Corn cultivation environment intelligent adjusting method based on multi-sensor control

Abstract

The invention relates to the technical field of environment regulation, in particular to a corn cultivation environment intelligent regulation method based on multi-sensor control, which comprises the following steps: S1, generating a soil moisture three-dimensional distribution diagram in real time through humidity sensor arrays arranged at different depths of a corn root zone; s2, combining infrared temperature data of the canopy and the three-dimensional distribution diagram of the soil moisture, and calculating a moisture motion mismatch degree through transpiration-permeation conduction collaborative analysis; s3, generating a user-defined irrigation mode based on the water movement mismatch degree, and controlling the irrigation duration, the stop interval and the water flow rate. According to the method, coupling modeling is carried out on the plant physiological water demand behavior and the soil moisture supply characteristic, the limitation that a traditional irrigation system only depends on a soil humidity threshold value or meteorological index judgment is broken through, and the biological adaptability and the aging responsiveness of field moisture regulation and control are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental regulation, and in particular to an intelligent regulation method for a corn cultivation environment based on multi-sensor control. Background Art

[0002] In the cultivation of field crops like corn, precise water management is crucial for ensuring yield and water use efficiency. Traditional irrigation methods often rely on fixed-cycle irrigation or simple threshold control based on shallow soil moisture, failing to fully account for the dynamic changes in the crop's actual water requirements. This often leads to problems such as "shallow overwetness, deep drought" or "delayed response and excessive water supply." Furthermore, most existing systems base irrigation decisions on a single factor, such as meteorological conditions or soil moisture content. This lacks real-time feedback on plant physiological status, resulting in limited control accuracy.

[0003] In recent years, although some studies have attempted to introduce sensor arrays to achieve stratified monitoring of soil moisture, most of them have only focused on single-point or single-layer humidity data and failed to build a three-dimensional humidity model with spatial continuity and crop response specificity; at the same time, current intelligent irrigation control methods generally do not establish a linkage mechanism between crop transpiration behavior and soil moisture conduction process, making it difficult to capture the spatiotemporal mismatch between "water demand-water supply", resulting in water resource waste or difficulty in timely identification of potential crop stress.

[0004] In addition, in terms of irrigation execution control, most existing technologies adopt rigid control strategies with fixed flow rates and durations. They are unable to flexibly adjust irrigation parameters according to different soil textures, growth stages or sudden environmental stresses. They lack adaptive capabilities and are unable to meet the needs of modern precision agriculture for efficient water conservation and simultaneous protection of crop health. Summary of the Invention

[0005] The present invention provides a method for intelligently regulating a corn cultivation environment based on multi-sensor control.

[0006] The method for intelligently adjusting the corn cultivation environment based on multi-sensor control includes the following steps: S1. Generate a real-time 3D soil moisture distribution map using an array of moisture sensors deployed at different depths within the corn root zone. S2. Calculate the water movement mismatch by combining the canopy infrared temperature data with the three-dimensional soil moisture distribution map through transpiration-infiltration conduction synergistic analysis; S3. Generate a customized irrigation pattern based on the water movement mismatch to control irrigation duration, rest interval, and water flow rate.

[0007] Optionally, the S1 includes dividing the corn root zone into spatial grids of fixed size and arranging moisture sensor nodes at multiple depths.

[0008] Optionally, the S1 further includes performing an inverse distance weighted interpolation operation on the humidity values of the grids where no humidity sensors are deployed.

[0009] Optionally, the construction of the three-dimensional soil moisture distribution map includes assigning a weight correction coefficient to the humidity values monitored at each layer based on the root distribution characteristics of different depth layers to reflect the actual water absorption capacity differences of the root zone. The three-dimensional soil moisture distribution map is represented by a data matrix: ;in, Indicates the spatial position Psychrometric node values, It is expressed as the soil moisture value after interpolation, infiltration compensation and root weight correction. Indicates the corresponding 、 、 The index coordinates of the grid position in the row, column, and depth directions.

[0010] Optionally, the S2 specifically includes: S21, transpiration rate inversion: Use an infrared thermometer to scan the crop canopy to obtain canopy infrared temperature data. Based on the canopy infrared temperature data, the real-time transpiration rate is inverted using the energy balance equation; S22, Calculation of longitudinal water conduction rate: from the three-dimensional distribution map of soil moisture Extract the humidity change rate at the effective root depth from Represents the spatial position Psychrometric node value of S23, Quantification of water movement mismatch: Calculate the mismatch by combining transpiration rate and water conduction rate.

[0011] Optionally, the real-time transpiration rate is expressed as: ; in, is the net radiation, is the soil heat flux, is the air density, is the constant pressure specific heat of air, is the canopy temperature, is the air temperature, is the airflow boundary layer resistance, is the latent heat of vaporization of water, the transpiration rate Indicates the amount of water evaporation per unit area per unit time.

[0012] Optionally, the water movement mismatch measures the degree of mismatch between crop transpiration rate and root zone water recharge rate, and is defined as the weighted sum of the normalized difference intensity between transpiration demand and water supply and the response lag time.

[0013] Optionally, the water movement mismatch is calculated as: ; in, is the water movement mismatch, ranging from 0 to 1. is the weight coefficient in the mismatch calculation, is the unit conversion factor, is the daily maximum potential transpiration rate, Indicates the delay time of water conduction from shallow to deep layers. is the longitudinal water conduction rate.

[0014] Optionally, generating a customized irrigation pattern based on the water movement mismatch in S3 specifically includes: S31, based on the currently calculated water movement mismatch, generating multiple irrigation control parameters according to set rules; S32, based on soil texture, makes differential corrections to multiple irrigation control parameters: If the soil is sandy, shorten the irrigation time and increase the water flow rate; If the soil is clayey, extend the irrigation period and shorten the rest interval; S33, after corn enters the tasseling stage, dynamically adjust the irrigation strategy based on the ambient humidity: When the relative humidity is low, extend the irrigation time to compensate for the water loss caused by high transpiration risk; At the same time, reduce the water flow rate, prolong the duration of wetting, and avoid rapid erosion of the root surface.

[0015] Optionally, the multiple irrigation control parameters specifically include: The water movement mismatch is mapped to the duration of a single irrigation. When the water movement mismatch is low, the standard duration is adopted; in the medium range, a linear decrease strategy is adopted; when the mismatch exceeds the set threshold, the pulse irrigation mode is switched. The irrigation pause interval is generated based on the mismatch value, and a nonlinear function relationship is used to make the pause time the shortest under medium mismatch conditions and gradually extend under low and high mismatch conditions. The water flow rate is calculated based on the mismatch degree. The more severe the mismatch is, the higher the water flow rate is.

[0016] Beneficial effects of the present invention: This method uses a multi-layered deep moisture sensor array combined with spatial gridding and a dynamic interpolation algorithm to convert moisture information in the corn root zone into a matrix-like three-dimensional moisture distribution map. This system also incorporates a weighted correction mechanism based on root density distribution, enabling it to accurately restore the "effective moisture supply map" of the crop root zone. Compared to traditional single-point or single-layer sensor systems, this method enhances the ability to identify dynamic gradient changes in rhizosphere moisture distribution, providing high-resolution data support for subsequent precise regulation.

[0017] This study constructs a transpiration-conduction mismatch index based on real-time transpiration rates derived from canopy infrared temperature inversion and combined with the root zone moisture change rate and delay time. This index comprehensively quantifies the intensity and timing differences between crop "water demand signals" and "water supply capacity." This mechanism couples plant physiological water demand behavior with soil moisture supply characteristics, transcending the limitations of traditional irrigation systems that rely solely on soil moisture thresholds or meteorological indicators, thereby enhancing the biological adaptability and timely responsiveness of field water regulation.

[0018] The present invention maps the mismatch degree into three parameters: irrigation duration, pause interval and water flow rate, and performs multi-level correction in combination with soil type and crop growth period characteristics to establish a pulse irrigation control model with nonlinear adaptive capabilities. This model can achieve on-demand, time-sharing and quantity-based precise irrigation in different physiological stages and different soil environments, maximize water use efficiency, avoid common problems such as over-irrigation, erosion or lag, and has significant water-saving efficiency and crop resistance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention; Figure 2 Schematic diagram of the water movement mismatch calculation process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art may also implement some known technologies in other alternative ways. The accompanying drawings are only for describing the embodiments in more detail and are not intended to limit the present invention in any specific way.

[0022] like Figure 1-Figure 2As shown, the intelligent adjustment method of corn cultivation environment based on multi-sensor control includes the following steps: S1. A three-dimensional soil moisture distribution map is generated in real time by deploying a moisture sensor array at different depths in the corn root zone.

[0023] S1 specifically includes: S11, spatial gridding processing: The corn root zone is divided into a three-dimensional grid of 20 cm × 20 cm × 10 cm. The grid nodes cover the sensor deployment sites, which are located at soil depths of 10 cm, 25 cm, and 40 cm, respectively.

[0024] S12, dynamic interpolation calculation: Based on the real-time data of the humidity sensors at each depth, the humidity value of the grid node without sensor deployment is calculated using the inverse distance weighted algorithm. The interpolation calculation is as follows: ; in, Indicates the humidity value of the node to be estimated, Indicates the The humidity value of the known sensor nodes, Indicates the node to be evaluated and the The spatial distance between the sensors, is the weight index, and its value is 2.

[0025] Interpolation estimates missing values based solely on spatial distance. However, the vertical movement of water in the soil is affected by multiple factors, including ground temperature, capillary forces, and gravitational infiltration. This leads to delays in propagation speed at different depths, necessitating corrections for estimates at deeper nodes. Water takes time to penetrate from the surface, especially in low temperatures, dense soil structures, or high viscosity. Moisture changes at a depth of 40 cm can lag behind those detected by surface sensors. Using the IDW interpolation algorithm directly will underestimate the actual moisture content at the 40 cm layer.

[0026] The higher the ground temperature, the lower the soil capillary tension and viscosity, and the faster the water infiltration. Therefore, for the nodes between the 25cm and 40cm layers, the interpolated values introduce a water gravity infiltration compensation factor: ;in, is the current ground temperature, Represents the water gravity infiltration compensation factor, which is calculated and combined with Multiply to get the corrected .

[0027] S13, root weight correction: Assign root density weight correction coefficients to grid nodes at different depths as shown in the following table: Table 1 Recommended weight correction coefficients

[0028] The moisture interpolation result will be multiplied by the weight coefficient of the corresponding depth for correction to more accurately reflect the effective moisture distribution in the root zone.

[0029] Set 0.5 at 40cm: Corn enters a high-intensity water demand stage from the tasseling to the grain filling stage. About 70% of the capillary roots are concentrated in the soil layer of about 40cm, which is the main channel for water and nutrient absorption. The soil also has a stronger water retention capacity and maintains it for a long time, so it is given the maximum weight.

[0030] 25cm is set to 0.3: This is a typical secondary root layer. Although the root density is slightly lower than that at 40cm, it is still an important area for medium-term water demand response, with both water storage and response functions, and its weight is second.

[0031] 10cm depth is set to 0.2: The surface layer has few and shallow roots, resulting in significant evaporation losses. Humidity changes in this layer are more influenced by climate rather than plant physiological needs, so it is given a lower weight to prevent it from influencing the main irrigation decision.

[0032] The aforementioned S13 water gravity infiltration compensation factor is used to improve the vertical rationality of the interpolation results, while the root density weight is used to reflect the differences in the water absorption efficiency of crops at different depths. The two factors act respectively in the interpolation generation stage and the subsequent weight correction stage.

[0033] S14, time domain smoothing filter: Perform sliding average filtering on the raw humidity data collected continuously for 5 minutes to eliminate instantaneous measurement errors caused by soil disturbance, sensor voltage fluctuation, etc.

[0034] Fault tolerance mechanism: When the mutation amplitude of a certain sensor data exceeds 35% within a unit time, the data of the three adjacent sensor groups will be immediately activated for cross-validation; if the abnormality lasts for more than 10 minutes, the node data will be replaced by the mean value within the historical time window of the node.

[0035] S15, after completing the interpolation operation and weight correction, the corrected moisture value of each spatial grid node in the corn root zone is mapped to a unified coordinate system to construct a complete three-dimensional soil moisture distribution map, which is represented by a matrix: ;in, Represents the spatial position Psychrometric node values, It is expressed as the soil moisture value after interpolation, infiltration compensation and root weight correction. Indicates the corresponding 、 、 The grid position is in X (row), Y (column), Index coordinate in the (depth) direction.

[0036] S2. Combine canopy infrared temperature data with the three-dimensional soil moisture distribution map and calculate the water movement mismatch through transpiration-infiltration conduction collaborative analysis.

[0037] S2 specifically includes: S21, transpiration rate inversion: Based on the canopy energy conservation principle, the "latent heat flux generated by transpiration" is inversely calculated as the "remaining energy after net energy input minus soil heat loss and sensible heat loss." By dividing by the latent heat of vaporization of water, the transpiration rate in the form of water mass flux is obtained. The vegetation canopy acts as an energy exchange interface, and its overall energy balance can be written as: ;in, is the net radiation, the total energy absorbed by the plant, is the soil heat flux, the energy conducted downward to the soil, is the sensible heat flux, the heat removed by air convection, is the latent heat flux, the energy consumed by the evaporation of water vapor, is the latent heat of vaporization of water, is the transpiration per unit area per unit time; The derivation is as follows: Step 1: Starting from the conservation of energy, according to the conservation of canopy energy: ; Step 2: Sensible heat flux: Sensible heat flux It is a heat convection process driven by air temperature difference, which can be calculated by Newton's law of cooling: ;in, is the temperature difference between the crop canopy and the air, is the air boundary layer resistance (i.e. heat transfer resistance), , is the air density and specific heat capacity, which is the classic heat transfer formula in energy flux expression, used to describe the heat conduction from the canopy to the air.

[0038] Step 3: Substitute the expression of H to get the solution of ET: Substitute the expression into the energy balance formula: ; Divide both sides by , we can get the real-time transpiration rate based on the canopy infrared temperature data and invert it through the energy balance equation : ; in, is the net radiation, measured by a net radiometer installed in the field, is the soil heat flux, obtained by the soil heat flux plate, is the air density, is the constant pressure specific heat of air, The canopy temperature is obtained by scanning the crop canopy with an infrared thermal imager or infrared thermometer. is the air temperature, is the airflow boundary layer resistance, is the latent heat of vaporization of water.

[0039] Transpiration rate The physical meaning is the amount of water evaporation per unit area per unit time (kg / m 2 / s), but in plant physiology it is often expressed in mm / day, that is, converted into water layer height.

[0040] But its essential unit can be expressed as: ; Molecular unit analysis: , The unit is W / m 2 , that is, J / s·m 2 , is the sensible heat flux, which should also be W / ma; :kg / m 3 , : , : K (temperature difference), : m; Substitute: ; Therefore the entire partial unit of the molecule remains: ; Denominator unit analysis: The latent heat of vaporization of water, in J / kg; The whole formula is unit analysis: ; This is the standard unit of transpiration rate, that is, water mass flux (kg / m2 / s), which can be converted to mm / h or mm / day (using the density of water to convert to water layer thickness).

[0041] S22, Calculation of longitudinal water conduction rate: from three-dimensional soil moisture distribution map Extracting effective root depth Humidity change rate at : ; in, is the moisture value of the coordinate point in the three-dimensional soil moisture distribution map, The effective root depth is 40cm by default (during the tasting period).

[0042] S23, Quantification of water movement mismatch: Combine transpiration rate and water conduction rate to calculate the mismatch : ; in, are weight coefficients in mismatch calculation, with initial values of 0.7 / 0.3, is the unit conversion factor (converting moisture rate to equivalent transpiration rate), is the daily maximum potential transpiration rate, Indicates the delay time for water to be transferred from shallow to deep layers.

[0043] when C and C, indicating that high temperature stress causes stomatal closure and transpiration to be weakly correlated: increase ; reduce ; When soil porosity Sandy soil), indicating faster infiltration and more significant delayed effect: reduce ;Increase .

[0044] Unit Conversion Factor The mass flux unit used to convert the volumetric moisture content change rate into the transpiration rate. The formula is: ;in, is the density of water, is the effective thickness of the root system, that is, the root zone depth range used for conduction calculation. If is the volumetric water content change rate at a certain depth per unit time, then It indicates the equivalent water supply per square meter of soil surface per unit time.

[0045] Delay time It is used to describe the timing delay of shallow water change signal transmission to the root layer, which can be solved by cross-correlation analysis: ;in, is the volumetric water content at a depth of 25 cm that changes with time, is the volumetric water content at a depth of 40 cm that changes with time, is the time offset, Indicates the time offset that maximizes the cross-correlation function, i.e., the best matching delay. Indicates the time delay at which shallow moisture changes best explain the synchronous response of the deeper layer. Constraints: When the detected rainfall flag is True, indicating a rainfall event, the mandatory settings are: .

[0046] Rainwater directly impacts the entire soil profile, especially during heavy rainfall. This can cause simultaneous increases or fluctuations in moisture levels in both shallow and deep layers, disrupting the original "propagation path" of moisture from shallow to deep. This disrupts the infiltration process, allowing moisture to be "transiently conducted" along cracks or capillary channels. Furthermore, the humidity change sequence between 25cm and 40cm loses its causal relationship, potentially distorting the cross-correlation function. Continuing to use the time delay algorithm in this situation would produce spurious differences, so a safe strategy is adopted: upon detecting rainfall, the shallow and deep moisture levels are assumed to be changing synchronously.

[0047] When wilting symptoms are detected, prioritize crop water stress responses: increase ;freeze .

[0048] The construction of this mismatch calculation stems from the problem of spatiotemporal decoupling of crop water use. In field environments, there is often a discrepancy between the crop's real-time transpiration demand (reflected by the leaf canopy) and the soil's root zone water supply capacity (derived from soil conduction). To accurately assess this inconsistency, the following two core factors must be considered: Intensity difference dimension: By comparing the relative difference between the actual transpiration rate of the crop and the upward conduction rate of soil water in the root zone, it reflects the intensity imbalance between the "desired water" and "available water" of the plant at the current moment; Response delay dimension: By analyzing the time delay in the transmission of shallow soil moisture changes to the main root layer, the "lag" of the current water recharge process is quantified to capture the timing mismatch in the water response chain.

[0049] This calculation method uses transpiration as the water demand indicator and vertical changes in soil moisture as the water supply indicator, forming a dual mismatch quantitative calculation model that takes into account both quantity difference and time difference.

[0050] By quantifying the mismatch, it's possible to determine if a crop is in the early stages of water stress, rather than relying on traditional soil moisture thresholds. This significantly improves identification sensitivity. When the mismatch exceeds a set threshold, the system automatically triggers a localized irrigation response, adjusting the irrigation flow rate, timing, or range to achieve precise, on-demand watering, avoiding overwatering or watering too late.

[0051] S3. Generate a customized irrigation pattern based on the water movement mismatch to control irrigation duration, rest interval, and water flow rate.

[0052] S3 specifically includes: S31, mode parameter mapping based on mismatch , establish the function mapping relationship of the three irrigation control parameters S311. Irrigation duration : ; S312. Rest interval : ; S313. Water flow rate : ; in, represents the transpiration-conduction mismatch, Indicates the duration of a single irrigation. Indicates the duration of rest after irrigation. Indicates the water flow rate of the nozzle.

[0053] 1. Irrigation duration Set as a piecewise function to reflect the water supply strategy under different degrees of mismatch: When the mismatch When the value is very low (≤0.3), the crop is in a state of little or no need for water, and the basic water volume is maintained, and the irrigation time is set to 60 seconds; when When the mismatch is in the middle range (0.3-1.0), a linear decreasing strategy is adopted. The more severe the mismatch is, the shorter the irrigation time is to avoid excessive water replenishment causing leaching or runoff. when When it is higher than 1.0, it indicates that the conduction in the root zone is seriously delayed. The use of ultra-short pulse (15 seconds) multiple iteration mode is beneficial to enhance the response of capillary roots to water and avoid surface loss caused by rapid irrigation.

[0054] This design emphasizes a small-dose, quick-response strategy, which is in line with the water-saving principles of precision irrigation and the physiological characteristics of root absorption.

[0055] 2. Rest Interval The dwell time uses an S-shaped nonlinear function to match the dynamic characteristics of vertical water conduction in the soil: At the medium mismatch degree Dm≈0.5, the root zone moisture is in a controllable adjustment state, and the shortest waiting time is set to increase the response frequency; When the mismatch is too low or too high, the rest period is extended respectively to prevent frequent irrigation from causing soil disturbance or reducing water supply efficiency; The S-type function provides a smooth and controllable response mechanism to avoid sudden changes in control logic caused by artificial thresholds.

[0056] This setting is designed to allow soil moisture enough time to conduct downward and be absorbed by the roots after each irrigation, reflecting the infiltration rhythm of "irrigate first and then stop, small amounts and multiple times".

[0057] 3. Water flow rate Using exponential enhancement function ( ), the purpose is to: Rapidly increase irrigation intensity to ensure sufficient water supply in a short period of time; Strong water flow can overcome the compaction or hardening of some surface soil structures and enhance infiltration efficiency; Maintain low flow rate when the mismatch is small to prevent shallow water from saturating or washing away the root surface.

[0058] The exponential form reflects the "nonlinear amplification response" to high mismatch conditions and is an adaptive regulation mechanism driven by physiological conditions.

[0059] S32, soil type adaptation correction: Soil texture directly affects the water infiltration rate, retention capacity and lateral diffusion characteristics, so a correction factor is introduced into the pulse irrigation parameters: 1. Amendment of sandy soil: Sandy soil has large pores, a fast infiltration rate, and water infiltrates quickly, so irrigation water easily loses from the root zone; Therefore, the irrigation duration is shortened to 70% of the original value to avoid water waste caused by deep seepage; At the same time, the water flow rate is increased by 30%, and stronger water pressure is used to promote the lateral expansion of water and increase the root water absorption coverage rate.

[0060] 2. Clay soil amendment: Clay has a dense structure and strong water holding capacity but slow infiltration, which can easily cause surface water accumulation; Therefore, the irrigation duration was extended to 140% of the original value to ensure that water could slowly penetrate into the effective root zone; The pause time is shortened to 60% of the original value to prevent the soil from being too wet due to a long pause after irrigation, which would inhibit root respiration.

[0061] The above parameters are corrected according to soil texture and expressed as: Sandy soil: ; Clay soil: .

[0062] S33, dynamic adjustment of the growth period (exclusive rules for the tasting period): The tasting stage is a critical phase in corn growth, with the highest water demand and sensitivity to environmental stress. Therefore, irrigation control must be dynamically adjusted according to the crop's physiological state. During the tasting stage, the lower the relative humidity and the higher the transpiration, the more susceptible the crop is to water deficit. Setting the irrigation duration inversely proportional to the humidity helps maintain a balance between foliar water supply and demand and reduce stress. During the tasting stage, capillary roots are dense and active, and excessively fast water flow can scour the rhizosphere or cause shallow water accumulation. Reducing the flow rate to 80% of the baseline value increases water retention in the root zone, promoting deep absorption and strengthening the root system.

[0063] Therefore, during the tasseling period, according to the relative humidity of the air Further corrections to the parameters: Irrigation duration adjustment: ; Water flow rate decreased: ; Indicates the current relative humidity of the air (%).

[0064] The irrigation duration Ton is controlled by a PLC timer to precisely control the opening time of the solenoid valve or pump; The rest interval Toff is delayed by the PLC program logic to control the interruption time of the irrigation cycle and ensure sufficient soil infiltration process; The water flow rate Fr is adjusted in real time through an electric proportional valve or a variable frequency pump controller.

[0065] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0066] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for intelligently adjusting corn cultivation environment based on multi-sensor control, characterized in that: The following steps are involved: S1. Generate a real-time 3D soil moisture distribution map using an array of moisture sensors deployed at different depths within the corn root zone. S2. Calculate the water movement mismatch by combining the canopy infrared temperature data with the three-dimensional soil moisture distribution map through transpiration-infiltration conduction synergistic analysis; S3. Generate a customized irrigation pattern based on the water movement mismatch to control irrigation duration, rest interval, and water flow rate.

2. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 1, characterized in that: The S1 includes dividing the corn root zone into spatial grids of fixed size and respectively arranging moisture sensor nodes at multiple depths.

3. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 2, characterized in that: The step S1 further includes performing an inverse distance weighted interpolation operation on the humidity values of the grids where no humidity sensors are deployed.

4. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 3, characterized in that: The construction of the three-dimensional soil moisture distribution map includes assigning weight correction coefficients to the humidity values monitored at each layer based on the root distribution characteristics at different depths to reflect the actual water absorption capacity differences of the root zone. The three-dimensional soil moisture distribution map is represented by a data matrix: ;in, Represents the spatial position Psychrometric node values, It is expressed as the soil moisture value after interpolation, infiltration compensation and root weight correction. Indicates the corresponding 、 、 The index coordinates of the grid position in the row, column, and depth directions.

5. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 1, characterized in that: The S2 specifically includes: S21, transpiration rate inversion: Use an infrared thermometer to scan the crop canopy to obtain canopy infrared temperature data. Based on the canopy infrared temperature data, the real-time transpiration rate is inverted using the energy balance equation; S22, Calculation of longitudinal water conduction rate: from the three-dimensional distribution map of soil moisture Extract the humidity change rate at the effective root depth from Represents the spatial position Psychrometric node value; S23, Quantification of water movement mismatch: Calculate the mismatch by combining transpiration rate and water conduction rate.

6. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 5, characterized in that: The real-time transpiration rate is expressed as: ; in, is the net radiation, is the soil heat flux, is the air density, is the constant pressure specific heat of air, is the canopy temperature, is the air temperature, is the airflow boundary layer resistance, is the latent heat of vaporization of water, the transpiration rate Indicates the amount of water evaporation per unit area per unit time.

7. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 6, characterized in that: The water movement mismatch measures the degree of mismatch between crop transpiration rate and root zone water recharge rate, and is defined as the weighted sum of the normalized difference intensity between transpiration demand and water supply and the response lag time.

8. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 7, characterized in that: The water movement mismatch is calculated as: ； in, is the water movement mismatch, ranging from 0 to 1. is the weight coefficient in the mismatch calculation, is the unit conversion factor, is the daily maximum potential transpiration rate, Indicates the delay time of water conduction from shallow to deep layers. is the longitudinal water conduction rate.

9. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 8, characterized in that: The generation of a custom irrigation pattern based on the water movement mismatch in S3 specifically includes: S31, based on the currently calculated water movement mismatch, generating multiple irrigation control parameters according to set rules; S32, based on soil texture, makes differential corrections to multiple irrigation control parameters: If the soil is sandy, shorten the irrigation time and increase the water flow rate; If the soil is clayey, extend the irrigation period and shorten the rest interval; S33, after corn enters the tasseling stage, dynamically adjust the irrigation strategy based on the ambient humidity: When the relative humidity is low, extend the irrigation time to compensate for the water loss caused by high transpiration risk; At the same time, reduce the water flow rate, prolong the duration of wetting, and avoid rapid erosion of the root surface.

10. The method for intelligently adjusting the corn cultivation environment based on multi-sensor control according to claim 9, characterized in that: The multiple irrigation control parameters specifically include: The water movement mismatch is mapped to the duration of a single irrigation. When the water movement mismatch is low, the standard duration is adopted; in the medium range, a linear decreasing strategy is adopted; when the mismatch exceeds the set threshold, the pulse irrigation mode is switched. The irrigation pause interval is generated based on the mismatch value, and a nonlinear function relationship is used to make the pause time the shortest under medium mismatch conditions and gradually extend under low and high mismatch conditions. The water flow rate is calculated based on the mismatch degree. The more severe the mismatch is, the higher the water flow rate is.

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