A drip irrigation system and water pressure regulation method based on Q-learning learning method

Abstract

This invention discloses a drip irrigation system based on the Q-learning method. The system comprises: a data acquisition module for installing pressure sensors at each outlet of the drip irrigation pipeline to collect water pressure data and record the terrain information at the outlet location; a Q-learning module for receiving the water pressure data and terrain information collected by the data acquisition module, learning based on the Q-learning algorithm to establish a correlation model between outlet water pressure and terrain, and outputting adjustment commands based on the model; a distributed wireless sensor network module for constructing a distributed wireless sensor network based on the water pressure values ​​generated by the Q-learning module, where each sensor node dynamically adjusts the network topology according to its own water pressure value and the communication status of adjacent nodes, achieving adaptive network construction and node management; and a control module for receiving the water pressure adjustment commands output by the Q-learning module and controlling the flow regulation device of the drip irrigation pipeline to ensure that each outlet reaches a preset water pressure balance.

Description

Technical Field

[0001] This application relates to agricultural automation control, wireless sensor networks, irrigation system management, and machine learning applications, specifically to a drip irrigation system and water pressure regulation method based on the Q-learning method. Background Technology

[0002] A drip irrigation system based on Q-learning aims to optimize water pressure management through intelligent algorithms and advanced sensor technology. This system utilizes a wireless sensor network to monitor key parameters such as water pressure and soil moisture at each drip point in real time, and dynamically adjusts the water pressure using a Q-learning algorithm to ensure crops receive a uniform and adequate water supply, thereby improving water resource utilization efficiency and crop yield. However, in uneven terrain, differences in water pressure at the drip irrigation pipe outlets can occur, resulting in inconsistent water supply to crops at different heights and locations, thus affecting crop growth and yield. This has always been one of the main challenges facing this system. Summary of the Invention

[0003] In view of this, the present disclosure provides a drip irrigation system based on the Q-learning method, which at least partially solves the problems existing in the prior art.

[0004] A drip irrigation system based on the Q-learning method includes: a data acquisition module for installing pressure sensors at each outlet of the drip irrigation pipeline to collect water pressure data and record the topographic information of the outlet location; a Q-learning module for receiving the water pressure data and topographic information collected by the data acquisition module, learning based on the Q-learning algorithm to establish a correlation model between outlet water pressure and topography, and outputting adjustment commands based on the model; a distributed wireless sensor network module for constructing a distributed wireless sensor network based on the water pressure values ​​generated by the Q-learning module, wherein each sensor node dynamically adjusts the network topology according to its own water pressure value and the communication status of neighboring nodes, realizing adaptive network construction and node management; and a control module for receiving the water pressure adjustment commands output by the Q-learning module and controlling the flow regulation device of the drip irrigation pipeline to ensure that each outlet reaches a preset water pressure balance.

[0005] Preferably, the Q-learning module includes a feedback loop to update the Q table based on the deviation between the actual water pressure value and the target water pressure value at each outlet, thereby optimizing the water pressure regulation effect. The specific steps of the Q-learning module including a feedback loop are as follows:

[0006] S1: Establish the status representation of the water outlet of the drip irrigation pipeline.

[0007] S2: Action set design; Based on the slope angle θ and valve opening ψ, design n typical valve opening datasets {ψ-1, ψ-2, ..., ψ-m} for m typical slope angles {θ-1, θ-2, ..., θ-m} respectively;

[0008] S3: Reward function design; The maximum cumulative reward from the starting point to the target point is obtained through environmental feedback, and a reward function is established.

[0009] S4: Action selection strategy design; First, determine the size of the terrain elevation angle θ, and the selection range of θ is the set m typical elevation angles {θ-1, θ-2, ..., θ-m}; Based on the elevation angle, select the corresponding n typical gate opening datasets ai = {ψ-(i1), ψ-(i2), ..., ψ-(in)}; Then, set a greedy value ε, and generate a random value p∈(0,1) each time an action is selected, which is used to determine whether the next action is randomly selected or strategically selected, to avoid the algorithm getting trapped in local optima; When p is less than ε, randomly select an action value; When p is greater than ε, select the action with the largest Q value in the action set as the action selected this time;

[0010] S5: Q-value table initialization method design; Q-value is initialized using the difference between the outlet water pressure and the target water pressure value, that is, the closer to the target value, the smaller the Q-value;

[0011] S6: Select action a from the action selection set ai corresponding to the terrain elevation angle according to the action selection strategy; S7: Obtain the immediate reward value Reward and the new valve opening S' after executing action a according to the reward function;

[0012] S8: Update the Q-value corresponding to state S and action a according to the following Q-value update function: Q(S,a)←(1-α)Q(S,a)+α[r+γmaxQ(S',a)]

[0013] S9: Determine if the water pressure has reached the target value. If not, continue to select the next action based on the action selection strategy; otherwise, end this round of learning.

[0014] Preferably, the distributed wireless sensor network module forms an adaptive connection relationship between nodes based on the water pressure value and terrain information of each sensor node, so as to realize dynamic topology adjustment and improve the scalability and fault tolerance of the network.

[0015] Preferably, the data acquisition module includes a 5G module for recording the geographical location of each water outlet, thereby providing accurate terrain data for analysis by the Q-learning module.

[0016] A water pressure regulation method for a drip irrigation system based on Q-learning includes the following steps: collecting water pressure data and terrain information from each outlet through a data acquisition module; using a Q-learning module to learn the water pressure data and terrain information to construct a correlation model between outlet water pressure and terrain; outputting water pressure regulation commands based on the model and sending them to a control module to adjust the water pressure at each outlet; updating the Q-table of the Q-learning model based on real-time water pressure feedback information from each outlet to gradually optimize the water pressure regulation effect.

[0017] Preferably, the distributed wireless sensor network module is used to construct a distributed wireless sensor network based on the water pressure value of each outlet, so that the network topology can be adaptively adjusted according to changes in water pressure data.

[0018] Preferably, when constructing a distributed wireless sensor network, the sensor nodes reorganize the network based on their own water pressure values ​​and the connection status of adjacent nodes to adapt to the network coverage requirements under different terrain conditions.

[0019] This disclosure provides a drip irrigation system based on the Q-learning method. The system includes: a data acquisition module for installing pressure sensors at each outlet of the drip irrigation pipeline to collect water pressure data and record the terrain information at the outlet location; a Q-learning module for receiving the water pressure data and terrain information collected by the data acquisition module, learning based on the Q-learning algorithm to establish a correlation model between outlet water pressure and terrain, and outputting adjustment commands based on the model; a distributed wireless sensor network module for constructing a distributed wireless sensor network based on the water pressure value generated by the Q-learning module, wherein each sensor node dynamically adjusts the network topology according to its own water pressure value and the communication status of adjacent nodes to achieve adaptive network construction and node management; and a control module for receiving the water pressure adjustment commands output by the Q-learning module and controlling the flow regulation device of the drip irrigation pipeline to ensure that each outlet reaches a preset water pressure balance.

[0020] The Q-learning module includes a feedback loop to update the Q-table based on the deviation between the actual and target water pressure values ​​at each outlet, thereby optimizing the water pressure regulation effect. A distributed wireless sensor network module forms adaptive connections between nodes based on the water pressure values ​​and terrain information of each sensor node, enabling dynamic topology adjustments and improving network scalability and fault tolerance. The data acquisition module includes a GPS module to record the geographical location of each outlet, providing accurate terrain data for analysis by the Q-learning module. A water pressure regulation method for a drip irrigation system based on the Q-learning approach includes the following steps: collecting water pressure data and terrain information from each outlet through the data acquisition module; using the Q-learning module to learn from the water pressure data and terrain information, constructing a correlation model between outlet water pressure and terrain; outputting water pressure regulation commands based on the model and sending them to the control module to adjust the water pressure at each outlet; updating the Q-table of the Q-learning model based on real-time water pressure feedback information from each outlet, gradually optimizing the water pressure regulation effect. The distributed wireless sensor network module constructs a network based on the water pressure value of each outlet, enabling the network topology to adaptively adjust according to changes in water pressure data. During network construction, sensor nodes reorganize the network based on their own water pressure value and the connection status of adjacent nodes to adapt to network coverage requirements under different terrain conditions. The solution presented in this embodiment addresses the impact of varying water pressure at drip irrigation outlets on crop growth under uneven terrain. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the exemplary embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this disclosure and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a water pressure regulation method based on Q-learning.

[0023] Figure 2 This is a flowchart of the Q-learning module. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings. The illustrative implementation methods and descriptions of the embodiments of this disclosure are only used to explain the embodiments of this disclosure and are not intended to limit the embodiments of this disclosure.

[0025] Next, referring to the accompanying drawings, this invention describes how a drip irrigation system based on the Q-learning method solves the problem of varying water pressure at the drip irrigation pipe outlet affecting crop growth under uneven terrain conditions through various steps.

[0026] The first step involves the data acquisition module installing pressure sensors at each outlet of the drip irrigation pipeline to collect water pressure data and record the terrain information of the outlet's location. In this step, each outlet is equipped with a pressure sensor to monitor water pressure. Simultaneously, the 5G module records the geographical location of each outlet, providing accurate terrain data. This data is crucial for subsequent learning and adjustments. For example, if an outlet is located at a higher elevation, its water pressure will typically be lower than that of outlets at lower elevations. This difference in terrain will result in varying amounts of irrigation water received by crops in different locations, thus affecting crop growth. Specifically, suppose a drip irrigation pipeline is laid on a hill, with outlet A at the top and outlet B at the foot. The data acquisition module will record the low water pressure value displayed by the pressure sensor at point A and the high water pressure value displayed by the pressure sensor at point B, and combine this with their geographical location information, transmitting this data to the subsequent learning module.

[0027] The second step involves using the Q-learning module to learn from the received water pressure data and terrain information, constructing a correlation model between the outlet water pressure and the terrain. In this process, the Q-learning algorithm learns from a large amount of historical data to identify the variation patterns of outlet water pressure under different terrain conditions, thus establishing a correlation model. This model uses water pressure as the dependent variable and terrain parameters (such as altitude and slope) as independent variables to predict the expected water pressure value under different terrain conditions. For example, based on historical data, the Q-learning module might discover a pattern where the water pressure decreases by 0.1 MPa for every 1 meter increase in outlet height. Specifically, when the system receives new terrain data, such as an outlet with an altitude of 50 meters, the Q-learning model will predict the expected water pressure value at that location to be 0.9 MPa (assuming a baseline water pressure value of 1.4 MPa), and compare it with the actual measured water pressure value to determine the deviation.

[0028] The third step involves outputting a water pressure adjustment command based on the model and sending it to the control module to adjust the water pressure at each outlet. In this step, after receiving the adjustment command from the Q-learning module, the control module adjusts the water pressure at each outlet by controlling the flow regulating device in the drip irrigation pipeline. This step ensures that even on uneven terrain, the water pressure at all outlets remains within the preset target range, thus guaranteeing irrigation consistency. For example, if the Q-learning model predicts that the water pressure at an outlet at a high elevation is 0.8 MPa, while the target value is 1.0 MPa, the system will issue an adjustment command to increase the water pressure at that outlet. Specifically, the control module may adjust the valve opening to increase the water pressure at high-pressure locations to the target value while simultaneously decreasing the water pressure at low-pressure locations to maintain the balance of the entire system.

[0029] The fourth step involves updating the Q-table of the Q-learning model based on real-time water pressure feedback from each outlet, gradually optimizing the water pressure regulation effect. The Q-learning module continuously receives real-time water pressure data from each outlet, compares it with the target value, and updates the Q-value in the Q-table according to the deviation. A higher Q-value indicates a greater probability of taking a certain action, ultimately enabling the system to gradually learn the optimal water pressure regulation strategy under different terrain conditions. For example, if the water pressure at an outlet at a certain elevation is still insufficient after adjustment, the system will continue to increase the regulation intensity at that location until the target value is reached. Specifically, the Q-learning module records the effect after each adjustment, iteratively learning and gradually optimizing the water pressure regulation scheme to ensure the long-term stability of the entire system.

[0030] The fifth step involves constructing a distributed wireless sensor network based on the water pressure values ​​at each outlet, using the distributed wireless sensor network module. This allows the network topology to adaptively adjust according to changes in water pressure data. This module not only transmits data but also enables dynamic network reconfiguration. When the water pressure at a node changes, the network topology adjusts accordingly to ensure efficient signal transmission and stable system operation. For example, in high-altitude areas, if the water pressure at a node drops significantly, surrounding nodes will dynamically adjust network connections to form a more stable transmission path. Specifically, assuming node A is located on a steep slope and surrounded by multiple nodes, if A's water pressure is abnormally low, other nodes in the network will adjust their routes to bypass the faulty node A, maintaining the connectivity of the entire network.

[0031] The sixth step involves reorganizing the network based on the sensor nodes' own water pressure values ​​and the connection status of neighboring nodes during the construction of the distributed wireless sensor network. This reorganization adapts to the network coverage requirements under different terrain conditions. This adaptive adjustment not only improves the network's flexibility but also enhances its fault tolerance. Even if some nodes fail, other nodes can quickly fill the gap.

[0032] The present invention discloses a drip irrigation system based on the Q-learning method, comprising the following main steps, aiming to solve the technical problem that varying water pressure at the drip irrigation pipe outlet affects crop growth under uneven terrain conditions:

[0033] Data Acquisition: Pressure sensors are installed at each outlet of the drip irrigation pipeline to collect water pressure data from each outlet in real time. Simultaneously, the data acquisition module records the geographical location of each outlet via a built-in 5G module, obtaining precise terrain information. This data will be used for subsequent analysis and adjustment processes.

[0034] Q-learning: After receiving water pressure data and terrain information from the data acquisition module, the Q-learning module learns based on the Q-learning algorithm. This module establishes a correlation model between outlet water pressure and terrain, analyzing the ideal water pressure distribution at each outlet under different terrain conditions. Through continuous learning and optimization, this model can gradually improve the accuracy of water pressure regulation.

[0035] Network Construction: The distributed wireless sensor network module uses the water pressure values ​​of each outlet as node information to construct a dynamic wireless sensor network. Each node (i.e., the outlet) in this network can dynamically adjust the network topology based on its own water pressure value and the communication status of neighboring nodes. In this way, even if terrain changes cause some nodes to lose contact, the network can still maintain stability and adaptability.

[0036] Water pressure regulation: The control module receives water pressure regulation commands from the Q-learning module and controls the flow regulation device in the drip irrigation pipeline. Specifically, the control module adjusts the flow valve or other regulating devices to ensure that the water pressure at each outlet reaches a preset equilibrium state. Through this precise control, each outlet can output stable water pressure under different terrain conditions to meet the needs of crop growth.

[0037] Feedback Optimization: To further improve the accuracy and effectiveness of water pressure regulation, the system also includes a feedback loop. In practical applications, real-time water pressure feedback information from each outlet is continuously transmitted back to the Q-learning module. By comparing the deviation between the actual water pressure value and the target water pressure value, the Q-learning model continuously updates the Q-table, gradually optimizing the water pressure regulation strategy.

[0038] Through the above steps, this invention can achieve precise water pressure control in complex terrain environments, ensuring that the drip irrigation system provides crops with uniform and appropriate amounts of water, effectively solving the problem of inconsistent water pressure caused by uneven terrain, and improving irrigation efficiency and crop growth quality.

[0039] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this invention, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A drip irrigation system based on the Q-learning method, the system comprising: The data acquisition module is used to install pressure sensors at each outlet of the drip irrigation pipeline to collect water pressure data and record the terrain information of the outlet location. The Q-learning module receives water pressure data and terrain information collected by the data acquisition module, learns based on the Q-learning algorithm, establishes a correlation model between outlet water pressure and terrain, and outputs adjustment commands based on the model. The distributed wireless sensor network module constructs a distributed wireless sensor network based on the water pressure values ​​generated by the Q-learning module. Each sensor node dynamically adjusts the network topology according to its own water pressure value and the communication status of neighboring nodes, achieving adaptive network construction and node management. The control module receives water pressure adjustment commands output by the Q-learning module and controls the flow regulation device of the drip irrigation pipeline to ensure that each outlet reaches a preset water pressure balance. The Q-learning module includes a feedback loop to update the Q-table based on the deviation between the actual water pressure value and the target water pressure value at each outlet, thereby optimizing the water pressure regulation effect. The Q-learning module includes a feedback loop, and the specific steps are as follows: S1: Establish a status representation for the drip irrigation pipeline outlet; S2: Action set design; Based on the slope angle θ and valve opening ψ, design n typical valve opening datasets {ψ-1, ψ-2, ..., ψ-m} for m typical slope angles {θ-1, θ-2, ..., θ-m} respectively; S3: Reward function design; The maximum cumulative reward from the starting point to the target point is obtained through environmental feedback, and a reward function is established. S4: Action selection strategy design; First, determine the size of the terrain elevation angle θ, and the selection range of θ is the set m typical elevation angles {θ-1, θ-2, ..., θ-m}; Based on the elevation angle, select the corresponding n typical gate opening datasets ai = {ψ-(i1), ψ-(i2), ..., ψ-(in)}; Then, set a greedy value ε, and generate a random value p∈(0,1) each time an action is selected, which is used to determine whether the next action is randomly selected or strategically selected, to avoid the algorithm getting trapped in local optima; When p is less than ε, randomly select an action value; When p is greater than ε, select the action with the largest Q value in the action set as the action selected this time; S5: Q-value table initialization method design; Q-value is initialized using the difference between the outlet water pressure and the target water pressure value, that is, the closer to the target value, the smaller the Q-value; S6: Based on the action selection strategy, select action a from the action selection set ai corresponding to the terrain elevation angle; S7: Obtain the immediate reward value Reward and the new valve opening S' after executing action a based on the reward function; S8: Update the Q value corresponding to state S and action a according to the following Q value update function: Q(S,a)←(1-α)Q(S,a)+α[r+γmaxQ(S',a)], where α is the learning rate, which controls the speed of experience update, and γ is the discount factor, which evaluates the importance of future rewards. S9: Determine if the water pressure has reached the target value. If not, continue to select the next action based on the action selection strategy; otherwise, end this round of learning.

2. The system according to claim 1, characterized in that, The distributed wireless sensor network module forms adaptive connections between nodes based on the water pressure values ​​and terrain information of each sensor node, so as to realize dynamic topology adjustment and improve the scalability and fault tolerance of the network.

3. The system according to claim 1, characterized in that, The data acquisition module includes a 5G module, which is used to record the geographical location of each water outlet, thereby providing accurate terrain data for analysis by the Q-learning module.

4. The water pressure regulation method of the system according to any one of claims 1 to 3, characterized in that, Includes the following steps: The data acquisition module collects water pressure data and terrain information from each water outlet; The Q-learning module is used to learn the water pressure data and terrain information to construct a correlation model between the outlet water pressure and the terrain. Based on the model, water pressure adjustment commands are output and sent to the control module to adjust the water pressure of each outlet. According to the real-time water pressure feedback information of each outlet, the Q table of the Q-learning model is updated to gradually optimize the water pressure adjustment effect.

5. The method according to claim 4, characterized in that, The distributed wireless sensor network module is used to construct a distributed wireless sensor network based on the water pressure value of each outlet, enabling the network topology to be adaptively adjusted according to changes in water pressure data.

6. The method according to claim 5, characterized in that, When constructing a distributed wireless sensor network, the sensor nodes reorganize the network based on their own water pressure values ​​and the connection status of adjacent nodes to adapt to the network coverage requirements under different terrain conditions.

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