Intelligent drip irrigation control method, system and device and storage medium

Abstract

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

Description

Technical Field

[0001] The present invention relates to the field of smart agriculture, and specifically to an intelligent drip irrigation control method, system, device and storage medium. Background Art

[0002] As agricultural modernization continues, traditional irrigation systems are increasingly facing challenges, particularly in terms of water efficiency and crop growth management. Existing technologies primarily rely on schedule-based automatic drip irrigation systems or manual irrigation methods, which suffer from several drawbacks.

[0003] First, traditional timed drip irrigation systems lack flexibility. Existing drip irrigation systems typically open and close valves based on a preset schedule, with fixed irrigation volumes and durations. This approach cannot accommodate dynamic changes in soil moisture or the varying water requirements of crops at different growth stages. For example, during droughts or rapid soil evaporation, soil moisture may drop rapidly, yet scheduled irrigation continues as usual, resulting in water waste and restricted crop growth. Traditional systems are unable to adapt to environmental changes in real time, leading to inefficient use of water resources.

[0004] Secondly, manual adjustment of irrigation quantity and frequency is inefficient. Many small-scale farms still rely on manual judgment of soil moisture and irrigation timing. Manual intervention is not only inefficient but also prone to errors. For example, when weather conditions or soil conditions change, farmers may not be able to adjust irrigation quantity or frequency in a timely manner, which puts crops at risk of over- or under-irrigation. Even automated irrigation systems, without real-time feedback mechanisms, cannot flexibly respond to crop needs in complex environments.

[0005] Furthermore, existing technologies inadequately consider crop growth status and soil type. Current irrigation systems often rely solely on soil moisture as a single indicator, ignoring the crop's growth stage and type. Water requirements vary significantly among crops, and traditional systems fail to account for this. For example, some crops require less water in the early stages of growth but may require more during flowering or fruiting. Traditional systems fail to flexibly adjust irrigation strategies to these variations, resulting in some crops not receiving adequate water, while others may suffer from root diseases and other problems due to over-irrigation. Summary of the Invention

[0006] In response to the deficiencies in the prior art, the present invention provides an intelligent drip irrigation control method, system, device, and storage medium, which solve the problems of traditional drip irrigation systems in terms of water resource utilization efficiency, irrigation accuracy, and flexibility.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent drip irrigation control method, comprising the following steps: Data acquisition: Acquire soil moisture data, environmental meteorological data, and crop growth status data of the target area through multiple sensors, and transmit the data to the data processing module; Data processing: perform anomaly detection and standardization on the received sensor data and store it in the database; Intelligent analysis: Based on processed data, the real-time water requirement of crops is calculated, and combined with time series, the trend of soil moisture changes in the future is predicted to predict irrigation needs; Irrigation decision-making: Based on intelligent analysis results, an irrigation plan is formulated, including determining the irrigation area, irrigation water volume, and irrigation duration. Irrigation execution: Based on the irrigation decision, irrigation instructions are sent to the intelligent solenoid valve through remote control to control the opening duration and opening degree of the solenoid valve to achieve precise water supply. Feedback adjustment: Monitor changes in soil moisture after irrigation. If soil moisture does not reach the target value, adjust the irrigation plan and update irrigation parameters to optimize subsequent irrigation operations.

[0008] Preferably, the data collection step specifically includes: Soil moisture collection: Use a capacitive soil moisture sensor to detect soil moisture content and transmit the data to the data processing module via wireless communication; Meteorological data collection: obtain ambient temperature, air humidity, rainfall, light intensity and wind speed through weather stations; Crop growth status collection: Multispectral imaging equipment is used to measure chlorophyll content and calculate the normalized vegetation index to assess crop water requirements.

[0009] Preferably, the intelligent analysis step specifically includes: Calculation of crop water requirements: Using a deep neural network model, soil moisture, environmental weather, and crop growth status data are used as input to calculate the optimal water requirement of crops; Soil moisture trend forecast: Based on time series analysis methods and combined with historical soil moisture data, the soil moisture change trend in the next 1-3 days is predicted, and the irrigation plan is optimized based on the prediction results.

[0010] Preferably, the irrigation decision-making steps specifically include: Calculate irrigation water volume: Calculate the required irrigation water volume based on crop water requirements, current soil moisture, and soil infiltration rate. The irrigation water volume is calculated as follows: The amount of irrigation water is equal to the difference between the crop water requirement and the current soil moisture divided by the soil infiltration rate; Optimize irrigation duration: Determine the duration of irrigation based on the calculated irrigation water volume and current water pressure.

[0011] The present invention also provides an intelligent drip irrigation control system for assisting the use of the intelligent drip irrigation control method described above, comprising: a data acquisition module for collecting soil moisture data, environmental meteorological data, and crop growth status data, and transmitting the collected data to a data processing and storage module; The data processing and storage module is used to receive the data transmitted by the data acquisition module, perform anomaly detection and standardization on the data, and store the data in a database; The intelligent analysis module is used to calculate crop water requirements and predict future soil moisture trends based on data provided by the data processing and storage module to assist in irrigation decision-making; The irrigation control module receives the prediction results provided by the intelligent analysis module, formulates an irrigation plan based on crop water requirements and soil moisture data, and calculates the irrigation water volume and duration; A remote control module is used to send control instructions to the intelligent solenoid valve through a wireless communication network according to the irrigation plan generated by the irrigation control module; The feedback monitoring module is used to monitor the changes in soil moisture after irrigation. If the soil moisture does not reach the target value, the irrigation plan is adjusted to optimize the subsequent irrigation strategy.

[0012] Preferably, the intelligent analysis module includes: The deep neural network computing unit is used to calculate crop water requirements based on soil moisture, environmental meteorological and crop growth status data; the time series analysis unit is used to predict the soil moisture change trend in the next 1-3 days based on historical soil moisture data and optimize irrigation strategies.

[0013] Preferably, the remote control module includes: a wireless communication unit for remotely transmitting irrigation control instructions; The wired communication backup unit is used to automatically switch to wired communication mode when wireless communication fails.

[0014] The present invention also provides an intelligent drip irrigation control device for use with the above-mentioned intelligent drip irrigation control method, comprising: A chassis housing, internally mounted with an intelligent control chip, a wireless communication module, a solenoid valve, and a water flow meter. The wireless communication module is connected to the intelligent control chip. The intelligent control chip is configured to receive data collected by a sensor, execute an irrigation control algorithm, and send an irrigation control signal to the solenoid valve. The solenoid valve is connected to the water flow meter and is configured to regulate the on / off state and flow rate of the irrigation water flow according to the irrigation control signal generated by the intelligent control chip. A soil moisture sensor is mounted on the outside of the chassis housing and connected to the intelligent control chip via a data transmission line, and is used to detect ambient temperature and humidity and transmit the detected data to the intelligent control chip; A temperature and humidity sensor is installed on the outside of the chassis shell and is connected to the intelligent control chip through a data transmission line, and is used to detect the ambient temperature and humidity and transmit the detection data to the intelligent control chip; a solar power supply unit, which is installed outside the chassis housing and connected to the intelligent control chip, the solenoid valve, the wireless communication module and the remote monitoring screen through a power line, and is used to provide operating power for the intelligent drip irrigation control device; A remote monitoring screen is installed outside the chassis shell and is data-connected to the intelligent control chip for displaying the operating status of the device, sensor detection data, and irrigation control information.

[0015] Preferably, the intelligent control chip includes: Data processing unit, used to clean, store and analyze sensor data; AI computing unit, used to calculate crop water requirements based on deep neural networks and optimize irrigation strategies.

[0016] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0017] The present invention provides an intelligent drip irrigation control method, system, device, and storage medium. It has the following beneficial effects: 1. The present invention, through its integrated sensor network, intelligent analysis module, and real-time feedback adjustment mechanism, can accurately calculate the real-time water requirements of crops and changes in soil moisture, and automatically adjust the irrigation water volume and duration based on this data, achieving the technical effect of efficient use of water resources. Compared with the traditional drip irrigation system in the prior art that relies on timer switches, the traditional system cannot cope with the dynamic changes in soil moisture and the actual needs of crops, often leading to over-irrigation or insufficient water, thereby wasting water resources. The intelligent control method of the present invention can respond to the needs of soil and crops in real time, reduce unnecessary water loss, significantly improve the efficiency of water resource use, and ensure that crops receive accurate water supply during their growth process.

[0018] 2. This invention combines deep neural networks and time series analysis technology to intelligently calculate crop water requirements based on soil moisture, meteorological data, and crop growth status. It also predicts soil moisture trends based on historical moisture data, thereby formulating personalized irrigation plans. Compared with existing technologies that rely on fixed schedules or simple experience-based irrigation methods, this intelligent analysis process can more accurately reflect the water needs of crops at different growth stages. Through this precise irrigation management, crops can grow under optimal moisture conditions, avoiding the negative effects of excessive or insufficient moisture on crop growth. Compared with traditional methods, this invention can dynamically optimize irrigation volume based on the real-time water requirements of crops and soil conditions, ensuring that crops receive the optimal moisture conditions required for healthy growth.

[0019] 3. The present invention significantly improves the automation and intelligence level of irrigation operations through deeply integrated data acquisition, intelligent analysis and automatic adjustment mechanisms. In traditional drip irrigation systems, manual intervention is inevitable, and manual monitoring of factors such as soil moisture and climate change is usually required, and irrigation parameters are manually adjusted, which is inefficient and prone to human errors. In contrast, the intelligent control system of the present invention can automatically collect soil moisture data and process it in real time, intelligently optimize irrigation strategies based on crop needs and meteorological conditions. This automated irrigation method effectively reduces manual operations and reduces management costs, while improving the system's response speed and operating efficiency, ensuring the accuracy and stability of irrigation operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the method flow of the present invention; Figure 2 It is a framework diagram of the system of the present invention; Figure 3 This is a framework diagram of the intelligent analysis module of the present invention; Figure 4 This is a framework diagram of the remote control module of the present invention; Figure 5 Schematic diagram of the intelligent drip irrigation control device of the present invention; Figure 6 This is a framework diagram of the intelligent control chip of the present invention.

[0021] Among them, 1. Chassis shell; 2. Intelligent control chip; 3. Wireless communication module; 4. Solenoid valve; 5. Water flow meter; 6. Soil moisture sensor; 7. Temperature and humidity sensor; 8. Solar power supply unit; 9. Remote monitoring screen. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Please see the attached Figure 1 , an embodiment of the present invention provides an intelligent drip irrigation control method, comprising the following steps: S1. Data acquisition: acquiring soil moisture data, environmental meteorological data, and crop growth status data of the target area through multiple sensors, and transmitting the data to a data processing module; In this embodiment, data collection involves using multiple sensors to acquire key environmental data from the target area, including soil moisture, weather data, and crop growth status data. This data is then transmitted to a data processing module for subsequent processing and intelligent analysis, thereby generating a precise irrigation control plan.

[0024] In this embodiment, the data collection step specifically includes monitoring soil moisture, environmental weather, and crop growth status. Each data collection method uses different sensors and equipment to ensure that the required agricultural environmental information can be fully and accurately obtained.

[0025] Soil moisture collection: The soil moisture in the target area is monitored in real time using a capacitive soil moisture sensor 6. The soil moisture sensor 6 measures the change in soil capacitance to estimate the moisture content in the soil. Generally, a capacitive sensor senses the change in soil moisture through changes in the electric field. The capacitance value of the soil (S) is related to the soil moisture content (θ s ) can be expressed by the following formula: θ s =a1+a2·S+a3·S 2 ; Among them, θ s is the soil moisture (unit: m 3 / m 3 ), S is the capacitance value measured by the sensor, and a1, a2, and a3 are the calibration coefficients of the capacitance sensor, adjusted based on experiments and soil type. This method effectively acquires soil moisture information in real time and transmits the data to the data processing module via wireless communication for subsequent analysis and processing.

[0026] Meteorological data collection: The target area's environmental meteorological data, including temperature (T), humidity (H), rainfall (R), light intensity (I), and wind speed (V), are acquired through the weather station. This data is crucial for assessing irrigation needs. The temperature and humidity sensor 7 can provide real-time ambient temperature and humidity data, from which information such as evaporation needs can be calculated. The rainfall sensor is used to monitor precipitation to avoid excessive irrigation. Light intensity and wind speed help assess plant transpiration needs and water evaporation. After all meteorological data is acquired through the weather station, it is transmitted to the data processing module via the wireless communication module 3 and further analyzed in combination with soil moisture and crop growth status.

[0027] Crop growth status collection: Multispectral imaging equipment is used to measure the chlorophyll content of crops and calculate the Normalized Difference Vegetation Index (NDVI). NDVI is an important indicator of crop growth health, and its calculation formula is: NIR stands for near-infrared reflectance, and RED stands for red reflectance. This index reflects the greenness of crops and their moisture status. Areas with high Normalized Difference Vegetation Index (NDVI) values ​​generally indicate good crop growth and low water requirements. Conversely, low NDVI values ​​indicate that crops may be under stress and require more irrigation. Data acquired by the multispectral imaging equipment is transmitted to the data processing module for further processing.

[0028] This data is transmitted in real time to the data processing and storage module via the sensor network system's wireless communication module 3, ensuring the system maintains real-time monitoring in a constantly changing environment. The real-time collection and transmission of this data is the foundation of the intelligent drip irrigation system, effectively providing accurate input data for subsequent intelligent analysis and irrigation decision-making.

[0029] Alternatively, in some embodiments, the soil moisture sensor 6 and the temperature and humidity sensor 7 can be monitored using an integrated all-in-one sensor device, thereby simplifying installation and reducing equipment maintenance costs. Such devices typically use more advanced sensor technology and integrated circuit design to improve measurement accuracy and device reliability.

[0030] In one possible implementation, comprehensive data collection is achieved by deploying a suite of sensor devices and weather stations. These sensors include capacitive soil moisture sensors 6, temperature and humidity sensors 7, rainfall meters, light intensity meters, anemometers, and multispectral imaging equipment. All devices are connected via a unified wireless communication network, ensuring stable transmission of real-time data to the data processing module. After preprocessing, data is further used to calculate crop water requirements, predict soil moisture trends, make irrigation decisions, and precisely adjust irrigation execution controls.

[0031] Through precise and diverse data collection, crop growth needs can be accurately assessed. Fluctuations in soil moisture, weather conditions, and changes in crop growth can be monitored in real time and irrigation strategies adjusted accordingly. In this way, smart drip irrigation systems can improve water resource utilization efficiency while ensuring healthy and efficient crop growth.

[0032] S2. Data Processing: Received sensor data is subjected to anomaly detection and standardization, and then stored in a database. In the intelligent drip irrigation control method, the data processing step effectively preprocesses the raw data collected from various sensors (including soil moisture sensors 6, meteorological data collectors, crop growth monitoring equipment, etc.). The goal of this step is to ensure data accuracy and consistency and provide high-quality input for subsequent intelligent analysis. The main tasks of data processing include anomaly detection, data cleaning, standardization, and data storage. All processed data is stored in a database for subsequent use by the intelligent analysis module.

[0033] In this embodiment, the data processing steps involve multiple steps. First, the received raw data undergoes anomaly detection and normalization, two core steps that form the core of data processing. Anomalous data may be caused by environmental interference, equipment failure, or other factors, and therefore must be effectively identified and removed to avoid affecting the results of intelligent analysis. Normalization ensures that the output data of different sensor types can be compared and analyzed under a unified standard.

[0034] In the anomaly detection phase, this embodiment uses statistical methods to analyze sensor data to identify and remove unexpected outliers. Generally, during the data acquisition process, sensors may produce extreme error data due to various factors (such as electromagnetic interference, sensor failure, etc.). In order to ensure data quality, the Z-score method is usually used for outlier detection. Specifically, the Z-score calculation formula is as follows: Where: X is the original data value; μ is the mean of the data; σ is the standard deviation of the data.

[0035] Generally, when |Z| > 3, data point X is considered an outlier and needs to be removed from the dataset. This is because data points with a value greater than three times the standard deviation are typically considered outliers far from the mean. Such data may not represent the actual environmental conditions, and if not removed, will directly affect subsequent analysis results.

[0036] Alternatively, in some embodiments, the data quality control system may be calibrated based on the historical performance of the sensor and dynamically adjust the threshold for outlier detection to adapt to different environments and the operating characteristics of different sensors.

[0037] Standardization is another important step in data preprocessing, the purpose of which is to convert the collected data of different dimensions (such as soil moisture, temperature, humidity, wind speed, etc.) into a unified standard scale. In this embodiment, data standardization is achieved through normalization.

[0038] Specifically, data standardization not only eliminates dimensional differences between sensor data but also improves the efficiency and accuracy of subsequent intelligent analysis models. After standardization, all data is converted to a uniform interval (e.g., [0, 1]), allowing data of different units and dimensions to be compared on the same scale, facilitating subsequent processing by intelligent analysis modules.

[0039] As an option, for some special sensor data, it may be necessary to use Z-score standardization (standard normalization) instead of normalization, especially when the data has an obvious Gaussian distribution, this standardization method is more suitable.

[0040] After the data processing steps are completed, all cleaned and standardized data will be stored in a database to ensure data persistence and facilitate subsequent use. In this embodiment, a distributed database (such as InfluxDB) is used to store this real-time data. Through distributed storage, data can be accessed efficiently while ensuring data integrity during the storage process.

[0041] Data storage involves not only the preservation of sensor data but also the archiving and retrieval of historical data. Specifically, the data in the database is sorted according to the timestamp of acquisition, and a specific storage format is assigned to each type of data to ensure efficient retrieval of the required information during subsequent analysis and queries.

[0042] In one possible implementation, all data storage operations are seamlessly connected through the interface between the data acquisition module and the data processing module, ensuring efficient and stable data flow. In addition, the data storage process also includes a data backup mechanism to ensure that historical data can be restored in the event of a system failure.

[0043] S3. Intelligent Analysis: Based on the processed data, the real-time water requirement of crops is calculated. In combination with time series, the trend of soil moisture changes in the future is predicted to predict irrigation needs. In intelligent drip irrigation control systems, the intelligent analysis step calculates the crop's real-time water requirements and, combined with historical data, predicts soil moisture trends to help develop an optimized irrigation strategy. This step further calculates and predicts the sensor data acquired and processed in the aforementioned data collection and processing steps, providing a more precise basis for irrigation control. Through intelligent analysis, the system can timely adjust irrigation plans based on current and future environmental conditions, ensuring that crops receive the water they need while avoiding water waste.

[0044] In this embodiment, the intelligent analysis step includes two key components: crop water requirement calculation and soil moisture trend prediction. These two components utilize deep learning and time series analysis methods to perform real-time analysis and prediction based on the cleaned and standardized data transmitted from the aforementioned data collection and processing steps. The following is a detailed description of these steps: Calculating crop water requirements is one of the most critical aspects of intelligent analysis. In this embodiment, crop water requirement calculation utilizes a deep neural network (DNN) model, which can process multivariate inputs and predict crop water requirements under different environmental conditions. The DNN model is trained by learning the complex, nonlinear relationship between input parameters (such as soil moisture, meteorological data, and crop growth status) and crop water requirements from historical data.

[0045] In the calculation of crop water requirements, the model inputs include the following variables: Soil moisture (θ s ), which indicates the water content in the soil, in cubic meters per cubic meter (m 3 / m 3 ); Environmental meteorological data: including temperature (T), humidity (H), rainfall (R), light intensity (I) and wind speed (V), which affect crop transpiration and water requirements; Crop growth status: For example, through chlorophyll content (C l ) and the Normalized Difference Vegetation Index (NDVI) to measure the growth health of crops and infer their water requirements.

[0046] The output of the deep neural network model is the water requirement of the crop (W n ), which is the amount of water required by crops under specific conditions. The formula for calculating crop water requirement is as follows: W n =f(T,H,I,V,θ s ,C l ,NDVI); Where: W n is the water requirement of crops (unit: L / m 2); T is temperature (unit: ℃); H is humidity (unit: %); I is light intensity (unit: W / m 2 ); V is wind speed (unit: m / s); θ s is the soil moisture (unit: m 3 / m 3 );C l is the chlorophyll content (unit: mg / m 2 ); NDVI is the normalized difference vegetation index (unit: dimensionless).

[0047] Typically, deep neural networks are trained using historical data, allowing the network to continuously adjust its weights and biases to improve prediction accuracy. During training, a backpropagation algorithm is used to adjust network parameters to minimize the model's prediction error.

[0048] Optionally, to further improve forecast accuracy, the model can also incorporate external factors such as rainfall (R) and soil permeability (η) to more precisely calculate crop water requirements. These additional factors can reduce irrigation requirements when there is sufficient rainfall or when soil permeability is high.

[0049] Soil moisture trend prediction is the second key step in intelligent analysis. Its purpose is to predict future soil moisture trends based on historical soil moisture data and adjust irrigation plans based on the predictions. In this example, soil moisture prediction uses time series analysis methods, including the Autoregressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) network.

[0050] Specifically, time series analysis can capture the changing trends of soil moisture data over time and predict moisture changes over the next 1-3 days. These predictions help the smart drip irrigation system adjust irrigation strategies in advance based on future moisture conditions.

[0051] In the implementation, the long short-term memory network (LSTM) model is used to predict soil moisture data. LSTM can capture long-term dependencies and is suitable for predicting time series data. The historical data of soil moisture θ s (t) will be used as the input of the LSTM model, and the model predicts the future soil moisture θ s (t+1),θ s (t+2),….

[0052] The soil moisture prediction formula can be expressed as: θ s (t+1),θ s (t+2),…=g(θ s (t),θ s(t-1),…); Where: θ s (t) is the soil moisture at the current moment; g(·) is the prediction function of the LSTM network, which generates future soil moisture values.

[0053] Specifically, the LSTM model not only predicts future trends based on past soil moisture data, but also combines it with external meteorological data (such as temperature, humidity, and rainfall) for joint predictions to improve prediction accuracy. This joint model can take into account the complex interactions between soil moisture and other meteorological factors, thereby optimizing irrigation plans.

[0054] As an option, in some embodiments, soil moisture trend prediction can also be combined with automatic learning algorithms, such as reinforcement learning (RL), to further dynamically adjust the model weights and prediction strategies to adapt to changing climate and soil conditions.

[0055] In this embodiment, calculating crop water requirements and predicting soil moisture trends are two core tasks of intelligent analysis. Through these two tasks, the system can assess crop water needs in real time and predict soil moisture changes. Ultimately, the intelligent analysis results provide a basis for the irrigation control module, which optimizes irrigation plans based on real-time water requirements and future moisture trends.

[0056] For example, if the forecast shows that soil moisture will continue to decrease, the system will increase irrigation volume and frequency in advance to ensure that crops do not suffer from drought. Conversely, if the forecast shows that soil moisture will increase, the system will reduce or suspend irrigation. In this way, the smart drip irrigation system can make precise irrigation decisions based on current and future environmental changes.

[0057] Specifically, by combining crop water requirement calculations with soil moisture trend forecasts, the intelligent analysis module can flexibly adjust irrigation plans to meet the current water needs of crops while taking into account future climate change, ensuring that the water supply for crops remains within the most appropriate range.

[0058] As an option, the intelligent analysis module can also dynamically correct the predicted results of crop water demand and soil moisture trend based on the soil moisture change information after irrigation fed back by the sensor, thereby further improving the adaptability and accuracy of the system.

[0059] S4. Irrigation decision-making: Based on the results of intelligent analysis, an irrigation plan is formulated, including determining the irrigation area, irrigation water volume, and irrigation duration; In smart drip irrigation control systems, the irrigation decision-making step is crucial to the entire irrigation management process. It ensures that crops receive precise water supply based on their needs without wasting water resources. This step is based on the previously mentioned intelligent analysis results, including the real-time water requirements of crops and predicted soil moisture trends, to further develop a specific irrigation plan. This plan includes the determination of irrigation areas, calculation of irrigation water quantities, and optimization of irrigation duration. Through these calculations and optimizations, the smart drip irrigation system can provide the most accurate water supply plan, avoiding over- or under-irrigation.

[0060] In this embodiment, the irrigation decision-making process mainly includes two core aspects: calculating the irrigation water amount and optimizing the irrigation duration. These aspects complement each other and work together to ensure that crops receive the appropriate amount of water while avoiding over-irrigation and water waste. The following is a detailed description of this step: Irrigation water quantity calculation is the first step in irrigation decision making. Its purpose is to calculate the required irrigation water quantity based on the water requirements of the crops and the existing moisture content of the soil.

[0061] In this example, the irrigation water calculation not only considers the crop's water requirement but also incorporates soil moisture and soil permeability. The crop's water requirement is calculated using a deep neural network (DNN) model that predicts crop water needs based on real-time environmental data, such as meteorological data and soil moisture.

[0062] Irrigation water volume (V w ) is calculated as follows: Where: V w The required irrigation water volume (unit: L / m 2 );W n is the water requirement of crops (unit: L / m 2 ), calculated by the intelligent analysis module; θ s is the current soil moisture (unit: m 3 / m 3 ), measured by the soil moisture sensor 6; η is the soil permeability (unit: m / day), which indicates the water penetration capacity of the soil.

[0063] Specifically, irrigation water volume is calculated based on the difference between soil moisture and crop water requirements. Crop water requirements are calculated using an intelligent analysis module, taking into account current soil moisture, meteorological data, and crop growth status. Soil permeability, which describes the rate at which water penetrates the soil, directly influences irrigation water volume calculations. By combining these three factors, the actual amount of irrigation water required can be calculated.

[0064] Generally, if soil moisture is high, irrigation water volume will be lower. Conversely, when soil moisture is low, the system will increase irrigation water volume. Soil permeability is a key factor affecting irrigation water volume, because different soil types (such as sand and clay) have different permeabilities. Soils with high permeability require less water, while soils with low permeability require more water.

[0065] Alternatively, in some embodiments, the value of soil permeability (η) can be obtained through long-term monitoring and soil analysis, and dynamically adjusted according to seasonal changes to adapt to different soil conditions and environmental changes.

[0066] The optimization of irrigation time is based on the calculation results of irrigation water volume and combined with the real-time water pressure to determine the duration of irrigation.

[0067] In this embodiment, the calculation process of irrigation duration takes into account the influence of water flow rate and water pressure to ensure that the required amount of water can be evenly distributed to the soil in each irrigation cycle. irrigation ) is calculated as follows: Where: T irrigation is the irrigation time (unit: hour); V w The required irrigation water volume (unit: L / m 2 ), calculated in the previous step; Q is the irrigation water flow rate (unit: L / hour), which indicates the amount of water that the irrigation system can provide per hour.

[0068] Specifically, irrigation water flow (Q) is related to water pressure (P s ), water pressure directly affects the speed and flow of water. The calculation formula of water flow (Q) is: Where: k is a constant related to the irrigation system pipe diameter, equipment performance and water flow path; P s is the water pressure (unit: Pa), which indicates the driving force of the water flow.

[0069] Generally speaking, the higher the water pressure, the greater the water flow (Q), which can shorten the irrigation time (T irrigation ). Therefore, by monitoring the water pressure of the system in real time, the irrigation duration can be dynamically adjusted to ensure that the irrigation effect is not affected by changes in water pressure.

[0070] Alternatively, if the irrigation system is configured with different types of irrigation equipment (e.g., drip irrigation, micro-sprinkler irrigation, etc.), the water flow and water pressure response of each type of equipment may be different. In this case, the irrigation system will dynamically adjust the irrigation duration and water flow based on the type and performance of the equipment to ensure that crops in different areas receive appropriate water supply.

[0071] In this embodiment, the irrigation decision-making process combines crop water requirement calculation with irrigation duration optimization to ensure that each irrigation event meets crop water needs. The irrigation duration is optimized based on real-time water pressure, achieving precise irrigation. The combination of crop water requirements and soil moisture differentials enables the system to quickly respond to current soil conditions. By optimizing water flow and irrigation duration, the system ensures that water is accurately infiltrated into the soil within a reasonable timeframe.

[0072] Specifically, crop water requirement calculations provide the basis for irrigation water quantity, while optimized irrigation duration ensures that each irrigation is completed fully and efficiently. Based on real-time water pressure and irrigation equipment performance feedback, the system automatically adjusts irrigation duration to ensure even water distribution during each irrigation, preventing crop growth from being impacted by over- or under-irrigation.

[0073] As an option, if the system finds that the soil moisture in certain areas is close to the target value, it can reduce the irrigation water volume or irrigation duration in that area through an intelligent feedback mechanism to avoid unnecessary waste of water resources.

[0074] S5. Execute irrigation: Based on the irrigation decision, send irrigation instructions to the intelligent solenoid valve 4 through remote control to control the opening time and opening degree of the solenoid valve 4 to achieve precise water supply; In an intelligent drip irrigation control system, the irrigation step is a core component of the entire irrigation process. Its mission is to execute specific irrigation operations based on the results of irrigation decision-making calculations, ensuring that crops receive a precise water supply. In this embodiment, the irrigation step involves remotely sending irrigation instructions to the intelligent solenoid valve 4, controlling its opening duration and degree, thereby achieving precise water supply. Through this process, the intelligent drip irrigation system can automatically adjust the irrigation volume based on crop water requirements, soil moisture, and other environmental conditions, ensuring uniform water distribution during the irrigation process and avoiding over-irrigation and water waste.

[0075] In this embodiment, the specific process of performing irrigation is completed through the following steps: controlling the opening time and opening degree of the solenoid valve 4 and performing precise water supply.

[0076] Controlling solenoid valve 4 is key to achieving precise irrigation. In a smart drip irrigation system, solenoid valve 4 controls the on and off flow of water, thereby controlling the irrigation volume. In this embodiment, the operation of solenoid valve 4 is based on the irrigation volume and duration calculated in the aforementioned irrigation decision-making step. Irrigation commands are sent to solenoid valve 4 via a remote control system, precisely controlling the water flow rate and irrigation duration.

[0077] The opening time and opening degree of the solenoid valve 4 are adjusted depending on the following factors: Irrigation water volume: derived from the aforementioned irrigation water volume calculation formula, indicating the amount of water required by the crop; Irrigation duration: calculated from the irrigation water volume and water flow (Q), indicating the time it takes for water to flow into the soil; Water flow and water pressure: Water pressure affects the water flow rate and flow rate of the irrigation system, and directly determines the water flow rate when the solenoid valve 4 is opened.

[0078] The opening of solenoid valve 4 controls the water flow rate during each irrigation process. A wider opening results in a higher water flow rate; a narrower opening results in a smaller water flow rate. Generally, the opening of solenoid valve 4 is linearly related to the water flow rate (Q), and the water flow rate is closely related to the water pressure. Therefore, the formula for setting the opening of solenoid valve 4 is: Where: Q is the water flow rate (unit: L / hour); k is a constant related to the type of irrigation equipment, pipe diameter, pumping capacity, etc.; P s is the water pressure (unit: Pa); A is the opening of the solenoid valve 4 (unit: m 2 ).

[0079] Specifically, the opening time of the solenoid valve 4 (T valve ) and opening (A) determine the water flow and time of each irrigation process, ensuring that crops receive appropriate water during the irrigation process.

[0080] Precision water supply relies on the control of the aforementioned solenoid valve 4. When irrigation instructions are sent to solenoid valve 4 via the remote control system, solenoid valve 4 precisely adjusts its opening and duration according to the irrigation plan. In this embodiment, precision water supply requires not only the timely opening and closing of solenoid valve 4 but also appropriate adjustments based on real-time soil moisture and crop water requirements.

[0081] Typically, irrigation instructions are sent and executed remotely via wireless communication module 3 and solenoid valve 4. Alternatively, long-distance, low-power control can be achieved using LoRa or NB-IoT communication protocols to ensure system stability and responsiveness.

[0082] The implementation steps of precision water supply are as follows: Receiving irrigation instructions: After the intelligent analysis module determines the irrigation needs based on factors such as crop water requirements and soil moisture, the irrigation instructions are sent to the remote control system.

[0083] Calculate the required water flow and opening degree: According to the irrigation water calculation formula and water flow control formula, calculate the opening degree (A) and opening time (T valve ).

[0084] Control the solenoid valve 4: send a switch command to the solenoid valve 4 through the wireless communication module 3 to control the opening degree and opening time of the solenoid valve 4 and adjust the water flow.

[0085] Real-time monitoring of water flow: During the irrigation process, the changes in soil moisture are monitored in real time. When the moisture reaches the preset target value, the system automatically stops supplying water.

[0086] In one possible implementation, to improve irrigation control accuracy, the water flow rate (Q) during the irrigation process can be monitored in real time by a water flow meter 5 and fed back to the intelligent control system. The system then adjusts the opening of the solenoid valve 4 based on the feedback, ensuring more accurate water supply during each irrigation cycle.

[0087] In this embodiment, the irrigation process is closely linked to irrigation decision-making and intelligent analysis. The irrigation volume and duration determined in the irrigation decision-making step, combined with remote control of the solenoid valve 4 by the intelligent control system, ensure precise execution of each irrigation. The intelligent drip irrigation system dynamically adjusts irrigation volume and duration based on soil moisture, crop water requirements, and water pressure, avoiding over-irrigation or under-watering.

[0088] Specifically, the intelligent drip irrigation system uses real-time soil moisture feedback and irrigation water flow monitoring to continuously optimize the opening and duration of the solenoid valve 4, ensuring that crops in each area receive the required water. Through remote control and precise water supply, the irrigation system can reduce manual intervention and improve the system's automation and intelligence.

[0089] As an option, if the system detects that the soil moisture in certain irrigation areas is close to the target value, it can reduce the irrigation water volume in that area or suspend irrigation through an intelligent feedback mechanism to avoid unnecessary water supply.

[0090] S6. Feedback adjustment: Monitor the changes in soil moisture after irrigation. If the soil moisture does not reach the target value, adjust the irrigation plan and update the irrigation parameters to optimize subsequent irrigation operations.

[0091] In intelligent drip irrigation control systems, feedback adjustment is a critical step in ensuring precise irrigation execution. By monitoring soil moisture in real time after irrigation is complete, the system automatically adjusts subsequent irrigation plans based on the difference between actual soil moisture and the preset target value. Feedback adjustment not only optimizes irrigation water volume but also ensures that crops receive the appropriate amount of water during growth by adjusting parameters such as irrigation duration and frequency, while also avoiding water waste and over-wetting the soil.

[0092] In this embodiment, the goal of the feedback adjustment step is to determine whether the irrigation effect meets expectations based on the soil moisture data after irrigation. If the soil moisture does not reach the target value, the irrigation plan is adjusted. The adjustments mainly include irrigation water volume, irrigation duration, and irrigation frequency, ensuring that subsequent irrigation operations can more accurately meet crop needs. The following is a detailed description of this step: Monitoring soil moisture changes is the first step in feedback adjustment. In this embodiment, soil moisture changes are monitored in real time by soil moisture sensors 6 (such as capacitive or frequency domain reflective sensors). These sensors can measure soil moisture changes in real time after the irrigation operation is completed and transmit the data to the intelligent control system. The soil moisture value after irrigation is and target soil moisture Compare and the system determines whether feedback adjustment is needed based on the comparison results.

[0093] Specifically, the target soil moisture The target humidity is typically determined by the crop's water requirements and soil type. Setting the target humidity value requires considering various factors, including the crop's growth stage, climate conditions, and soil type. For example, during a crop's rapid growth period, the system might set a higher soil humidity target to meet its needs; during a dormant period, a lower target humidity might be set to reduce water supply.

[0094] In general, soil moisture after irrigation Should approach or reach the preset target soil moisture value If the soil moisture does not reach the target value, it means that the current irrigation fails to meet the water needs of the crops, and the system needs to be adjusted to increase the irrigation amount or extend the irrigation time.

[0095] When the soil moisture is detected to be below the target value, the system will adjust the irrigation water volume to replenish the soil moisture and ensure that the crops are adequately supplied with water. w ) is calculated as follows: Where: ΔV w The amount of irrigation water that needs to be increased (unit: L / m 2 ); is the target soil moisture (unit: m 3 / m 3 ); is the current soil moisture (unit: m 3 / m 3 ); η is the soil permeability (unit: m / day), which indicates the permeability of the soil.

[0096] Specifically, the amount of water that needs to be replenished (ΔVw ) is determined by the soil moisture difference. If the soil moisture does not reach the target value, the amount of supplemental irrigation water required is calculated based on this difference. This formula shows that the amount of supplemental water is directly proportional to the soil moisture difference and inversely proportional to the soil's infiltration capacity (η). Soils with higher permeability require less supplemental water, while soils with lower permeability require more water to reach the target moisture level.

[0097] Optimizing irrigation duration is another important step in feedback adjustment. Based on the calculated irrigation water volume and water flow (Q), the system will update the irrigation duration (T irrigation ) to ensure that water penetrates deep into the soil within an appropriate time. The formula for calculating irrigation duration is: Where: T irrigation is the irrigation duration (unit: hours); ΔV w The amount of irrigation water that needs to be supplemented (unit: L / m 2 ), which is obtained from the irrigation water volume calculation formula; Q is the water flow rate (unit: L / hour), which represents the amount of water the system can provide per unit time and is usually determined by the water pressure and equipment performance of the irrigation system.

[0098] Specifically, by adjusting the irrigation duration, the system ensures that the amount of water applied during each irrigation is evenly distributed throughout the soil, preventing water loss. If a large amount of water is needed, the system will extend the irrigation duration; if less water is needed, the irrigation duration will be shortened accordingly.

[0099] As an alternative, if the irrigation system's water flow is limited, the system can choose to increase the number of irrigations rather than extend the duration of a single irrigation session, thereby avoiding excessive accumulation of moisture on the soil surface. Adjusting the irrigation frequency is closely related to optimizing the irrigation duration to ensure that crops are always under the appropriate water conditions.

[0100] Optimizing irrigation frequency is another important step in ensuring the soil receives the appropriate amount of water at different times. In this embodiment, irrigation frequency is adjusted based on soil type, crop needs, and climatic conditions. By dynamically adjusting irrigation frequency based on soil moisture trends, over-irrigation or under-watering can be avoided.

[0101] Specifically, when soil moisture is close to the target value, the irrigation frequency can be appropriately reduced; when soil moisture is lower than the target value, the irrigation frequency will be increased to ensure that water can be replenished to the crop roots in time.

[0102] As an option, irrigation frequency can be optimized through adaptive algorithms, which dynamically adjust the frequency and interval of irrigation based on real-time feedback of soil moisture data, crop growth stage, climate change and other factors.

[0103] In this embodiment, the feedback adjustment step is closely integrated with the aforementioned irrigation decision-making, intelligent analysis, and irrigation execution steps. By leveraging real-time soil moisture data, the system can dynamically adjust irrigation plans to ensure crops receive the required water and optimize irrigation operations to avoid over- or under-watering. Feedback data from each irrigation session provides new input to the system, enabling it to more precisely adjust various parameters for the next irrigation.

[0104] Specifically, feedback adjustment ensures that the smart drip irrigation system can dynamically optimize irrigation strategies based on changes in soil moisture during the actual irrigation process, maximizing water resource utilization and promoting healthy crop growth. Through continuous adjustment and optimization, the system can flexibly respond to environmental changes and crop needs, avoiding blind and inefficient irrigation.

[0105] Alternatively, the feedback adjustment step can be combined with more advanced data analysis methods (such as machine learning, reinforcement learning, etc.) to automatically optimize irrigation parameters through training algorithms, allowing the system to adapt to the needs of different environments and crops over a wider range.

[0106] The intelligent drip irrigation control system described below and the intelligent drip irrigation control method described above can refer to each other.

[0107] Please see the attached Figure 2 , an intelligent drip irrigation control system, used to assist the use of the above-mentioned intelligent drip irrigation control method, including: a data acquisition module, used to collect soil moisture data, environmental meteorological data and crop growth status data, and transmit the collected data to a data processing and storage module; The data processing and storage module is used to receive the data transmitted by the data acquisition module, perform anomaly detection and standardization on the data, and store the data in a database; The intelligent analysis module is used to calculate crop water requirements and predict future soil moisture trends based on data provided by the data processing and storage module to assist in irrigation decision-making; The irrigation control module receives the prediction results provided by the intelligent analysis module, formulates an irrigation plan based on crop water requirements and soil moisture data, and calculates the irrigation water volume and duration; A remote control module, configured to send control instructions to the intelligent solenoid valve 4 via a wireless communication network according to the irrigation plan generated by the irrigation control module; The feedback monitoring module is used to monitor the changes in soil moisture after irrigation. If the soil moisture does not reach the target value, the irrigation plan is adjusted to optimize the subsequent irrigation strategy.

[0108] Please see the attached Figure 3 , the intelligent analysis module includes: The deep neural network computing unit is used to calculate crop water requirements based on soil moisture, environmental meteorological and crop growth status data; the time series analysis unit is used to predict the soil moisture change trend in the next 1-3 days based on historical soil moisture data and optimize irrigation strategies.

[0109] Please see the attached Figure 4 , the remote control module includes: a wireless communication unit for remotely transmitting irrigation control instructions; The wired communication backup unit is used to automatically switch to wired communication mode when wireless communication fails.

[0110] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, so they will not be repeated here.

[0111] The intelligent drip irrigation control device described below and the intelligent drip irrigation control method described above can refer to each other.

[0112] Please see the attached Figure 5 , an intelligent drip irrigation control device, used for use with the above-mentioned intelligent drip irrigation control method, comprising: a chassis shell 1, inside which is installed an intelligent control chip 2, a wireless communication module 3, a solenoid valve 4 and a water flow meter 5, the wireless communication module 3 being connected to the intelligent control chip 2, the intelligent control chip 2 being used to receive data collected by a sensor, execute an irrigation control algorithm, and send an irrigation control signal to the solenoid valve 4, the solenoid valve 4 being connected to the water flow meter 5, which is used to adjust the on / off and flow rate of the irrigation water flow according to the irrigation control signal generated by the intelligent control chip 2; A soil moisture sensor 6 is mounted on the outside of the chassis housing 1 and connected to the intelligent control chip 2 via a data transmission line, and is used to detect ambient temperature and humidity and transmit the detected data to the intelligent control chip 2; A temperature and humidity sensor 7 is installed on the outside of the chassis housing 1 and is connected to the intelligent control chip 2 through a data transmission line, and is used to detect the ambient temperature and humidity and transmit the detection data to the intelligent control chip 2; a solar power supply unit 8, which is installed outside the chassis housing 1 and is connected to the intelligent control chip 2, the solenoid valve 4, the wireless communication module 3 and the remote monitoring screen 9 through a power line, and is used to provide operating power for the intelligent drip irrigation control device; The remote monitoring screen 9 is installed outside the chassis shell 1 and is data-connected to the intelligent control chip 2 for displaying the operating status of the device, sensor detection data and irrigation control information.

[0113] Please see the attached Figure 6 , the intelligent control chip 2 includes: Data processing unit, used to clean, store and analyze sensor data; AI computing unit, used to calculate crop water requirements based on deep neural networks and optimize irrigation strategies. The device of this embodiment can be used to execute the above method embodiments. Its principles and technical effects are similar and will not be repeated here.

[0114] The present invention also provides a storage medium on which a computer program is stored. When the computer program is run by a processor, the above method is executed.

[0115] Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0116] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent drip irrigation control method, characterized in that: The following steps are involved: Data acquisition: Acquire soil moisture data, environmental meteorological data, and crop growth status data of the target area through multiple sensors, and transmit the data to the data processing module; Data processing: perform anomaly detection and standardization on the received sensor data and store it in the database; Intelligent analysis: Based on processed data, the real-time water requirement of crops is calculated, and combined with time series, the trend of soil moisture changes in the future is predicted to predict irrigation needs; Irrigation decision-making: Based on intelligent analysis results, formulate irrigation plans, including determining irrigation areas, irrigation water volume, and irrigation duration; Execute irrigation: according to the irrigation decision, send irrigation instructions to the intelligent solenoid valve (4) through remote control, control the opening time and opening degree of the solenoid valve (4), and realize accurate water supply; Feedback adjustment: Monitor changes in soil moisture after irrigation. If soil moisture does not reach the target value, adjust the irrigation plan and update irrigation parameters to optimize subsequent irrigation operations.

2. The intelligent drip irrigation control method according to claim 1, characterized in that: The data collection step specifically includes: Soil moisture collection: using a capacitive soil moisture sensor (6) to detect soil moisture content and transmit the data to a data processing module via wireless communication; Meteorological data collection: obtain ambient temperature, air humidity, rainfall, light intensity and wind speed through weather stations; Crop growth status collection: Multispectral imaging equipment is used to measure chlorophyll content and calculate the normalized vegetation index to assess crop water requirements.

3. The intelligent drip irrigation control method according to claim 1, characterized in that: The intelligent analysis step specifically includes: Calculation of crop water requirements: Using a deep neural network model, soil moisture, environmental weather, and crop growth status data are used as input to calculate the optimal water requirement of crops; Soil moisture trend forecast: Based on time series analysis methods and combined with historical soil moisture data, the soil moisture change trend in the next 1-3 days is predicted, and the irrigation plan is optimized based on the prediction results.

4. The intelligent drip irrigation control method according to claim 1, characterized in that: The irrigation decision-making steps specifically include: Calculate irrigation water volume: Calculate the required irrigation water volume based on crop water requirements, current soil moisture, and soil infiltration rate. The irrigation water volume is calculated as follows: The amount of irrigation water is equal to the difference between the crop water requirement and the current soil moisture divided by the soil infiltration rate; Optimize irrigation duration: Determine the duration of irrigation based on the calculated irrigation water volume and current water pressure.

5. Intelligent drip irrigation control system, characterized by: Used to assist the use of the intelligent drip irrigation control method according to any one of claims 1 to 4, comprising: The data acquisition module is used to collect soil moisture data, environmental meteorological data and crop growth status data, and transmit the collected data to the data processing and storage module; The data processing and storage module is used to receive the data transmitted by the data acquisition module, perform anomaly detection and standardization on the data, and store the data in a database; The intelligent analysis module is used to calculate crop water requirements and predict future soil moisture trends based on data provided by the data processing and storage module to assist in irrigation decision-making; The irrigation control module receives the prediction results provided by the intelligent analysis module, formulates an irrigation plan based on crop water requirements and soil moisture data, and calculates the irrigation water volume and duration; A remote control module for sending control instructions to the intelligent solenoid valve (4) via a wireless communication network according to the irrigation plan generated by the irrigation control module; The feedback monitoring module is used to monitor the changes in soil moisture after irrigation. If the soil moisture does not reach the target value, the irrigation plan is adjusted to optimize the subsequent irrigation strategy.

6. The intelligent drip irrigation control system according to claim 5, characterized in that: The intelligent analysis module includes: A deep neural network computing unit is used to calculate crop water requirements based on soil moisture, environmental weather, and crop growth status data; The time series analysis unit is used to predict the soil moisture change trend in the next 1-3 days based on historical soil moisture data and optimize irrigation strategies.

7. The intelligent drip irrigation control system according to claim 5, characterized in that: The remote control module includes: a wireless communication unit for remotely transmitting irrigation control instructions; The wired communication backup unit is used to automatically switch to wired communication mode when wireless communication fails.

8. Intelligent drip irrigation control device, characterized in that, Used in conjunction with the intelligent drip irrigation control method according to any one of claims 1 to 4, comprising: A chassis shell (1) is provided with an intelligent control chip (2), a wireless communication module (3), a solenoid valve (4) and a water flow meter (5) installed therein, wherein the wireless communication module (3) is connected to the intelligent control chip (2), the intelligent control chip (2) is used to receive data collected by a sensor, execute an irrigation control algorithm, and send an irrigation control signal to the solenoid valve (4), and the solenoid valve (4) is connected to the water flow meter (5) and is used to adjust the on / off and flow rate of the irrigation water flow according to the irrigation control signal generated by the intelligent control chip (2); A soil moisture sensor (6) is mounted on the outside of the chassis housing (1) and is connected to the intelligent control chip (2) via a data transmission line, and is used to detect ambient temperature and humidity and transmit the detected data to the intelligent control chip (2); a temperature and humidity sensor (7), which is mounted on the outside of the chassis housing (1) and connected to the intelligent control chip (2) via a data transmission line, and is used to detect ambient temperature and humidity and transmit the detected data to the intelligent control chip (2); a solar power supply unit (8), which is installed outside the chassis housing (1) and is connected to the intelligent control chip (2), the solenoid valve (4), the wireless communication module (3) and the remote monitoring screen (9) via a power line, and is used to provide operating power for the intelligent drip irrigation control device; A remote monitoring screen (9) is installed outside the chassis housing (1) and is data-connected to the intelligent control chip (2) for displaying the operating status of the device, sensor detection data, and irrigation control information.

9. The intelligent drip irrigation control device according to claim 8, characterized in that: The intelligent control chip (2) comprises: Data processing unit, used to clean, store and analyze sensor data; AI computing unit, used to calculate crop water requirements based on deep neural networks and optimize irrigation strategies.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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