultural water use efficiency are the keys to solving the contradiction between agricultural water shortage and food security. Improving crop water productivity is the ultimate goal of efficient water-saving agriculture and the core task of ensuring water resources and food security. Crop water productivity expresses the value and benefits generated by crop water consumption in the process of irrigation water management, and is a comprehensive index to measure the level of agricultural production and the scientificity and rationality of agricultural water use.

[0003] Currently, the research on crop water productivity in the prior art mainly focuses on crop water productivity from a single angle. Crop water productivity from a single angle (irrigation water productivity, economic water productivity) often focuses on certain aspects, such as crop yield, water use, etc. However, agricultural crop water productivity is affected by multiple factors. Only a comprehensive study of crop water productivity (such as considering crop yield, water use efficiency and nutritional value at the same time) can comprehensively reflect the effect of crops using water resources. Focusing only on the simulation from a single angle will make it difficult to improve crop water productivity. Summary of the Invention

[0004] The present invention provides a crop water allocation method, device, medium and equipment to solve the above problems existing in the prior art, that is, the problem of how to comprehensively improve crop water productivity in the prior art. The present invention provides a crop water allocation method, which includes:

[0005] Obtain meteorological data, crop data, soil data, channel data and groundwater data of the area to be measured;

[0006] Construct a multi-objective simulation-optimization model for improving crop water productivity; the multi-objective simulation-optimization model includes an EPIC crop growth model, and an optimization model with maximizing economic water productivity, maximizing irrigation water productivity and maximizing nutritional water productivity as objective functions, and with the irrigation water volume during the crop growth period and salt accumulation as constraint conditions;

[0007] Input the obtained meteorological data, crop data, soil data, channel data and groundwater data into the simulation model of the multi-objective simulation-optimization model to determine the simulation results of crop actual evapotranspiration, crop final yield and straw amount, and use the simulation results as the input of the optimization model to determine the irrigation water volume allocation plan;

[0008] Construct a water-salt transport model for simulating the interaction between soil moisture and salinity; update the simulation results through the water-salt transport model to provide feedback for optimizing the model, so as to optimize the irrigation water allocation plan until the preset optimization goal is achieved.

[0009] Optionally, the following formula is used to obtain the maximized economic water productivity:

[0010]

[0011] where MaxF1 is the maximized economic water productivity, which is the ratio of benefit to total irrigation water volume; n is the irrigation subsystem number; NB i is the market price of the i-th crop; A i is the planting area of the i-th crop; Y i is the yield of the i-th crop; Ω is a set of parameters, including meteorological parameters, leaf area index, and harvest index; Y i = Y ci (IW1,…,IW t ,…,IW 173 ,Ω); σ i is the utilization rate of straw as feed for the i-th crop; S i The straw amount is calculated by the EPIC model; FP i Feed price; CW is the irrigation cost; IW it is the irrigation water volume of the i-th crop at time t; CP i is the production cost of the i-th crop; is the irrigation water use efficiency;

[0012] The following formula is used to obtain the maximized irrigation water productivity:

[0013]

[0014] where MaxF2 is the maximized irrigation water productivity, which is the ratio of the total crop yield to the total irrigation water volume;

[0015] The following formula is used to obtain the maximized nutrient water productivity:

[0016]

[0017] where Max F3 is the maximized nutrient water productivity, which is the ratio of the conversion of crop yield into nutrient content to the actual water consumption; NP i is the nutrient content per kilogram of food crop; ET a,it is the actual transpiration of the i-th crop at time t.

[0018] Optionally, the meteorological data includes daily average temperature, maximum temperature, minimum temperature, solar radiation, and effective rainfall; the crop data includes optimum growth temperature, minimum temperature, maximum leaf area index, harvest index, yield response coefficient, etc.; the canal data includes the lengths of irrigation canals at all levels, water conveyance loss coefficient, and canal water utilization coefficient; the soil data includes field water holding capacity, wilting water content, saturated water content, residual water content, saturated hydraulic conductivity, initial water content, and initial soil salinity concentration; the canal data includes the lengths of irrigation canals at all levels, water conveyance loss coefficient, and canal water utilization coefficient; the groundwater data includes specific yield, initial groundwater depth, and initial groundwater salinity concentration.

[0019] The present invention provides a crop water distribution device, comprising:

[0020] An acquisition module, configured to acquire meteorological data, crop data, soil data, canal data, and groundwater data of a to-be-measured area;

[0021] A construction module, configured to construct a multi-objective simulation-optimization model for improving crop water productivity; the multi-objective simulation-optimization model includes an EPIC crop growth model, and an optimization model with maximizing economic water productivity, maximizing irrigation water productivity, and maximizing nutrient water productivity as objective functions, and with crop growth period irrigation water volume and salt accumulation as constraint conditions;

[0022] An irrigation water volume allocation scheme determination module, configured to input the acquired meteorological data, crop data, soil data, canal data, and groundwater data into the simulation model of the multi-objective simulation-optimization model, determine the simulation results of actual crop evapotranspiration, final crop yield, and straw amount, use the simulation results as the input of the optimization model, and determine the irrigation water volume allocation scheme;

[0023] An optimization module, configured to construct a water-salt transport model for simulating the interaction between soil water and salt; update the simulation results through the water-salt transport model, provide feedback to the optimization model, so as to optimize the irrigation water volume allocation scheme until a preset optimization target is reached.

[0024] The present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned crop water distribution method is implemented.

[0025] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the above-mentioned crop water distribution method is implemented.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a crop water allocation method. By considering the three aspects of crop economy, irrigation, and nutrient water productivity, integrating the EPIC crop growth module and the water-salt dynamic balance module, a multi-objective simulation-optimization model for improving the comprehensive water productivity of crops is proposed. Considering the impacts of multiple processes such as "supply-consumption-drainage" in the irrigation area and canal seepage on crop growth and the formation process of water productivity, it can comprehensively consider water productivity from multiple perspectives, thereby effectively improving water resource utilization efficiency and reducing soil secondary salinization, and providing a scientific solution for the efficient utilization of irrigation water in the irrigation area. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0028] Figure 1 It is a flowchart of a crop water allocation method provided by an embodiment of the present invention;

[0029] Figure 2 It is a framework diagram of a crop water allocation method provided by an embodiment of the present invention;

[0030] Figure 3 It is a schematic diagram of a computer device for the crop water allocation method provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0032] The technical solutions of the present invention and how the technical solutions of the present invention solve the above technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0033] As Figure 1 and Figure 2 shown, a crop water allocation method shown in this embodiment includes:

[0034] S1: Obtain meteorological data, crop data, soil data, canal data, and groundwater data of the area to be measured.

[0035] Optionally, the meteorological data includes daily average temperature, maximum temperature, minimum temperature, solar radiation, and effective rainfall; the crop data includes optimal growth temperature, minimum temperature, maximum leaf area index, harvest index, yield response coefficient, etc.; the canal data includes the lengths of irrigation canals at all levels, water conveyance loss coefficient, and canal water use coefficient; the soil data includes field water holding capacity, wilting water content, saturated water content, residual water content, saturated hydraulic conductivity, initial water content, and initial soil salt concentration; the canal data includes the lengths of irrigation canals at all levels, water conveyance loss coefficient, and canal water use coefficient; the groundwater data includes specific yield, initial groundwater depth, and initial groundwater salt concentration.

[0036] S2: Construct a multi-objective simulation-optimization model for improving crop water productivity; the multi-objective simulation-optimization model includes an EPIC crop growth model and an optimization model with maximizing economic water productivity, maximizing irrigation water productivity, and maximizing nutrient water productivity as objective functions and with crop growth stage irrigation water volume and salt accumulation as constraint conditions.

[0037] Exemplarily, economic water productivity is the economic value obtained by a crop per unit of irrigation water consumed and can be used as an important economic indicator for evaluating water resource allocation, irrigation water use efficiency, and sustainable development. The maximization of economic water productivity is obtained using the following formula:

[0038]

[0039] where F1 is economic water productivity, which is the ratio of benefit to total irrigation water volume; n is the irrigation subsystem number; NB i is the market price of the i-th crop; A i is the planting area of the i-th crop; Y i is the yield of the i-th crop; Ω is a set of parameters, including meteorological parameters, leaf area index, and harvest index; Y i = Y ci (IW1,...,IW t ,...,IW 173 ,Ω); σ i is the utilization rate of the straw obtained by the i-th crop as feed; S i is the straw amount calculated by the EPIC model; FP i is the feed price; CW is the irrigation cost; IW it is the irrigation water volume of the i-th crop at time t; CP i is the production cost of the i-th crop; is the irrigation water use efficiency;

[0040] Generally, irrigation water productivity is the yield obtained by a crop per unit of irrigation water consumed. Narrowing the gap in irrigation water productivity is an effective way to alleviate water scarcity and ensure food security. The irrigation water productivity can be obtained using the following formula:

[0041]

[0042] where F2 is the maximum irrigation water productivity, which is the ratio of the total crop yield to the total irrigation water volume;

[0043] Nutrient water productivity is the nutritional value produced by a crop per unit of water consumption. Incorporating nutrients into all aspects of crop and water productivity research can alleviate food and nutritional insecurity. At the same time, the transformation of research on efficient agricultural water use towards healthy diets may bring various environmental benefits, including water conservation. The nutrient water productivity can be obtained using the following formula:

[0044]

[0045] where F3 is the maximum nutrient water productivity, which is the ratio of the conversion of crop yield into nutrient content to the actual water consumption; NP i is the nutrient content per kilogram of food crop; ET a,it is the actual transpiration of the i-th crop at time t.

[0046] Exemplarily, the irrigation water volume constraint may include:

[0047]

[0048]

[0049] According to the actual irrigation plans of the three crops, the upper and lower limits of the single irrigation water volume can be formulated respectively. It is necessary to set the lower limit of the total irrigation water demand during the growth period to meet the minimum water demands of the three crops;

[0050] The salt accumulation constraint includes:

[0051]

[0052] S3: Input the obtained meteorological data, crop data, soil data, canal data, and groundwater data into the simulation model of the multi-objective simulation-optimization model to determine the simulation results of the actual crop evapotranspiration, the final crop yield, and the straw amount. Use the simulation results as the input of the optimization model to determine the irrigation water volume allocation plan.

[0053] S4: Construct a water-salt transport model for simulating the interaction between soil water and salt; update the simulation results through the water-salt transport model to provide feedback to the optimization model to optimize the irrigation water volume allocation plan until the preset optimization goal is achieved.

[0054] Exemplarily, the actual crop evapotranspiration, the final crop yield, and the straw amount output by the simulation model are used as the input data for the optimization model; a group of irrigation water distribution schemes is randomly generated as the initial solution, and the elite opposition-based learning strategy is used to process the initial population to increase the diversity and quality of the initial population; the above process is repeated until a predetermined number of iterations or convergence conditions are reached, and one or more groups of optimal solutions are selected from the final population as the optimized irrigation water distribution scheme.

[0055] Exemplarily, the multi-objective simulation-optimization model is solved by the NSGA-Ⅲ algorithm. The specific process may include: (1) Initialization: Define three water productivity objective functions to be optimized, water volume and salt constraints; set the population size, termination conditions, hyperparameters, etc. of NSGA-Ⅲ; create a group of initial populations (decision variables) that meet the constraints. (2) Evolution loop: Input the population (the population in the first loop = the initial population); calculate the fitness of the current population through the simulation model (i.e., the coupled EPIC and water-salt transport models). The operations of crossover (simulated binary crossover) and mutation (polynomial mutation) are used to generate offspring, the offspring and the current population are merged, and the non-dominated sorting and reference point guiding strategy are used to select individuals with good fitness values, and finally the next generation population is selected from the merged ones. (3) End condition: When the preset termination condition is reached, the algorithm ends. Output the final Pareto front (a series of non-dominated solutions) for the optimization of the irrigation regime.

[0056] To ensure the sustainable development of agriculture, three scenarios are set for the increment of the salt concentration in the root zones of maize, sunflower, and spring wheat crops. In this application, the crop planting depth can be set to 10 cm, and the salt concentration at the crop planting depth can be obtained by actual measurement and separately calculating the water-salt balance of the 10-cm soil layer to obtain the salt concentration at the sowing time.

[0057] Exemplarily, the EPIC crop growth model is a multi-crop general growth model that simulates the phenological development process based on accumulated temperature. The EPIC input parameters are relatively few and the simulation accuracy is relatively high. At the same time, through a large number of verifications, it can simulate plant height, leaf area index, evapotranspiration, root depth, biomass, nutrient and water absorption, environmental stress, and crop yield, etc.

[0058] ①Phenological development

[0059] The phenological development of crops is based on the daily accumulation of heat units. The accumulated amount of heat units on the t-th day after crop sowing is expressed as:

[0060]

[0061] The crop heat unit coefficient is 0 at sowing and 1 at maturity, and is calculated by the following formula:

[0062]

[0063] In the formula: HU t , T max,t , T min,t are the heat unit, the maximum and minimum air temperatures (°C) on the t-th day respectively; T b is the base temperature of the crop (°C); HUI t is the heat unit coefficient on the t-th day, and its value range is 0 - 1; PHU is the maximum heat unit required for crop maturity.

[0064] ② Potential biomass growth

[0065] The solar radiation intercepted by the crop is calculated using Beer's law:

[0066] PAR t = 0.5·RA t ·[1 - exp(-0.65·LAI t )]

[0067] ΔB p,t = 0.001·BE·PAR t

[0068] In the formula: PAR t is the intercepted photosynthetically active radiation (MJ / m 2 ); RA t is the total solar radiation (MJ / m 2 ); LAI t is the leaf area index; ΔB p,t is the daily potential biomass increment (kg / hm 2 ); BE is the energy-biomass conversion factor (kg / hm 2 ) / (MJ / m 2 ).

[0069] ③ Leaf area index change

[0070] The leaf area index is a function of the heat unit, crop stress, and crop growth and development stages. From emergence to the start of leaf area decline, it is calculated using the following formula:

[0071] LAI t = LAI t-1 + ΔLAI t

[0072]

[0073] From the start of leaf area decline to the end of growth, LAI is calculated using the following formula t :

[0074]

[0075] In the formula: LAI t is the leaf area index, HUF t is the heat unit factor, REG t is the minimum crop stress factor value, which is the minimum value of the moisture, temperature, and salinity stress factors, LAI max is the maximum crop leaf area index, and ah1 and ah2 are the parameters controlling the leaf area change curve; ad is the parameter determining the LAI t attenuation rate, LAI0 is the actual maximum leaf area index during the simulation process, and HUI0 is the heat unit coefficient when reaching the actual leaf area index.

[0076] ④ Plant height growth

[0077] The plant height of the crop is calculated by the following formula:

[0078] H c,t = H c,max ·HUF t 0.5

[0079] In the formula: H c,max is the maximum plant height of the crop.

[0080] ⑤ Root growth

[0081] The dry matter mass allocated to the roots is calculated by the following formula:

[0082] H c,t = H c,max ·HUF t 0.5

[0083] Before physiological maturity, the root length of the crop usually reaches the maximum root depth, and the root depth is expressed as a function of the heat unit factor and the maximum root depth:

[0084] RD t = RD t-1 +ΔRD t

[0085] ΔRD t = 2.5·RD max ·ΔHUF t RD t ≤RD max

[0086] RD t = 2RD max RD t >RD max

[0087] In the formula: ΔRWT tis the change in root weight (t / hm 2 ); ΔRD it is the change in root depth on the t-th day (cm); RD t is the root depth on the t-th day; RD max is the maximum root depth (cm).

[0088] ⑥ Crop yield

[0089] Crop yield is calculated using the harvest index:

[0090] YLD = HI·B a

[0091]

[0092]

[0093] YLD = HI adj ·B a

[0094] In the formula: YLD is the crop yield at harvest (t / hm 2 ); HI is the harvest index on the t-th day; B a is the aboveground biomass (t / hm 2 ); HUFH is the heat unit factor affecting the harvest index; HI adj is the restriction of environmental stress on the harvest index; YLD is the actual crop yield; WSYF is the sensitivity index of the crop to drought, which is also the lower limit of the harvest index.

[0095] ⑦ Influence of environmental stress on biomass growth

[0096] When any one of the environmental stress factors such as water salinity and temperature is less than 1, the actual biomass increment is calculated using the following formula:

[0097] ΔB t = ΔB p,t ·REG t

[0098] REG t = min(WS t ·SS t , TS t )

[0099]

[0100] EC e = 1.33 + 5.88·EC 1：5

[0101] In the formula: ΔB tis the actual biomass growth increment on the t-th day (t / hm 2 ); ΔB p,t is the potential biomass growth increment on the t-th day (t / hm 2 ); REG is the crop growth regulation factor; WS is the water stress factor; TS is the temperature stress factor; T p is the potential transpiration rate of the crop (mm / d); T a is the actual transpiration rate of the crop (mm / d); TG is the daily average air temperature (°C); T b is the base temperature of the crop (°C); T0 is the optimum temperature of the crop (°C); Ky is the yield response factor; B is the percentage reduction in yield for each unit increase in EC e ; EC e is the saturated aqueous solution electrical conductivity value of the soil root layer (ms / cm); EC e,threshold is the threshold value of EC e when the crop yield starts to be lower than the maximum yield (ms / cm).

[0102] Exemplarily, the soil water and salt transport process affects the data such as the actual evapotranspiration of the crop, the final crop yield, and the straw amount output by the simulation model; these data affect the objective function of the optimization model; through the objective function and iteration, the irrigation regime can be updated, so as to obtain the optimal solution by continuously adjusting and optimizing the irrigation regime. The soil interface can be divided into 5 regions: the seed burial depth layer (region 0), the actual root zone layer (region 1), the potential root zone layer (region 2), the transition layer (region 3), and the saturated layer (region 4). Region 0 is the soil layer with a thickness of 10 cm on the surface layer. The initial salt concentration in the root zone at the sowing time of corn and sunflower is mainly calculated through the water-salt balance equation. When calculating the parameters of each part of the actual root zone, regions 0 and 1 are merged and named region M; the lower boundary of region 3 is at the groundwater level, and the upper boundary is the lower boundary of the potential root zone. Irrigation, rainfall, crop growth, and surface evaporation will cause downward leakage or upward groundwater recharge in the soil body.

[0103] (1) Soil water movement

[0104] ① Before the root system reaches the maximum root length (RD it ≤RD max,i ), the water balance calculation of region M is as follows:

[0105] Wr i(t+1) =Wr it +P it +I it +U M,2,it +CR it -ET a,it -J M,2,it

[0106]

[0107] Where: Wr is the water storage in area M (mm); P is the rainfall (mm); I is the irrigation water volume (mm); ET a is the actual evapotranspiration of the crop (mm); CR is the water volume increased in area 1 due to root growth (mm); J M,2 is the water volume infiltrated from area M to area 2 (mm); U M,2 is the water volume replenished from area 2 to area M (mm); mr and mg are the soil water contents of area M and area 2 (cm3 / cm3); RD t is the crop root depth on the t-th day (mm).

[0108] When mr it ≥ mf it :

[0109] ET a,it = ET p,it

[0110] U 1,2,it = 0

[0111] J M,2,it = Wr it + P it + I it + U M,2,it + CR it - ET a,it - mf it · MD it

[0112] When mr it < mf it :

[0113] J M,2,it = 0

[0114]

[0115] mr i(t+1) = Wr i(t+1) / MD i(t+1)

[0116] The water balance calculation of area 2 is as follows:

[0117] Wg i(t+1) = Wg it + J M,2,it - U M,2,it - CR it - J 2,3,it - U 2,3,it

[0118] Where: MD itis the thickness of the merged soil layer, with a value of MD it = D + RD it ; D is the seeding thickness, set to 10 cm; D r is the average diffusion rate of region M (cm2 / day); D0 is the diffusion rate of region M at the wilting point (cm2 / day); b is the empirical parameter of the soil; Wg is the water content of region 2 (mm); J 2,3 is the downward flux at the lower boundary of region 2 (mm); U 2,3 is the downward flux at the lower boundary of region 2 (mm).

[0119] When mg it ≥ mf it , the amount of groundwater recharged by region 2 is:

[0120]

[0121] When mg it <mf it , there will be no downward flux generated, and the amount of water recharged to the soil by phreatic evaporation can be obtained from the function of the groundwater level:

[0122]

[0123]

[0124] wgh t = λ1h 1t + λ2h 2t + λ3h 3t

[0125] mg i(t+1) = Wg i(t+1) / D 2,i(t+1)

[0126]

[0127] In the formula: ms is the saturated water content of region 2, md is the residual water content of region 2, k2s is the saturated hydraulic conductivity of regions M and 2; ks is the saturated hydraulic conductivity of the seepage layer (mm / day); C is a constant; D2 is the soil layer thickness of region 2; h is the groundwater depth (mm); wgh is the weighted groundwater depth; Q loss Water conveyance loss (mm); DR is the drainage volume (mm); λ1, λ2, λ3 are the area weights of three crops; α is the diffusion rate of the seepage layer, and its value is the reciprocal of the air entry value; is the matrix potential (mm); is the air entry suction (cm); dp is the specific yield; mg is the soil water content of region 2 (cm3 / cm3).

[0128] ②When the root growth reaches the maximum root system (RD it = RD max,i ), region 2 disappears, and regions M and 2 will merge into region M. The calculation of the water balance in region M is similar to that of region 2 before the maximum root system is reached.

[0129] Wr i(t+1) = Wr it + P it + I it - J M,3,it + U M,3,it

[0130] When mg it ≥ mf it , the amount of water replenishing the groundwater in region M is:

[0131]

[0132] When mg it < mf it , there is no downward flux, and the amount of water replenishing the soil water by phreatic evaporation is:

[0133]

[0134] (2) Soil salt movement

[0135] ①When the root growth has not reached the maximum root system, the salt balance calculation in region M is as follows:

[0136]

[0137] The salt balance calculation in region 2 is as follows:

[0138] When the downward boundary flux in region 2 is upward, the calculation formula is as follows:

[0139]

[0140] When the downward boundary flux in region 2 is downward, the calculation formula is:

[0141]

[0142] In the formula: Csr and Csg are the salt concentrations in regions M and 2 (mg / L); Cgw is the salt concentration of groundwater (mg / L); G is the groundwater depth of the groundwater level fluctuation (mm), taking 2 m.

[0143] ②When the root growth reaches the maximum root system, region 2 disappears, and regions M and 2 will merge into region M. The salt balance calculation in region M is similar to that of region 2 before the maximum root system is reached, but is different from that of water.

[0144] When the flux at the lower boundary of region M is upward, the calculation formula is as follows:

[0145]

[0146] When the flux at the lower boundary of region M is downward, the calculation formula is:

[0147]

[0148] (3) Actual crop evapotranspiration

[0149] The actual evapotranspiration in the crop root zone is affected by water and salt stress. ET a consists of actual evaporation and actual transpiration, and is calculated from the potential evaporation E p and the potential transpiration T p The ratio of potential evaporation to potential transpiration is determined by the canopy growth stage and is represented by τ:

[0150] τ = exp(-kb·LAI)(

[0151] E p = τ·ET p T p = (1 - τ)·ET p

[0152]

[0153] ET a = E a + T a

[0154] In the formula: kb is the dimensionless canopy extinction coefficient; ET a is the actual evapotranspiration (mm); ET p is the potential evapotranspiration; be and bt are empirical parameters respectively; E a is the actual evaporation; T a is the actual transpiration.

[0155] The above is the crop water distribution method provided by one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding crop water distribution device, including:

[0156] An acquisition module, configured to acquire meteorological data, crop data, soil data, channel data, and groundwater data of the area to be measured;

[0157] A building module for building a multi-objective simulation-optimization model for improving crop water productivity; the multi-objective simulation-optimization model includes an EPIC crop growth model, and an optimization model with maximizing economic water productivity, maximizing irrigation water productivity, and maximizing nutrient water productivity as objective functions, and with the irrigation water volume during the crop growth period and salt accumulation as constraint conditions.

[0158] An irrigation water volume allocation scheme determination module for inputting the obtained meteorological data, crop data, soil data, channel data, and groundwater data into the simulation model of the multi-objective simulation-optimization model to determine the simulation results of crop actual evapotranspiration, crop final yield, and straw volume, and using the simulation results as the input of the optimization model to determine the irrigation water volume allocation scheme.

[0159] An optimization module for building a water-salt transport model for simulating the interaction between soil water and salt; updating the simulation results through the water-salt transport model and providing feedback to the optimization model to optimize the irrigation water volume allocation scheme until a preset optimization goal is achieved.

[0160] For the specific limitations of the crop water distribution device, reference can be made to the limitations of the crop water distribution method in the above text, which will not be elaborated here. Each module in the above crop water distribution device can be implemented in whole or in part through software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0161] The present invention also provides a computer-readable storage medium storing a computer program, which can be used to execute the crop water distribution method provided above.

[0162] The present invention also provides Figure 3 The structural schematic diagram of the computer device shown, as Figure 3 shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, there may also be other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the crop water distribution method provided in the above embodiment.

[0163] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0164] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded by the present invention.

Claims

1. A crop water distribution method, characterized in that, Including: Obtain meteorological data, crop data, soil data, canal data, and groundwater data of the area to be measured; Construct a multi-objective simulation-optimization model for improving crop water productivity; the multi-objective simulation-optimization model includes the EPIC crop growth model, and an optimization model with maximizing economic water productivity, maximizing irrigation water productivity, and maximizing nutrient water productivity as objective functions, and with the irrigation water volume during the crop growth period and salt accumulation as constraint conditions; Input the obtained meteorological data, crop data, soil data, canal data, and groundwater data into the simulation model of the multi-objective simulation-optimization model to determine the simulation results of actual crop evapotranspiration, final crop yield, and straw amount, and use the simulation results as the input of the optimization model to determine the irrigation water volume allocation plan; Construct a water-salt transport model for simulating the interaction between soil water and salt; update the simulation results through the water-salt transport model to provide feedback to the optimization model to optimize the irrigation water volume allocation plan until the preset optimization goal is achieved.

2. The crop water distribution method according to claim 1, wherein, The following formula is used to obtain the maximized economic water productivity: Where Max F1 is the maximum economic water productivity, which is the ratio of benefit to total irrigation water volume; n is the irrigation subsystem number; NB i is the market price of the i-th crop; A i is the planting area of the i-th crop; Y i is the yield of the i-th crop; Ω is a set of parameters, including meteorological parameters, leaf area index and harvest index; Y i =Y ci (IW1,…,IW t ,…,IW 173 ,Ω);σ i is the utilization rate of straw obtained from the i-th crop as feed; S i The amount of straw was calculated by the EPIC model; FP i Feed price; CW is irrigation cost; IW is it is the irrigation water volume of the i-th crop at time t; CP i is the production cost of the i-th crop; is the irrigation water use efficiency; The following formula is used to obtain the maximized irrigation water productivity: Wherein, Max F2 is the maximized irrigation water productivity, which is the ratio of the total crop yield to the total irrigation water volume; The following formula is used to obtain the maximized nutrient water productivity: Among them, Max F3 is the maximized nutrient water productivity, which is the ratio of crop yield converted into nutrient content to the actual water consumption; NP i is the nutrient content per kilogram of food crops; ET a,it is the actual transpiration of the i-th crop at time t.

3. The crop water distribution method according to claim 1, wherein The meteorological data includes daily average temperature, maximum temperature, minimum temperature, solar radiation amount, and effective rainfall; the crop data includes the optimum growth temperature, minimum temperature, maximum leaf area index, harvest index, yield response coefficient, etc.; the canal data includes the lengths of irrigation canals at all levels, water conveyance loss coefficients, and canal water utilization coefficients; the soil data includes field water holding capacity, wilting water content, saturated water content, residual water content, saturated hydraulic conductivity, initial water content, and initial soil salt concentration; the canal data includes the lengths of irrigation canals at all levels, water conveyance loss coefficients, and canal water utilization coefficients; the groundwater data includes specific yield, initial groundwater depth, and initial groundwater salt concentration.

4. A crop water distribution device, characterized in that, Including: An acquisition module for obtaining meteorological data, crop data, soil data, canal data, and groundwater data of the area to be measured; A construction module for constructing a multi-objective simulation-optimization model for improving crop water productivity; the multi-objective simulation-optimization model includes the EPIC crop growth model, and an optimization model with maximizing economic water productivity, maximizing irrigation water productivity, and maximizing nutrient water productivity as objective functions, and with the irrigation water volume during the crop growth period and salt accumulation as constraint conditions; An irrigation water volume allocation plan determination module for inputting the obtained meteorological data, crop data, soil data, canal data, and groundwater data into the simulation model of the multi-objective simulation-optimization model to determine the simulation results of actual crop evapotranspiration, final crop yield, and straw amount, and using the simulation results as the input of the optimization model to determine the irrigation water volume allocation plan; An optimization module for constructing a water-salt transport model for simulating the interaction between soil moisture and salinity; updating the simulation results through the water-salt transport model to provide feedback to the optimization model for optimizing the irrigation water allocation scheme until a preset optimization goal is achieved.

5. A computer-readable storage medium, characterized in that, The storage medium stores a computer program which, when executed by a processor, implements the crop water allocation method according to any one of claims 1 to 3 above.

6. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the crop water allocation method according to any one of claims 1 to 3 above.

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