Irrigation duration control method based on soil permeability

Abstract

The application discloses an irrigation time length control method based on soil permeability, and particularly relates to the technical field of agricultural irrigation control. The target irrigation area is divided into multiple sub-areas, and crop root system configuration parameters and ground biological crust coverage parameters are obtained. Root system infiltration influence coefficients and crust infiltration resistance coefficients are calculated, and then input into a comprehensive reasoning model to output a comprehensive permeability correction factor. The baseline soil permeability is corrected to obtain a dynamic soil permeability. The irrigation time length required for irrigation water to reach a preset root layer depth is calculated as the target irrigation time length according to the dynamic soil permeability. The irrigation equipment is controlled according to the target irrigation time length of each sub-area to perform irrigation operation. The baseline soil permeability is dynamically corrected by the root system configuration parameters and the biological crust coverage parameters, deep seepage and insufficient irrigation are avoided, and the irrigation water use efficiency is improved. The comprehensive permeability correction factor is output according to the game relationship between root system promotion and crust inhibition, and the correction reliability is ensured.

Description

Technical Field

[0001] This invention relates to the field of agricultural irrigation control technology, and more specifically, to a method for controlling irrigation duration based on soil permeability. Background Technology

[0002] Precision irrigation is a crucial means of water conservation and yield increase in agriculture. Its core lies in delivering the appropriate amount of water to the soil layer where crop roots reside at the right time. Determining the irrigation duration directly determines the total irrigation volume and the depth of water distribution in the soil profile, making it one of the most critical decision variables in precision irrigation control. Soil permeability is a fundamental parameter influencing the setting of irrigation duration. It reflects the rate at which water vertically infiltrates the soil; soils with high permeability allow water to infiltrate quickly, requiring shorter irrigation times, and vice versa. However, field soil permeability is not a fixed physical property but a dynamic quantity that continuously changes with soil biological activity and environmental conditions.

[0003] During the crop growing season, the growth and development of plant roots continuously reshape the soil structure. The biopores formed during root penetration provide preferential channels for water infiltration. These channels can bypass the dense soil matrix and directly guide water to deeper layers, thus significantly accelerating the vertical transport of water. Meanwhile, the widespread development of biocrusts on the soil surface is another significant biological variable. Biocrusts are formed by the cementation of soil particles with organisms such as cyanobacteria, lichens, and mosses. After covering the surface, they directly block water from entering the soil through their dense structure, and the extracellular polymers they secrete can clog surface soil pores. Both of these factors significantly inhibit water infiltration.

[0004] The two biological factors mentioned above, root system and soil crust, have opposite effects on soil permeability, and they often coexist in the field, canceling or superimposing each other. This makes it difficult to accurately grasp the true state of soil permeability during actual irrigation. Most existing methods for determining irrigation duration treat soil permeability as a static input parameter. That is, a baseline permeability value is measured in the field before actual irrigation by using a ring sampler or a double-ring infiltration test. This baseline value is then used as a fixed parameter for the entire irrigation season or at least a longer period to calculate the irrigation duration. This approach completely ignores the real-time changes in soil permeability caused by the continuous biological processes of root penetration and expansion and the continuous development and thickening of the topsoil crust during crop growth.

[0005] When roots grow rapidly and form numerous preferential flow channels, the actual soil permeability is significantly higher than the pre-measured baseline value. If irrigation is still supplied according to the baseline value for the set irrigation duration, water will quickly penetrate the root layer and enter the deeper soil, resulting in the ineffective loss of water resources and nutrients. Conversely, when biological crusts develop extensively to form a dense water-blocking layer, the actual soil permeability is significantly lower than the baseline value. If irrigation is still performed according to the baseline value, water will have difficulty reaching the root layer within the predetermined time, leading to insufficient irrigation and thus affecting the normal growth of crops. Therefore, this invention proposes an irrigation duration control method based on soil permeability to solve the above problems. Summary of the Invention

[0006] To achieve the above objectives, the present invention provides the following technical solution: The irrigation duration control method based on soil permeability includes the following steps: The target irrigation area is divided into multiple sub-regions, and each sub-region is assigned an independent sub-region identifier; Obtain crop root system configuration parameters and surface biocrust coverage parameters for each sub-region. Crop root system configuration parameters include the number of lateral roots, root length density, and root distribution depth. Surface biocrust coverage parameters include the percentage of biocrust coverage area, average biocrust thickness, and biomass density. The root development index is calculated based on the number of lateral roots and root length density in each sub-region. The thickness of the soil layer affected by the root system is determined based on the root distribution depth in the sub-region. The root infiltration influence coefficient of the sub-region is calculated using the root development index and the thickness of the soil layer affected by the root system. The crust density index of each sub-region is calculated based on the crust coverage area ratio, average crust thickness, and crust biomass density. The crust infiltration resistance coefficient of the sub-region is then calculated using the crust density index and average crust thickness. The root infiltration influence coefficient and crust infiltration inhibition coefficient of each sub-region are input into the preset comprehensive reasoning model. The comprehensive reasoning model is used to output the comprehensive infiltration correction factor of the sub-region based on the game relationship between the root's water conduction promotion effect and the crust's water blocking inhibition effect in the same sub-region. The baseline soil permeability is corrected using the comprehensive permeability correction factor for each sub-region, and the permeability value obtained after correction for each sub-region is taken as the dynamic soil permeability of the sub-region. The irrigation time required for the target infiltration depth of irrigation water to reach the preset root layer depth in each sub-region is calculated based on the dynamic soil permeability of each sub-region, and the irrigation time is used as the target irrigation time for the sub-region. According to the target irrigation duration for each sub-region, the irrigation equipment is controlled to perform irrigation operations for each sub-region respectively.

[0007] In a preferred embodiment, the target irrigation area is divided into multiple sub-regions, and each sub-region is assigned an independent sub-region identifier, specifically including the following steps: Multiple sampling points are arranged at a first preset interval within the target irrigation area to collect crop root system architecture parameters and surface biological crust coverage parameters at each sampling point. For any sampling point, determine each of the sampling points adjacent to the sampling point based on the spatial coordinates of the sampling point, calculate the spatial distance between the sampling point and each of its adjacent sampling points, and use the reciprocal of each spatial distance value as the weight coefficient corresponding to the spatial distance value. For any sampling point, the number of lateral roots, root length density, and root distribution depth of the sampling point are used to form a first parameter vector. The number of lateral roots, root length density, and root distribution depth of each adjacent sampling point are used to form the first parameter vectors corresponding to each adjacent sampling point. The first vector difference value between the first parameter vector of the sampling point and the first parameter vectors of each adjacent sampling point is calculated. Each first vector difference value is multiplied by the corresponding weight coefficient and then summed to obtain the first spatial variation value of the root configuration parameter of the local area where the sampling point is located. The percentage of crust coverage area, average crust thickness, and crust biomass density of the sampling point are used to form a second parameter vector. The percentage of crust coverage area, average crust thickness, and crust biomass density of each adjacent sampling point are used to form the second parameter vector of each adjacent sampling point. The difference value of the second vector between the second parameter vector of the sampling point and the second parameter vector of each adjacent sampling point is calculated. Each difference value of the second vector is multiplied by the corresponding weight coefficient and then summed to obtain the second spatial variation value of the biological crust coverage parameter of the local area where the sampling point is located. The first spatial variation value and the second spatial variation value are weighted and fused twice to obtain the comprehensive spatial variation index of the local area where each sampling point is located. The local area where the comprehensive spatial variability index exceeds the preset variability threshold is located is identified as a high variability region, and vice versa. In the high-variability region, the grid is divided according to the second preset interval, and in the low-variability region, the grid is divided according to the third preset interval. Each grid cell obtained after division is taken as a sub-region. Generate an independent sub-region identifier for each sub-region, and store the sub-region identifier associated with the boundary coordinates of the sub-region.

[0008] In a preferred embodiment, the root development index is calculated based on the number of lateral roots and root length density in each sub-region, and the thickness of the soil layer affected by the root system is determined based on the root distribution depth in the sub-region. This specifically includes the following steps: Multiply the number of lateral roots in the subregion by the root length density, and use the product as the total root density of the subregion. Divide the root distribution depth of the sub-region into a preset number of depth segments, count the lateral root density in each depth segment, calculate the standard deviation of the lateral root density of all depth segments, multiply the reciprocal of the standard deviation by the preset root uniformity benchmark value, and use the product as the root distribution uniformity of the sub-region. Multiply the total root density by the root distribution uniformity, and use the product as the root development index of the sub-region. Multiply the root distribution depth of the sub-region by the preset root extension coefficient, and use the product as the root influence soil layer thickness of the sub-region.

[0009] In a preferred embodiment, calculating the root infiltration influence coefficient of a sub-region specifically includes the following steps: Divide the root development index of the sub-region by the soil layer thickness affected by the root system of the sub-region, and use the result as the root density value per unit soil layer of the sub-region. The root density value per unit soil layer is substituted into the preset root infiltration influence function for calculation. The preset root infiltration influence function is used to map the root density value per unit soil layer to a power function and output the root infiltration influence coefficient. The output value of the root infiltration influence function is used as the root infiltration influence coefficient of the sub-region.

[0010] In a preferred embodiment, calculating the crust density index of a sub-region specifically includes the following steps: Divide the percentage of crust coverage area in the sub-region by the preset coverage area benchmark value, and use the result as the normalized coverage area value of the sub-region. Divide the average crust thickness of the sub-region by the preset thickness benchmark value, and use the result as the normalized thickness value of the sub-region. Divide the crust biomass density of the sub-region by the preset biomass density benchmark value, and use the result as the normalized biomass density value of the sub-region. The normalized coverage area, normalized thickness, and normalized biomass density values ​​are weighted and summed to obtain the crustal density index of the subregion.

[0011] In a preferred embodiment, calculating the crust infiltration resistance coefficient of a sub-region specifically includes the following steps: Multiply the density index of the crust layer of the sub-region by the preset retardation base coefficient, and add the preset retardation growth bias value to the result to obtain the retardation rate per unit thickness of the crust of the sub-region. Multiply the unit thickness resistance rate of the crust in the sub-region by the average thickness of the crust in the sub-region, and use the result as the crust infiltration resistance coefficient of the sub-region.

[0012] In a preferred embodiment, the comprehensive inference model is a fuzzy logic device. The root infiltration influence coefficient and crust infiltration resistance coefficient of each sub-region are input into the preset comprehensive inference model, and the comprehensive infiltration correction factor of the sub-region is output. Specifically, the following steps are included: The root infiltration influence coefficient of each sub-region is used as the first input variable, and the crust infiltration resistance coefficient of the sub-region is used as the second input variable. The first input variable is divided into three fuzzy linguistic terms: low influence level, medium influence level, and high influence level, according to the preset first membership function. The second input variable is divided into three fuzzy linguistic terms: low blockage level, medium blockage level, and high blockage level, according to the preset second membership function. The fuzzy linguistic terms of the first input variable and the second input variable are combined to form nine input state rule combinations. Nine input state rules are combined and input into a preset fuzzy rule table for matching. Each rule in the fuzzy rule table records the correspondence between an input state combination and an output fuzzy linguistic item. The output fuzzy linguistic item is one of five levels: minimal correction factor, small correction factor, medium correction factor, large correction factor, and maximum correction factor. Aggregate the fuzzy membership values ​​corresponding to the matched output fuzzy linguistic terms, and use the correction factor value corresponding to the output fuzzy linguistic term with the largest aggregated fuzzy membership value as the comprehensive penetration correction factor for the sub-region.

[0013] In a preferred embodiment, dynamic soil permeability is obtained through the following steps: Obtain the comprehensive infiltration correction factor for the sub-region. The comprehensive infiltration correction factor is a dimensionless value. When its value is greater than one, it indicates that the comprehensive effect promotes infiltration. When its value is less than one, it indicates that the comprehensive effect inhibits infiltration. When its value is equal to one, it indicates that the comprehensive effect has no effect on infiltration. Determine whether the comprehensive permeability correction factor of the sub-region is greater than the preset effective lower limit and less than the preset effective upper limit. When the comprehensive permeability correction factor of the sub-region is greater than or equal to the preset effective lower limit and less than or equal to the preset effective upper limit, the comprehensive permeability correction factor is determined as the effective correction factor of the sub-region. When the comprehensive permeability correction factor of the sub-region is less than the preset effective lower limit, the preset effective lower limit is determined as the effective correction factor of the sub-region. When the comprehensive permeability correction factor of the sub-region is greater than the preset effective upper limit, the preset effective upper limit is determined as the effective correction factor of the sub-region. Multiply the baseline soil permeability of the subregion by the effective correction factor of the subregion, and use the product as the dynamic soil permeability of the subregion.

[0014] The technical effects and advantages of this invention are as follows: This invention, by separately acquiring crop root system configuration parameters and surface biocrust coverage parameters, and separately calculating the root infiltration influence coefficient and biocrust infiltration blocking coefficient, incorporates two key biological factors affecting soil permeability into the irrigation duration decision-making process. This overcomes the limitations of existing technologies that rely solely on static baseline soil permeability or single sensor data for irrigation decisions. It enables the determination of irrigation duration to adaptively adjust to real-time changes in field biological conditions, effectively avoiding two irrigation misconceptions: deep water leakage due to root-preferred flow channels and insufficient irrigation due to biocrust water-blocking layers. This significantly improves irrigation water use efficiency.

[0015] This invention inputs the root infiltration influence coefficient and crust infiltration inhibition coefficient of each sub-region into a preset comprehensive reasoning model. Using a fuzzy logic device, it outputs a comprehensive infiltration correction factor based on the game relationship between the root conduction promotion effect and the crust inhibition effect within the same sub-region. This mechanism can handle the complex situation where roots and crusts cancel each other out or superimpose when they coexist in the field. It avoids the problems of unclear physical meaning and distorted results caused by simply adding or multiplying the two coefficients. This makes the output result of the comprehensive infiltration correction factor more consistent with the actual infiltration state in the field, providing a reliable basis for the subsequent dynamic correction of soil infiltration rate.

[0016] This invention divides the target irrigation area into multiple sub-regions and assigns an independent sub-region identifier to each sub-region. It obtains crop root system configuration parameters and surface biocrust coverage parameters for each sub-region, and calculates the root infiltration influence coefficient and biocrust infiltration resistance coefficient for each sub-region. This ensures that each sub-region obtains an independent comprehensive infiltration correction factor and an independent dynamic soil infiltration rate. Ultimately, each sub-region obtains an independent target irrigation duration and controls irrigation equipment to perform irrigation operations accordingly. This differentiated irrigation strategy addresses the problem of uneven spatial distribution of root development and biocrust coverage within the same target irrigation area, leading to varying irrigation needs. It avoids over-irrigation or under-irrigation in different areas due to uniform irrigation duration, thereby improving the irrigation uniformity and crop water management accuracy of the entire target irrigation area. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the irrigation duration control method based on soil permeability in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Reference Figure 1 The following examples were obtained: Example 1: A method for controlling irrigation duration based on soil permeability, comprising the following steps: The target irrigation area is divided into multiple sub-regions, and each sub-region is assigned an independent sub-region identifier; Obtain crop root system configuration parameters and surface biocrust coverage parameters for each sub-region. Crop root system configuration parameters include the number of lateral roots, root length density, and root distribution depth. Surface biocrust coverage parameters include the percentage of biocrust coverage area, average biocrust thickness, and biomass density. The root development index is calculated based on the number of lateral roots and root length density in each sub-region. The thickness of the soil layer affected by the root system is determined based on the root distribution depth in the sub-region. The root infiltration influence coefficient of the sub-region is calculated using the root development index and the thickness of the soil layer affected by the root system. The crust density index of each sub-region is calculated based on the crust coverage area ratio, average crust thickness, and crust biomass density. The crust infiltration resistance coefficient of the sub-region is then calculated using the crust density index and average crust thickness. The root infiltration influence coefficient and crust infiltration inhibition coefficient of each sub-region are input into the preset comprehensive reasoning model. The comprehensive reasoning model is used to output the comprehensive infiltration correction factor of the sub-region based on the game relationship between the root's water conduction promotion effect and the crust's water blocking inhibition effect in the same sub-region. The baseline soil permeability is corrected using the comprehensive permeability correction factor for each sub-region, and the permeability value obtained after correction for each sub-region is taken as the dynamic soil permeability of the sub-region. The irrigation time required for the target infiltration depth of irrigation water to reach the preset root layer depth in each sub-region is calculated based on the dynamic soil permeability of each sub-region, and the irrigation time is used as the target irrigation time for the sub-region. According to the target irrigation duration for each sub-region, the irrigation equipment is controlled to perform irrigation operations for each sub-region respectively.

[0020] The target irrigation area is divided into multiple sub-regions, and each sub-region is assigned an independent sub-region identifier. The specific steps include: Multiple sampling points are arranged at a first preset interval within the target irrigation area. The first preset interval is set according to the area of ​​the target irrigation area and the degree of spatial variation of soil texture. The typical value range of the first preset interval is 5 meters to 20 meters. When the area of ​​the target irrigation area is less than 1 hectare, the first preset interval is 5 meters. When the area of ​​the target irrigation area is greater than 1 hectare and less than 10 hectares, the first preset interval is 10 meters. When the area of ​​the target irrigation area is greater than 10 hectares, the first preset interval is 20 meters.

[0021] Crop root system architecture parameters and surface biocrust coverage parameters were collected at each sampling point. Crop root system architecture parameters included the number of lateral roots, root length density, and root distribution depth. Surface biocrust coverage parameters included the percentage of biocrust coverage area, average biocrust thickness, and biomass density.

[0022] For any given sampling point, the spatial straight-line distance between the sampling point and all surrounding sampling points is calculated based on the spatial coordinates of the sampling point. Surrounding sampling points whose spatial straight-line distance is less than a preset adjacent distance threshold are identified as adjacent sampling points of the sampling point. The preset adjacent distance threshold is determined based on a first preset spacing and is 1.5 times the first preset spacing.

[0023] Calculate the spatial distance between each sampling point and each of its adjacent sampling points. Use the reciprocal of each spatial distance value as the weight coefficient corresponding to the spatial distance value. The larger the spatial distance value, the smaller the corresponding weight coefficient, and the smaller the spatial distance value, the larger the corresponding weight coefficient.

[0024] For any given sampling point, the number of lateral roots, root length density, and root distribution depth of the sampling point are combined in a fixed order to form the first parameter vector of the sampling point. The number of lateral roots, root length density, and root distribution depth of each adjacent sampling point are combined in the same fixed order to form the first parameter vector corresponding to each adjacent sampling point.

[0025] The first vector difference value is calculated between the first parameter vector of each sampling point and the first parameter vector of each adjacent sampling point. The first vector difference value is calculated by subtracting the parameter values ​​at corresponding positions in the two first parameter vectors, taking the absolute value, and then summing them. Each first vector difference value is multiplied by the corresponding weight coefficient and then summed. The summation result is used as the first spatial variation value of the root system configuration parameter in the local region where the sampling point is located.

[0026] For any given sampling point, the percentage of crust coverage area, average crust thickness, and crust biomass density of the sampling point are combined in a fixed order to form the second parameter vector of the sampling point. The percentage of crust coverage area, average crust thickness, and crust biomass density of each adjacent sampling point are combined in the same fixed order to form the second parameter vector of each adjacent sampling point.

[0027] The second vector difference value between the second parameter vector of each sampling point and the second parameter vector of each adjacent sampling point is calculated. The second vector difference value is calculated by subtracting the parameter values ​​at corresponding positions in the two second parameter vectors, taking the absolute value, and then summing them. Each second vector difference value is multiplied by its corresponding weight coefficient and then summed. The summation result is used as the second spatial variation value of the biological crust coverage parameter of the local area where the sampling point is located.

[0028] The first and second spatial variability values ​​are weighted and fused twice. The weighting coefficient for the first spatial variability value is determined based on the influence of root architecture parameters on soil permeability, with a typical value of 0.6. The weighting coefficient for the second spatial variability value is determined based on the influence of biological crust coverage parameters on soil permeability, with a typical value of 0.4. The sum of the two weighting coefficients is 1. The first spatial variability value is multiplied by 0.6, and then the second spatial variability value is multiplied by 0.4. The sum is used as the comprehensive spatial variability index for the local area of ​​each sampling point.

[0029] The comprehensive spatial variability index is compared with the preset variability threshold. The preset variability threshold is determined based on the statistical distribution of the comprehensive spatial variability index in the same irrigation area in historical experimental data, and the typical value of the preset variability threshold is 0.5 times the average value of the comprehensive spatial variability index of all sampling points.

[0030] When the comprehensive spatial variability index exceeds the preset variability threshold, the local area where the sampling point is located is identified as a high variability area; when the comprehensive spatial variability index does not exceed the preset variability threshold, the local area where the sampling point is located is identified as a low variability area.

[0031] Within the spatial area identified as a high-variability region, a grid is divided according to a second preset spacing. The second preset spacing is determined based on the first preset spacing and the degree to which the comprehensive spatial variation index exceeds a preset variation threshold. The second preset spacing is the first preset spacing multiplied by an adjustment coefficient. The adjustment coefficient ranges from 0.2 to 0.5. The larger the extent to which the comprehensive spatial variation index exceeds the preset variation threshold, the smaller the adjustment coefficient becomes. When the extent to which the comprehensive spatial variation index exceeds the preset variation threshold reaches 100% or more, the adjustment coefficient is 0.2. When the extent to which the comprehensive spatial variation index exceeds the preset variation threshold is between 50% and 100%, the adjustment coefficient is 0.35. When the extent to which the comprehensive spatial variation index exceeds the preset variation threshold is less than 50%, the adjustment coefficient is 0.5.

[0032] Within the spatial range identified as a low-variability region, the area is divided into grids according to a third preset spacing. The third preset spacing is determined based on the first preset spacing and is 1.5 to 2 times the first preset spacing. Each grid cell obtained after division is treated as a sub-region, and an independent sub-region identifier is generated for each sub-region. The sub-region identifiers are associated with and stored with the boundary coordinates of the sub-regions, and a sub-region spatial index table is generated.

[0033] For example, when the target irrigation area is 5 hectares, the first preset spacing is 10 meters, and the preset adjacent distance threshold is 15 meters. When the spatial distance between a sampling point and its adjacent sampling points is 10 meters, its weight coefficient is 0.1. The first parameter vector of the sampling point is (150 lateral roots, root length density of 2.5 cm per cubic centimeter, root distribution depth of 40 cm), and the first parameter vector of its adjacent sampling points is (120 lateral roots, root length density of 2.0 cm per cubic centimeter, root distribution depth of 35 cm). The absolute values ​​of the corresponding position differences are 30, 0.5, and 5. After summing, the difference of the first vector is 35.5. Multiplying 35.5 by the weight coefficient 0.1 yields 3.55.

[0034] When the comprehensive spatial variation index of the local area where the sampling point is located is 0.75 and the preset variation threshold is 0.6, if 0.75 exceeds 0.6, the local area is identified as a high variation area. At this time, the second preset spacing is 10 meters multiplied by 0.35, which equals 3.5 meters. In the high variation area, the grid is divided according to the spacing of 3.5 meters, while in the low variation area, the grid is divided according to the spacing of 10 meters multiplied by 1.5, which equals 15 meters.

[0035] The root development index is calculated based on the number of lateral roots and root length density in each sub-region. The thickness of the soil layer affected by the root system is determined based on the root distribution depth in the sub-region. The specific steps include: The root density of a subregion is calculated by multiplying the number of lateral roots by the root length density. The number of lateral roots is the average of the number of lateral roots at all sampling points within the subregion, and the root length density is the average of the root length density at all sampling points within the subregion. The product is the total root density of the subregion, which characterizes the total distribution of roots per unit volume of soil within the subregion. For example, if the average number of lateral roots in a subregion is 200 and the average root length density is 3.0 cm / cm³, the total root density of the subregion is 200 multiplied by 3.0, which equals 600.0 cm / cm³.

[0036] The root distribution depth of the sub-region is divided into a predetermined number of depth segments. The predetermined number is determined based on the root distribution depth and the degree of variation in root distribution in the vertical direction. The typical range for the predetermined number of segments is 3 to 5 segments. For root distribution depths greater than 60 cm, the predetermined number is 5 segments; for root distribution depths between 30 and 60 cm, the predetermined number is 4 segments; and for root distribution depths less than 30 cm, the predetermined number is 3 segments. The lateral root density within each depth segment is calculated. The lateral root density is the ratio of the total number of lateral roots within the depth segment to the volume of the depth segment. The standard deviation of the lateral root density across all depth segments is calculated; the standard deviation characterizes the dispersion of lateral root density among different depth segments. Multiply the reciprocal of the standard deviation by the preset root uniformity benchmark value, which is determined based on the statistical average of the lateral root density of the crop variety under fully uniform distribution conditions. The typical range of the preset root uniformity benchmark value is 0.3 roots per cubic centimeter to 0.8 roots per cubic centimeter. When the crop is corn, the preset root uniformity benchmark value is 0.5 roots per cubic centimeter; when the crop is wheat, the preset root uniformity benchmark value is 0.4 roots per cubic centimeter; and when the crop is fruit tree, the preset root uniformity benchmark value is 0.7 roots per cubic centimeter. The product result is the root distribution uniformity of the sub-region.

[0037] Multiplying the total root density by the root distribution evenness yields the root development index for a subregion. This index comprehensively characterizes the overall development level of the root system within a subregion, considering both total root density and evenness of distribution. For example, if a subregion has a total root density of 600.0 roots per cubic centimeter and a root distribution evenness of 1.2, its root development index is 600.0 multiplied by 1.2, which equals 720.0. When the total root density is high but the lateral root density distribution is uneven across different depths, the standard deviation is large, and the reciprocal of the standard deviation is small, indicating a correspondingly low root distribution evenness. The root development index is then corrected downwards. This correction mechanism avoids overestimating the infiltration-promoting effect of roots solely based on high total root density while ignoring uneven depth distribution.

[0038] The root distribution depth of a sub-region is multiplied by a preset root extension coefficient. This coefficient is the ratio of the maximum horizontal radius of root extension to the maximum vertical depth for the crop variety belonging to that sub-region. Typical values ​​for the preset root extension coefficient range from 0.3 to 0.8. For example, a preset root extension coefficient of 0.4 is used for corn, 0.3 for wheat, and 0.7 for fruit trees. The product represents the root-affected soil layer thickness of the sub-region. For instance, if the root distribution depth of a sub-region is 40 cm and the preset root extension coefficient for the crop variety is 0.4, the root-affected soil layer thickness of that sub-region is 40 multiplied by 0.4, which equals 16 cm. This root-affected soil layer thickness characterizes the effective soil layer thickness range within the sub-region where roots influence water infiltration.

[0039] The calculation of the root infiltration influence coefficient of a subregion specifically includes the following steps: Divide the root development index of the sub-region by the root influence soil layer thickness of the sub-region to obtain the root density value per unit soil layer of the sub-region. The root density value per unit soil layer is used to characterize the concentration of root development within each unit thickness of soil layer. The larger the value, the denser the root development in the unit soil layer.

[0040] The root density value per unit soil layer is substituted into the preset root infiltration influence function for calculation. The preset root infiltration influence function is a power function, and its function form is that the output value is equal to the power function exponent raised to the power function base. The power function base is the ratio of the root density value per unit soil layer to the preset density benchmark value. The preset density benchmark value is determined based on the statistical average of the root density per unit soil layer of the crop variety in the sub-region under standard growth conditions. The typical range of the preset density benchmark value is a root development index of 100 to 500 per centimeter of soil thickness. When the crop is corn, the preset density benchmark value is 300 per centimeter of soil thickness; when the crop is wheat, the preset density benchmark value is 150 per centimeter of soil thickness; and when the crop is fruit tree, the preset density benchmark value is 450 per centimeter of soil thickness.

[0041] The power function exponent is determined by fitting the nonlinear relationship between the root density value per unit soil layer and the actual infiltration promotion effect in the experimental data. The typical value range of the power function exponent is 0.3 to 0.7. When the root density value per unit soil layer is less than the preset density benchmark value, the power function exponent is 0.6. When the root density value per unit soil layer is greater than or equal to the preset density benchmark value, the power function exponent is 0.4. The output value of the preset root infiltration influence function is the root infiltration influence coefficient. The output value of the root infiltration influence function is used as the root infiltration influence coefficient of the sub-region.

[0042] For example, if the root development index of a certain sub-region is 720.0 and the soil layer thickness affected by the root system is 16 cm, the root density per unit soil layer is 720.0 divided by 16 equals 45.0. If the crop of the sub-region is corn and the preset density benchmark value is 300, the power function base is 45.0 divided by 300 equals 0.15. Since 0.15 is less than 1, the power function exponent is taken as 0.6. Therefore, the root infiltration influence coefficient is 0.15 raised to the power of 0.6 equals 0.35. A root infiltration influence coefficient less than 1 indicates that the root system's effect on infiltration in the sub-region is lower than the standard level. In another sub-region, when the root development index is 1500.0 and the soil layer thickness affected by the roots is 15 cm, the root density per unit soil layer is 1500.0 divided by 15, which equals 100.0. When the crop in this sub-region is corn and the preset density benchmark is 300, the power function base is 100.0 divided by 300, which equals 0.333. With a power function exponent of 0.6, the root infiltration influence coefficient is 0.333 raised to the power of 0.6, which equals 0.50. A root infiltration influence coefficient of 0.50 indicates that the root system's promoting effect on infiltration in this sub-region is 50% of the corresponding standard promoting effect. When the root density per unit soil layer equals the preset density benchmark of 300, the power function base is 1, and any power of 1 equals 1. Therefore, the root infiltration influence coefficient is 1, indicating that the root system's promoting effect on infiltration in this sub-region reaches the standard level.

[0043] The preset root infiltration influence function also has upper and lower cutoff conditions. When the root density per unit soil layer is 0, the base of the power function is 0, and the lower limit of the root infiltration influence coefficient is set to 0.01. When the root density per unit soil layer is greater than 10 times the preset density benchmark value, the base of the power function is greater than 10, and the upper limit of the root infiltration influence coefficient is set to 1.8. The lower and upper cutoff conditions are used to avoid the function output value from physically meaningless extreme values.

[0044] The calculation of the crust density index of a subregion specifically includes the following steps: Divide the percentage of crust cover area in the sub-region by the preset coverage area benchmark value. The crust cover area percentage is the ratio of the crust cover area in the sub-region to the total surface area of ​​the sub-region. The preset coverage area benchmark value is determined based on the statistical upper limit of the percentage of crust cover area in the geographical region to which the sub-region belongs under the condition of fully developed crust. The typical range of the preset coverage area benchmark value is 0.7 to 0.9. When the sub-region is located in an arid or semi-arid region, the preset coverage area benchmark value is 0.8. When the sub-region is located in a humid region, the preset coverage area benchmark value is 0.7. The result of the division is the normalized coverage area value of the sub-region. When the normalized coverage area value is greater than 1, it means that the percentage of crust cover area in the sub-region exceeds the fully developed benchmark level. When the normalized coverage area value is less than 1, it means that the percentage of crust cover area in the sub-region does not reach the fully developed benchmark level.

[0045] Divide the average crust thickness of the sub-region by the preset thickness benchmark value. The average crust thickness is the average of the crust thickness at multiple sampling points within the sub-region. The preset thickness benchmark value is determined based on the statistical average of crust thickness under fully developed crust conditions for the soil type of the sub-region. The typical range of the preset thickness benchmark value is 1.0 cm to 3.0 cm. When the soil type is sandy soil, the preset thickness benchmark value is 1.5 cm; when the soil type is loam, the preset thickness benchmark value is 2.0 cm; and when the soil type is clay, the preset thickness benchmark value is 2.5 cm. The result of the division is the normalized thickness value of the sub-region. A normalized thickness value greater than 1 indicates that the average crust thickness of the sub-region exceeds the fully developed benchmark level, and a normalized thickness value less than 1 indicates that the average crust thickness of the sub-region does not reach the fully developed benchmark level.

[0046] Divide the crust biomass density of the sub-region by the preset biomass density benchmark value. The crust biomass density is the biomass of microorganisms and algae contained in the crust per unit area within the sub-region. The preset biomass density benchmark value is determined based on the statistical average value of crust biomass density in the geographical region to which the sub-region belongs under the condition of full crust development. The typical range of the preset biomass density benchmark value is 0.5 grams per square meter to 2.0 grams per square meter. When the sub-region is located in an arid or semi-arid region, the preset biomass density benchmark value is 0.8 grams per square meter. When the sub-region is located in a humid region, the preset biomass density benchmark value is 1.5 grams per square meter. The result of the division is the normalized biomass density value of the sub-region. When the normalized biomass density value is greater than 1, it indicates that the crust biomass density of the sub-region exceeds the full development benchmark level. When the normalized biomass density value is less than 1, it indicates that the crust biomass density of the sub-region has not reached the full development benchmark level.

[0047] The normalized cover area, normalized thickness, and normalized biomass density values ​​are weighted and summed. The weighting coefficient for the normalized cover area value is determined based on the contribution of the crust cover area ratio to the crust density, with a typical value of 0.35. The weighting coefficient for the normalized thickness value is determined based on the contribution of the average crust thickness to the crust density, with a typical value of 0.45. The weighting coefficient for the normalized biomass density value is determined based on the contribution of the crust biomass density to the crust density, with a typical value of 0.20. The sum of the three weighting coefficients is 1. The normalized cover area value is multiplied by 0.35, the normalized thickness value by 0.45, and the normalized biomass density value by 0.20, and then summed. The summation result is the crust density index of the sub-region.

[0048] For example, if the crust coverage area of ​​a sub-region is 0.50 and the preset coverage area benchmark is 0.80, the normalized coverage area value is 0.50 divided by 0.80, which equals 0.625. If the average crust thickness of the sub-region is 1.2 cm and the preset thickness benchmark is 2.0 cm, the normalized thickness value is 1.2 divided by 2.0, which equals 0.60. If the crust biomass density of the sub-region is 0.6 g per square meter and the preset biomass density benchmark is 0.8 g per square meter, the normalized biomass density value is 0.6 divided by 0.8, which equals 0.75. Then, the crust density index of the sub-region is 0.625 multiplied by 0.35 plus 0.60 multiplied by 0.45 plus 0.75 multiplied by 0.20, which equals 0.21875 plus 0.27 plus 0.15, which equals 0.63875.

[0049] When the crust coverage area of ​​another sub-region is 0.90 and the preset coverage area benchmark is 0.80, the normalized coverage area value is 0.90 divided by 0.80 equals 1.125. When the average crust thickness of the sub-region is 2.8 cm and the preset thickness benchmark is 2.0 cm, the normalized thickness value is 2.8 divided by 2.0 equals 1.40. When the crust biomass density of the sub-region is 1.6 g per square meter and the preset biomass density benchmark is 0.8 g per square meter, the normalized biomass density value is 1.6 divided by 0.8 equals 2.0. Therefore, the crust density index of the sub-region is 1.125 multiplied by 0.35 plus 1.40 multiplied by 0.45 plus 2.0 multiplied by 0.20 equals 0.39375 plus 0.63 plus 0.40 equals 1.42375.

[0050] In the weighted summation method, the weighting coefficient corresponding to the average crust thickness has the largest value because crust thickness has the most direct and significant effect on blocking water infiltration. The weighting coefficient corresponding to the crust coverage area ratio is the second largest, and the weighting coefficient corresponding to crust biomass density is the smallest because biomass density indirectly affects the density by influencing the internal pore structure of the crust. The crust density index is used to comprehensively characterize the overall density level of the crust layer in a sub-region across the three dimensions of coverage, thickness, and biomass. The larger the index value, the denser the crust layer and the stronger its barrier effect on water infiltration.

[0051] The calculation of the crust infiltration resistance coefficient of a sub-region specifically includes the following steps: The density index of the crust layer in the sub-region is multiplied by a preset retardation base coefficient. The preset retardation base coefficient is determined based on the linear regression slope between the density index of the crust layer and the retardation rate per unit thickness in the experimental data. The typical value range of the preset retardation base coefficient is 0.3 to 0.7. When the soil type of the sub-region is sandy soil, the preset retardation base coefficient is 0.4; when the soil type of the sub-region is loam, the preset retardation base coefficient is 0.5; and when the soil type of the sub-region is clay, the preset retardation base coefficient is 0.6.

[0052] The result of the multiplication is added to a preset stagnation growth bias value. The preset stagnation growth bias value is determined based on the baseline value of the stagnation rate per unit thickness of the crust when the crust density index is zero in the experimental data. The typical range of the preset stagnation growth bias value is 0.1 to 0.3. When the geographical region to which the sub-region belongs is an arid or semi-arid region, the preset stagnation growth bias value is 0.15. When the geographical region to which the sub-region belongs is a humid region, the preset stagnation growth bias value is 0.25. The result obtained is the stagnation rate per unit thickness of the crust of the sub-region.

[0053] The unit thickness resistance rate of crust is the amount of resistance effect of each unit thickness of crust on water infiltration. There is a linear positive correlation between the unit thickness resistance rate of crust and the density index of crust layer. That is, the larger the density index of crust layer, the larger the unit thickness resistance rate of crust, and the smaller the density index of crust layer, the smaller the unit thickness resistance rate of crust.

[0054] Multiply the unit thickness resistance rate of the crust in the sub-region by the average crust thickness of the sub-region. The average crust thickness is the average of the crust thickness at multiple sampling points in the sub-region. The result is the crust infiltration resistance coefficient of the sub-region. The crust infiltration resistance coefficient is used to characterize the total resistance effect of the crust layer on water infiltration in the sub-region. The total resistance effect is the product of the unit thickness resistance rate of the crust and the actual thickness of the crust.

[0055] For example, when the density index of the crust layer in a certain sub-region is 0.63875 and the preset retardation basement coefficient is 0.5, the result is 0.63875 multiplied by 0.5 equals 0.319375. When the geographical region to which the sub-region belongs is an arid or semi-arid region and the preset retardation growth bias value is 0.15, the crust unit thickness retardation rate is 0.319375 plus 0.15 equals 0.469375. When the average crust thickness of the sub-region is 1.2 cm, the crust infiltration retardation coefficient is 0.469375 multiplied by 1.2 equals 0.56325. When the density index of the crust layer in another sub-region is 1.42375 and the preset basement resistance coefficient is 0.5, the result is 1.42375 multiplied by 0.5 equals 0.711875. When the geographical region to which the sub-region belongs is a humid area and the preset basement resistance growth bias value is 0.25, the crust unit thickness resistance rate is 0.711875 plus 0.25 equals 0.961875. When the average crust thickness of the sub-region is 2.8 cm, the crust infiltration resistance coefficient is 0.961875 multiplied by 2.8 equals 2.69325.

[0056] When the crust density index is 0, the crust resistance rate per unit thickness equals the preset resistance growth bias value. At this point, the crust infiltration resistance coefficient equals the preset resistance growth bias value multiplied by the average crust thickness. The lower limit cutoff mechanism ensures that the crust still has a basic resistance effect even when it is not completely dense. In the linear mapping method, the preset resistance base coefficient controls the contribution of the crust density index to the crust resistance rate per unit thickness, and the preset resistance growth bias value controls the baseline level of the crust resistance rate per unit thickness. Both together ensure that the crust resistance rate per unit thickness is always positive and monotonically increases with the crust density index.

[0057] The comprehensive inference model is a fuzzy logic device. The root infiltration influence coefficient and crust infiltration resistance coefficient of each sub-region are input into the preset comprehensive inference model, and the comprehensive infiltration correction factor for the sub-region is output. Specifically, the following steps are included: The root infiltration influence coefficient of each sub-region is used as the first input variable. The value range of the first input variable is determined to be 0.01 to 1.8 according to the upper and lower limit cutoff conditions of the root infiltration influence function. The crust infiltration resistance coefficient of each sub-region is used as the second input variable. The value of the second input variable is determined to be 0.1 to 3.0 according to the calculation method of the crust infiltration resistance coefficient.

[0058] The first input variable is divided into three fuzzy linguistic terms: low influence level, medium influence level, and high influence level, according to a preset first membership function. The first membership function is a triangular membership function. The center point of the first membership function corresponding to the low influence level is taken as the 0.1 quantile (0.2) of the range of the first input variable. The lower limit of the support domain of the first membership function corresponding to the low influence level is taken as the 0th quantile (0.01) of the range of the first input variable. The upper limit of the support domain of the first membership function corresponding to the low influence level is taken as the 0.25 quantile (0.45) of the range of the first input variable. The center point of the first membership function corresponding to the medium influence level is taken as the 0.5 quantile of the range of the first input variable. That is, 0.9. The lower limit of the support domain of the first membership function corresponding to the medium influence level is the 0.25th quantile of the range of the first input variable, which is 0.45. The upper limit of the support domain of the first membership function corresponding to the medium influence level is the 0.75th quantile of the range of the first input variable, which is 1.35. The center point of the first membership function corresponding to the high influence level is the 0.9th quantile of the range of the first input variable, which is 1.62. The lower limit of the support domain of the first membership function corresponding to the high influence level is the 0.75th quantile of the range of the first input variable, which is 1.35. The upper limit of the support domain of the first membership function corresponding to the high influence level is the 1.0 quantile of the range of the first input variable, which is 1.80.

[0059] The second input variable is divided into three fuzzy linguistic terms—low-blockage level, medium-blockage level, and high-blockage level—according to a preset second membership function. The second membership function is a triangular membership function. The center point of the second membership function corresponding to the low-blockage level is taken as the 0.1 quantile (0.4) of the second input variable's range. The lower limit of the support domain of the second membership function corresponding to the low-blockage level is taken as the 0th quantile (0.1) of the second input variable's range. The upper limit of the support domain of the second membership function corresponding to the low-blockage level is taken as the 0.25 quantile (0.8) of the second input variable's range. The center point of the second membership function corresponding to the medium-blockage level is taken as the 0.5 quantile of the second input variable's range. The value is 1.55. The lower limit of the support domain of the second membership function corresponding to the medium stagnation level is the 0.25 quantile of the range of the second input variable, which is 0.8. The upper limit of the support domain of the second membership function corresponding to the medium stagnation level is the 0.75 quantile of the range of the second input variable, which is 2.3. The center point of the second membership function corresponding to the high stagnation level is the 0.9 quantile of the range of the second input variable, which is 2.7. The lower limit of the support domain of the second membership function corresponding to the high stagnation level is the 0.75 quantile of the range of the second input variable, which is 2.3. The upper limit of the support domain of the second membership function corresponding to the high stagnation level is the 1.0 quantile of the range of the second input variable, which is 3.0.

[0060] The three fuzzy linguistic terms of low influence level, medium influence level, and high influence level of the first input variable are combined in pairs with the three fuzzy linguistic terms of low stagnation level, medium stagnation level, and high stagnation level of the second input variable. After the combinations, nine input state rule combinations are formed. The nine input state rule combinations are: the combination of low influence level of the first input variable and low stagnation level of the second input variable; the combination of low influence level of the first input variable and medium stagnation level of the second input variable; the combination of low influence level of the first input variable and high stagnation level of the second input variable; the combination of medium influence level of the first input variable and low stagnation level of the second input variable; the combination of medium influence level of the first input variable and medium stagnation level of the second input variable; the combination of medium influence level of the first input variable and high stagnation level of the second input variable; the combination of high influence level of the first input variable and low stagnation level of the second input variable; the combination of high influence level of the first input variable and medium stagnation level of the second input variable; and the combination of high influence level of the first input variable and high stagnation level of the second input variable.

[0061] Nine input state rules are combined and matched against a pre-defined fuzzy rule table. The fuzzy rule table is based on the statistical distribution of measured comprehensive permeability correction factors under different combinations of root infiltration influence coefficients and crust infiltration resistance coefficients in experimental data. Each rule in the fuzzy rule table records the correspondence between an input state combination and an output fuzzy linguistic term. The rules are set as follows: when the first input variable is at a low influence level and the second input variable is at a low resistance level, the output fuzzy linguistic term is a medium correction factor; when the first input variable is at a low influence level and the second input variable is at a medium resistance level, the output fuzzy linguistic term is a small correction factor; and when the first input variable is at a low influence level and the second input variable is at a high resistance level, the output fuzzy linguistic term is a minimal correction factor. The output fuzzy linguistic term is a large correction factor when the first input variable is of medium influence level and the second input variable is of low stagnation level; a medium correction factor when the first input variable is of medium influence level and the second input variable is of medium stagnation level; a small correction factor when the first input variable is of medium influence level and the second input variable is of high stagnation level; a large correction factor when the first input variable is of high influence level and the second input variable is of low stagnation level; a large correction factor when the first input variable is of high influence level and the second input variable is of medium stagnation level; and a medium correction factor when the first input variable is of high influence level and the second input variable is of high stagnation level.

[0062] The output fuzzy language terms are divided into five levels: minimum correction factor, moderate correction factor, medium correction factor, large correction factor, and maximum correction factor. The minimum correction factor corresponds to a correction factor value of 0.3, the moderate correction factor corresponds to a correction factor value of 0.6, the medium correction factor corresponds to a correction factor value of 1.0, the large correction factor corresponds to a correction factor value of 1.4, and the maximum correction factor corresponds to a correction factor value of 1.8.

[0063] For example, the first input variable of a sub-region, the root infiltration influence coefficient, is 0.35. 0.35 has a membership value of 0.75 on the first membership function at the low influence level and 0.25 on the first membership function at the medium influence level. The second input variable of the sub-region, the crust infiltration inhibition coefficient, is 0.56. 0.56 has a membership value of 0.75 on the second membership function at the low inhibition level and 0.25 on the second membership function at the medium inhibition level. The output fuzzy linguistic term corresponding to the combination of the low influence level of the first input variable and the low inhibition level of the second input variable is a medium correction factor; the output fuzzy linguistic term corresponding to the combination of the low influence level of the first input variable and the medium inhibition level of the second input variable is a small correction factor; the output fuzzy linguistic term corresponding to the combination of the medium influence level of the first input variable and the low inhibition level of the second input variable is a large correction factor; and the output fuzzy linguistic term corresponding to the combination of the medium influence level of the first input variable and the medium inhibition level of the second input variable is... A medium correction factor aggregates the fuzzy membership values ​​corresponding to the matched output fuzzy linguistic terms. The aggregated membership value for the medium correction factor is the product of the membership value of the first input variable at its low influence level (0.75) and the membership value of the second input variable at its low hindrance level (0.75), which is 0.5625, plus the product of the membership value of the first input variable at its influence level (0.25) and the membership value of the second input variable at its hindrance level (0.25), which is 0.0625, equaling 0.625. A smaller correction factor aggregates the membership value of the first input variable at its low influence level (0.75) and the membership value of the second input variable at its low hindrance level (0.75), which is 0.5625. The product of the membership value of the low-impact level of the input variable (0.75) and the membership value of the stagnation level of the second input variable (0.25) is 0.1875. The aggregated membership value of the larger correction factor is the product of the membership value of the impact level of the first input variable (0.25) and the membership value of the low-stagnation level of the second input variable (0.75), which is 0.1875. The output fuzzy linguistic term with the largest fuzzy membership value after aggregation is the medium correction factor. The correction factor value of 1.0 corresponding to the medium correction factor is taken as the comprehensive penetration correction factor of the sub-region.

[0064] Dynamic soil permeability is obtained through the following steps: The comprehensive permeability correction factor for the sub-region is obtained. The comprehensive permeability correction factor is a dimensionless value. When the comprehensive permeability correction factor is greater than 1, it means that the root conduction promoting effect is greater than the crust barrier inhibiting effect, and the comprehensive effect promotes soil water infiltration. When the comprehensive permeability correction factor is less than 1, it means that the crust barrier inhibiting effect is greater than the root conduction promoting effect, and the comprehensive effect inhibits soil water infiltration. When the comprehensive permeability correction factor is equal to 1, it means that the root conduction promoting effect and the crust barrier inhibiting effect cancel each other out, and the comprehensive effect has no net impact on soil water infiltration.

[0065] Determine whether the comprehensive permeability correction factor of the sub-region is greater than the preset effective lower limit and less than the preset effective upper limit. The preset effective lower limit is determined based on the physical lower limit of the comprehensive permeability correction factor when the crust infiltration resistance coefficient reaches its maximum value. The typical value of the preset effective lower limit is 0.2. The preset effective upper limit is determined based on the physical upper limit of the comprehensive permeability correction factor when the root infiltration influence coefficient reaches its maximum value. The typical value of the preset effective upper limit is 2.5.

[0066] When the comprehensive permeability correction factor of a sub-region is greater than or equal to the preset effective lower limit and less than or equal to the preset effective upper limit, the comprehensive permeability correction factor is determined as the effective correction factor of the sub-region. When the comprehensive permeability correction factor of a sub-region is less than the preset effective lower limit, the preset effective lower limit of 0.2 is determined as the effective correction factor of the sub-region. When the comprehensive permeability correction factor of a sub-region is greater than the preset effective upper limit, the preset effective upper limit of 2.5 is determined as the effective correction factor of the sub-region. The effective correction factor is used to ensure that the corrected soil permeability does not have physically unreasonable extreme values.

[0067] The baseline soil permeability of the subregion is multiplied by the effective correction factor of the subregion. The baseline soil permeability is the initial soil permeability of the subregion under the condition of no biological activity. The baseline soil permeability is obtained directly on site by the ring cutter method or the double ring infiltration method. The product result is used as the dynamic soil permeability of the subregion. The dynamic soil permeability is the actual soil permeability of the subregion under the combined influence of the current root system configuration and crust cover status.

[0068] For example, if the comprehensive permeability correction factor for a certain sub-region is 1.8 and the preset effective lower limit is 0.2 and the preset effective upper limit is 2.5, then 1.8 is greater than 0.2 and less than 2.5. Therefore, 1.8 is determined as the effective correction factor for the sub-region. When the baseline soil permeability of the sub-region is 2.5 cm per hour, the dynamic soil permeability of the sub-region is 2.5 multiplied by 1.8, which equals 4.5 cm per hour. This means that the actual soil permeability of the sub-region under the influence of comprehensive biological effects is 4.5 cm per hour.

[0069] In another sub-region, the comprehensive permeability correction factor is 3.2, and the preset effective upper limit is 2.5. Since 3.2 is greater than 2.5, the preset effective upper limit of 2.5 is determined as the effective correction factor for the sub-region. When the baseline soil permeability of the sub-region is 2.0 cm / hour, the dynamic soil permeability of the sub-region is 2.0 multiplied by 2.5, which equals 5.0 cm / hour. This truncation prevents the dynamic soil permeability from reaching an extremely high value due to an excessively large comprehensive permeability correction factor. In yet another sub-region, the comprehensive permeability correction factor is 0.15, and the preset effective lower limit is 0.2. Since 0.15 is less than 0.2, the preset effective lower limit of 0.2 is determined as the effective correction factor for the sub-region. When the baseline soil permeability of the sub-region is 3.0 cm / hour, the dynamic soil permeability of the sub-region is 3.0 multiplied by 0.2, which equals 0.6 cm / hour. This truncation prevents the dynamic soil permeability from reaching an extremely low value that is too close to zero due to an excessively small comprehensive permeability correction factor. The upper and lower limit truncations together ensure that the dynamic soil permeability always remains within a physically reasonable range.

[0070] When performing irrigation operations on each sub-region, the dynamic soil permeability of the sub-region is divided by the target infiltration depth of the sub-region. The target infiltration depth is equal to the sum of the preset root layer depth and the converted depth of the water required to replenish the soil when the current soil moisture content reaches the field capacity. The preset root layer depth is determined based on the average vertical distribution depth of the roots of the crop variety in the sub-region. The typical value range of the preset root layer depth is 20 cm to 60 cm. When the crop is wheat, the preset root layer depth is 30 cm; when the crop is corn, the preset root layer depth is 40 cm; and when the crop is fruit tree, the preset root layer depth is 50 cm. The reciprocal of the result is the irrigation time required for the irrigation water to reach the target infiltration depth of the sub-region to reach the preset root layer depth. The irrigation time is taken as the target irrigation time of the sub-region.

[0071] For example, if the dynamic soil permeability of a sub-region is 4.5 cm per hour and the target infiltration depth is 30 cm, the target irrigation duration for the sub-region is 30 divided by 4.5, which equals 6.67 hours. Similarly, if the dynamic soil permeability of another sub-region is 0.6 cm per hour and the target infiltration depth is 40 cm, the target irrigation duration for the sub-region is 40 divided by 0.6, which equals 66.67 hours.

[0072] The target irrigation duration for each sub-region is obtained and sorted in ascending order. When the difference between the maximum and minimum target irrigation durations of all sub-regions is less than the preset duration difference threshold, the average of the maximum and minimum values ​​is taken as the unified irrigation duration and the irrigation equipment is controlled to perform irrigation operations on all sub-regions simultaneously. The preset duration difference threshold is determined based on the minimum time control accuracy of the irrigation equipment, and a typical value for the preset duration difference threshold is 10 minutes.

[0073] When the difference between the maximum and minimum target irrigation durations of each sub-region is greater than or equal to a preset duration difference threshold, the sub-region identifier and its corresponding boundary coordinates are obtained. Irrigation control instructions for each sub-region are generated according to the target irrigation duration of each sub-region. The irrigation control instructions include the boundary coordinates of the sub-region and the target irrigation duration. The irrigation control instructions are sent to the control system of the irrigation equipment. The control system controls the irrigation execution unit of the irrigation equipment to move to the corresponding sub-region according to the boundary coordinates of the sub-region. The control system controls the irrigation execution unit to continuously supply water in the sub-region according to the target irrigation duration. When the cumulative water supply time of the sub-region reaches the target irrigation duration of the sub-region, the control system controls the irrigation execution unit to stop supplying water to the sub-region and move to the next sub-region to continue the irrigation operation.

[0074] For example, a target irrigation area is divided into three sub-areas. The target irrigation duration for the first sub-area is 6.67 hours, for the second sub-area it is 8.33 hours, and for the third sub-area it is 66.67 hours. The difference between the maximum value of 66.67 hours and the minimum value of 6.67 hours is 60 hours. This difference is greater than a preset duration difference threshold of 10 minutes. Therefore, irrigation control instructions are generated for the first sub-area, the second sub-area, and the third sub-area, respectively. The irrigation equipment's control system first controls the irrigation execution unit to move to the first sub-area and continuously supply water for 6.67 hours. After 6.67 hours, the water supply stops and the unit moves to the second sub-area to continuously supply water for 8.33 hours. After 8.33 hours, the water supply stops and the unit moves to the third sub-area to continuously supply water for 66.67 hours. After 66.67 hours, the water supply stops and all irrigation operations are completed.

[0075] The above algorithms or formulas are all dimensionless and numerical calculations, and the results are obtained by software simulation based on a large amount of collected data to obtain the most recent real-world results. The preset parameters are set by those skilled in the art according to the actual situation.

[0076] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0077] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling irrigation duration based on soil permeability, characterized in that, Includes the following steps: The target irrigation area is divided into multiple sub-regions, and each sub-region is assigned an independent sub-region identifier; the crop root system configuration parameters and surface biocrust coverage parameters are obtained in each sub-region. The crop root system configuration parameters include the number of lateral roots, root length density and root distribution depth, and the surface biocrust coverage parameters include the percentage of biocrust coverage area, average biocrust thickness and biomass density. The root development index is calculated based on the number of lateral roots and root length density in each sub-region. The thickness of the soil layer affected by the root system is determined based on the root distribution depth in the sub-region. The root infiltration influence coefficient of the sub-region is calculated using the root development index and the thickness of the soil layer affected by the root system. The crust density index of each sub-region is calculated based on the crust coverage area ratio, average crust thickness, and crust biomass density. The crust infiltration resistance coefficient of the sub-region is then calculated using the crust density index and average crust thickness. The root infiltration influence coefficient and crust infiltration inhibition coefficient of each sub-region are input into the preset comprehensive reasoning model. The comprehensive reasoning model is used to output the comprehensive infiltration correction factor of the sub-region based on the game relationship between the root's water conduction promotion effect and the crust's water blocking inhibition effect in the same sub-region. The baseline soil permeability is corrected using a comprehensive permeability correction factor for each sub-region, and the corrected permeability value for each sub-region is taken as the dynamic soil permeability of that sub-region. Based on the dynamic soil permeability of each sub-region, the irrigation time required for the irrigation water to reach the target infiltration depth of the sub-region to reach the preset root layer depth is calculated, and the irrigation time is taken as the target irrigation time for the sub-region. According to the target irrigation time for each sub-region, the irrigation equipment is controlled to perform irrigation operations for each sub-region.

2. The irrigation duration control method based on soil permeability according to claim 1, characterized in that, Dividing the target irrigation area into multiple sub-regions specifically includes the following steps: Multiple sampling points are arranged at a first preset interval within the target irrigation area to collect crop root system architecture parameters and surface biological crust coverage parameters at each sampling point. For any sampling point, determine each of the sampling points adjacent to the sampling point based on the spatial coordinates of the sampling point, calculate the spatial distance between the sampling point and each of its adjacent sampling points, and use the reciprocal of each spatial distance value as the weight coefficient corresponding to the spatial distance value. For any sampling point, calculate the first spatial variation value of the root system configuration parameter and the second spatial variation value of the biological crust coverage parameter in the local area where the sampling point is located; The first spatial variation value and the second spatial variation value are weighted and fused twice to obtain the comprehensive spatial variation index of the local region where each sampling point is located, and the region is divided into high variation region and low variation region. In the high-variability region, the grid is divided according to the second preset interval, and in the low-variability region, the grid is divided according to the third preset interval. Each grid cell obtained after division is taken as a sub-region. An independent sub-region identifier is generated for each sub-region, and the sub-region identifier is associated with the boundary coordinates of the sub-region and stored.

3. The irrigation duration control method based on soil permeability according to claim 2, characterized in that, The root development index is calculated based on the number of lateral roots and root length density in each sub-region. The thickness of the soil layer affected by the root system is determined based on the root distribution depth in the sub-region. The specific steps include: Multiply the number of lateral roots in the subregion by the root length density, and use the product as the total root density of the subregion. Divide the root distribution depth of the sub-region into a preset number of depth segments, count the lateral root density in each depth segment, calculate the standard deviation of the lateral root density of all depth segments, multiply the reciprocal of the standard deviation by the preset root uniformity benchmark value, and use the product as the root distribution uniformity of the sub-region. Multiply the total root density by the root distribution uniformity, and use the product as the root development index of the sub-region. Multiply the root distribution depth of the sub-region by the preset root extension coefficient, and use the product as the root influence soil layer thickness of the sub-region.

4. The irrigation duration control method based on soil permeability according to claim 3, characterized in that, The calculation of the root infiltration influence coefficient of a subregion specifically includes the following steps: Divide the root development index of the sub-region by the soil layer thickness affected by the root system in the sub-region, and use the result as the root density value per unit soil layer of the sub-region. The root density value per unit soil layer is substituted into the preset root infiltration influence function for calculation. The preset root infiltration influence function is used to map the root density value per unit soil layer to a power function and output the root infiltration influence coefficient. The output value of the root infiltration influence function is used as the root infiltration influence coefficient of the sub-region.

5. The irrigation duration control method based on soil permeability according to claim 4, characterized in that, The calculation of the crust density index of a subregion specifically includes the following steps: Divide the percentage of crust coverage area in the sub-region by the preset coverage area benchmark value to obtain the normalized coverage area value of the sub-region. Divide the average crust thickness of the sub-region by the preset thickness benchmark value to obtain the normalized thickness value of the sub-region. Divide the crust biomass density of the sub-region by the preset biomass density benchmark value to obtain the normalized biomass density value of the sub-region. The normalized coverage area, normalized thickness, and normalized biomass density values ​​are weighted and summed to obtain the crustal density index of the subregion.

6. The irrigation duration control method based on soil permeability according to claim 5, characterized in that, The calculation of the crust infiltration resistance coefficient of a subregion specifically includes the following steps: Multiply the density index of the crust layer of the sub-region by the preset retardation base coefficient, and add the preset retardation growth bias value to the result to obtain the retardation rate per unit thickness of the crust of the sub-region. Multiply the unit thickness resistance rate of the crust in the sub-region by the average thickness of the crust in the sub-region, and use the result as the crust infiltration resistance coefficient of the sub-region.

7. The irrigation duration control method based on soil permeability according to claim 6, characterized in that, The comprehensive inference model is a fuzzy logic device. The root infiltration influence coefficient and crust infiltration resistance coefficient of each sub-region are input into the preset comprehensive inference model, and the comprehensive infiltration correction factor for the sub-region is output. Specifically, the following steps are included: The root infiltration influence coefficient of each sub-region is used as the first input variable, and the crust infiltration resistance coefficient of the sub-region is used as the second input variable. The first input variable is divided into three fuzzy linguistic terms with different influence levels according to the preset first membership function, and the second input variable is divided into three fuzzy linguistic terms with different blocking levels according to the preset second membership function. The fuzzy linguistic terms of the first input variable and the second input variable are combined to form the input state rule combination. The input state rule combination is input into a preset fuzzy rule table for matching. Each rule in the fuzzy rule table records the correspondence between an input state combination and an output fuzzy language item. The output fuzzy language item is one of five preset levels. Aggregate the fuzzy membership values ​​corresponding to the matched output fuzzy linguistic terms, and use the correction factor value corresponding to the output fuzzy linguistic term with the largest aggregated fuzzy membership value as the comprehensive penetration correction factor for the sub-region.

8. The irrigation duration control method based on soil permeability according to claim 7, characterized in that, Dynamic soil permeability is obtained through the following steps: Obtain the comprehensive penetration correction factor of the sub-region and determine whether the comprehensive penetration correction factor of the sub-region is greater than the preset effective lower limit and less than the preset effective upper limit. When the comprehensive penetration correction factor of the sub-region is greater than or equal to the preset effective lower limit and less than or equal to the preset effective upper limit, the comprehensive penetration correction factor is determined as the effective correction factor of the sub-region. When the comprehensive permeability correction factor of a sub-region is less than the preset effective lower limit, the preset effective lower limit is determined as the effective correction factor of the sub-region; when the comprehensive permeability correction factor of a sub-region is greater than the preset effective upper limit, the preset effective upper limit is determined as the effective correction factor of the sub-region. Multiply the baseline soil permeability of the subregion by the effective correction factor of the subregion, and use the product as the dynamic soil permeability of the subregion.

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