A Time-Based Irrigation Optimization Method for Saline-Alkali Paddy Fields Based on Water-Saline Simulation

Abstract

A method for optimizing periodic irrigation in saline-alkali paddy fields based on water-salt simulation. This method relates to the field of water and salt regulation technology in agricultural saline-alkali land, specifically to a method for optimizing periodic irrigation in saline-alkali paddy fields based on water-salt simulation. The method includes the following steps: collecting basic data of saline-alkali paddy fields; constructing and calibrating a HYDRUS water-salt transport model; constructing several phased irrigation scenario combinations using rice growth stage, irrigation frequency adjustment amount, single additional irrigation amount, and total irrigation amount as scenario variables; calculating the salt leaching rate of the irrigation scenario combinations; calculating the marginal leaching efficiency per unit water volume for each candidate phased irrigation scenario combination; calculating the marginal leaching efficiency per unit irrigation frequency for each candidate phased irrigation scenario combination; and, based on the marginal leaching efficiency per unit water volume and per unit irrigation frequency for each candidate phased irrigation scenario combination, eliminating inefficient water volume adjustments and inefficient frequency adjustments to optimize the periodic irrigation method for saline-alkali paddy fields.

Description

Technical Field

[0001] This invention relates to the field of water and salt regulation technology for agricultural saline-alkali land, specifically to a method for optimizing irrigation of saline-alkali paddy fields in different time periods based on water and salt simulation. Background Technology

[0002] Saline-alkali land is one of my country's important reserve arable land resources. Its rational development and utilization are crucial for ensuring food security, improving the regional ecological environment, and enhancing land resource utilization efficiency. Rice cultivation, by maintaining a surface water layer, can promote soil salt leaching and saline-alkali land improvement to a certain extent, thus exhibiting strong adaptability in some saline-alkali areas. However, the water movement and salt migration processes in saline-alkali paddy fields are influenced by a variety of factors, including soil texture, groundwater conditions, initial salinity, irrigation system, and crop growth stage, resulting in a complex water-salt response relationship.

[0003] The basic idea behind salt control through irrigation in alkaline paddy fields is to reduce salt concentration in the root zone by allowing irrigation water to infiltrate and cause soil salts to migrate downwards. However, in actual irrigation management, there is no stable proportional relationship between the amount of additional irrigation water and the salt leaching effect. For the same field, when the amount of additional irrigation water is low, increasing irrigation may significantly promote the downward migration of salts in the root zone; but as the amount of irrigation water continues to increase, the salt leaching gain brought about by the additional water volume may gradually weaken, and may even mainly manifest as increased deep seepage, increased drainage load, or increased water consumption, while the improvement in salt control effect in the root zone is limited. Therefore, it is difficult to determine how much effective salt control gain each additional level of additional irrigation water can bring based solely on empirical irrigation water volume, fixed irrigation regime, or final salt leaching rate.

[0004] This problem is particularly prominent in saline-alkali rice paddy production. If the irrigation volume is set too low, salt in the root zone may not be leached in time, affecting the normal growth of rice; if the irrigation volume is set too high, it may lead to water waste, increased deep seepage, and salt migration to deeper layers. Existing irrigation management methods can usually provide the overall salt control results under a certain irrigation scheme, but it is difficult to further determine whether "continuing to increase the irrigation volume by one level is still worthwhile," or to determine the critical range at which additional irrigation transitions from the effective range to the inefficient range. Therefore, under different salinity backgrounds, different soil water and salt states, and different crop growth stages, irrigation managers often lack quantifiable evidence to judge the actual benefits of additional irrigation.

[0005] Furthermore, irrigation regimes involve not only the amount of irrigation water but also the frequency and amount of water per irrigation. When the irrigation frequency remains constant, increasing the amount of water per irrigation can increase the total additional irrigation amount, but the salt leaching gain from different water volume levels may vary. Similarly, when the amount of water per irrigation remains constant, increasing the number of irrigations can also increase the total additional irrigation amount, but the salt leaching gain from each additional irrigation may gradually decrease. Therefore, based solely on the final salt leaching rate of a particular irrigation scenario, it is difficult to determine whether further increasing the amount of water per irrigation or the number of irrigations still has effective salt control value, and it is also difficult to distinguish whether the improvement in salt leaching effect mainly comes from an increase in irrigation volume or an increase in the number of irrigations. Summary of the Invention

[0006] Existing irrigation management methods for rice in saline-alkali land struggle to quantitatively determine the salt leaching gain resulting from each additional irrigation level and each additional irrigation session. This makes it difficult to accurately identify the effective salt control range and inefficient irrigation range for each additional irrigation level and frequency, thus hindering the rational determination of irrigation frequency and the amount of additional irrigation per session based on different rice growth stages and soil salinity responses. The purpose of this invention is to propose a time-phased irrigation optimization method for saline-alkali rice fields based on water-salt simulation.

[0007] The method includes the following steps: S1. Collect basic data on paddy fields in saline-alkali land; S2. Based on the basic data collected in step S1, construct and calibrate the initial HYDRUS water-salt transport model to obtain the HYDRUS water-salt transport model. S3. Using rice growth stage, irrigation frequency adjustment amount, single additional irrigation amount and total irrigation amount as scenario variables, construct several phased irrigation scenario combinations; S4. Select a portion of the irrigation scenario combinations from the candidate phased irrigation scenario combinations and input them into the HYDRUS water-salt transport model. Based on the output results, calculate the salt leaching rate of the selected irrigation scenario combinations respectively. S5. Using the partial irrigation scenario combination as input and the salt leaching rate of the partial irrigation scenario combination as output, construct and train the GPR proxy prediction model, and then predict the combined salt leaching rate of the remaining irrigation scenarios that were not simulated in the irrigation scenario combination based on the trained GPR proxy prediction model. S6. For several candidate phased irrigation scenario combinations, the additional irrigation volume is grouped according to the rice growth stage and the adjustment amount of irrigation frequency, and the marginal leaching efficiency per unit volume of each candidate phased irrigation scenario combination is calculated. S7. For several candidate phased irrigation scenario combinations, group the irrigation frequency adjustment amount according to the rice growth stage and the amount of additional irrigation per time, and calculate the marginal leaching efficiency per unit irrigation number for each candidate phased irrigation scenario combination. S8. Based on the marginal leaching efficiency per unit water volume and marginal leaching efficiency per unit irrigation frequency for each candidate phased irrigation scenario combination, eliminate inefficient water volume adjustments and inefficient frequency adjustments, and optimize the phased irrigation method for saline-alkali paddy fields.

[0008] Furthermore, the basic data for paddy fields in saline-alkali land include: meteorological data of the study area, soil physical parameters, initial soil moisture content, initial soil salinity, soil electrical conductivity, groundwater depth, groundwater salinity, irrigation system, and information on rice growth stages.

[0009] Furthermore, the construction of the initial HYDRUS water-salt transport model includes: Set the simulation process and simulation period, establish the simulation domain, set the water and salt transport boundary conditions, and set the root water absorption parameters; The calibration of the initial HYDRUS water-salt transport model includes: The simulation results of the initial HYDRUS water-salt transport model are compared with the soil electrical conductivity collected in step S1. When the simulation accuracy meets the preset accuracy requirements, the HYDRUS water-salt transport model is obtained.

[0010] Furthermore, the construction of several phased irrigation scenario combinations includes: An initial irrigation scenario combination is generated based on the rice growth stage, the amount of irrigation frequency adjustment, and the amount of additional irrigation water per instance; Calculate the total irrigation volume for the entire growth period corresponding to each initial irrigation scenario combination based on the irrigation system; Initial irrigation scenario combinations that do not meet crop water requirements or total irrigation volume constraints throughout the growth period are eliminated to obtain candidate phased irrigation scenario combinations.

[0011] Furthermore, the salt leaching rate for each irrigation scenario combination is composed of the salt leaching rate of the corresponding target soil layer; The salt leaching rate of the target soil layer corresponding to the partial irrigation scenario combinations The calculation formula is as follows:

[0012] in, This represents the index value of the target soil layer in each irrigation scenario combination. This is the index value of the partial irrigation scenario combination. Indicates the first In the aforementioned partial irrigation scenario combinations, the first Initial electrical conductivity of the target soil layer After applying the irrigation scenario, the output of the HYDRUS water-salt transport model is the first... In the first of the aforementioned partial irrigation scenario combinations The target soil layer electrical conductivity.

[0013] Furthermore, the marginal leaching efficiency per unit volume of water for each irrigation scenario combination is composed of the marginal leaching efficiency per unit volume of water for the corresponding target soil layer. The formula for calculating the marginal leaching efficiency per unit volume of water for each candidate phased irrigation scenario combination is as follows: in, This represents the index value of candidate phased irrigation scenario combinations within the same additional irrigation assessment group, arranged in ascending order of total additional irrigation amount. Indicates the first Among the candidate phased irrigation scenario combinations, the first Marginal leaching efficiency per unit volume of water for a target soil layer Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer, with an additional irrigation volume of Salt leaching rate at that time Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer, with an additional irrigation volume of Salt leaching rate at that time and They represent the first The and the first The total amount of additional irrigation for each candidate phased irrigation scenario.

[0014] Furthermore, the marginal leaching efficiency per unit irrigation frequency for each irrigation scenario combination is composed of the marginal leaching efficiency per unit irrigation frequency for the corresponding target soil layer; The formula for calculating the marginal leaching efficiency per unit irrigation frequency for each candidate phased irrigation scenario combination is as follows:

[0015] This represents the index value of the candidate phased irrigation scenario combination within the same irrigation frequency evaluation group, arranged in ascending order of adjusted irrigation frequency. Indicates the first Among the candidate phased irrigation scenario combinations, the first Marginal leaching efficiency per unit number of irrigations for a target soil layer Indicates the number of irrigations from the first The number of files has been increased to the first. When the file is in use, the first Marginal leaching efficiency per unit irrigation frequency for a target soil layer; Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer was irrigated a number of times. Salt leaching rate at that time; Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer was irrigated a number of times. Salt leaching rate at that time; and They represent the first The and the first The number of irrigations after adjusting the combination of candidate phased irrigation scenarios.

[0016] Furthermore, in step S8, when When it is below the preset threshold, The corresponding single additional irrigation amount is determined as an adjustment for inefficient irrigation. when When it is below the preset threshold, The corresponding irrigation frequency adjustment amount is determined to be the inefficient irrigation frequency adjustment.

[0017] An electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the above method.

[0018] A computer-readable storage medium for storing computer instructions that, when executed by a processor, implement the steps of the above-described method.

[0019] The beneficial effects of the method described in this invention are as follows: (1) This invention constructs candidate staged irrigation scenarios based on the rice's greening, tillering, jointing, and grain-filling stages, and associates the irrigation frequency adjustment amount and the single additional irrigation amount with the corresponding growth stages, so that each candidate irrigation scenario has a clear irrigation adjustment stage, irrigation frequency, and irrigation amount. As a result, the salt leaching response of each target soil layer under different growth stages, different irrigation frequencies, and different additional irrigation amounts can be obtained, avoiding the use of a uniform irrigation index throughout the entire growth period to mask the differences in water and salt responses at different growth stages, and providing a scenario evaluation basis for determining the irrigation frequency and single additional irrigation amount at different growth stages.

[0020] (2) This invention establishes a correspondence between the increase in salt leaching rate and the increase in additional irrigation volume between adjacent additional irrigation volume levels by calculating the marginal leaching efficiency per unit volume of water, quantitatively characterizing the salt control gain generated by each additional irrigation volume level. Simultaneously, by calculating the marginal leaching efficiency per unit irrigation frequency, it establishes a correspondence between the increase in salt leaching rate and the increase in irrigation frequency between adjacent irrigation frequency levels, quantitatively characterizing the salt control gain generated by each additional irrigation. This invention can not only evaluate the salt leaching effect of candidate staged irrigation scenarios, but also determine whether continuing to increase the single additional irrigation volume or the number of irrigations still has effective salt control value, thereby identifying inefficient irrigation volume adjustments and inefficient irrigation frequency adjustments. This provides a quantitative basis for the rational allocation of irrigation frequency and single additional irrigation volume at different growth stages, reducing water resource waste caused by blindly increasing irrigation volume or the number of irrigations. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method described in this invention; Figure 2 This is a schematic diagram of the simulation domain of the HYDRUS water-salt transport model described in this invention; Figure 3 This is a graph showing the relationship between the additional irrigation volume, the predicted salt leaching rate, and the marginal leaching efficiency per unit volume of water, as described in this invention. Figure 4 This is a three-dimensional response surface plot of the surface soil salinity leaching rate during the jointing stage as described in this invention. Figure 5 This is a distribution diagram of the marginal leaching efficiency per unit volume of water corresponding to different rice growth stages and additional irrigation amounts as described in this invention. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0023] Example 1 This embodiment provides a time-phased irrigation optimization method for saline-alkali paddy fields based on water-salt simulation. The flowchart of the method is as follows: Figure 1 As shown, the method includes the following steps: S1. Collect basic data on paddy fields in saline-alkali land; The relevant operations in step S1 will be introduced with specific examples: A saline-alkali paddy field to be optimized was selected as the study area. Basic data for constructing water-salt transport models and irrigation scenarios were collected before the start of the rice growing season and during the rice growth process. The basic data included meteorological data, soil physical parameters, initial soil moisture content, initial soil salinity, soil electrical conductivity, groundwater depth, groundwater salinity, irrigation regime, and rice growth stage information. The rice growth stages included the greening stage, tillering stage, jointing stage, and grain-filling stage.

[0024] The meteorological data is obtained through small field weather stations, regional weather stations, irrigation district meteorological monitoring systems, or publicly available meteorological data. The meteorological data includes one or more of the following: daily or hourly rainfall, temperature, relative humidity, wind speed, solar radiation, evaporation, and reference crop evapotranspiration. In practice, automatic weather stations can be deployed near the study area to record rainfall, temperature, humidity, wind speed, and solar radiation during the rice growing season. This meteorological data is used to subsequently determine the upper boundary conditions of the HYDRUS water-salt transport model, including rainfall infiltration, soil surface evaporation, and crop transpiration.

[0025] The soil physical parameters were obtained through field soil profile surveys, stratified sampling, and laboratory testing. Specifically, representative sampling points were selected in the study area, and stratified sampling was conducted at soil depths of 0–20 cm, 20–60 cm, and 60–100 cm. Laboratory measurements were performed on soil samples from each layer to obtain one or more parameters, including soil texture, bulk density, porosity, saturated water content, residual water content, saturated hydraulic conductivity, and soil moisture characteristic curves. For hydraulic parameters that were inconvenient to test directly, parameters could be estimated or inverted based on soil texture, bulk density, and measured water content data, and corrected during subsequent model calibration. These soil physical parameters are used to subsequently determine the soil stratification properties, soil hydraulic parameters, and salt migration parameters for the HYDRUS water-salt transport model.

[0026] The initial soil moisture content is obtained through undisturbed field soil sampling, oven drying, soil moisture sensors, TDR probes, or FDR probes. Specifically, at the start of the model simulation or at the start of each growth stage, soil samples are collected from different target soil layers. The mass moisture content is determined by oven drying and weighing, and then converted to volumetric moisture content based on soil bulk density. Alternatively, soil moisture sensors are embedded in different soil layers to continuously record changes in soil moisture content. The initial soil moisture content is used to determine the initial moisture distribution of the HYDRUS water-salt transport model and can serve as measured data for model calibration and validation.

[0027] The initial soil salinity and soil electrical conductivity were obtained by collecting soil samples at different layers and conducting indoor leaching tests or in-situ electrical conductivity measurements. Specifically, at the start of the model simulation or at the start of each growth stage, target soil layers (0–20 cm, 20–60 cm, 60–100 cm) were sampled to measure soil salinity, soluble salt ion concentration, and the electrical conductivity of the saturated paste-like extract or the pre-set soil-water ratio extract. When soil electrical conductivity is used to characterize soil salinity, based on a pre-established calibration relationship between electrical conductivity and salt concentration, the soil electrical conductivity is converted into salt concentration, solute concentration, or relative salinity index recognizable by the HYDRUS water-salt transport model. The initial soil salinity and soil electrical conductivity are used to determine the initial salt distribution of the model and for subsequent calculations of salt leaching rate and verification of salt simulation results.

[0028] The groundwater depth is obtained through field groundwater observation wells, water level gauges, groundwater monitoring data from irrigation areas, or manual measurement. In practice, groundwater observation wells are deployed in or near the study area to periodically record the groundwater level relative to the surface, and groundwater samples are collected to determine groundwater conductivity, mineralization, or salinity. This groundwater depth is used to determine the lower boundary conditions of the HYDRUS water-salt transport model. When the groundwater depth is relatively deep and has little impact on water-salt movement in the target soil layer, the lower boundary can be set as a free drainage boundary. When the groundwater depth is relatively shallow and may affect water-salt transport in the root zone, the lower boundary can be set as a groundwater level boundary, a constant head boundary, or a pressure head boundary, and the lower boundary salinity input conditions are determined in conjunction with groundwater salinity data.

[0029] The irrigation regime is obtained through irrigation district management records, field irrigation records, flow meter measurements, field water gauge observations, or farmer surveys. The irrigation regime includes one or more of the following: existing irrigation dates, irrigation frequency, irrigation intervals, single irrigation volume, irrigation duration, irrigation method, field surface water depth, and irrigation water salinity. In practice, flow meters or water measurement facilities can be installed at the inlet to record the inflow volume and duration of each irrigation; alternatively, irrigation water samples can be collected and their conductivity or salinity measured. The irrigation regime is used to determine baseline irrigation boundary conditions and serves as a benchmark for subsequently constructing candidate phased irrigation scenarios.

[0030] The rice growth stage information was obtained through field surveys, agricultural records, transplanting dates, variety growth period data, and plant morphology observations. In practice, the start and end times of the rice's greening-up stage, tillering stage, jointing stage, and grain-filling stage were recorded according to the rice varieties and cultivation systems in the study area. If necessary, the growth stages were corrected by combining plant height, tiller number, leaf age, panicle differentiation status, and grain filling status. This rice growth stage information was used to subsequently divide irrigation scenarios into phases and to set root water uptake parameters, transpiration requirements, and irrigation control periods for different growth stages.

[0031] After completing the above data collection, the collected data were uniformly organized and preprocessed. Meteorological data were compiled into time series at daily or hourly scales; soil moisture content, soil salinity, and soil electrical conductivity were established into data tables according to sampling time, sampling point, and soil depth; irrigation regimes were archived according to growth stage, irrigation date, irrigation frequency, and single irrigation volume; and groundwater depth and groundwater salinity were compiled into dynamic monitoring sequences according to monitoring dates. The organized basic data were used in subsequent step S2 for the construction of the simulation domain, assignment of initial conditions, setting of boundary conditions, setting of root water uptake parameters, and model calibration and verification of the HYDRUS water-salt transport model.

[0032] S2. Based on the basic data collected in step S1, construct and calibrate the initial HYDRUS water-salt transport model to obtain the HYDRUS water-salt transport model. The relevant operations in step S2 will be introduced with specific examples: Based on the basic data collected in step S1, the process of constructing the initial HYDRUS water-salt transport model includes the following steps: S21. Set the simulation process and simulation period. In HYDRUS-2D, water movement, standard solute transport, and root water uptake processes were selected, with soil salinity used as the solute to be simulated. The time unit was set to days, with the start of the rice greening stage as the simulation start time. Based on the rice growth stage information collected in step S1, the greening stage, tillering stage, jointing stage, and grain-filling stage were sequentially mapped to the simulation timeline. In this embodiment, the total simulation duration was 112 days, and the corresponding time period in the simulation timeline was determined according to the actual start and end dates of each growth stage.

[0033] S22. Establish the simulation domain A rectangular two-dimensional simulation domain with a width of 200 cm and a depth of 100 cm was established in HYDRUS-2D. This simulation domain was used to characterize the soil profile of paddy fields. The simulation domain was divided into three target soil layer regions according to a preset depth: 0–20 cm, 20–60 cm, and 60–100 cm.

[0034] Based on the soil physical parameters collected in step S1 for each target soil layer region, soil hydraulic parameters for each target soil layer region are set. These soil physical parameters include bulk density, saturated water content, residual water content, saturated hydraulic conductivity, and soil moisture characteristic curve parameters. Initial moisture conditions are set based on the initial soil moisture content for each target soil layer region, and initial salt concentration conditions are set based on the initial soil salinity for each target soil layer region.

[0035] Figure 2The two-dimensional simulation domain of the initial HYDRUS water-salt transport model is shown. This simulation domain characterizes a paddy field soil profile and is divided into three target soil layers according to soil depth: 0–20 cm, 20–60 cm, and 60–100 cm. Rainfall, irrigation, evaporation, and transpiration inputs are set at the upper boundary of the simulation domain, and observation points are set within different target soil layers to obtain the changes in soil moisture content and equivalent salinity conductivity over time. HYDRUS-2D can be used to simulate water flow and solute transport processes in two-dimensional variable-saturation porous media.

[0036] S23. Set boundary conditions for water and salt transport. The upper boundary of the simulation domain is set as an atmospheric boundary with a surface water layer. The precipitation, soil evaporation and rice transpiration during the simulation period are determined based on the meteorological data collected in step S1 and input into the model in chronological order. Based on the irrigation regime collected in step S1, the irrigation time, irrigation volume and irrigation duration of each irrigation are converted into irrigation infiltration flux for the corresponding time period to form the upper boundary water flux time series.

[0037] The left and right sides of the simulation domain are set as flux-free boundaries. In this embodiment, the simulation domain depth of 100 cm is used as the groundwater depth determination threshold; when the groundwater depth is greater than 100 cm, the lower boundary is set as a free drainage boundary; when the groundwater depth is less than or equal to 100 cm, a time-varying pressure head boundary is set according to the groundwater depth.

[0038] The 100 cm threshold is a boundary condition selection threshold determined based on the simulation domain depth in this embodiment, used to determine whether the groundwater level is within the simulation domain range, and is not a fixed threshold applicable to all study areas.

[0039] S24. Set root water absorption parameters Based on the root water uptake parameters corresponding to each growth stage of rice, the root water uptake parameters of the greening stage, tillering stage, jointing stage and grain filling stage were respectively input into the root water uptake module of HYDRUS-2D to complete the construction of the initial HYDRUS water and salt transport model.

[0040] In this embodiment, soil electrical conductivity is used as an equivalent solute variable characterizing soil salinity and input into HYDRUS-2D, so that the initial HYDRUS water-salt transport model outputs the changes in soil electrical conductivity of each target soil layer over time. A set of output results is listed in Table 1.

[0041] Table 1

[0042] Note: In Table 1, the initial salinity of each target soil layer in the initial HYDRUS water-salt transport model is 4.4 dS / m. L1, L2, and L3 represent the target soil layers of 0–20 cm, 20–60 cm, and 60–100 cm, respectively. The values ​​in the table are the salinity conductivity of the corresponding target soil layers.

[0043] The initial HYDRUS water and salt transport model is calibrated to obtain the HYDRUS water and salt transport model. Specifically, the soil moisture content, initial soil salinity, or soil electrical conductivity data collected in step S1 are used as measured values ​​to calibrate the model. The simulation accuracy is judged by calculating R², RMSE, or MAE. When the error between the simulated value and the measured value meets the preset accuracy requirements, the model is confirmed to be suitable for candidate irrigation scenario simulation, and the HYDRUS water and salt transport model is obtained.

[0044] In this embodiment, the preset accuracy requirement is set as follows: the coefficient of determination R² of the soil moisture content and soil electrical conductivity simulation results are not less than 0.80, and the RMSE or MAE is not greater than 10% of the average value of the corresponding measured data. When the above requirements are met, the initial HYDRUS water-salt transport model is considered to have reached the preset simulation accuracy.

[0045] Among them, the closer R² is to 1, and the closer RMSE and MAE are to 0, the better the fitting effect. However, the above values ​​are the judgment criteria used in this embodiment and are not the general mandatory standards of HYDRUS.

[0046] The model simulation results were compared with the measured data of soil moisture content and soil electrical conductivity collected at the corresponding monitoring times.

[0047] Those skilled in the art can adjust the construction process of the initial HYDRUS water-salt transport model and the preset accuracy of the initial HYDRUS water-salt transport model according to actual needs.

[0048] S3. Using rice growth stage, irrigation frequency adjustment amount, single additional irrigation amount and total irrigation amount as scenario variables, construct several phased irrigation scenario combinations; The relevant operations in step S3 will be introduced with specific examples: Rice growth stages refer to the key periods of rice growth and development, including the greening stage, tillering stage, jointing stage, yellow ripening stage, milk ripening stage, and harvest stage. These stages can also be combined or subdivided according to local planting systems. Rice growth stages can be defined and determined through field cultivation records or agricultural manuals.

[0049] The adjustment of irrigation frequency refers to the increase or decrease in the number of irrigations during each growth stage based on the baseline irrigation system. It can be set within a range of ±1 or ±2 times based on historical field irrigation records.

[0050] Additional irrigation volume per cycle refers to the additional irrigation volume (mm) under the baseline irrigation regime. Different increments can be set based on soil water storage capacity, crop water requirements, or historical irrigation data.

[0051] The total irrigation volume constraint throughout the entire growth period is used to limit the total water consumption of candidate irrigation scenarios. The upper and lower limits can be set according to the water resource constraints of the irrigation area, the water demand of crops, and water-saving targets.

[0052] Furthermore, the above four variables are combined to form a candidate phased irrigation scenario set, and scenarios that do not meet the crop water requirements or total irrigation volume constraints are eliminated. Under the condition that at least one irrigation is retained in each growth stage and the total irrigation volume of the entire growth period is constrained, multiple phased irrigation scenarios are generated.

[0053] Each candidate scenario includes a scenario number, rice growth stage, irrigation frequency adjustment, single additional irrigation amount, and total irrigation amount for the entire growth period. This scenario set can be used for subsequent HYDRUS water and salt transport simulations and GPR surrogate prediction model training, thereby supporting the selection of staged irrigation optimization schemes.

[0054] In a specific embodiment, the greening stage, tillering stage, jointing stage, and fruiting stage are coded as 1–4 respectively; the irrigation frequency adjustment is set to The irrigation scenarios were 2, -1, 0, +1, and +2 times; the additional irrigation amount per irrigation was 0, 24, 30, 40, and 50 mm / time; the total irrigation amount for the growing season was constrained to be 400–800 mm. Combining these three variables generated 68 candidate staged irrigation scenarios suitable for HYDRUS simulation after screening under a single constraint.

[0055] For example, a candidate phased irrigation scenario combination is (1, +1, 24), where 1 represents the greening period, +1 represents an additional irrigation on top of the baseline irrigation frequency, and 24 represents an additional irrigation volume of 24 mm per additional irrigation event. When the baseline total growing season irrigation volume is 600 mm, the total growing season irrigation volume corresponding to this irrigation scenario is 624 mm, which meets the constraint of 400–800 mm of total growing season irrigation volume. Therefore, this irrigation scenario is retained as a candidate phased irrigation scenario.

[0056] S4. Select a portion of the irrigation scenario combinations from the candidate phased irrigation scenario combinations and input them into the HYDRUS water-salt transport model. Based on the output results, calculate the salt leaching rate of the selected irrigation scenario combinations respectively. The relevant operations in step S4 will be introduced with specific examples: The selected irrigation scenario combinations were chosen based on the principles of covering different rice growth stages, adjusting irrigation frequency, and allocating additional irrigation water per application. In the output of the HYDRUS water and salt transport model, target soil salinity variation data were extracted layer by layer according to the rice root zone soil layer and / or soil profile within a preset depth range, and the salt leaching rate (LE) of the corresponding target soil layer was calculated. The selected irrigation scenario combinations were then converted into input for the HYDRUS water and salt transport model through the following steps: S41. Generate irrigation event sequence For each candidate staged irrigation scenario, the simulation period for irrigation adjustment is determined according to the rice growth stage corresponding to the candidate staged irrigation scenario; the baseline irrigation frequency for the corresponding growth stage is adjusted according to the amount of irrigation frequency adjustment, and the adjusted irrigation time and irrigation frequency are mapped to the simulation time axis of the HYDRUS water-salt transport model in chronological order to form the corresponding irrigation event sequence.

[0057] When the irrigation frequency adjustment is negative, it means that the number of irrigations is reduced relative to the baseline irrigation frequency; when the irrigation frequency adjustment is 0, it means that the baseline irrigation frequency is maintained; when the irrigation frequency adjustment is positive, it means that the number of irrigations is increased relative to the baseline irrigation frequency.

[0058] S42, Set Irrigation Upper Boundary Input Based on the single additional irrigation amount in the candidate phased irrigation scenario, the single additional irrigation amount is set as the upper boundary irrigation input corresponding to each irrigation event in the irrigation event sequence, forming the upper boundary irrigation input sequence of the corresponding candidate phased irrigation scenario.

[0059] S43. Set initial salinity conditions The initial soil salinity of each target soil layer collected in step S1 is set as the initial salinity conditions for the target soil layers of 0–20 cm, 20–60 cm, and 60–100 cm, respectively, according to the predetermined salinity input format of the initial HYDRUS water-salt transport model.

[0060] S44, Set Atmospheric Boundary Input Evaporation and transpiration are calculated based on the meteorological data collected in step S1, and the evaporation and transpiration are set as the atmospheric boundary conditions of the HYDRUS water-salt transport model.

[0061] Through steps S41 to S44, the rice growth stage and irrigation frequency adjustment amount corresponding to each candidate staged irrigation scenario are converted into an irrigation event sequence. The single additional irrigation amount is converted into an upper boundary irrigation input sequence corresponding to the irrigation event sequence. Combined with the initial salinity conditions of each target soil layer and the evaporation and transpiration calculated from meteorological data, the HYDRUS water and salt transport model input data corresponding to each candidate staged irrigation scenario is formed.

[0062] The input data of the HYDRUS water-salt transport model were respectively input into the initial HYDRUS water-salt transport model for simulation, and the soil electrical conductivity of the target soil layers of 0-20 cm, 20-60 cm and 60-100 cm under each candidate staged irrigation scenario was obtained as a sequence of changes in soil electrical conductivity over time.

[0063] The salt leaching rate for each irrigation scenario combination is composed of the salt leaching rate of the corresponding target soil layer. The calculation formula is as follows:

[0064] in, This represents the index value of the target soil layer in each irrigation scenario combination. The index value for the partial irrigation scenario combination, in this embodiment , Indicates the first In the aforementioned partial irrigation scenario combinations, the first Initial electrical conductivity of the target soil layer After applying the irrigation scenario, the output of the HYDRUS water-salt transport model is the first... In the first of the aforementioned partial irrigation scenario combinations The target soil layer electrical conductivity. It can be summarized by soil layer or root zone as a whole for GPR model training and MLE evaluation.

[0065] , and Composition of the first The salt leaching rate of the aforementioned partial irrigation scenario combinations, in this step... The index range is a subset of irrigation scenario combinations selected from the candidate phased irrigation scenario combinations. It is traversed using formula (1). and The index range is used to obtain the salt leaching rate of the partial irrigation scenario combination, which is then used to construct and train the GPR surrogate prediction model.

[0066] The target soil layer division can be adjusted according to different rice varieties or irrigation strategies. For example, the root zone can be further subdivided into 30–45 cm, 45–60 cm, etc. The salinity measurement index can be selected as electrical conductivity, soluble salt content, or total salt content. The layer number and initial salinity information must be consistent with the HYDRUS input to ensure that the simulation results are reproducible.

[0067] S5. Using the partial irrigation scenario combination as input and the salt leaching rate of the partial irrigation scenario combination as output, construct and train the GPR proxy prediction model, and then predict the combined salt leaching rate of the remaining irrigation scenarios that were not simulated in the irrigation scenario combination based on the trained GPR proxy prediction model. The relevant operations in step S5 will be introduced with specific examples: Using the aforementioned partial irrigation scenario combinations as input and the salt leaching rate of these combinations as output, a Gaussian Process Regression (GPR) surrogate prediction model is established. Specifically, the partial irrigation scenarios are transformed into input feature vectors, including rice growth stage codes, irrigation frequency adjustments, and soil layer numbers or depth information for single additional irrigation amounts. The salt leaching rate (LE) calculated in step 4 is used as the output variable, constructing a nonlinear mapping relationship between input and output. The sample data is divided into training and validation sets. The GPR model is trained by selecting a suitable kernel function (such as a radial basis function or a Matérn kernel function) and optimizing hyperparameters. The prediction accuracy is evaluated using the validation set. Once the accuracy meets a preset threshold, the GPR surrogate prediction model can be used for rapid LE prediction of candidate staged irrigation scenarios that have not been directly simulated using HYDRUS water and salt transport. The GPR prediction results are used for subsequent marginal leaching efficiency (MLE) calculation per unit water volume and for screening phased irrigation optimization schemes. This reduces the computational burden of HYDRUS simulating all scenarios one by one, improves the evaluation efficiency of multi-scenario irrigation schemes, provides a data foundation for the quantitative screening of phased irrigation optimization schemes, and ensures logical consistency and reproducibility between each step. This step can also be replaced by other nonlinear regression models, such as random forest regression or support vector regression, as long as the input-output structure and data flow remain consistent.

[0068] S6. For several candidate phased irrigation scenario combinations, the additional irrigation volume is grouped according to the rice growth stage and the adjustment amount of irrigation frequency, and the marginal leaching efficiency per unit volume of each candidate phased irrigation scenario combination is calculated. The relevant operations in step S6 will be described using specific examples: Candidate staged irrigation scenarios were grouped according to rice growth stage and irrigation frequency adjustment amount, ensuring that scenario variables other than the single additional irrigation amount remained the same within the same additional irrigation amount evaluation group, and that the irrigation frequency adjustment amount was positive. Furthermore, the initial salinity conditions were identical for all candidate staged irrigation scenarios within the same group. Since the irrigation frequency adjustment amount remained constant within the same group, the total additional irrigation amount for the candidate staged irrigation scenarios increased with the increase in the single additional irrigation amount.

[0069] Within the same additional irrigation volume evaluation group, candidate phased irrigation scenarios are ranked from smallest to largest according to the total additional irrigation volume. For each target soil layer, the marginal leaching efficiency per unit volume is calculated based on the increase in salt leaching rate corresponding to two adjacent total additional irrigation volumes and the increase in total additional irrigation volume. :

[0070] in, This represents the index value of candidate phased irrigation scenario combinations within the same additional irrigation assessment group, arranged in ascending order of total additional irrigation amount. Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer, with an additional irrigation volume of Salt leaching rate at that time Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer, with an additional irrigation volume of Salt leaching rate at that time and They represent the first The and the first Total additional irrigation volume for each candidate phased irrigation scenario.

[0071] Among them, the The total additional irrigation amount for each candidate phased irrigation scenario is the product of the number of additional irrigation sessions and the amount of additional irrigation per session. The total irrigation amount itself is determined by both the number of irrigation sessions and the amount of irrigation per session.

[0072] When the irrigation frequency adjustment is positive, the value of the irrigation frequency adjustment is the number of additional irrigations; otherwise, the number of additional irrigations is zero.

[0073] Formula (2) can be used to quantify the marginal gain of increasing additional irrigation volume on the salt leaching rate of each target soil layer. When, it indicates that the additional irrigation amount is from the first The number of files has been increased to the first. The gear can improve the first The salt leaching rate of each target soil layer, and The larger the value, the greater the salt leaching gain per unit of newly added irrigation water; when When this occurs, it indicates that increasing the additional water volume in this stage did not result in salt leaching gain; when When the amount of additional water is increased, it indicates that the salt leaching rate decreases. The corresponding single additional irrigation volume is identified as an inefficient irrigation volume adjustment and is restricted or eliminated.

[0074] , and Composition of the first Marginal leaching efficiency (MLE) per unit volume for each candidate phased irrigation scenario combination.

[0075] Within the same additional irrigation volume evaluation group, the candidate phased irrigation scenario without additional irrigation is used as the baseline scenario. The candidate phased irrigation scenario corresponding to the lowest positive additional irrigation volume is compared with the baseline scenario to calculate the marginal leaching efficiency per unit volume corresponding to the lowest positive additional irrigation volume. For the remaining candidate phased irrigation scenarios, they are compared with the candidate phased irrigation scenario corresponding to the next lower additional irrigation volume in ascending order of additional irrigation volume to calculate the corresponding marginal leaching efficiency per unit volume.

[0076] Figure 3 This figure illustrates the relationship between additional irrigation volume and the predicted salt leaching rate and the marginal leaching efficiency per unit volume of water. The horizontal axis represents the additional irrigation volume, the left vertical axis represents the predicted salt leaching rate, and the right vertical axis represents the marginal leaching efficiency per unit volume of water. The blue curve represents the change in the predicted salt leaching rate with the additional irrigation volume, and the green curve represents the change in the marginal leaching efficiency per unit volume of water between adjacent additional irrigation volume levels. This figure is used to illustrate the salt leaching effect and its marginal gain when the additional irrigation volume is increased.

[0077] Figure 4 This figure illustrates the three-dimensional response of salt leaching rate in the target surface soil layer during the jointing stage. The two horizontal axes represent initial soil salinity and additional irrigation volume, respectively, while the vertical axis represents the salt leaching rate. The response surface and color changes indicate the salt leaching rate corresponding to different combinations of initial soil salinity and additional irrigation volume. This figure visually represents the variation of salt leaching rate in the target surface soil layer during the jointing stage with changes in initial soil salinity and additional irrigation volume.

[0078] Figure 5 This figure illustrates the marginal leaching efficiency per unit volume of water at different additional irrigation amounts during the greening, tillering, jointing, and grain-filling stages of rice. The horizontal axis represents the additional irrigation amount, and the vertical axis represents the rice growth stage. The values ​​and colors in each cell indicate the marginal leaching efficiency per unit volume of water at the corresponding rice growth stage and additional irrigation amount. This figure is used to compare the marginal gain of salt leaching at different rice growth stages and with different additional irrigation amounts, and to identify candidate staged irrigation scenarios with low marginal leaching efficiency per unit volume of water.

[0079] S7. For several candidate phased irrigation scenario combinations, group the irrigation frequency adjustment amount according to the rice growth stage and the amount of additional irrigation per time, and calculate the marginal leaching efficiency per unit irrigation number for each candidate phased irrigation scenario combination. The relevant operations in step S7 will be introduced with specific examples: Candidate staged irrigation scenarios were grouped according to rice growth stage and the amount of additional irrigation per instance, ensuring that scenario variables other than irrigation frequency adjustment remained the same within the same irrigation frequency evaluation group. Furthermore, the initial salinity conditions were identical for all candidate staged irrigation scenarios within the same group. Since the amount of additional irrigation per instance remained constant within the same group, the total irrigation amount increased with the number of irrigations.

[0080] Within the same irrigation frequency evaluation group, candidate phased irrigation scenario combinations were ranked from smallest to largest according to the adjusted number of irrigations. For each target soil layer, the marginal leaching efficiency per unit irrigation frequency was calculated based on the salt leaching rate increment and the irrigation frequency increment corresponding to two adjacent irrigation frequency levels. : Marginal leaching efficiency per unit irrigation frequency The formula for calculation is:

[0081] in, This represents the index value of the candidate phased irrigation scenario combination within the same irrigation frequency evaluation group, arranged in ascending order of adjusted irrigation frequency. Indicates the number of irrigations from the first The number of files has been increased to the first. When the file is in use, the first Marginal leaching efficiency per unit irrigation frequency for a target soil layer; Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer was irrigated a number of times. Salt leaching rate at that time; Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer was irrigated a number of times. Salt leaching rate at that time; and They represent the first The and the first The number of irrigations after adjusting the combination of candidate phased irrigation scenarios.

[0082] The adjusted irrigation frequency is the sum of the baseline irrigation frequency for the corresponding growth stage and the irrigation frequency adjustment amount. The irrigation frequency adjustment amount is... 2. 1, 0, +1, and +2 represent reducing the number of irrigations by 2, reducing it by 1, keeping it unchanged, increasing it by 1, and increasing it by 2, respectively, based on the baseline number of irrigations.

[0083] Formula (3) allows for the quantification of the marginal gain of increasing irrigation frequency on the salt leaching rate of each target soil layer. When, it indicates that the number of irrigations has increased from the first. The number of files has been increased to the first. After the file, the first The salt leaching rate of the target soil layer increased, and The larger the volume, the greater the salt leaching gain with each additional irrigation; when When, it indicates that increasing the number of irrigations at that level did not result in salt leaching gain; when When the number of irrigation sessions is increased, the salt leaching rate decreases. Will At that time, The corresponding irrigation frequency adjustment was identified as an inefficient irrigation frequency adjustment, and the corresponding candidate phased irrigation scenario combinations were restricted or eliminated.

[0084] , and Composition of the first Marginal leaching efficiency per unit irrigation frequency for each candidate phased irrigation scenario combination .

[0085] Within the same irrigation frequency evaluation group, the candidate phased irrigation scenario combination corresponding to the lowest irrigation frequency is used as the starting scenario for comparison of adjacent frequencies, and the marginal leaching efficiency per unit irrigation frequency is not calculated separately; the remaining scenarios are compared with the scenarios corresponding to the next lower irrigation frequency.

[0086] S8. Based on the marginal leaching efficiency per unit water volume and marginal leaching efficiency per unit irrigation frequency for each candidate phased irrigation scenario combination, eliminate inefficient water volume adjustments and inefficient frequency adjustments, and optimize the phased irrigation method for saline-alkali paddy fields.

[0087] The relevant operations in step S8 will be introduced with specific examples: Based on the marginal leaching efficiency per unit water volume (MLE) obtained in step S6 and the marginal leaching efficiency per unit irrigation frequency obtained in step S7... The candidate phased irrigation scenario combinations were evaluated.

[0088] When developing a phased irrigation method, inefficient irrigation volume adjustments and inefficient irrigation frequency adjustments are restricted or eliminated, while the marginal leaching efficiency per unit volume (MLE) and marginal leaching efficiency per unit irrigation frequency are retained. Candidate phased irrigation scenario combinations that meet the corresponding requirements. Based on the rice growth stage, irrigation frequency adjustment amount, and single additional irrigation amount corresponding to the retained candidate phased irrigation scenario combinations, the phased irrigation method for saline-alkali land paddy fields is determined.

[0089] The phased irrigation method includes at least one adjustment in irrigation frequency and an additional amount of water per irrigation corresponding to at least one rice growth stage.

Claims

1. A method for optimizing irrigation in saline-alkali paddy fields based on water-salt simulation, characterized in that, The method includes the following steps: S1. Collect basic data on paddy fields in saline-alkali land; S2. Based on the basic data collected in step S1, construct and calibrate the initial HYDRUS water-salt transport model to obtain the HYDRUS water-salt transport model. S3. Using rice growth stage, irrigation frequency adjustment amount, single additional irrigation amount and total irrigation amount as scenario variables, construct several phased irrigation scenario combinations; S4. Select a portion of the irrigation scenario combinations from the candidate phased irrigation scenario combinations and input them into the HYDRUS water-salt transport model. Based on the output results, calculate the salt leaching rate of the selected irrigation scenario combinations respectively. S5. Using the partial irrigation scenario combination as input and the salt leaching rate of the partial irrigation scenario combination as output, construct and train the GPR proxy prediction model, and then predict the combined salt leaching rate of the remaining irrigation scenarios that were not simulated in the irrigation scenario combination based on the trained GPR proxy prediction model. S6. For several candidate phased irrigation scenario combinations, the additional irrigation volume is grouped according to the rice growth stage and the adjustment amount of irrigation frequency, and the marginal leaching efficiency per unit volume of each candidate phased irrigation scenario combination is calculated. S7. For several candidate phased irrigation scenario combinations, group the irrigation frequency adjustment amount according to the rice growth stage and the amount of additional irrigation per time, and calculate the marginal leaching efficiency per unit irrigation number for each candidate phased irrigation scenario combination. S8. Based on the marginal leaching efficiency per unit water volume and marginal leaching efficiency per unit irrigation frequency for each candidate phased irrigation scenario combination, eliminate inefficient water volume adjustments and inefficient frequency adjustments, and optimize the phased irrigation method for saline-alkali paddy fields.

2. The method for optimizing irrigation of saline-alkali paddy fields based on water-salt simulation according to claim 1, characterized in that, The basic data for paddy fields in saline-alkali land include: meteorological data of the study area, soil physical parameters, initial soil moisture content, initial soil salinity, soil electrical conductivity, groundwater depth, groundwater salinity, irrigation system, and information on rice growth stages.

3. The method for optimizing irrigation in saline-alkali paddy fields based on water-salt simulation according to claim 2, characterized in that, The construction of the initial HYDRUS water-salt transport model includes: setting the simulation process and simulation period, establishing the simulation domain, setting water-salt transport boundary conditions, and setting root water absorption parameters; The calibration of the initial HYDRUS water-salt transport model includes: The simulation results of the initial HYDRUS water-salt transport model are compared with the soil electrical conductivity collected in step S1. When the simulation accuracy meets the preset accuracy requirements, the HYDRUS water-salt transport model is obtained.

4. The method for optimizing irrigation of saline-alkali paddy fields based on water-salt simulation according to claim 3, characterized in that, The construction of several phased irrigation scenario combinations includes: An initial irrigation scenario combination is generated based on the rice growth stage, the amount of irrigation frequency adjustment, and the amount of additional irrigation water per instance; Calculate the total irrigation volume for the entire growth period corresponding to each initial irrigation scenario combination based on the irrigation system; Initial irrigation scenario combinations that do not meet crop water requirements or total irrigation volume constraints throughout the growth period are eliminated to obtain candidate phased irrigation scenario combinations.

5. The method for optimizing irrigation of saline-alkali paddy fields based on water-salt simulation according to claim 4, characterized in that, The salt leaching rate for each irrigation scenario combination is composed of the salt leaching rate of the corresponding target soil layer; The salt leaching rate of the target soil layer corresponding to the partial irrigation scenario combinations The calculation formula is as follows: in, This represents the index value of the target soil layer in each irrigation scenario combination. This is the index value of the partial irrigation scenario combination. Indicates the first In the aforementioned partial irrigation scenario combinations, the first Initial electrical conductivity of the target soil layer After applying the irrigation scenario, the output of the HYDRUS water-salt transport model is the first... In the first of the aforementioned partial irrigation scenario combinations The target soil layer electrical conductivity.

6. The method for optimizing irrigation of saline-alkali paddy fields based on water-salt simulation according to claim 5, characterized in that, The marginal leaching efficiency per unit volume of each irrigation scenario combination is composed of the marginal leaching efficiency per unit volume of the corresponding target soil layer. The formula for calculating the marginal leaching efficiency per unit volume of water for each candidate phased irrigation scenario combination is as follows: in, This represents the index value of candidate phased irrigation scenario combinations within the same additional irrigation assessment group, arranged in ascending order of total additional irrigation amount. Indicates the first Among the candidate phased irrigation scenario combinations, the first Marginal leaching efficiency per unit volume of water for a target soil layer Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer, with an additional irrigation volume of Salt leaching rate at that time Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer, with an additional irrigation volume of Salt leaching rate at that time and They represent the first The and the first The total amount of additional irrigation for each candidate phased irrigation scenario.

7. The method for optimizing irrigation of saline-alkali paddy fields based on water-salt simulation according to claim 6, characterized in that, The marginal leaching efficiency per unit irrigation frequency for each irrigation scenario combination is composed of the marginal leaching efficiency per unit irrigation frequency for the corresponding target soil layer. The formula for calculating the marginal leaching efficiency per unit irrigation frequency for each candidate phased irrigation scenario combination is as follows: This represents the index value of the candidate phased irrigation scenario combination within the same irrigation frequency evaluation group, arranged in ascending order of adjusted irrigation frequency. Indicates the first Among the candidate phased irrigation scenario combinations, the first Marginal leaching efficiency per unit number of irrigations for a target soil layer Indicates the number of irrigations from the first The number of files has been increased to the first. When the file is in use, the first Marginal leaching efficiency per unit irrigation frequency for a target soil layer; Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer was irrigated a number of times. Salt leaching rate at that time; Indicates the first Among the candidate phased irrigation scenario combinations, the first The target soil layer was irrigated a number of times. Salt leaching rate at that time; and They represent the first The and the first The number of irrigations after adjusting the combination of candidate phased irrigation scenarios.

8. The method for optimizing irrigation of saline-alkali paddy fields based on water-salt simulation according to claim 7, characterized in that, In step S8, when When it is below the preset threshold, The corresponding single additional irrigation amount is determined as an adjustment for inefficient irrigation. when When it is below the preset threshold, The corresponding irrigation frequency adjustment amount is determined to be the inefficient irrigation frequency adjustment.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium for storing computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-8.

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