Intelligent drip irrigation control system based on real-time monitoring of soil moisture content

Abstract

The application discloses an intelligent drip irrigation control system based on real-time monitoring of soil moisture conditions, and relates to the technical field of agricultural irrigation.The system comprises a moisture condition environment acquisition module, which uses sensors to acquire soil moisture and environmental parameters and transmits them; a moisture condition analysis module, which receives data, constructs a matrix to analyze rules, excavates correlations and predicts the risk of irrigation leakage; an irrigation strategy formulation module, which generates comprehensive decision instructions accordingly; an irrigation parameter adjustment module, which optimizes parameters to form control instructions; and an irrigation execution feedback module, which drives equipment operation and feeds back the results after completion.The application integrates sensors to realize real-time and accurate monitoring of soil moisture and environmental parameters, constructs a moisture condition space-time distribution matrix, excavates correlations and rules by combining algorithms, generates differentiated irrigation instructions, improves irrigation efficiency and promotes the improvement of crop yield and quality; at the same time, a dynamic optimization algorithm is introduced to calibrate parameters, a leakage compensation strategy is used for rapid response, irrigation is coordinated, waste and pollution are avoided, and the risk of crop damage is reduced.

Description

Technical Field

[0001] This invention relates to the field of agricultural irrigation technology, specifically to an intelligent drip irrigation control system based on real-time monitoring of soil moisture. Background Technology

[0002] With the increasing severity of global water scarcity and the growing demands for agricultural water efficiency, precision irrigation technology has become crucial for sustainable agricultural development. Traditional irrigation methods rely heavily on manual experience or extensive management models based on fixed times and quantities, making it difficult to adapt to the dynamic needs of different crops, soil types, and climatic conditions. This leads to water waste, secondary soil salinization, and reduced crop yields. In recent years, the integration of IoT, big data, and AI technologies has provided new solutions for agricultural irrigation. By monitoring soil moisture and environmental parameters in real time and combining them with intelligent algorithms, irrigation decisions can be automated and refined. However, existing intelligent irrigation systems mostly focus on single-point data collection or simple threshold control, lacking in-depth analysis of the spatiotemporal distribution patterns of soil moisture and the ability to optimize multiple factors collaboratively. This makes it difficult to meet the needs of efficient management in complex farmland scenarios. Therefore, developing an intelligent drip irrigation control system with spatiotemporal dynamic analysis, adaptive decision-making, and collaborative compensation functions has become an urgent industry need.

[0003] Traditional irrigation technologies suffer from several drawbacks: First, data acquisition is limited to a single point or a small number of sensors, failing to reflect soil moisture differences at varying depths and spatial locations, resulting in insufficient decision-making support. Second, control strategies lack dynamic adaptability, often relying on fixed thresholds or empirical durations for irrigation without considering variables such as crop water requirements, soil water retention capacity, and pipeline pressure, easily leading to over-irrigation or under-irrigation. Third, system coordination is poor, with each irrigation unit operating independently, unable to address localized water shortage risks through coordinated compensation mechanisms. This is especially problematic in complex terrain or large-scale farmland, where under-irrigation areas can easily spread, impacting overall crop growth. Furthermore, traditional systems suffer from lagging feedback mechanisms, making it difficult to adjust irrigation parameters in real time, further exacerbating resource waste and production risks. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent drip irrigation control system based on real-time soil moisture monitoring. Through a multi-depth soil sensor and environmental sensor network, it achieves accurate perception and risk warning of soil moisture in time and space. The system integrates a soil moisture time-space coupling degree algorithm, a leak irrigation risk prediction model and a dynamic priority decision function to generate differentiated irrigation instructions. It also combines parameter optimization algorithms based on soil characteristics and topographic pressure to drive valves and flow controllers to work together.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intelligent drip irrigation control system based on real-time soil moisture monitoring, the system comprising:

[0006] Soil moisture and environmental data acquisition module: includes soil moisture sensor and environmental sensor. Soil moisture sensor monitors soil moisture data at different depths in real time, and environmental sensor collects environmental parameters of the planting area and transmits them to soil moisture analysis module via wireless transmission network.

[0007] Soil moisture analysis module: Receives transmitted soil moisture data, constructs a soil moisture spatiotemporal distribution matrix, analyzes the spatial lateral infiltration characteristics and temporal dynamic change patterns of soil moisture, mines the correlation of soil moisture changes in different regions through a soil moisture spatiotemporal coupling degree algorithm, and predicts irrigation leakage risk through an irrigation leakage risk prediction algorithm.

[0008] Irrigation strategy formulation module: Receives the moisture analysis results, leakage risk prediction information and environmental parameters from the moisture analysis module, compares the moisture data with the preset stratified threshold to trigger an early warning, determines the irrigation priority through the dynamic irrigation priority function, formulates a coordinated compensation strategy for surrounding valves in combination with leakage risk prediction, selects the irrigation period according to the environmental parameters, and generates a comprehensive decision instruction including irrigation area, priority, compensation plan and period, which is transmitted to the execution parameter optimization module.

[0009] Irrigation parameter calibration module: Receives comprehensive instructions, combines the characteristics of soil water retention, topography and pipeline pressure in each zone, optimizes irrigation parameters through a dynamic optimization algorithm, and generates equipment control instructions;

[0010] Irrigation execution feedback module: Receives control commands, drives intelligent drip irrigation valves and flow controllers to complete irrigation operations. The intelligent drip irrigation valves adjust their opening according to the commands, the flow controller monitors and adjusts the flow rate in real time, and controls the surrounding valves to work together through a leak compensation strategy. After irrigation is completed, the execution results are fed back to the soil moisture analysis module.

[0011] Furthermore, in the soil moisture environment acquisition module, the soil moisture sensors are deployed at a gradient depth according to the distribution of crop roots, including shallow, middle and deep layers, with a acquisition frequency of 10±5 minutes / time. The acquired soil moisture data includes soil volumetric water content and matrix potential. The environmental sensors are deployed at a height of 2±0.5 meters on the poles in the planting area, with a acquisition frequency of 5±1 minutes / time. The acquired environmental data includes light intensity, air temperature, relative humidity and wind speed.

[0012] Furthermore, in the soil moisture analysis module, the specific steps for constructing the spatiotemporal distribution matrix of soil moisture are as follows: using the planar coordinates of the planting area as the horizontal and vertical axes, time as the vertical axis, and soil depth as the hierarchical axis; using soil moisture data collected by various soil moisture sensors at different depths, times, and locations as the basic node values ​​of the matrix; for spatial locations and time nodes not covered by the sensors, data is supplemented using a spatial interpolation algorithm to form a continuous matrix; the calculation formula for the spatial interpolation algorithm is: ,in Points to be interpolated Soil moisture value, For the first Soil moisture values ​​from a known sensor, For the interpolation point and the first The straight-line distance between the sensors The number of neighboring sensors participating in the interpolation. The distance weighting coefficients are used, and each element in the matrix contains the measured or interpolated soil moisture values ​​for the corresponding coordinates, time, and depth.

[0013] Furthermore, in the soil moisture analysis module, the calculation formula for the soil moisture spatiotemporal coupling degree algorithm is as follows: ,in, coordinates At any moment The spatiotemporal coupling degree of soil moisture, It is the first The weight of the soil layer, For the first Soil layer in coordinates time Volumetric water content, It is the first The rate of change of soil moisture content over time It is the first Spatial gradient of soil moisture content in the soil layer coordinates No. The maximum water content that the soil layer can retain. Total number of soil layers.

[0014] Furthermore, in the soil moisture analysis module, the calculation formula for the leakage irrigation risk prediction algorithm is as follows: ,in, coordinates time The probability of missed irrigation It is the time-space coupling sensitivity coefficient. coordinates The average coupling degree under normal irrigation conditions in historical data. It is the soil heterogeneity influence coefficient. coordinates The saturated hydraulic conductivity of soil represents the soil's ability to conduct water when it is saturated.

[0015] Furthermore, in the irrigation strategy formulation module, the specific content of triggering an early warning by comparing soil moisture data with preset stratified thresholds is as follows: Preset suitable threshold ranges, lower limit early warning thresholds, and emergency early warning thresholds for soil moisture at each depth. The suitable threshold range is set according to the crop type and growth stage; the lower limit early warning threshold is 95% of the lower limit of the suitable threshold range; and the emergency early warning threshold is 85% of the lower limit of the suitable threshold range. The received soil moisture data at different depths are compared with the corresponding preset thresholds. When the soil moisture data at a certain depth is within the suitable threshold range, no early warning is triggered. When the moisture level drops to or below the lower limit early warning threshold but is higher than the emergency early warning threshold, a mild early warning is triggered, and the warning information includes the area coordinates, soil depth, and current soil moisture value. When the moisture level drops to or below the emergency early warning threshold, a severe early warning is triggered, and the warning information, in addition to including the area coordinates, soil depth, and current soil moisture value, includes an additional marker indicating priority processing.

[0016] Furthermore, in the irrigation strategy formulation module, the calculation formula for the dynamic irrigation priority function is as follows: ,in, coordinates At any moment Irrigation priority, , , These are the weighting coefficients for the spatiotemporal coupling degree of soil moisture, the probability of missed irrigation, and the criticality of crop water requirements, respectively. coordinates time Actual water requirements of crops coordinates Maximum water requirement of crops Coordinates At any moment The spatiotemporal coupling degree of soil moisture, coordinates time The probability of missed irrigation.

[0017] Furthermore, in the irrigation parameter calibration module, the calculation formula for the dynamic optimization algorithm of irrigation parameters is as follows: ,in, coordinates time The optimized integrated irrigation parameters include normalized coefficients for valve opening, irrigation duration, and flow rate adjustment range. coordinates time Basic irrigation parameters The soil water retention coefficient is defined as follows: high water retention capacity is taken as 0.8-1.0, medium as 1.0-1.2, and low as 1.2-1.5. This is a terrain correction factor, set to 1.0 for flat land and increasing by 0.1 for every 5° increase in slope. This is the pipeline pressure coefficient. This area is the priority for irrigation. This is the highest priority value across all regions within the current irrigation cycle. This is the topographic pressure influence coefficient, with a value ranging from 0.02 to 0.05. This represents the elevation difference between the area and the irrigation water source.

[0018] Furthermore, the specific content of the response to the leakage irrigation compensation strategy in the irrigation execution feedback module is as follows: After receiving the control command containing the coordinates of the leakage irrigation risk area, the risk level, and the compensation range, all smart drip irrigation valves within a radius of 3-8 meters centered on the risk area are activated; the compensation participation rate of each valve is determined according to the risk level, with the valve participation rate corresponding to the high-risk area being 80%-100% and the valve participation rate corresponding to the medium-risk area being 50%-80%; the valves are activated in order from farthest to closest to the risk center, with an interval of 10-30 seconds between adjacent valve activations; each valve executes the opening command according to the proportion corresponding to its participation rate, and the compensation irrigation time is 1.2-1.5 times the conventional irrigation time; during the compensation irrigation process, soil moisture data in the compensation area is collected in real time, and when the data rises to 90% of the upper limit of the suitable threshold range, the valves are closed sequentially in reverse order of activation to complete the compensation operation.

[0019] Furthermore, in the irrigation execution feedback module, the suitable threshold range is 60%-80% of the field water holding capacity of the soil at the corresponding depth, wherein the suitable threshold range for shallow soil is 65%-80% of the field water holding capacity, the suitable threshold range for middle soil is 60%-75% of the field water holding capacity, and the suitable threshold range for deep soil is 60%-70% of the field water holding capacity.

[0020] Compared with existing technologies, this intelligent drip irrigation control system based on real-time soil moisture monitoring has the following advantages:

[0021] I. This invention integrates soil moisture sensors and environmental sensors to achieve real-time and accurate monitoring of soil moisture dynamics and environmental parameters. The system constructs a soil moisture spatiotemporal distribution matrix, combined with a soil moisture spatiotemporal coupling degree algorithm, which can deeply explore the spatial correlation and temporal variation patterns of soil moisture in different regions, providing a scientific basis for irrigation strategies. The irrigation strategy formulation module uses a dynamic priority function to comprehensively consider soil moisture, the risk of missed irrigation, and the criticality of crop water requirements to generate differentiated irrigation instructions, ensuring that water resources are accurately allocated to high-demand areas. This data-driven decision-making model avoids the blindness of traditional irrigation, significantly improves irrigation efficiency, and indirectly promotes crop yield and quality by meeting the water requirements of crops during critical growth stages.

[0022] Second, this invention introduces a dynamic optimization algorithm for irrigation parameters, combined with soil water retention capacity, topography, and pipeline pressure characteristics, to calibrate irrigation parameters in real time, achieving precise adaptation of equipment control. The irrigation execution feedback module, through a leakage compensation strategy, can quickly respond to risk areas and coordinate with surrounding valves for irrigation, effectively overcoming the limitations of traditional single-point control. Through a real-time feedback mechanism, it dynamically adjusts irrigation duration and flow rate, avoiding water waste and groundwater pollution caused by over-irrigation. At the same time, through preventive leakage intervention, it significantly reduces the risk of crop damage due to water shortage.

[0023] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0025] Figure 1 This is a flowchart of the intelligent drip irrigation control system based on real-time soil moisture monitoring.

[0026] Figure 2 This is a diagram showing the module connection framework of an intelligent drip irrigation control system based on real-time soil moisture monitoring.

[0027] Figure 3 This is a flowchart of the soil moisture analysis module. Detailed Implementation

[0028] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0029] Example 1:

[0030] A smart drip irrigation system for greenhouse tomato cultivation.

[0031] Based on the gradient depth of tomato root distribution, shallow, medium, and deep soil moisture sensors were deployed in different planting areas within the greenhouse to ensure comprehensive capture of moisture conditions at different depths of the tomato roots. Soil volumetric water content and matrix potential at each depth were collected every 10 minutes, providing fundamental data for subsequent analysis of soil moisture changes. Environmental sensors were installed 2 meters high on the greenhouse poles, collecting data on light intensity, air temperature, relative humidity, and wind speed every 5 minutes. These environmental parameters reflect the external conditions for tomato growth. All data was transmitted wirelessly to the soil moisture spatiotemporal coupling analysis module in a timely manner, ensuring the timeliness and accuracy of subsequent analysis. Figure 1 As shown.

[0032] After receiving the data, a soil moisture spatiotemporal distribution matrix is ​​constructed, with the greenhouse planar coordinates as the horizontal and vertical axes, time as the vertical axis, and soil depth as the hierarchical axis. Soil moisture data collected by sensors at different depths, times, and locations are used as the basic node values ​​of the matrix. For spatial locations and time nodes not covered by the sensors, data is supplemented using a spatial interpolation algorithm to form a continuous matrix. The calculation formula for the spatial interpolation algorithm is as follows: ,in Points to be interpolated Soil moisture value, For the first Soil moisture values ​​from a known sensor, For the interpolation point and the first The straight-line distance between the sensors The number of neighboring sensors participating in the interpolation. The distance weighting coefficient is used to fully represent the soil moisture conditions of the entire greenhouse. Next, the spatial lateral infiltration characteristics and temporal dynamic changes of soil moisture in tomato roots in different regions are analyzed using a soil moisture spatiotemporal coupling degree algorithm. The calculation formula for the soil moisture spatiotemporal coupling degree algorithm is as follows: ,in, coordinates At any moment The spatiotemporal coupling degree of soil moisture, It is the first The weight of the soil layer, For the first Soil layer in coordinates time Volumetric water content, It is the first The rate of change of soil moisture content over time It is the first Spatial gradient of soil moisture content in the soil layer coordinates No. The maximum water content that the soil layer can retain. The total number of soil layers is used to explore the correlation between soil moisture changes in different areas, clearly understanding the movement and changing trends of water within the greenhouse; then, a leakage irrigation risk prediction algorithm is used to predict the potential leakage irrigation risk in each area. The calculation formula for the leakage irrigation risk prediction algorithm is as follows: ,in, coordinates time The probability of missed irrigation It is the time-space coupling sensitivity coefficient. coordinates The average coupling degree under normal irrigation conditions in historical data. It is the soil heterogeneity influence coefficient. coordinates Soil saturated hydraulic conductivity represents the soil's ability to conduct water under saturated conditions. This allows for the early identification of potential irrigation problems. Finally, these analytical results and predictive information are transmitted to the irrigation decision generation module, providing a basis for developing scientific irrigation plans. Figure 3 As shown.

[0033] After receiving the analysis results, irrigation leakage risk prediction results, and environmental parameters from the soil moisture spatiotemporal coupling analysis module, the soil moisture data at each depth is compared with preset stratified thresholds. The preset suitable threshold range is set according to the tomato growth stage, with the lower warning threshold being 95% of the suitable threshold and the emergency warning threshold being 85% of the suitable threshold. When the soil moisture in the middle layer of a certain area drops to the lower warning threshold, a mild warning is triggered to promptly indicate abnormal water conditions in that area. Through a priority arbitration mechanism, based on the dynamic irrigation priority function, combined with the spatiotemporal coupling degree of soil moisture, the probability of irrigation leakage risk, and the criticality of tomato water requirements, the irrigation priority is determined. The calculation formula for the dynamic irrigation priority function is as follows: ,in, coordinates At any moment Irrigation priority, , , These are the weighting coefficients for the spatiotemporal coupling degree of soil moisture, the probability of missed irrigation, and the criticality of crop water requirements, respectively. coordinates time Actual water requirements of crops coordinates Maximum water requirement of crops Coordinates At any moment The spatiotemporal coupling degree of soil moisture, coordinates time The system assesses the probability of irrigation leakage to ensure that water is prioritized for delivery to the areas that need it most. Based on the leakage risk prediction results, a coordinated compensation strategy for surrounding valves is developed to address potential leakage problems. According to the light and temperature parameters collected by environmental sensors, the system selects periods with lower temperatures and weaker light as irrigation periods to reduce water evaporation and improve irrigation efficiency. Finally, a comprehensive decision instruction containing irrigation area, priority, compensation plan, and time period is generated and transmitted to the execution parameter optimization module to clarify the specific content of subsequent irrigation operations.

[0034] After receiving the comprehensive instructions, and considering factors such as soil water retention (moderate water retention in the tomato growing area), topography (the greenhouse is on flat ground), and pipeline pressure characteristics in each zone of the greenhouse, which directly affect the irrigation effect, the irrigation parameters (valve opening, irrigation duration, and flow rate adjustment range) are optimized using a dynamic optimization algorithm. The calculation formula for the dynamic optimization algorithm is as follows: ,in, coordinates time The optimized integrated irrigation parameters include normalized coefficients for valve opening, irrigation duration, and flow rate adjustment range. coordinates time Basic irrigation parameters The soil water retention coefficient is defined as follows: high water retention capacity is taken as 0.8-1.0, medium as 1.0-1.2, and low as 1.2-1.5. This is a terrain correction factor, set to 1.0 for flat land and increasing by 0.1 for every 5° increase in slope. This is the pipeline pressure coefficient. This area is the priority for irrigation. This is the highest priority value across all regions within the current irrigation cycle. This is the topographic pressure influence coefficient, with a value ranging from 0.02 to 0.05. The elevation difference between the area and the irrigation water source is used to match the irrigation parameters with the actual planting conditions, ensuring that irrigation is both sufficient and not wasteful. This generates equipment control commands that are transmitted to the intelligent execution control module, providing precise guidance for specific irrigation operations.

[0035] Upon receiving control commands, the system drives the intelligent drip irrigation valves and flow controllers. The valves adjust their opening according to the commands, and the flow controller monitors and adjusts the flow rate in real time to ensure that the irrigation volume accurately meets the needs. If there is a risk of leakage, the system responds to the leakage compensation strategy, locating all intelligent drip irrigation valves within a 5-meter radius of the center of the risk area. The compensation participation of each valve is determined according to the risk level, and the valves are activated in order of distance from the risk center, with a 20-second interval between adjacent valve activations. Each valve executes the opening command according to the proportion corresponding to its participation level. The compensation irrigation time is 1.3 times the regular irrigation time, ensuring that the risk area is replenished through collaborative compensation. During the compensation irrigation process, soil moisture data in the compensation area is collected in real time. When the data rises to 90% of the upper limit of the suitable threshold range, the valves are closed sequentially in reverse order of activation to avoid excessive watering. After the compensation operation is completed, the execution results are fed back to the soil moisture spatiotemporal coupling analysis module for subsequent evaluation and adjustment of the irrigation effect.

[0036] In summary, in the greenhouse tomato cultivation scenario, the system comprehensively captures soil moisture and environmental parameters through a multi-dimensional information acquisition module, laying the foundation for subsequent analysis; the soil moisture spatiotemporal coupling analysis module uses spatial interpolation algorithms and soil moisture spatiotemporal coupling degree algorithms to fully present the soil moisture status and predict the risk of irrigation leakage; the irrigation decision generation module combines threshold early warning and dynamic priority functions to formulate a plan; the execution parameter optimization module adapts to the planting conditions to optimize parameters; and the intelligent execution control module performs precise operations and responds with compensation strategies. All modules work together to achieve precise and efficient irrigation for tomatoes, ensuring their growth needs are met.

[0037] Example 2:

[0038] A smart drip irrigation system in an open-air vineyard.

[0039] Based on the characteristics of grape root distribution, soil moisture sensors were deployed at varying depths (shallow, medium, and deep) in different areas of the vineyard to accurately capture moisture levels at each root depth. Soil volumetric water content and matrix potential were collected every 8 minutes at each depth, providing detailed data for subsequent analysis of soil moisture changes. Environmental sensors were installed at a height of 1.8 meters on poles in the vineyard, collecting data on light intensity, air temperature, relative humidity, and wind speed every 6 minutes. These data reflect the external environmental conditions for grape growth. All data were transmitted wirelessly to the soil moisture spatiotemporal coupling analysis module in a timely manner, ensuring that subsequent analysis was based on the latest field conditions. Figure 2 As shown.

[0040] After receiving the data, a soil moisture spatiotemporal distribution matrix is ​​constructed, with the plantation planar coordinates as the horizontal and vertical axes, time as the vertical axis, and soil depth as the hierarchical axis. Soil moisture data collected by the sensor at different depths, times, and locations are used as the basic node values ​​of the matrix. For spatial locations and time nodes not covered by the sensor, data is supplemented using a spatial interpolation algorithm to form a continuous matrix. The calculation formula for the spatial interpolation algorithm is as follows: This allows for a complete view of the soil moisture conditions throughout the entire vineyard. The spatial lateral infiltration and temporal dynamic changes of soil moisture in the grape-growing area are analyzed using a spatiotemporal coupling degree algorithm. The calculation formula for the spatiotemporal coupling degree algorithm is as follows: This study aims to uncover the correlation between soil moisture changes in different regions, clearly understand the movement and changing trends of water within the plantation, and predict the risk of irrigation leakage in each region using a leakage risk prediction algorithm. This allows for the early identification of areas potentially experiencing insufficient irrigation. The calculation formula for the leakage risk prediction algorithm is as follows: Finally, the analysis results and prediction information are transmitted to the irrigation decision generation module to provide a basis for developing targeted irrigation plans.

[0041] After receiving the data, the soil moisture at each depth is compared with preset stratification thresholds. The appropriate threshold range is set according to the grape growth stage. When the deep soil moisture in a certain area drops to the emergency warning threshold, a severe warning is triggered and the area is marked for priority handling, promptly indicating that the area is severely lacking in water. Through a priority arbitration mechanism, based on the dynamic irrigation priority function, the calculation formula of the dynamic irrigation priority function is as follows: The system determines irrigation priorities by combining the spatiotemporal coupling degree of soil moisture, the probability of leakage irrigation risk, and the criticality of grape water requirements, ensuring that water is supplied preferentially to areas with the greatest impact on growth. Based on the leakage irrigation risk prediction results, a coordinated compensation strategy for surrounding valves is formulated to address potential leakage irrigation problems. Based on environmental parameters, the system selects the period with lower wind speed at night as the irrigation period to reduce water evaporation loss caused by wind and high temperature, improve water use efficiency, and generates a comprehensive decision instruction containing irrigation area, priority, compensation plan, and time period, which is transmitted to the execution parameter optimization module to clarify the specific arrangements for subsequent irrigation operations.

[0042] After receiving the comprehensive instructions, and considering factors such as soil water retention (low water retention in some areas), topography (slope of approximately 5°), and pipeline pressure characteristics in each zone of the plantation—factors that directly affect water retention and distribution in the soil—an irrigation parameter dynamic optimization algorithm is used to optimize irrigation parameters (valve opening, irrigation duration, and flow rate adjustment range). The calculation formula for the irrigation parameter dynamic optimization algorithm is as follows: This allows irrigation parameters to adapt to different soil and terrain conditions in different areas, avoiding over- or under-irrigation. It also generates equipment control commands that are transmitted to the intelligent execution control module, providing precise parameters for specific irrigation operations.

[0043] Upon receiving control commands, the system drives the intelligent drip irrigation valves and flow controllers to perform irrigation operations. The valves adjust their opening according to the commands, and the flow controller monitors and adjusts the flow rate in real time to ensure that the irrigation amount accurately meets the needs of each area. For high-risk areas of irrigation leakage, the system responds to the leakage compensation strategy, locates all intelligent drip irrigation valves within a 7-meter radius of the center of the risk area, determines the participation rate to be 90%, and starts the valves in order from farthest to near, with an interval of 25 seconds between the start of adjacent valves. Each valve executes the opening command according to the proportion corresponding to its participation rate, and the compensation irrigation time is 1.4 times the regular irrigation time. Through collaborative compensation, it ensures that the risk area receives sufficient water replenishment. During the compensation irrigation process, soil moisture data of the compensation area is collected in real time. When the data rises back to 90% of the upper limit of the suitable threshold range, the valves are closed in reverse order of start-up to prevent excessive water from affecting the growth of grape roots. After completion, the execution results are fed back to the soil moisture spatiotemporal coupling analysis module to facilitate subsequent evaluation of irrigation effects and provide a reference for the next irrigation decision.

[0044] In summary, in the open-air vineyard setting, all modules of the system operate collaboratively. The multi-dimensional information collection module provides accurate data, the soil moisture spatiotemporal coupling analysis module presents soil moisture patterns and risks through a series of algorithms, the irrigation decision generation module determines the plan based on early warnings and priorities, the execution parameter optimization module optimizes parameters for soil and terrain, and the intelligent execution control module provides precise irrigation and compensates for missed irrigation. The entire process takes into account the characteristics of the open-air environment, reduces water loss, ensures reasonable water supply for grapes, and improves the scientific and effective nature of irrigation.

[0045] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An intelligent drip irrigation control system based on real-time soil moisture monitoring, characterized in that, The system includes: Soil moisture and environmental data acquisition module: includes soil moisture sensor and environmental sensor. Soil moisture sensor monitors soil moisture data at different depths in real time, and environmental sensor collects environmental parameters of the planting area and transmits them to soil moisture analysis module via wireless transmission network. Soil moisture analysis module: Receives transmitted soil moisture data, constructs a soil moisture spatiotemporal distribution matrix, analyzes the spatial lateral infiltration characteristics and temporal dynamic change patterns of soil moisture, mines the correlation of soil moisture changes in different regions through a soil moisture spatiotemporal coupling degree algorithm, and predicts the risk of irrigation leakage through an irrigation leakage risk prediction algorithm. Irrigation strategy formulation module: Receives the moisture analysis results, leakage risk prediction information and environmental parameters from the moisture analysis module, compares the moisture data with the preset stratified threshold to trigger an early warning, determines the irrigation priority through the dynamic irrigation priority function, formulates a coordinated compensation strategy for surrounding valves in combination with leakage risk prediction, selects the irrigation period according to the environmental parameters, and generates a comprehensive decision instruction including irrigation area, priority, compensation plan and period, which is transmitted to the execution parameter optimization module. Irrigation parameter calibration module: Receives comprehensive instructions, combines the characteristics of soil water retention, topography and pipeline pressure in each zone, optimizes irrigation parameters through a dynamic optimization algorithm, and generates equipment control instructions; Irrigation execution feedback module: Receives control commands, drives intelligent drip irrigation valves and flow controllers to complete irrigation operations. The intelligent drip irrigation valves adjust their opening according to the commands, the flow controller monitors and adjusts the flow rate in real time, and controls the surrounding valves to work together through a leak compensation strategy. After irrigation is completed, the execution results are fed back to the soil moisture analysis module. The specific steps for constructing the spatiotemporal distribution matrix of soil moisture in the soil moisture analysis module are as follows: using the planar coordinates of the planting area as the horizontal and vertical axes, time as the vertical axis, and soil depth as the hierarchical axis; using soil moisture data collected by each soil moisture sensor at different depths, times, and locations as the basic node values ​​of the matrix; and supplementing data for spatial locations and time nodes not covered by the sensors using a spatial interpolation algorithm to form a continuous matrix. The specific content of the response to the leakage irrigation compensation strategy in the irrigation execution feedback module is as follows: After receiving the control command containing the coordinates of the leakage irrigation risk area, the risk level, and the compensation range, it locates all smart drip irrigation valves within a radius of 3-8 meters centered on the risk area; it determines the compensation participation degree of each valve according to the risk level, with the valve participation degree corresponding to high-risk areas being 80%-100% and the valve participation degree corresponding to medium-risk areas being 50%-80%; it starts the valves in order from farthest to near the risk center, with an interval of 10-30 seconds between the start of adjacent valves; each valve executes the opening command according to the proportion corresponding to its participation degree, and the compensation irrigation time is 1.2-1.5 times the conventional irrigation time; during the compensation irrigation process, it collects soil moisture data in the compensation area in real time, and when the data rises to 90% of the upper limit of the appropriate threshold range, it closes the valves in reverse order of start-up to complete the compensation operation.

2. The intelligent drip irrigation control system based on real-time soil moisture monitoring according to claim 1, characterized in that, In the soil moisture and environmental data acquisition module, the soil moisture sensors are deployed at a gradient depth according to the distribution of crop roots, including shallow, middle and deep layers, with a acquisition frequency of 10±5 minutes / time. The acquired soil moisture data includes soil volumetric water content and matrix potential. The environmental sensors are deployed at a height of 2±0.5 meters on poles in the planting area, with a acquisition frequency of 5±1 minutes / time. The acquired environmental data includes light intensity, air temperature, relative humidity and wind speed.

3. The intelligent drip irrigation control system based on real-time soil moisture monitoring according to claim 1, characterized in that, The formula for spatial interpolation is: ,in Points to be interpolated Soil moisture value, For the first Soil moisture values ​​from a known sensor, For the point to be interpolated and the first The straight-line distance between the sensors The number of neighboring sensors participating in the interpolation. The distance weighting coefficients are used, and each element in the matrix contains the measured or interpolated soil moisture values ​​for the corresponding coordinates, time, and depth.

4. The intelligent drip irrigation control system based on real-time soil moisture monitoring according to claim 1, characterized in that, In the soil moisture analysis module, the calculation formula for the soil moisture spatiotemporal coupling degree algorithm is as follows: ,in, coordinates At any moment The spatiotemporal coupling degree of soil moisture, It is the first The weight of the soil layer, For the first Soil layer in coordinates time Volumetric water content, It is the first The rate of change of soil moisture content over time It is the first Spatial gradient of soil moisture content in the soil layer coordinates No. The maximum water content that the soil layer can retain. Total number of soil layers.

5. The intelligent drip irrigation control system based on real-time soil moisture monitoring according to claim 4, characterized in that, In the soil moisture analysis module, the calculation formula for the leakage irrigation risk prediction algorithm is as follows: ,in, coordinates time The probability of missed irrigation It is the time-space coupling sensitivity coefficient. coordinates The average coupling degree under normal irrigation conditions in historical data. It is the soil heterogeneity influence coefficient. coordinates The saturated hydraulic conductivity of soil represents the soil's ability to conduct water when it is saturated.

6. The intelligent drip irrigation control system based on real-time soil moisture monitoring according to claim 1, characterized in that, In the irrigation strategy formulation module, the specific content of triggering an early warning by comparing soil moisture data with preset stratified thresholds is as follows: preset suitable threshold ranges, lower limit early warning thresholds, and emergency early warning thresholds for soil moisture at each depth. The suitable threshold range is set according to the crop type and growth stage, the lower limit early warning threshold is 95% of the lower limit of the suitable threshold range, and the emergency early warning threshold is 85% of the lower limit of the suitable threshold range. The received soil moisture data at different depths are compared with the preset thresholds for the corresponding depths. When the soil moisture data at a certain depth is within the suitable threshold range, no early warning is triggered. When the soil moisture level drops to or below the lower warning threshold but above the emergency warning threshold, a mild warning is triggered. The warning information includes the area coordinates, soil depth, and current soil moisture value. When the soil moisture level drops to or below the emergency warning threshold, a severe warning is triggered. In addition to the area coordinates, soil depth, and current soil moisture value, the warning information includes additional markers that should be prioritized for processing.

7. The intelligent drip irrigation control system based on real-time soil moisture monitoring according to claim 1, characterized in that, In the irrigation strategy formulation module, the calculation formula for the dynamic irrigation priority function is as follows: ,in, coordinates At any moment Irrigation priority, , , These are the weighting coefficients for the spatiotemporal coupling degree of soil moisture, the probability of missed irrigation, and the criticality of crop water requirements, respectively. coordinates time Actual water requirements of crops coordinates Maximum water requirement of crops Coordinates At any moment The spatiotemporal coupling degree of soil moisture, coordinates time The probability of missed irrigation.

8. The intelligent drip irrigation control system based on real-time soil moisture monitoring according to claim 7, characterized in that, In the irrigation parameter calibration module, the calculation formula for the dynamic optimization algorithm of irrigation parameters is as follows: ,in, coordinates time The optimized integrated irrigation parameters include normalized coefficients for valve opening, irrigation duration, and flow rate adjustment range. coordinates time Basic irrigation parameters The soil water retention coefficient is defined as follows: high water retention capacity is taken as 0.8-1.0, medium as 1.0-1.2, and low as 1.2-1.

5. This is a terrain correction factor, set to 1.0 for flat land and increasing by 0.1 for every 5° increase in slope. This is the pipeline pressure coefficient. This area is the priority for irrigation. This is the highest priority value across all regions within the current irrigation cycle. This is the topographic pressure influence coefficient, with a value ranging from 0.02 to 0.

05. This represents the elevation difference between the area and the irrigation water source.

9. The intelligent drip irrigation control system based on real-time soil moisture monitoring according to claim 1, characterized in that, In the irrigation execution feedback module, the suitable threshold range is 60%-80% of the field water holding capacity of the soil at the corresponding depth, of which the suitable threshold range for shallow soil is 65%-80% of the field water holding capacity, the suitable threshold range for middle soil is 60%-75% of the field water holding capacity, and the suitable threshold range for deep soil is 60%-70% of the field water holding capacity.

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