An artificial intelligence-based irrigation control system for hilly slopes

Abstract

The present invention discloses an artificial intelligence-based irrigation control system for hilly slopes. The system includes a wind speed and direction detection module for obtaining wind speed and direction data; a sprinkler head adjustment module for adjusting the angle and spraying distance of the sprinkler head based on the wind speed and direction data; a soil humidity detection module for connecting to a soil humidity sensor and determining the irrigation demand according to the data of the soil humidity sensor; an artificial intelligence control module for optimizing the irrigation time and irrigation amount through artificial intelligence algorithms; a sprinkler system control module for controlling the sprinkler system to perform irrigation according to the optimization results. This artificial intelligence-based irrigation control system for hilly slopes reduces the phenomenon of uneven crop growth, is beneficial to the healthy and uniform growth of crops, ensures that crops can obtain an appropriate amount of water supply, thereby promoting the normal growth and development of crops, improving the yield and quality of agricultural crops, and further enhancing the agricultural production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural automation and intelligent irrigation, and particularly relates to an irrigation control system for hilly slopes based on artificial intelligence. Background Art

[0002] In the existing agricultural irrigation technologies, sprinkler irrigation systems are widely used due to their water-saving and high-efficiency characteristics. However, the existing sprinkler irrigation systems show obvious deficiencies in the case of strong winds. Due to the influence of wind speed, the water droplets ejected by the sprinkler heads are easily blown away in the air, resulting in the water droplets not accurately falling on the target area, and the phenomenon of water droplet dispersion occurs. This not only causes waste of water resources, but also reduces the irrigation effect of sprinkler irrigation and cannot fully meet the water requirements of crops. Especially in areas with frequent wind speed changes, this problem is particularly prominent.

[0003] Most of the existing sprinkler irrigation systems lack an adjustment mechanism for wind speed changes and cannot effectively control the sprinkler irrigation water flow according to the wind speed, which further exacerbates the waste of water resources. In addition, the influence of wind on water droplet dispersion also leads to over-irrigation or under-irrigation in local areas, affecting the uniform growth of crops. To solve this problem, there is an urgent need in the existing technology for an improved solution that can reduce water droplet dispersion in the case of strong winds to improve the irrigation accuracy and resource utilization efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide an irrigation control system for hilly slopes based on artificial intelligence, which solves the problem that most of the existing sprinkler irrigation systems lack an adjustment mechanism for wind speed changes and cannot effectively control the sprinkler irrigation water flow according to the wind speed, which further exacerbates the waste of water resources. In addition, the influence of wind on water droplet dispersion also leads to over-irrigation or under-irrigation in local areas, affecting the uniform growth of crops.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An irrigation control system for hilly slopes based on artificial intelligence, the system includes:

[0006] A wind speed and wind direction detection module for obtaining wind speed and wind direction data;

[0007] A sprinkler head adjustment module connected to the wind speed and wind direction detection module for adjusting the angle and spraying distance of the sprinkler head based on the wind speed and wind direction data. Specifically, utility functions for different wind speeds and wind directions are designed to represent the optimal states of spraying distance, angle, and water flow distribution, and the optimal sprinkler head parameter combination under the current wind speed and wind direction is obtained by solving. The spraying angle and distance are automatically adjusted according to the optimal strategy combination. The solving process is specifically as follows: ;

[0008] Among them, Utility functions representing different wind speeds and directions Represents the setting of the angle or spraying distance of the sprinkler head Represents the optimal angle or spraying distance setting of the sprinkler head under the current wind speed and direction conditions Represents the angle and distance settings of the sprinkler head under other wind speed and direction conditions Represents the sprinkler head parameter combination when all other wind speed and direction conditions adopt their respective optimal strategies

[0009] Soil humidity detection module, used to connect to the soil humidity sensor and determine the irrigation demand based on the data of the soil humidity sensor

[0010] An artificial intelligence control module connected to the wind speed and direction detection module and the soil humidity detection module, used to optimize the irrigation time and irrigation volume through artificial intelligence algorithms. Specifically, the irrigation demand and environmental data are constructed into a small-world network model. Each node in the network represents different irrigation conditions, and the edges between nodes represent the mutual influence between conditions. Calculate the average path length and clustering coefficient of each node. The calculation formulas are:

[0011] , ;

[0012] Among them, Represents the number of network nodes Represents the average node degree Represents the average path length of each node Represents the clustering coefficient

[0013] A sprinkler system control module connected to the artificial intelligence control module, used to control the sprinkler system to perform irrigation according to the optimization results

[0014] The sprinkler head adjustment module adjusts the angle and spraying distance of the sprinkler head based on wind speed and direction data and further includes:

[0015] Determine the wind force level based on the wind speed data

[0016] Adjust the angle of the sprinkler head according to the wind force level

[0017] Adjust the spraying distance of the sprinkler head according to the wind force level

[0018] When the wind speed exceeds the preset threshold, reduce the water spraying amount of the sprinkler head

[0019] The soil humidity detection module connects to the soil humidity sensor and determines the irrigation demand based on the data of the soil humidity sensor and further includes:

[0020] Obtain the real-time data S of the soil humidity sensor

[0021] Compare the real-time data S with the preset soil moisture threshold S min ;

[0022] When the soil moisture is lower than the preset threshold S min a trigger irrigation demand signal is generated.

[0023] Preferably, the wind speed and direction detection module obtains wind speed and direction data including:

[0024] Collect historical wind speed and direction data, calculate the fluctuation of the data and apply the autoregressive conditional heteroskedasticity model for conditional variance estimation. The specific formula is:

[0025] ;

[0026] where represents the variance at the current moment, represents the error at the previous moment, represents the standard deviation of the variance at the previous moment, , , represents the parameter of the model.

[0027] Preferably, the soil moisture detection module is connected to a soil moisture sensor and determines the irrigation demand according to the data of the soil moisture sensor, including:

[0028] Collect soil moisture sensing data, perform Fourier transform, analyze its frequency domain characteristics, identify seasonal or fluctuating frequency components in the soil moisture signal, and predict the trend of water loss or soil drying. The Fourier transform formula is:

[0029] ;

[0030] where represents the component of the frequency domain signal, represents the data points of the time domain signal, represents the total number of signal sampling points, represents the frequency index, represents the time index.

[0031] Preferably, the sprinkler irrigation system control module controls the sprinkler irrigation system for irrigation according to the optimization result, including:

[0032] Define the irrigation time, irrigation amount, wind speed, and soil moisture as random variables, simulate and generate irrigation scenarios. In each simulation scenario, calculate the corresponding irrigation effect, generate the irrigation time and irrigation amount under this combination, perform expectation calculation on all simulation results, select the optimal irrigation plan that can perform stably in different environments, and use it as the final optimization result. The expectation calculation formula is:

[0033] ;

[0034] Among them, the expected value of the variable , represents a random variable, represents the number of simulations, represents the result of the

[0035] Preferably, the determination of the wind force level based on wind speed data includes:

[0036] Obtain real-time wind speed data V;

[0037] Compare the wind speed data with a preset wind force level table;

[0038] Determine the current wind force level L based on the comparison result;

[0039] Judge whether the wind force level L is greater than or equal to 3;

[0040] The specific formula is: L = f(V), where V represents the real-time wind speed data and L represents the wind force level.

[0041] Preferably, the adjustment of the sprinkler head angle according to the wind force level includes:

[0042] Obtain the current wind force level L;

[0043] Determine the angle adjustment value θ based on the wind force level L;

[0044] Adjust the angle of the sprinkler head to the current angle plus the adjustment value θ;

[0045] Judge whether the adjusted angle is within the safe range. If it is, perform the adjustment;

[0046] The specific formula is: θ = g(L), where L represents the wind force level and θ represents the angle adjustment value.

[0047] Preferably, the adjustment of the sprinkler head spraying distance according to the wind force level includes:

[0048] Obtain the current wind force level L;

[0049] Determine the spraying distance adjustment value D based on the wind force level L;

[0050] Adjust the spraying distance of the sprinkler head to the current spraying distance minus the adjustment value D;

[0051] Judge whether the adjusted spraying distance is within the safe range;

[0052] Formula: D = h(L), where L represents the wind force level and D represents the adjusted value of the jet distance;

[0053] When the wind speed exceeds the preset threshold, reducing the water spray volume of the sprinkler head includes:

[0054] Obtain the current wind speed V;

[0055] Determine the water spray volume adjustment coefficient K based on the wind speed V;

[0056] Reduce the current water spray volume by multiplying it by the adjustment coefficient K;

[0057] Judge whether the reduced water spray volume is within the allowable range;

[0058] Formula: K = i(V), where V represents the real-time wind speed data and K represents the water spray volume adjustment coefficient.

[0059] Preferably, obtaining the current wind speed V includes:

[0060] Collect the data of the wind speed sensor;

[0061] Smooth the wind speed data to reduce the influence of noise;

[0062] Calculate the average wind speed V based on the smoothed wind speed data;

[0063] Judge whether the average wind speed V exceeds the preset threshold;

[0064] Formula: V=(V1 + V2 +... + V r ) / r, where V1, V2,..., V r represent the wind speed data measured multiple times, r represents the number of measurements, and V represents the average wind speed after smoothing.

[0065] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0066] The artificial intelligence-based hilly slope irrigation control system obtains wind speed and wind direction data through a wind speed and wind direction detection module. The sprinkler head adjustment module adjusts the angle and spraying distance of the sprinkler head based on the wind speed and wind direction data. The soil humidity detection module is connected to a soil humidity sensor and determines the irrigation demand according to the data of the soil humidity sensor. The artificial intelligence control module optimizes the irrigation time and irrigation amount through an artificial intelligence algorithm. The sprinkler system control module controls the sprinkler system to irrigate according to the optimization results. It can automatically adjust the spraying angle and spraying distance of the sprinkler head according to the real-time change of the wind speed, so that the water droplets can fall more accurately onto the target area, reducing the deviation and dispersion of the water droplets caused by the wind, and reducing the phenomenon of the sprinkler water drifting and evaporating in the air, thereby effectively reducing the waste of water resources. Especially in areas with water shortages, this water-saving effect is particularly important, which helps to improve the efficiency of agricultural water use, ensure that the irrigation effect will not be significantly affected, improve the adaptability of the system in complex environments, avoid the problems of over-irrigation or under-irrigation in local areas, reduce the phenomenon of uneven crop growth, and is conducive to the healthy and uniform growth of crops, ensuring that the crops can obtain an appropriate amount of water supply, thereby promoting the normal growth and development of crops, improving the yield and quality of agricultural crops, and further improving the efficiency of agricultural production. It solves the problem that most of the existing sprinkler systems lack an adjustment mechanism for wind speed changes and cannot effectively control the sprinkler water flow according to the wind speed, which further exacerbates the waste of water resources. In addition, the influence of the wind on the dispersion of water droplets also causes over-irrigation or under-irrigation in local areas, affecting the uniform growth of crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0069] As Figure 1 shown, the present invention provides a technical solution: an artificial intelligence-based hilly slope irrigation control system, the system includes:

[0070] A wind speed and wind direction detection module for obtaining wind speed and wind direction data;

[0071] The sprinkler head adjustment module connected to the wind speed and direction detection module is used to adjust the angle and spraying distance of the sprinkler head based on wind speed and direction data. Specifically, utility functions for different wind speeds and directions are designed to represent the optimal states of spraying distance, angle, and water flow distribution. The best combination of sprinkler head parameters under the current wind speed and direction is obtained through solution. The solution process is as follows: ;

[0072] Among them, represents the utility function for different wind speeds and directions, represents the setting of the angle or spraying distance of the sprinkler head, represents the optimal angle or spraying distance setting of the sprinkler head under the current wind speed and direction conditions, represents the angle and distance settings of the sprinkler head under other wind speed and direction conditions, represents the sprinkler head parameter combination when all other wind speed and direction conditions adopt their respective optimal strategies;

[0073] The soil moisture detection module is used to connect to the soil moisture sensor and determine the irrigation demand based on the data of the soil moisture sensor;

[0074] The artificial intelligence control module connected to the wind speed and direction detection module and the soil moisture detection module is used to optimize the irrigation time and irrigation volume through artificial intelligence algorithms. Specifically, the irrigation demand and environmental data are constructed into a small-world network model. Each node in the network represents different irrigation conditions, and the edges between nodes represent the mutual influence between conditions. The average path length and clustering coefficient of each node are calculated. The calculation formulas are as follows:

[0075] , ;

[0076] Among them, represents the number of network nodes, represents the average node degree, represents the average path length of each node, represents the clustering coefficient;

[0077] The sprinkler system control module connected to the artificial intelligence control module is used to control the sprinkler system to perform irrigation according to the optimization results.

[0078] The system obtains wind speed and wind direction information through the wind speed and wind direction detection module and transmits it to the sprinkler head adjustment module. The sprinkler head adjustment module automatically adjusts the sprinkling angle and distance according to the current wind speed and wind direction to ensure that the water flow can accurately reach the target area. Subsequently, the soil moisture detection module monitors the soil moisture in real time and feeds it back to the artificial intelligence control module. The artificial intelligence control module analyzes the mutual influence between nodes by applying the small-world network model based on the wind speed, wind direction, and soil moisture data, calculates the average path length and clustering coefficient of each node, thereby optimizing the irrigation time and irrigation volume. Finally, the irrigation is executed by the sprinkler system control module. By dynamically adjusting the sprinkling angle and distance according to the wind speed and wind direction, the water flow can effectively cover the target area, reducing water resource waste. The artificial intelligence module optimizes the irrigation time and water volume, improving the irrigation efficiency, meeting the real-time requirements of soil moisture, reducing the waste of irrigation time and water volume, and having a significant water resource conservation effect. This system can adaptively adjust according to actual wind speed, wind direction, soil moisture and other conditions and is applicable to the variable hilly slope environment.

[0079] The wind speed and wind direction detection module obtains wind speed and wind direction data including:

[0080] Collect historical wind speed and wind direction data, calculate the fluctuation of the data and apply the autoregressive conditional heteroskedasticity model for conditional variance estimation. The specific formula is:

[0081] ;

[0082] Where represents the variance at the current moment, represents the error at the previous moment, represents the standard deviation of the variance at the previous moment, , , represents the parameter of the model.

[0083] The system collects current and historical wind speed and direction data through the wind speed and direction detection module, and uses this data for volatility analysis. The calculation module uses the autoregressive conditional heteroskedasticity model to estimate the conditional variance of the wind speed and direction data, that is, the fluctuation of the data. The ARCH model makes the current variance related to the error and variance values at the previous moment by introducing the lag terms of the time series, so as to dynamically capture the volatility of the changes in wind speed and direction. This method can effectively predict the change trends of wind speed and direction in the short term, provide a basis for the precise adjustment of the irrigation system, and help the system better adapt to the changes in environmental conditions. By dynamically analyzing the fluctuation of wind speed and direction data, the prediction accuracy of the system for future wind speed and direction changes is improved, which helps to more precisely control the irrigation parameters, can instantly capture the fluctuation trends of wind speed and direction, enables the system to quickly adapt to the environmental changes in a short time, optimizes the irrigation efficiency, and makes the irrigation control of the system more scientific based on the statistical model prediction of wind speed and direction, which helps to improve the utilization efficiency of water resources and reduce unnecessary water waste.

[0084] The soil moisture detection module is connected to the soil moisture sensor and determines the irrigation requirements according to the data of the soil moisture sensor, including:

[0085] Collect soil moisture sensing data, through Fourier transform, analyze its frequency domain characteristics, identify the seasonal or fluctuating frequency components in the soil moisture signal, predict the water loss or soil drying trend, and the Fourier transform formula is:

[0086] ;

[0087] Among them, represents the component of the frequency domain signal, represents the data points of the time domain signal, represents the total number of signal sampling points, represents the frequency index, represents the time index.

[0088] The system collects humidity data through the soil humidity detection module, applies Fourier transform to convert the humidity signal from the time domain to the frequency domain, and analyzes its frequency domain characteristics. By calculating the frequency components of the humidity signal, the system can identify the periodic and fluctuating characteristics of humidity changes. For example, certain frequency components may indicate the periodic fluctuations of soil humidity, and this information helps to predict the drying trend of the soil or the risk of water loss. In this way, the system can more effectively evaluate the actual irrigation needs of the soil, ensuring the rationality and effectiveness of irrigation. By analyzing the frequency components of the humidity signal, the periodicity of soil humidity changes can be identified, the future drying trend or water loss situation of the soil can be accurately predicted, and the fluctuating trend of soil moisture can be identified, making the system more scientific in judging irrigation needs, thereby reducing unnecessary irrigation and improving water resource utilization efficiency. The system can identify possible drought trends in advance and initiate irrigation in a timely manner to prevent the soil from drying out excessively and ensure the water supply required for crop growth.

[0089] The sprinkler irrigation system control module controls the sprinkler irrigation system for irrigation according to the optimization results, including:

[0090] Define the irrigation time, irrigation amount, wind speed, and soil humidity as random variables, simulate and generate irrigation scenarios. In each simulated scenario, calculate the corresponding irrigation effect, generate the irrigation time and irrigation amount under this combination, perform an expectation calculation on all simulation results, select the optimal irrigation plan that can perform stably under different environments, and use it as the final optimization result. The expectation calculation formula is:

[0091] ;

[0092] Among them, variable expectation value of, represents a random variable, represents the number of simulations, represents the result of the

[0093] The system constructs multiple different irrigation scenarios by setting parameters such as irrigation time, irrigation volume, wind speed, and soil humidity as random variables, and simulates and calculates the irrigation effects of each scenario. Each simulation generates a set of irrigation effects under specific conditions, and through multiple simulation calculations, an irrigation effect set under different environmental conditions is obtained. The system selects a parameter combination that can provide stable irrigation effects under various conditions through expected value calculation, and uses it as the optimized configuration for irrigation control to adapt to different hilly slope environments. Multiple simulations generate irrigation results under different conditions, enabling the system to find stable irrigation parameter configurations under different environmental conditions, reducing the impact of environmental fluctuations on irrigation effects. Expected value calculation analyzes the results of multiple irrigation scenarios through statistical methods, selects the best parameter combination, improves irrigation efficiency and resource utilization rate. The system can perform adaptive adjustment according to different environmental and climatic conditions to ensure that the system can continuously provide the best irrigation effects.

[0094] The sprinkler head adjustment module adjusts the angle and spraying distance of the sprinkler head based on wind speed and wind direction data. It also includes determining the wind force level based on wind speed data; adjusting the angle of the sprinkler head according to the wind force level; adjusting the spraying distance of the sprinkler head according to the wind force level; and reducing the water spraying volume of the sprinkler head when the wind speed exceeds a preset threshold.

[0095] The sprinkler head adjustment module first determines the current wind force level based on wind speed data, and obtains the corresponding level by comparing with a preset wind force level table. According to this wind force level, the system automatically adjusts the angle and spraying distance of the sprinkler head to ensure accurate coverage of the water flow. When the wind speed exceeds the preset threshold of the system, the system reduces the water spraying volume of the sprinkler head, thereby reducing the impact of the wind on the water flow distribution, avoiding water resource waste and reducing the irrigation effect. The system dynamically adjusts the angle and spraying distance of the sprinkler head according to the wind force level to ensure that the water flow accurately reaches the target area, reduces the water spraying volume under strong wind conditions, reduces water loss caused by the wind, and improves water resource utilization efficiency. The sprinkler irrigation system can respond to wind speed changes in real time, adaptively adjust sprinkler irrigation parameters under different wind speed conditions, and adapt to changing environmental conditions.

[0096] Determining the wind force level based on wind speed data includes obtaining real-time wind speed data V; comparing the wind speed data with a preset wind force level table; determining the current wind force level L based on the comparison result; and judging whether the wind force level L is greater than or equal to 3. The specific formula is: L = f(V), where V represents real-time wind speed data and L represents the wind force level.

[0097] The system obtains wind speed data in real time, compares it with the wind force level table, and determines the current wind force level. When the wind force level reaches or exceeds level 3, the system automatically adjusts the sprinkler parameters to ensure that the sprinkler effect is not affected by high wind speeds. This enables the system to adjust the irrigation parameters according to the real-time wind speed, reducing the impact of high wind speeds on the irrigation effect. The wind force level discrimination function allows the system to adaptively adjust the sprinkler parameters under different wind speed conditions, improving the consistency of the irrigation effect.

[0098] Adjusting the angle of the sprinkler head according to the wind force level includes obtaining the current wind force level L; determining the angle adjustment value θ based on the wind force level L; adjusting the angle of the sprinkler head to the current angle plus the adjustment value θ; and judging whether the adjusted angle is within the safe range. If it is, the adjustment is executed. The specific formula is: θ = g(L), where L represents the wind force level and θ represents the angle adjustment value.

[0099] The system automatically calculates the angle adjustment value θ according to the current wind force level L and adjusts the angle of the sprinkler head to adapt to the wind speed change. The adjusted angle needs to ensure that it is within the safe range. If it exceeds the range, the adjustment is not executed to avoid system failure under extreme conditions. By adjusting the angle, the water flow coverage range is optimized, reducing the impact of wind speed on the water flow distribution, ensuring that the sprinkler head angle is adjusted within the safe range, and reducing the risk of damage to system components under high wind speeds.

[0100] Adjusting the spraying distance of the sprinkler head according to the wind force level includes obtaining the current wind force level L; determining the spraying distance adjustment value D based on the wind force level L; adjusting the spraying distance of the sprinkler head to the current spraying distance minus the adjustment value D; and judging whether the adjusted spraying distance is within the safe range. The formula is: D = h(L), where L represents the wind force level and D represents the spraying distance adjustment value.

[0101] When the wind speed exceeds the preset threshold, reducing the water spray volume of the sprinkler head includes obtaining the current wind speed V; determining the water spray volume adjustment coefficient K based on the wind speed V; reducing the current water spray volume multiplied by the adjustment coefficient K; and judging whether the reduced water spray volume is within the allowable range. The formula is: K = i(V), where V represents the real-time wind speed data and K represents the water spray volume adjustment coefficient.

[0102] This module adapts to the wind speed change by adjusting the spraying distance and automatically reduces the water spray volume when the wind speed exceeds the threshold. The system calculates the spraying distance adjustment value D corresponding to the wind force level and executes the adjustment within the safe range. When the wind speed exceeds the threshold, based on the current wind speed V, the water spray volume adjustment coefficient K is determined, and the water spray volume is reduced proportionally to ensure irrigation uniformity. When the wind speed is high, the water spray volume is reduced to avoid water loss and improve irrigation efficiency. The system can automatically reduce the spraying distance according to the wind force level, making the water flow cover the target area more accurately.

[0103] Obtaining the current wind speed V includes collecting data from the wind speed sensor; smoothing the wind speed data to reduce the influence of noise; calculating the average wind speed V based on the smoothed wind speed data; determining whether the average wind speed V exceeds a preset threshold; formula: V = (V1 + V2 +... + V r ) / r, where V1, V2,..., V r represent the wind speed data measured multiple times, r represents the number of measurements, and V represents the average wind speed after smoothing.

[0104] The system collects wind speed sensor data multiple times and smooths it to reduce the influence of measurement noise on the average wind speed V, thereby obtaining more stable wind speed data. The system compares the calculated average wind speed with a preset threshold to determine whether to adjust the sprinkler irrigation parameters. By smoothing, the noise interference is reduced, and the accuracy of the wind speed data is improved. The smoothed average wind speed is used for threshold comparison, making the system more accurate in responding to wind speed changes.

[0105] The soil humidity detection module is connected to the soil humidity sensor and determines the irrigation requirement according to the data of the soil humidity sensor. It also includes obtaining the real-time data S of the soil humidity sensor; comparing the real-time data S with the preset soil humidity threshold S min ; when the soil humidity is lower than the preset threshold S min , triggering an irrigation requirement signal.

[0106] The system collects soil humidity sensor data in real time and compares it with the preset humidity threshold S min . When the humidity is lower than this threshold, the system automatically triggers an irrigation requirement signal to ensure that the soil moisture meets the crop growth requirements. When the soil humidity is lower than the set threshold, the irrigation requirement is automatically triggered, reducing manual intervention and only irrigating when the soil moisture is insufficient, thereby improving the utilization rate of water resources.

[0107] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based irrigation control system for hilly slopes, characterized in that, The system includes: A wind speed and direction detection module for obtaining wind speed and direction data; The sprinkler head adjustment module connected to the wind speed and wind direction detection module is used to adjust the angle and spraying distance of the sprinkler head based on wind speed and wind direction data. Specifically, utility functions for different wind speeds and wind directions are designed to represent the optimal states of spraying distance, angle, and water flow distribution. The optimal sprinkler head parameter combination under the current wind speed and wind direction is obtained through solution. The spraying angle and distance are automatically adjusted according to the optimal strategy combination. The solution process is specifically as follows: ; Among them, represents the utility function for different wind speeds and directions, represents the setting of the angle or spraying distance of the sprinkler head, represents the optimal angle or spraying distance setting of the sprinkler head under the current wind speed and direction conditions, represents the angle and distance settings of the sprinkler head under other wind speed and direction conditions, represents the sprinkler head parameter combination when all other wind speed and direction conditions adopt their respective optimal strategies; A soil humidity detection module for connecting to a soil humidity sensor and determining irrigation requirements based on the data of the soil humidity sensor; An artificial intelligence control module connected to the wind speed and direction detection module and the soil humidity detection module for optimizing irrigation time and irrigation volume through artificial intelligence algorithms. Specifically, the irrigation requirements and environmental data are constructed into a small-world network model. Each node in the network represents different irrigation conditions, and the edges between nodes represent the mutual influence between conditions. Calculate the average path length and clustering coefficient of each node. The calculation formulas are as follows: ， ； Among them, represents the number of network nodes, represents the average node degree, represents the average path length of each node, represents the clustering coefficient; A sprinkler system control module connected to the artificial intelligence control module for controlling the sprinkler system to perform irrigation according to the optimization results; The sprinkler head adjustment module adjusts the angle and spraying distance of the sprinkler head based on wind speed and direction data. It also includes: Determining the wind force level based on wind speed data; Adjusting the angle of the sprinkler head according to the wind force level; Adjusting the spraying distance of the sprinkler head according to the wind force level; When the wind speed exceeds the preset threshold, reducing the water spraying volume of the sprinkler head; The soil humidity detection module connects to a soil humidity sensor and determines irrigation requirements based on the data of the soil humidity sensor. It also includes: Obtaining the real-time data S of the soil humidity sensor; Compare the real-time data S with the preset soil moisture threshold S min ; When the soil humidity is lower than the preset threshold S min a signal for irrigation demand is triggered.

2. The hilly slope irrigation control system based on artificial intelligence according to claim 1, characterized in that: The wind speed and direction detection module obtaining wind speed and direction data includes: Collecting historical wind speed and direction data, calculating the fluctuation of the data, and applying the autoregressive conditional heteroskedasticity model for conditional variance estimation. The specific formula is: ； Among them, represents the variance at the current moment, represents the error at the previous moment, represents the standard deviation of the variance at the previous moment, , , represents the parameters of the model.

3. An artificial intelligence-based irrigation control system for hilly slopes according to claim 1, characterized in that: The soil humidity detection module connecting to a soil humidity sensor and determining irrigation requirements includes: Collecting soil humidity sensing data, performing Fourier transform, analyzing its frequency domain characteristics, identifying seasonal or fluctuating frequency components in the soil humidity signal, predicting water loss or soil drying trends. The Fourier transform formula is: ； Among them, represents the component of the frequency-domain signal, represents the data points of the time-domain signal, represents the total number of sampling points of the signal, represents the frequency index, represents the time index.

4. The hilly slope irrigation control system based on artificial intelligence according to claim 1, characterized in that: The sprinkler system control module controlling the sprinkler system to perform irrigation according to the optimization results includes: Defining irrigation time, irrigation volume, wind speed, and soil humidity as random variables, simulating and generating irrigation scenarios. In each simulation scenario, calculating the corresponding irrigation effect, generating the irrigation time and irrigation volume under this combination, performing expectation calculation on all simulation results, and selecting the optimal irrigation plan that can perform stably in different environments as the final optimization result. The expectation calculation formula is: ； Among them, variable expected value of denotes a random variable, denotes the number of simulations, denotes the result of the -th simulation.

5. The irrigation control system for hilly slopes based on artificial intelligence according to claim 1, wherein: The determining the wind force level based on wind speed data includes: Obtaining the real-time wind speed data V; Comparing the wind speed data with the preset wind force level table; Determining the current wind force level L based on the comparison result; Judging whether the wind force level L is greater than or equal to 3; The specific formula is: L = f(V), where V represents the real-time wind speed data and L represents the wind force level.

6. The irrigation control system for hilly slopes based on artificial intelligence according to claim 5, characterized in that: The adjusting the angle of the sprinkler head according to the wind force level includes: Obtaining the current wind force level L; Determining the angle adjustment value θ based on the wind force level L; Adjusting the angle of the sprinkler head to the current angle plus the adjustment value θ; Judging whether the adjusted angle is within the safe range. If it is, perform the adjustment; The specific formula is: θ = g(L), where L represents the wind force level and θ represents the angle adjustment value.

7. The irrigation control system for hilly slopes based on artificial intelligence according to claim 6, wherein: Adjusting the spraying distance of the sprinkler head according to the wind force level includes: Obtaining the current wind force level L; Determining the spraying distance adjustment value D based on the wind force level L; Adjusting the spraying distance of the sprinkler head to the current spraying distance minus the adjustment value D; Judging whether the adjusted spraying distance is within the safe range; Formula: D = h(L), where L represents the wind force level and D represents the spraying distance adjustment value; When the wind speed exceeds the preset threshold, reducing the water spraying amount of the sprinkler head includes: Obtaining the current wind speed V; Determining the water spraying amount adjustment coefficient K based on the wind speed V; Reducing the current water spraying amount by multiplying it by the adjustment coefficient K; Judging whether the reduced water spraying amount is within the allowable range; Formula: K = i(V), where V represents the real-time wind speed data and K represents the water spraying amount adjustment coefficient.

8. An irrigation control system for hilly slopes based on artificial intelligence according to claim 7, characterized in that: Obtaining the current wind speed V includes: Collecting the data of the wind speed sensor; Smoothing the wind speed data to reduce the influence of noise; Calculating the average wind speed V based on the smoothed wind speed data; Judging whether the average wind speed V exceeds the preset threshold; Formula: V = (V1 + V2 +... + V r ) / r, where V1, V2,..., V r represent the wind speed data measured multiple times, r represents the number of measurements, and V represents the average wind speed after smoothing processing.

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