Intelligent control system for agricultural irrigation

Abstract

The invention relates to the technical field of intelligent agriculture and precise irrigation, in particular to an intelligent control system for agricultural irrigation. Comprising a water-saving valve module, a sensing layer module, a control layer module, an execution layer module and a data and application layer module, and the water-saving valve module is used for achieving microsecond level flow regulation and control through piezoelectric ceramic driving and energy recovery and integrating a self-maintenance function. According to the invention, the LSTM network and the Duelling DQN algorithm are utilized to accurately discriminate the soil type and formulate an extreme weather coping scheme, dynamic optimization of an irrigation strategy is realized based on low-power-consumption edge calculation and an intelligent control algorithm, the adaptability and decision accuracy of the system are greatly improved, multiple advanced technologies are integrated, and the system is suitable for large-scale popularization and application. Comprising a magneto-rheological buffer, a TSN valve controller, an SMA emergency pressure release valve and a variable frequency water pump driver, pressure balance of a pipe network, high-precision synchronization of valves and safe treatment of sudden failures are ensured, and the stability and flexibility of an irrigation system are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent agriculture and precision irrigation technology, and specifically to an intelligent control system for agricultural irrigation. Background Art

[0002] Agricultural irrigation refers to the practice of artificially adding water to farmland to meet the water needs of crop growth and development. Its primary purpose is to regulate soil moisture, improve soil fertility and physical properties, promote root growth and nutrient absorption, and ensure stable and high crop yields. It can also maintain agricultural production in arid and semi-arid regions or during periods of insufficient or uneven rainfall. Agricultural irrigation methods include traditional surface irrigation, such as border irrigation, furrow irrigation, sprinkler irrigation, and drip irrigation, as well as modern water-saving irrigation technologies, and precision irrigation combined with intelligent control systems. The latter uses sensors to monitor parameters such as soil moisture and meteorological data to automatically control irrigation time and water volume, improving water resource utilization efficiency, reducing labor costs, and adapting to the large-scale, intensive, and intelligent development needs of modern agriculture. Existing technologies currently face problems with traditional agricultural irrigation systems, including low water resource utilization efficiency, poor environmental adaptability, insufficient system stability, and weak fault response capabilities.

[0003] Based on this, the present invention provides an intelligent control system for agricultural irrigation to solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent control system for agricultural irrigation. This system uses an LSTM network and the Dueling DQN algorithm to accurately identify soil types and develop extreme weather response plans. Relying on low-power edge computing and intelligent control algorithms, it achieves dynamic optimization of irrigation strategies, greatly improving the system's adaptability and decision-making accuracy. It also integrates multiple advanced technologies, including magnetorheological buffers, TSN valve controllers, SMA emergency pressure relief valves, and variable-frequency water pump drivers, to ensure pressure balance in the pipe network, high-precision valve synchronization, and safe handling of sudden failures, comprehensively enhancing the stability and flexibility of the irrigation system.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides an intelligent control system for agricultural irrigation, including a water-saving valve module, a perception layer module, a control layer module, an execution layer module, and a data and application layer module, wherein:

[0007] The water-saving valve module is used to achieve microsecond flow control through piezoelectric ceramic drive and energy recovery, and integrates self-maintenance function;

[0008] The perception layer module is used to collect environmental parameters such as soil moisture and meteorological data in real time;

[0009] The control layer module: Based on multi-source perception data, it dynamically optimizes irrigation parameters through a soil texture adaptive algorithm, generates valve control instructions in conjunction with a climate resilience decision engine, and implements low-power edge computing with an embedded AI acceleration unit;

[0010] The execution layer module is used to achieve dynamic balance of pipe network pressure through an intelligent water hammer suppression system, complete precise synchronization of valve groups based on a time-deterministic control architecture, and integrate a passive safety protection mechanism to deal with sudden failures;

[0011] The data and application layer module is used to store historical data through a local database and optionally connect to a cloud platform via 4G / Ethernet to support mobile APP visual monitoring and offline AI model updates for irrigation strategies.

[0012] The water-saving valve module includes a piezoelectric ceramic drive unit, a worm gear power generation unit, a self-cleaning valve core unit, and a wireless communication unit, wherein:

[0013] The piezoelectric ceramic drive unit is used to achieve microsecond flow regulation through nanometer-level control;

[0014] The worm gear power generation unit is used to utilize the irrigation water flow to drive the double-flow worm gear to recover energy;

[0015] The self-cleaning valve core unit is used to realize automatic anti-fouling of the valve core by using a DLC coating and an ultrasonic oscillator;

[0016] The wireless communication unit is used to integrate the LoRa module to realize remote monitoring of the valve status.

[0017] The self-cleaning valve core unit uses DLC coating and ultrasonic oscillator to realize automatic anti-fouling of the valve core. The specific operation is as follows:

[0018] A1: The DLC coating applied on the valve core surface has a thickness of 2-5μm and a surface roughness of Ra≤0.2μm, which is used to reduce the adhesion of impurities;

[0019] A2: The ultrasonic oscillator integrated into the valve body has an operating frequency of 20-40kHz. It is activated once during each irrigation cycle and lasts for 5-10 seconds each time. It removes the attachments on the valve core surface through high-frequency vibration.

[0020] The perception layer module includes a soil moisture sensor unit, a meteorological data acquisition unit, and a data preprocessing unit, wherein:

[0021] The soil moisture sensor unit is used to monitor key parameters of soil moisture, temperature, and pH value in real time;

[0022] The meteorological data acquisition unit is used to obtain environmental parameters such as light, wind speed and rainfall in real time using a micro-weather station;

[0023] The data pre-processing unit is used to perform preliminary processing on the acquired original data.

[0024] The control layer module includes a soil texture recognition unit, a climate resilience strategy library unit, an NPU acceleration unit, and a real-time control unit, wherein:

[0025] The soil texture recognition unit is used to automatically identify soil types, including sandy soil, loam, and clay, based on LSTM network analysis of moisture diffusion curves;

[0026] The climate resilience strategy library unit is used to store extreme weather response plans trained through reinforcement learning;

[0027] The NPU acceleration unit is used to execute the 8-bit integer quantized TensorFlow model;

[0028] The real-time control unit is used to run the fuzzy PID algorithm to output a PWM control signal.

[0029] The soil texture recognition unit automatically identifies soil types, including sandy soil, loam, and clay, by analyzing the moisture diffusion curve based on the LSTM network. The specific operations are as follows:

[0030] B1: The input layer receives time series data from multi-depth soil moisture sensor units, including soil volumetric water content at depths of 10 cm, 30 cm, and 50 cm, with a sampling interval of 15 minutes;

[0031] B2: The feature extraction layer uses three layers of LSTM units with 64, 32, and 16 neurons respectively to extract the spatiotemporal features of humidity diffusion;

[0032] B3: The output layer uses the Softmax function to calculate the probability distribution of sand, loam, and clay. When the maximum probability value is ≥90%, the corresponding classification result is output. The specific formula for calculating the probability of sand, loam, and clay using the Softmax function is:

[0033]

[0034] Where σ(z) i represents the probability of the i-th category; e is the base of the natural logarithm, which is approximately 2.71828; z i The input vector z is the i-th element; This is the denominator, representing the sum of the exponentials of all category scores;

[0035] B4: Integrated online learning mechanism, when the confidence level is less than 85% for three consecutive times, the incremental learning algorithm is automatically called to update the network weights.

[0036] The specific operations for extreme weather response in the climate resilience strategy library unit are as follows:

[0037] C1: Receives real-time monitoring data and 72-hour numerical weather forecasts from the meteorological data acquisition unit, including three-dimensional indicators: rainfall, wind speed, and evaporation;

[0038] C2: Built using the Dueling DQN algorithm, its network structure includes:

[0039] ① Value stream branch: calculate the state value function V(s);

[0040] ② Advantage flow branch: calculate the action advantage function A(s,a);

[0041] ③ Output layer: synthesize Q value through Q(s,a)=V(s)+(A(s,a)-mean(A(s)));

[0042] C3: Preview the strategy effect in the digital twin simulation environment and trigger the strategy optimization cycle when the predicted water saving rate is less than 15%;

[0043] C4: Quantize the trained policy network into an 8-bit integer model and store it in the read-only memory of the NPU acceleration unit.

[0044] The execution layer module includes a magnetorheological buffer unit, a TSN valve controller unit, an SMA emergency pressure relief valve unit, and a variable frequency water pump driver unit, wherein:

[0045] The magnetorheological buffer unit is used to suppress the water hammer effect by adjusting the damping through the magnetic field;

[0046] The TSN valve controller unit is used to implement μs-level valve synchronization using the IEEE 802.1Qbv protocol;

[0047] The SMA emergency pressure relief valve unit is used to realize automatic drainage when power is off by utilizing the temperature control characteristics of shape memory alloy;

[0048] The variable frequency water pump driver unit adjusts the water pump flow rate based on space vector PWM.

[0049] The TSN valve controller unit is used to implement μs-level valve synchronization using the IEEE 802.1Qbv protocol. The specific operations are as follows:

[0050] D1: Irrigation-specific time slots are divided based on the IEEE 802.1Qbv protocol, with the minimum time granularity set to 100 μs;

[0051] D2: Dynamically adjust the time slot duty cycle based on the current number of activated valves N. The formula is: Among them, T cycle 1ms, T Slot 100μs;

[0052] D3: Use IEEE 802.1Qbv protocol to calibrate the clock of each node, with synchronization error ≤ 5μs;

[0053] D4: When data packet loss is detected, the control instruction is retransmitted preferentially within the guard band time window.

[0054] The data and application layer module includes a data storage and transmission unit, a cloud platform analysis unit, and a human-computer interaction unit, wherein:

[0055] The data storage and transmission unit is used to cache irrigation data through a local SQLite database and supports 4G / Ethernet encrypted transmission to the cloud;

[0056] The cloud platform analysis unit is used to visualize valve status and soil moisture using GIS maps, train offline AI models based on historical data, and regularly update edge decision strategies;

[0057] The human-computer interaction unit is used to provide graphical configuration of irrigation plans, real-time alarm push and water-saving report generation through a mobile APP.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. This invention uses an LSTM network and the Dueling DQN algorithm to accurately identify soil types and develop extreme weather response plans. Relying on low-power edge computing and intelligent control algorithms, it achieves dynamic optimization of irrigation strategies, greatly improving the system's adaptability and decision-making accuracy. It also integrates multiple advanced technologies, including magnetorheological buffers, TSN valve controllers, SMA emergency pressure relief valves, and variable frequency water pump drivers, to ensure pressure balance in the pipe network, high-precision valve synchronization, and safe handling of sudden failures, comprehensively improving the stability and flexibility of the irrigation system.

[0060] 2. This invention uses advanced piezoelectric ceramic drive technology and energy recovery mechanism to achieve efficient and accurate flow control. It also has self-maintenance function and remote monitoring capability, significantly improving the intelligence level and reliability of the irrigation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a system diagram of an intelligent control system for agricultural irrigation according to the present invention.

[0062] Figure 2This is a flow chart of the control algorithm in an intelligent control system for agricultural irrigation according to the present invention.

[0063] Description of Figure Numbers:

[0064] 100. Water-saving valve module; 101. Piezoelectric ceramic drive unit; 102. Worm gear power generation unit; 103. Self-cleaning valve core unit; 104. Wireless communication unit; 200. Perception layer module; 201. Soil moisture sensor unit; 202. Meteorological data acquisition unit; 203. Data preprocessing unit; 300. Control layer module; 301. Soil texture identification unit; 302. Climate resilience strategy library unit; 303. NPU acceleration unit; 304. Real-time control unit; 400. Execution layer module; 401. Magnetorheological buffer unit; 402. TSN valve controller unit; 403. SMA emergency pressure relief valve unit; 404. Variable frequency water pump driver unit; 500. Data and application layer module; 501. Data storage and transmission unit; 502. Cloud platform analysis unit; 503. Human-computer interaction unit. DETAILED DESCRIPTION

[0065] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described 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 any creative efforts shall fall within the scope of protection of the present invention.

[0066] Example:

[0067] like Figure 1-Figure 2 As shown, this embodiment provides an intelligent control system for agricultural irrigation, including a water-saving valve module 100, a perception layer module 200, a control layer module 300, an execution layer module 400, and a data and application layer module 500, wherein: the water-saving valve module 100: is used to achieve microsecond flow control through piezoelectric ceramic drive and energy recovery, and integrates a self-maintenance function; the perception layer module 200: is used to collect environmental parameters such as soil moisture and meteorological data in real time; the control layer module 300: based on multi-source perception data, dynamically optimizes irrigation parameters through a soil texture adaptive algorithm, generates valve control instructions in combination with a climate resilience decision engine, and relies on an embedded AI acceleration unit to achieve low-power edge computing; the execution layer module 400: is used to achieve dynamic balance of pipe network pressure through an intelligent water hammer suppression system, complete precise synchronization of valve groups based on a time-deterministic control architecture, and integrate a passive safety protection mechanism to deal with sudden failures; the data and application layer module 500: is used to store historical data through a local database, and optionally connect to a cloud platform via 4G / Ethernet, supporting mobile APP visual monitoring and offline AI model updates for irrigation strategies.

[0068] In this embodiment, it should be noted that the perception layer module 200 collects soil moisture, meteorological data and other environmental parameters in real time as a basis, and generates valve control instructions through the control layer module 300 based on multi-source data through the soil texture adaptive algorithm and climate resilience decision engine, relying on the embedded AI acceleration unit to achieve low-power edge computing, and then drives the water-saving valve module 100 through piezoelectric ceramic drive and energy recovery to complete microsecond flow control and self-maintenance functions, and links the execution layer module 400 through the intelligent water hammer suppression system, time-deterministic control architecture and passive safety protection mechanism to achieve pipeline pressure balance, precise synchronization of valve groups and sudden fault response. Finally, the data and application layer module 500 is connected through the local database and cloud platform to support mobile APP visual monitoring and offline AI model updates of irrigation strategies.

[0069] In this invention, the water-saving valve module 100 includes a piezoelectric ceramic drive unit 101, a worm gear generator unit 102, a self-cleaning valve core unit 103, and a wireless communication unit 104. The piezoelectric ceramic drive unit 101 is used to achieve microsecond flow regulation through nanometer-level control; the worm gear generator unit 102 uses irrigation water to drive a dual-channel worm gear to recover energy; and the self-cleaning valve core unit 103 uses a DLC coating and an ultrasonic oscillator to automatically prevent the valve core from fouling. The specific operation is as follows: A1: The DLC coating on the valve core has a thickness of 2-5 μm and a surface roughness of Ra ≤ 0.2 μm to reduce the adhesion of impurities. A2: The ultrasonic oscillator integrated into the valve body operates at a frequency of 20-40 kHz and is activated once during each irrigation cycle for 5-10 seconds. It removes impurities from the valve core surface through high-frequency vibration. The wireless communication unit 104 integrates a LoRa module to enable remote monitoring of the valve status.

[0070] In this embodiment, it should be noted that the piezoelectric ceramic drive unit 101 achieves microsecond-level precise flow regulation, is powered by the worm gear power generation unit 102 which recovers energy, and achieves anti-fouling self-maintenance with the help of the DLC coating and ultrasonic oscillator of the self-cleaning valve core unit 103, and remotely monitors the status through the LoRa module of the wireless communication unit 104.

[0071] In addition, it should be noted that microsecond flow control utilizes the inverse piezoelectric effect of piezoelectric ceramics to generate micro-displacement by applying a 0-100V driving voltage, with a response time of <1ms, to achieve precise flow control. The dual-channel worm gear is integrated with a micro-generator. When the water flow velocity is ≥0.5m / s, the worm gear rotates to drive the generator, which, in conjunction with the supercapacitor to store energy, generates an average daily power generation of 6Wh, meeting the valve's self-powered needs. The DLC coating uses a magnetron sputtering process to apply a 2-5μm diamond-like carbon coating on the surface of the valve core; the ultrasonic oscillator, with an operating frequency of 20-40kHz, is activated once per irrigation cycle, lasting 5-10 seconds each time, and removes attachments from the valve core surface through high-frequency vibration.

[0072] In the present invention, the perception layer module 200 includes a soil moisture sensor unit 201, a meteorological data acquisition unit 202, and a data preprocessing unit 203, wherein: the soil moisture sensor unit 201 is used to monitor the key parameters of soil moisture, temperature, and pH value in real time; the meteorological data acquisition unit 202 is used to use a micro-meteorological station to obtain environmental parameters such as light, wind speed, and rainfall in real time; the data preprocessing unit 203 is used to perform preliminary processing on the obtained raw data.

[0073] In this embodiment, it should be noted that the soil moisture sensor unit 201 and the meteorological data acquisition unit 202 respectively collect soil and meteorological environmental parameters in real time, and then the data preprocessing unit 203 performs preliminary processing such as denoising and calibration on the raw data.

[0074] In addition, it should be noted that the initial processing is to perform sliding average filtering on the raw data to remove noise, calibrate sensor drift, mark outliers, and output JSON format for the control layer to call.

[0075] In the present invention, the control layer module 300 includes a soil texture identification unit 301, a climate resilience strategy library unit 302, an NPU acceleration unit 303, and a real-time control unit 304, wherein: the soil texture identification unit 301 is used to automatically identify the soil type, including sand, loam, and clay, based on the LSTM network analysis of the moisture diffusion curve; the specific operations are as follows: B1: the input layer receives time series data from the multi-depth soil moisture sensor unit 201, including the soil volume water content at depths of 10 cm, 30 cm, and 50 cm, with a sampling interval of 15 minutes; B2: the feature extraction layer uses three layers of LSTM units, with 64, 32, and 16 neurons set respectively to extract the spatiotemporal characteristics of moisture diffusion; B3: the output layer calculates the probability distribution of sand, loam, and clay through the Softmax function, and outputs the corresponding classification result when the maximum probability value is ≥90%. The specific formula for calculating the probability of sand, loam, and clay by the Softmax function is:

[0076]

[0077] Where σ(z) i represents the probability of the i-th category; e is the base of the natural logarithm, which is approximately 2.71828; z i The input vector z is the i-th element; This is the denominator, which represents the sum of the exponentials of the category scores;

[0078] B4: An integrated online learning mechanism automatically calls an incremental learning algorithm to update network weights when the confidence level is less than 85% for three consecutive times. The climate resilience strategy library unit 302 is used to store extreme weather response plans trained through reinforcement learning. The specific operations are as follows: C1: Receives real-time monitoring data and 72-hour numerical weather forecasts from the meteorological data acquisition unit 202, including three dimensional indicators: rainfall, wind speed, and evaporation. C2: Utilizes the Dueling DQN algorithm to construct a network structure comprising: ① Value stream branch: Calculates the state value function V(s); ② Advantage stream branch: Calculates the action advantage function A(s, a); ③ Output layer: Calculates the Q value using Q(s, a) = V(s) + (A(s, a) - mean(A(s))). C3: Previews the strategy effect in a digital twin simulation environment, triggering a strategy optimization loop when the predicted water savings rate is less than 15%. C4: Quantizes the trained strategy network into an 8-bit integer model and stores it in the read-only memory of the NPU acceleration unit 303. The NPU acceleration unit 303 is used to execute the 8-bit integer quantized TensorFlow model; the real-time control unit 304 is used to run the fuzzy PID algorithm to output the PWM control signal.

[0079] In this embodiment, it should be noted that the soil texture identification unit 301 determines the soil type based on multi-depth soil moisture data, and the climate resilience strategy library unit 302 generates extreme weather response plans based on meteorological data. After both are optimized by the NPU acceleration unit 303, the real-time control unit 304 outputs a control signal to drive the execution layer.

[0080] In addition, it should be noted that the specific formula of the state value function V(s) is: Among them, γ = 0.95 is the discount factor; r t As an immediate reward, it is calculated by weighting the water-saving benefit factor α and the crop water demand satisfaction β: t=0.6α+0.4β, α, β∈[0,1]. The specific formula of the action advantage function A(s,a) is: A(s,a)=Q(s,a)-V(s), where Q(s,a) is the state-action value function, which represents the expected long-term cumulative reward of executing action a in state s. The NPU acceleration unit 303 uses the Cambrian MLU220 chip, which supports 8-bit integer quantization model inference. The single inference time of the LSTM network is less than 10ms and the power consumption is less than 200mW. The fuzzy PID algorithm is specifically as follows: Input variables: soil moisture deviation e=set value-measured value; deviation change rate Δt=15 minutes; output variable: PWM signal duty cycle u, ranging from 0-100%, used to adjust the opening of the water-saving valve module 100.

[0081] In the present invention, the execution layer module 400 includes a magnetorheological buffer unit 401, a TSN valve controller unit 402, an SMA emergency pressure relief valve unit 403, and a variable frequency water pump driver unit 404. The magnetorheological buffer unit 401 is used to suppress the water hammer effect by adjusting the damping through the magnetic field; the TSN valve controller unit 402 is used to achieve μs-level valve synchronization using the IEEE 802.1Qbv protocol. The specific operations are as follows: D1: Dedicated irrigation time slots are divided based on the IEEE 802.1Qbv protocol, with the minimum time granularity set to 100 μs; D2: Dynamically adjust the time slot duty cycle based on the number of currently activated valves N, using the formula: Among them, T cycle 1ms, T Slot The time delay is 100μs; D3: IEEE 802.1Qbv protocol is used to calibrate each node clock, with a synchronization error of ≤5μs; D4: When packet loss is detected, control instructions are preferentially retransmitted within the guardband time window. The SMA emergency pressure relief valve unit 403 utilizes the temperature control properties of shape memory alloy to achieve automatic drainage during power outages; the variable frequency water pump driver unit 404 uses space vector PWM to adjust the water pump flow rate.

[0082] In this embodiment, it should be noted that the magnetorheological buffer unit 401 suppresses the water hammer effect to ensure the stability of the pipeline network, the TSN valve controller unit 402 adopts the IEEE 802.1Qbv protocol to achieve μs-level valve synchronization, the SMA emergency pressure relief valve unit 403 uses the characteristics of shape memory alloy to automatically drain water when the power is off, and the variable frequency water pump driver unit 404 adjusts the water pump flow based on space vector PWM.

[0083] In addition, it should be noted that a magnetorheological fluid shock absorber with adjustable damping is used. When the pipeline pressure fluctuation is detected to be greater than 10kPa, a 0-1.5T magnetic field is applied, and the damping force can be dynamically adjusted within the range of 50-500N, reducing the peak water hammer pressure by more than 60%. The shape memory alloy wire is made of NiTi alloy, and the phase change temperature is set to 25°C. When the valve is powered off and the pipeline temperature rises to 25°C, the SMA wire contracts and drives the valve disc to open, and the pressure relief speed is ≥5L / s. Space vector PWM technology, switching frequency 10kHz, motor efficiency increased by 5%, flow adjustment range 20-100m 3 / h, accuracy ±1%.

[0084] In the present invention, the data and application layer module 500 includes a data storage and transmission unit 501, a cloud platform analysis unit 502, and a human-computer interaction unit 503, wherein: the data storage and transmission unit 501 is used to cache irrigation data through a local SQLite database and support 4G / Ethernet encrypted transmission to the cloud; the cloud platform analysis unit 502 is used to visualize valve status and soil moisture using GIS maps, train offline AI models based on historical data, and regularly update edge decision strategies; the human-computer interaction unit 503 is used to provide graphical configuration of irrigation plans, real-time alarm push, and water-saving report generation through a mobile APP.

[0085] In this embodiment, it should be noted that the data storage and transmission unit 501 realizes local caching and cloud-encrypted transmission of irrigation data, and the cloud platform analysis unit 502 trains the AI model based on GIS visualization and historical data to optimize the decision-making strategy, and then the mobile APP of the human-computer interaction unit 503 enables users to configure the irrigation plan, monitor the status and generate reports.

[0086] In addition, it should be noted that the GIS map resolution reaches 0.5m, and it displays the valve status green = open, red = fault and soil moisture heat map in real time; the offline AI model is trained once a week and uses the XGBoost algorithm to predict the water demand for the next 7 days. 2 The goal of XGBoost is to minimize the objective function of the following form: Where, Is the loss function, which measures the model prediction value and the true value y i The gap between Ω(f k ) is the complexity penalty term of the K-th tree, and the specific formula is: Where T is the number of leaf nodes, w is the leaf weight vector, and γ and λ are regularization parameters. The mobile app supports gesture-based drawing of irrigation areas and automatically generates rotational irrigation plans. Alarms such as stuck valves and leaking pipes are sent via push notifications and text messages, with a response time of less than 1 minute.

[0087] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0088] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent control system for agricultural irrigation, characterized in that: It comprises a water-saving valve module (100), a perception layer module (200), a control layer module (300), an execution layer module (400), and a data and application layer module (500), wherein: The water-saving valve module (100) is used to achieve microsecond flow control through piezoelectric ceramic drive and energy recovery, and integrates a self-maintenance function; The perception layer module (200) is used to collect environmental parameters such as soil moisture and meteorological data in real time; The control layer module (300) dynamically optimizes irrigation parameters based on multi-source sensing data through a soil texture adaptive algorithm, generates valve control instructions in combination with a climate resilience decision engine, and implements low-power edge computing based on an embedded AI acceleration unit; The execution layer module (400) is used to achieve dynamic balance of pipe network pressure through an intelligent water hammer suppression system, complete precise synchronization of valve groups based on a time-deterministic control architecture, and integrate a passive safety protection mechanism to deal with sudden failures; The data and application layer module (500) is used to store historical data in a local database and optionally connect to a cloud platform via 4G / Ethernet to support offline AI model updates for mobile APP visual monitoring and irrigation strategies.

2. The intelligent control system for agricultural irrigation according to claim 1, characterized in that: The water-saving valve module (100) comprises a piezoelectric ceramic drive unit (101), a worm gear power generation unit (102), a self-cleaning valve core unit (103), and a wireless communication unit (104), wherein: The piezoelectric ceramic drive unit (101) is used to achieve microsecond-level flow regulation through nanometer-level control; The worm gear power generation unit (102) is used to utilize irrigation water flow to drive a double-channel worm gear to recover energy; The self-cleaning valve core unit (103) is used to realize automatic anti-fouling of the valve core by using a DLC coating and an ultrasonic oscillator; The wireless communication unit (104) is used to integrate a LoRa module to implement remote monitoring of valve status.

3. The intelligent control system for agricultural irrigation according to claim 2, characterized in that: The self-cleaning valve core unit (103) uses a DLC coating and an ultrasonic oscillator to achieve automatic anti-fouling of the valve core. The specific operation is as follows: A1: The DLC coating applied on the valve core surface has a thickness of 2-5μm and a surface roughness of Ra≤0.2μm, which is used to reduce the adhesion of impurities; A2: The ultrasonic oscillator integrated into the valve body has an operating frequency of 20-40kHz. It is activated once during each irrigation cycle and lasts for 5-10 seconds each time. It removes the attachments on the valve core surface through high-frequency vibration.

4. The intelligent control system for agricultural irrigation according to claim 1, characterized in that: The perception layer module (200) comprises a soil moisture sensor unit (201), a meteorological data acquisition unit (202), and a data preprocessing unit (203), wherein: The soil moisture sensor unit (201) is used to monitor key parameters such as soil moisture, temperature, and pH value in real time; The meteorological data acquisition unit (202) is used to obtain environmental parameters such as light intensity, wind speed, and rainfall in real time using a micro-meteorological station; The data pre-processing unit (203) is used to perform preliminary processing on the acquired original data.

5. The intelligent control system for agricultural irrigation according to claim 4, characterized in that: The control layer module (300) includes a soil texture identification unit (301), a climate resilience strategy library unit (302), an NPU acceleration unit (303), and a real-time control unit (304), wherein: The soil texture recognition unit (301) is used to automatically identify soil types, including sandy soil, loam, and clay, by analyzing the moisture diffusion curve based on the LSTM network; The climate resilience strategy library unit (302) is used to store extreme weather response plans trained through reinforcement learning; The NPU acceleration unit (303) is used to execute an 8-bit integer quantized TensorFlow model; The real-time control unit (304) is used to run a fuzzy PID algorithm to output a PWM control signal.

6. The intelligent control system for agricultural irrigation according to claim 5, characterized in that: The soil texture recognition unit (301) automatically identifies soil types, including sandy soil, loam, and clay, by analyzing the moisture diffusion curve based on the LSTM network. The specific operations are as follows: B1: The input layer receives time series data from the multi-depth soil moisture sensor unit (201), including the soil volume water content at the depths of 10 cm, 30 cm, and 50 cm, with a sampling interval of 15 minutes; B2: The feature extraction layer uses three layers of LSTM units with 64, 32, and 16 neurons respectively to extract the spatiotemporal features of humidity diffusion; B3: The output layer uses the Softmax function to calculate the probability distribution of sand, loam, and clay. When the maximum probability value is ≥90%, the corresponding classification result is output. The specific formula for calculating the probability of sand, loam, and clay using the Softmax function is: Where σ(z) i represents the probability of the i-th category; e is the base of the natural logarithm, which is approximately 2.71828; z i The input vector z is the i-th element; This is the denominator, representing the sum of the exponentials of all category scores; B4: Integrated online learning mechanism, when the confidence level is less than 85% for three consecutive times, the incremental learning algorithm is automatically called to update the network weights.

7. The intelligent control system for agricultural irrigation according to claim 6, characterized in that: The specific operations for responding to extreme weather in the climate resilience strategy library unit (302) are as follows: C1: Receives real-time monitoring data and 72-hour numerical weather forecasts from the meteorological data acquisition unit (202), including three-dimensional indicators of rainfall, wind speed, and evaporation; C2: Built using the Dueling DQN algorithm, its network structure includes: ① Value stream branch: calculate the state value function V(s); ② Advantage flow branch: calculate the action advantage function A(s,a); ③ Output layer: synthesize Q value through Q(s,a)=V(s)+(A(s,a)-mean(A(s))); C3: Preview the strategy effect in the digital twin simulation environment and trigger the strategy optimization cycle when the predicted water saving rate is less than 15%; C4: Quantize the trained policy network into an 8-bit integer model and store it in the read-only memory of the NPU acceleration unit (303).

8. The intelligent control system for agricultural irrigation according to claim 1, characterized in that: The execution layer module (400) includes a magnetorheological buffer unit (401), a TSN valve controller unit (402), an SMA emergency pressure relief valve unit (403), and a variable frequency water pump driver unit (404), wherein: The magnetorheological buffer unit (401) is used to suppress the water hammer effect by adjusting the damping through the magnetic field; The TSN valve controller unit (402) is used to implement μs-level valve synchronization using the IEEE 802.1Qbv protocol; The SMA emergency pressure relief valve unit (403) is used to realize automatic drainage in the event of power failure by utilizing the temperature control characteristics of the shape memory alloy; The variable frequency water pump driver unit (404) adjusts the water pump flow rate based on space vector PWM.

9. The intelligent control system for agricultural irrigation according to claim 8, characterized in that: The TSN valve controller unit (402) is used to implement μs-level valve synchronization using the IEEE 802.1Qbv protocol, and the specific operations are as follows: D1: Irrigation-specific time slots are divided based on the IEEE 802.1Qbv protocol, with the minimum time granularity set to 100 μs; D2: Dynamically adjust the time slot duty cycle based on the current number of activated valves N. The formula is: Among them, T cycle 1ms, T Slot 100μs; D3: Use IEEE 802.1Qbv protocol to calibrate the clock of each node, with synchronization error ≤ 5μs; D4: When data packet loss is detected, the control instruction is retransmitted preferentially within the guard band time window.

10. The intelligent control system for agricultural irrigation according to claim 1, characterized in that: The data and application layer module (500) includes a data storage and transmission unit (501), a cloud platform analysis unit (502), and a human-computer interaction unit (503), wherein: The data storage and transmission unit (501) is used to cache irrigation data through a local SQLite database and supports 4G / Ethernet encrypted transmission to the cloud; The cloud platform analysis unit (502) is used to visualize valve status and soil moisture using GIS maps, train offline AI models based on historical data, and regularly update edge decision strategies; The human-computer interaction unit (503) is used to provide graphical configuration of irrigation plans, real-time alarm push and water-saving report generation through a mobile terminal APP.

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