Drip irrigation anti-blocking system and method based on tail end working condition sensing and targeted cleaning

Abstract

The invention discloses a drip irrigation anti-blocking system and method based on terminal working condition sensing and targeted cleaning, and belongs to the technical field of intelligent agriculture. The device comprises a raw water tank; the front-end self-adaptive processing unit is configured to be capable of purifying irrigation water and killing microorganisms; the irrigation pipe network is configured to be capable of conveying irrigation water to an irrigation area; the plurality of intelligent drippers are arranged on the irrigation pipe network and are configured to realize intelligent drip irrigation; the system controller is connected with the intelligent water dropper and can control the drip irrigation action of the intelligent water dropper; a micro thermal pulse sensor and a targeted cleaning agent release unit are integrated in the intelligent water dropper; the micro thermal pulse sensor is configured to be capable of measuring the thickness of attachments on the wall surface of the inner flow channel of the water dropper; and the targeted cleaning agent release unit is configured to release a targeted cleaning agent to clean attachments when conditions are met. The blocking condition in the water dropper can be directly monitored in real time, and efficient blocking prevention can be achieved.

Description

Technical Field

[0001] This invention belongs to the field of smart agriculture technology, specifically relating to a drip irrigation anti-clogging system and method based on end-point condition sensing and targeted cleaning. Background Technology

[0002] Drip irrigation technology is the core of water-saving agriculture. However, when using fertilizer sources rich in suspended solids, organic matter, and microorganisms, such as aquaculture wastewater and biogas slurry, dripper clogging is a prominent problem, becoming a key bottleneck restricting its large-scale application.

[0003] Existing anti-clogging technologies are mainly divided into three categories: 1. Front-end filtration: Physical methods such as multi-stage sieves and cyclone separation are used to remove large particles. However, this method cannot remove colloids, microorganisms, or crystallizable salts, and the filtration device itself requires frequent backwashing, making operation and maintenance complex.

[0004] 2. Chemical cleaning: Periodically injecting acids, alkalis, or oxidants into the system for overall cleaning. This method carries the risk of chemical residue, corrosion of system components, and damage to the soil ecosystem. Furthermore, it is a "blind cleaning" method with high energy and chemical consumption.

[0005] 3. Physical flushing and special dripper design: Increasing pipeline pressure for pulse flushing, or using drippers with large flow channels or turbulent flow channels. This method is energy-intensive, has limited anti-clogging effect, and often comes at the cost of sacrificing irrigation uniformity.

[0006] In addition, existing technologies generally suffer from two fundamental defects: first, they lack the ability to perceive the blockage process in real time and directly, making it impossible to intervene in the early stages of blockage; second, the processing is detached from the end, with the front-end processing technology being fixed and unable to be dynamically optimized based on the actual blockage risk at the end, resulting in resource waste or insufficient processing. Summary of the Invention

[0007] The present invention aims to at least partially solve one of the technical problems in the aforementioned related technologies.

[0008] Therefore, the purpose of this invention is to provide a drip irrigation anti-clogging system and method based on end-point condition sensing and targeted cleaning, which can monitor the internal clogging status of drippers in real time and directly, realizing a fundamental shift from "passively responding to clogging" to "actively predicting and precisely controlling clogging", and dynamically optimizing the front-end processing technology through closed-loop feedback, so as to save water, electricity and chemical agents to the maximum extent while effectively preventing clogging.

[0009] To solve the above-mentioned technical problems, the present invention is implemented as follows: This invention provides a drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning, the system comprising: Raw water tank, used to hold irrigation raw water; The front-end adaptive processing unit is configured to purify and kill microorganisms in the irrigation water. The irrigation network, connected to the front-end adaptive processing unit, is configured to deliver irrigation water to the irrigation area. Several smart drippers are installed on the irrigation network and configured to enable intelligent drip irrigation; and, The system controller is connected to the smart dripper and is able to control the dripping action of the smart dripper.

[0010] In addition, the drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning according to the present invention may also have the following additional technical features: In some embodiments, the system further includes: Fertilizer pumps, installed on irrigation networks, are used to add fertilizers / pesticides. The filter, installed on the irrigation pipeline, is used to filter and purify irrigation water.

[0011] In some embodiments, the smart dripper integrates a micro-thermal pulse sensor and a targeted cleaning agent release unit; Micro-thermal pulse sensor: configured to measure the thickness of deposits on the inner channel wall of the dripper; Targeted cleaning agent release unit: configured to release targeted cleaning agent to clean the deposits when conditions are met.

[0012] In some embodiments, the micro-thermal pulse sensor includes a micro heating element and at least two micro temperature sensing elements; the micro heating element and the micro temperature sensing elements are arranged along the direction of water flow. By applying a short-duration thermal pulse to the micro heating element and detecting the time difference and amplitude change of the thermal signal arrival by the micro temperature sensing element, the thickness of the deposit is calculated based on the differences in the thermophysical parameters of the deposit, the channel wall material, and the water.

[0013] In some embodiments, the targeted cleaning agent release unit includes a reservoir, an electrically openable seal, and a solid or liquid cleaning agent located within the reservoir. The seal is disposed on the liquid storage chamber and releases the solid or liquid cleaning agent located in the liquid storage chamber when opened.

[0014] The cleaning agent in the targeted cleaning agent release unit is one or more of the following: a compound enzyme preparation for biofilms, acidic microspheres for mineral scale, or chelating agent microcapsules.

[0015] In some of these embodiments, the smart dripper also integrates an in-situ water quality monitoring sensor and an energy harvesting module; The water quality monitoring data is connected to the system controller, and the monitoring data is used for decision optimization by the system controller; The energy harvesting module is used to extract energy from the irrigation water flow and power the internal circuitry of the dripper.

[0016] In some of these embodiments, the front-end adaptive processing unit includes an electrocoagulation module and an ozone oxidation module; The electrocoagulation module and the ozone oxidation module are respectively connected to the system controller; The system controller dynamically optimizes and adjusts the operating parameters of the electrocoagulation module and the ozone oxidation module based on the global risk distribution.

[0017] In some of these embodiments, the electrically operable seal is any one of an electrolytically dissolvable film, a miniature solenoid valve, and a fusible wire.

[0018] This invention also provides a drip irrigation anti-clogging method based on end-point condition sensing and targeted cleaning, the method comprising: Real-time acquisition of the thickness data of the attached material measured by the micro-thermal pulse sensor inside each smart drip head; Based on the thickness data of the attached material, a predictive model is used to calculate the real-time clogging risk index of each smart dripper. If the risk index of a certain smart dripper exceeds the first threshold, the targeted cleaning agent release unit of the smart dripper is instructed to release cleaning agent to clean the attached substances. Based on the risk index distribution of all smart drippers, the optimal collaborative working parameters of the front-end electrocoagulation module and ozone oxidation module are dynamically calculated using an optimization algorithm and then executed. Collect system operation data and update the prediction model and optimization algorithm parameters regularly.

[0019] In addition, the drip irrigation anti-clogging method based on end-point condition sensing and targeted cleaning according to the present invention may also have the following additional technical features: In some implementations, the prediction model is a trained LSTM neural network model.

[0020] Compared with the prior art, the present invention has at least the following beneficial effects: In this embodiment of the invention, the drip irrigation anti-clogging system and method based on end-point condition sensing and targeted cleaning is the first to apply the "micro-thermal pulse method" to the direct online thickness measurement of the microchannels inside the dripper. This method has strong anti-pollution capabilities, fast response, and high accuracy, and can directly quantify the root cause of clogging—the accumulation of deposits on the wall surface, providing an unprecedented direct data source for precise control. In this embodiment of the invention, the drip irrigation anti-clogging system and method based on end-point condition sensing and targeted cleaning establishes an intelligent closed loop of "end-point sensing - risk prediction - front-end control - targeted cleaning". The system is no longer an open-loop fixed process, but dynamically adjusts the pretreatment intensity according to the actual needs of the end point, realizing "on-demand processing" and greatly improving the overall energy efficiency. In this embodiment of the invention, the drip irrigation anti-clogging system and method based on end-point condition sensing and targeted cleaning changes "periodic blind cleaning" or "replacement after failure" to "predictive targeted maintenance". It only performs micro-cleaning and in-situ cleaning on individual drippers with excessive risk, avoiding a complete shutdown and the use of large doses of chemicals, which is economical and environmentally friendly. In this embodiment of the invention, the drip irrigation anti-clogging system and method based on end-point condition sensing and targeted cleaning provides a control system that can continuously optimize the prediction model and control parameters based on historical data, so that the anti-clogging performance of the system can improve itself over time.

[0021] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall architecture of a drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning, as disclosed in an embodiment of the present invention. Figure 2 This is a cross-sectional view of the internal structure of an intelligent dripper disclosed in an embodiment of the present invention, which focuses on the integration of the micro-thermal pulse sensor and the targeted cleaning agent release unit; Figure 3 This is a schematic diagram illustrating the working principle of a micro-thermal pulse sensor disclosed in one embodiment of the present invention; Figure 4 This is a flowchart of the closed-loop control logic of a drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning, as disclosed in one embodiment of the present invention.

[0023] Explanation of reference numerals in the attached figures: 1- Raw water tank; 2-Front-end adaptive processing unit; 21-Electrocoagulation module; 22-Ozone oxidation module; 3-Fertilizer pump; 4-Filter; 5-Irrigation network; 51-Main pipe; 52-Branch pipe; 53-Capillary pipe; 6-Intelligent dripper; 61-Housing; 62-Main channel; 63-Miniature heating film; 64a-Upstream temperature sensor; 64b-Downstream temperature sensor; 65-Liquid storage chamber; 66-Solid cleaning agent; 67-Electrolytically dissolvable film; 68-Miniature electrode; 69-Miniature circuit board; 7-System controller; 8-Communication network. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and specific examples and application scenarios.

[0026] In some embodiments of the present invention, a drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning is provided, comprising: - Front-end adaptive processing unit: used for pretreatment of raw water, including at least an electrocoagulation module and an ozone oxidation module. The current density and electrode switching frequency of the electrocoagulation module are adjustable, and the ozone dosage concentration and contact time of the ozone oxidation module are adjustable. The electrocoagulation module separates and precipitates colloids, suspended solids, and some organic matter in the water into larger flocs, purifying the water quality. Further purification occurs after subsequent separation and sedimentation. The ozone oxidation module efficiently decomposes organic matter in the water and kills microorganisms, thereby reducing the pathogenicity of irrigation water. When applying microbial agents to the irrigation system, the ozone oxidation module is temporarily not activated to maintain microbial activity.

[0027] - Irrigation network: including main pipes, branch pipes and capillary pipes, used to transport irrigation water to specific areas for farmland irrigation.

[0028] - Several intelligent drippers: installed at the end of the capillary tube for intelligent drip irrigation; each intelligent dripper integrates a micro-thermal pulse sensor and a targeted cleaning agent release unit; Micro-thermal pulse sensor: used to directly measure the thickness of the deposits on the inner channel wall of the dripper; it includes a micro heating element and at least two micro temperature sensing elements, the heating element and the temperature sensing elements being arranged along the water flow direction; by applying a short thermal pulse to the heating element and detecting the time difference of the arrival of the thermal signal by the temperature sensing elements, the thickness of the deposits is calculated based on the difference in thermal conductivity between the deposits and the water. Targeted cleaning agent release unit: includes a sealed reservoir, an electrically controllable micro-valve, and a solid or highly concentrated liquid cleaning agent located within the reservoir. The cleaning agent in the targeted cleaning agent release unit is one or more of the following: a complex enzyme preparation targeting biofilms, acidic microspheres targeting mineral scale, or chelating agent microcapsules.

[0029] - System Controller: Communicates with the front-end adaptive processing unit and all smart drippers. The controller stores a blockage risk prediction model and a multi-objective optimization control algorithm.

[0030] - The system controller is configured to perform the following closed-loop control: a. Receive real-time data on the thickness of the deposited material and the operating status data sent by all smart drippers; b. Based on the data, use the blockage risk prediction model to calculate the real-time blockage risk index of each dripper and predict its future trend; c. If the risk index of a certain dropper exceeds the first threshold, a command is sent to the targeted cleaning agent release unit of that dropper to trigger local cleaning; d. Based on the risk distribution of the global drippers, the multi-objective optimization control algorithm is used to calculate the optimal solution of the cooperative working parameters of the electrocoagulation module and the ozone oxidation module with the goal of minimizing the total energy consumption and reagent consumption of the system, and adjustment instructions are issued.

[0031] In some embodiments of the present invention, the intelligent dripper also integrates a miniature conductivity sensor and / or pH sensor for monitoring in-situ water quality. The water quality data is sent to the system controller as input parameters for optimizing the control algorithm.

[0032] In some embodiments of the present invention, the cleaning agent of the targeted cleaning agent release unit is one or more of the following: a complex enzyme preparation for biofilms, acidic microspheres for mineral scale, or chelating agent microcapsules.

[0033] In some embodiments of the present invention, the system controller and the smart dripper communicate via low-power wireless communication. The smart dripper has a built-in energy harvesting module for extracting kinetic energy from the water flow and converting it into electrical energy.

[0034] In some embodiments of the present invention, the blockage risk prediction model is a time-series-based machine learning model, whose input features include historical and current deposit thickness, water quality parameters, cumulative working time, and front-end treatment unit operating parameters.

[0035] In some embodiments of the present invention, the multi-objective optimization control algorithm is a constraint-based optimization solver, wherein the constraint condition is that the predicted blockage risk index of all drippers is lower than the safety threshold, and the optimization variables are the current density of the electrocoagulation module, the electrode switching frequency, and the dosage concentration of the ozone oxidation module.

[0036] Please see Figure 1As shown, in some embodiments of the present invention, the drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning includes: a raw water tank 1, a front-end adaptive processing unit 2 (containing an electrocoagulation module 21 and an ozone oxidation module 22), a fertilizer pump 3, a filter 4, an irrigation pipeline network 5, several smart drippers 6, a system controller 7, and a communication network 8.

[0037] In the above embodiment, raw water (such as aquaculture wastewater) first enters the front-end adaptive treatment unit 2. The electrocoagulation module 21 generates micro-flocculations via an adjustable DC power supply, removing colloids and fine suspended solids; the ozone oxidation module 22 generates micro-nano bubbles via an ozone generator, oxidizing organic matter and sterilizing. Both are communicatively connected to the system controller 7, and their operating parameters (current, frequency, ozone concentration, etc.) are dynamically controlled by the system controller 7. The outlet of the front-end adaptive treatment unit 2 is connected to a filter 4. A fertilizer pump 3 is installed between the filter 4 and the front-end adaptive treatment unit 2 for adding fertilizer or pesticides to the irrigation water. The particle size of the irrigation water filtered by the filter 4 meets the requirements, preventing potential dripper clogging during irrigation.

[0038] In the above embodiment, the irrigation network 5 includes a main pipe 51, branch pipes 52 and capillary pipes 53. The main pipe 51 is connected to the filter 4 to guide the water outflow to a suitable location, and then connected to several branch pipes 52 to guide the water outflow to the irrigation area. Each branch pipe 52 is connected to several capillary pipes 53 at its end, and several smart drippers 6 are arranged on the capillary pipes 52 for drip irrigation.

[0039] like Figure 2 As shown, the intelligent dripper 6 is the core of this invention. It includes a housing 61, a micro heating film 63, a temperature sensor, a liquid storage chamber 65, a solid cleaning agent 66, an electrolytically dissolvable film 67, microelectrodes 68, and a microcircuit board 69. The housing 61 forms a main flow channel 62 within it. The micro-thermal pulse sensor includes a micro heating film 63 (such as a platinum film) embedded in the wall of the main flow channel, and micro high-precision upstream temperature sensors 64a and downstream temperature sensors 64b (such as thin-film thermocouples) located upstream and downstream, respectively. The targeted cleaning agent release unit includes a liquid storage chamber 65 fabricated using micromachining technology, filled with solid cleaning agent 66, and sealed at the opening by an electrolytically dissolvable film 67 (such as a polylactic acid film), with microelectrodes 68 connected to both ends of the film. The dripper also integrates a microcircuit board 69.

[0040] The impeller of the micro turbine generator is mechanically coupled within the main flow channel 62 of the smart dripper, and its power output is electrically connected to the power management unit of the micro circuit board 69. Its function is to utilize the kinetic energy of the irrigation water flowing through the dripper to drive the impeller's rotation, thereby generating electricity. Its core role is to provide the necessary power for the micro circuit board 69 and all the integrated electronic components, enabling the smart dripper to be self-sufficient in energy and ensuring its long-term autonomous operation in field environments without external power.

[0041] The microprocessor serves as the local control core of the smart dripper, and its connection relationships and operational functions are as follows: Connection Relationship: Through the internal circuitry of the micro-circuit board 69, the microprocessor engages in bidirectional data exchange with the integrated wireless communication module. Furthermore, through control and signal lines extended from the circuit board, it electrically connects to and drives the micro-heating element 63 and the micro-temperature sensing elements 64a and 64b in the micro-thermal pulse sensor, and connects to the control execution terminal 68 of the targeted cleaning agent release unit. In addition, the microprocessor manages and monitors the operating status of the micro-turbine generator and the power management unit.

[0042] The microprocessor is configured to perform the following localized tasks: a. Energy Management and Scheduling: Control the power supply sequence of each functional module so that the system is in a low-power sleep state most of the time and is woken up according to a predetermined cycle or event trigger to achieve energy-saving operation.

[0043] b. Sensor driving and data acquisition: Generate control signals to drive the micro-thermal pulse sensor to perform periodic measurements, and acquire and preprocess (e.g., filter, amplify) the signals fed back by the micro temperature sensing element in real time.

[0044] c. Edge-side calculation and preliminary diagnosis: Based on the preset algorithm, the collected sensor signals are processed in real time to directly calculate the thickness of the deposits on the dripper channel wall. Local threshold comparison can be performed to achieve preliminary judgment of blockage risk and immediate identification of abnormal events (such as a sudden increase in deposit thickness).

[0045] d. Command execution and equipment control: Based on its preliminary diagnostic results or remote commands from the system controller, a control signal is generated to directly trigger the targeted cleaning agent release unit to perform in-situ cleaning actions.

[0046] The hierarchy and function of the microprocessor and system controller are distinct: the microprocessor acts as an edge computing node, responsible for real-time data sensing, rapid response, and low-level device control of the smart dripper. The system controller 7, as the central decision-making unit, is responsible for aggregating data from all drippers across the network, performing global situational analysis, advanced model predictions (such as long-term trends in congestion risk), and cross-unit collaborative optimization decisions (such as dynamically adjusting front-end processing parameters). Together, they form a control architecture combining distributed and centralized approaches. The microprocessor ensures the real-time performance and reliability of the end-point response while reducing the real-time data processing burden on the system controller.

[0047] The wireless communication module connects to the microprocessor via an onboard bus or interface and establishes a wireless communication link with the remote system controller 7 via an antenna. Its function is to, under the microprocessor's scheduling, package and upload the dripper status data (such as deposit thickness, water quality parameters, and equipment health status) generated by the microprocessor to the system controller using low-power, long-distance wireless transmission methods (such as LoRa); simultaneously, it receives and parses various instructions issued by the system controller (such as parameter settings, cleaning triggers, and data reporting requests) and forwards them to the microprocessor for execution. Its core function is to build a two-way data communication bridge between the intelligent dripper and the system controller, serving as a key network infrastructure for realizing global information perception and closed-loop optimization control of the system.

[0048] like Figure 3 As shown, the micro-thermal pulse sensor operates as follows: the system initiates a measurement periodically (e.g., hourly). A short (e.g., 0.1 seconds) constant power pulse is applied to the micro-heating film 63. Heat is simultaneously transferred to the water flow and the channel walls. Because the thermal conductivity of biofilms or scale (approximately 0.5–1.5 W / m·K) is significantly lower than that of metal or plastic walls (>10 W / m·K), but higher than that of water (approximately 0.6 W / m·K), their presence alters the heat wave propagation path. Upstream temperature sensor 64a and downstream temperature sensor 64b detect the temperature rise curves. By analyzing the time difference Δt between the peaks of the two curves and the peak amplitude, combined with a pre-calibrated model, the deposit thickness d can be calculated. This method is highly sensitive to slowly thickening biofilms and scale.

[0049] like Figure 4 As shown, the closed-loop control flow of the system is as follows: S1: System initialization, all smart drippers periodically upload data such as the thickness and conductivity of the attached material.

[0050] S2: The data fusion module in system controller 7 receives all data.

[0051] S3: The clogging risk prediction model (e.g., a trained LSTM neural network) calculates the clogging risk probability value R for each dripper in the next cycle (such as the next irrigation) based on current and historical data.

[0052] S4: The decision module determines whether there are drippers with R > R_emergency (emergency threshold, such as 90%). If yes, proceed to S5; otherwise, proceed to S6.

[0053] S5: For drippers with excessive risk, send a command to energize the electrolytically dissolving membrane 67. After the membrane dissolves, the water flow dissolves the cleaning agent 66, forming a high-concentration cleaning solution to powerfully clean the local flow channels. After completion, return to S1.

[0054] S6: The multi-objective optimization algorithm module takes the risk probability set {R} of all current drippers and the current front-end unit operating parameters as inputs, and aims to minimize the weighted average of the total power consumption of the system and the estimated consumption of chemical agents (ozone, cleaning agent) to solve for a new set of optimal front-end control parameters (electrocoagulation current I*, ozone concentration C*).

[0055] S7: Send the optimized parameters I* and C* to the front-end adaptive processing unit 2 for execution.

[0056] S8: The system records all data and results from this loop for periodic updates and training of the risk prediction model and optimization algorithm, enabling self-learning. It then returns to S1 to begin the next control cycle.

[0057] For the parts of this invention not described in detail, please refer to the prior art or the art known to those skilled in the art. This embodiment does not limit these aspects and will not describe them in detail here.

[0058] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning, characterized in that, The system includes: Raw water tank, used to hold irrigation raw water; The front-end adaptive processing unit is configured to purify and kill microorganisms in the irrigation water. The irrigation network, connected to the front-end adaptive processing unit, is configured to deliver irrigation water to the irrigation area. Several smart drippers are installed on the irrigation network and configured to enable intelligent drip irrigation; and, The system controller is connected to the smart dripper and is able to control the dripping action of the smart dripper.

2. The drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning according to claim 1, characterized in that, The system also includes: Fertilizer pumps, installed on irrigation networks, are used to add fertilizers / pesticides. The filter, installed on the irrigation pipeline, is used to filter and purify irrigation water.

3. The drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning according to claim 1, characterized in that, The intelligent dripper integrates a micro-thermal pulse sensor and a targeted cleaning agent release unit. Micro-thermal pulse sensor: configured to measure the thickness of deposits on the inner channel wall of the dripper; Targeted cleaning agent release unit: configured to release targeted cleaning agent to clean the deposits when conditions are met.

4. The drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning according to claim 3, characterized in that, The micro-thermal pulse sensor includes a micro heating element and at least two micro temperature sensing elements; the micro heating element and the micro temperature sensing elements are arranged along the water flow direction; By applying a short-duration thermal pulse to the micro heating element and detecting the time difference and amplitude change of the thermal signal arrival by the micro temperature sensing element, the thickness of the deposit is calculated based on the differences in the thermophysical parameters of the deposit, the channel wall material, and the water.

5. The drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning according to claim 3, characterized in that, The targeted cleaning agent release unit includes a liquid storage chamber, an electrically controllable seal, and a solid or liquid cleaning agent located within the liquid storage chamber. The seal is disposed on the liquid storage chamber and releases the solid or liquid cleaning agent located in the liquid storage chamber when opened.

6. The drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning according to claim 3, characterized in that, The intelligent dripper also integrates an in-situ water quality monitoring sensor and an energy harvesting module; The water quality monitoring data is connected to the system controller, and the monitoring data is used for decision optimization by the system controller; The energy harvesting module is used to extract energy from the irrigation water flow and power the internal circuitry of the dripper.

7. The drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning according to claim 1, characterized in that, The front-end adaptive processing unit includes an electrocoagulation module and an ozone oxidation module; The electrocoagulation module and the ozone oxidation module are respectively connected to the system controller; The system controller dynamically optimizes and adjusts the operating parameters of the electrocoagulation module and the ozone oxidation module based on the global risk distribution.

8. The drip irrigation anti-clogging system based on end-point condition sensing and targeted cleaning according to claim 5, characterized in that, The electrically operable seal is any one of an electrolytically dissolvable membrane, a miniature solenoid valve, and a fusible wire.

9. A drip irrigation anti-clogging method based on end-point condition sensing and targeted cleaning, characterized in that, The method includes: Real-time acquisition of the thickness data of the attached material measured by the micro-thermal pulse sensor inside each smart drip head; Based on the thickness data of the attached material, a predictive model is used to calculate the real-time clogging risk index of each smart dripper. If the risk index of a certain smart dripper exceeds the first threshold, the targeted cleaning agent release unit of the smart dripper is instructed to release cleaning agent to clean the attached substances. Based on the risk index distribution of all smart drippers, the optimal collaborative working parameters of the front-end electrocoagulation module and ozone oxidation module are dynamically calculated using an optimization algorithm and then executed. Collect system operation data and update the prediction model and optimization algorithm parameters regularly.

10. The drip irrigation anti-clogging method based on end-point condition sensing and targeted cleaning according to claim 9, characterized in that, The prediction model is a trained LSTM neural network model.

Images