Method for predicting and optimizing anti-blocking life of emitter based on machine learning
By constructing a database and employing various machine learning algorithms and SHAP analysis, key driving factors of sprinklers are identified, and optimization design criteria are proposed. This solves the problems of insufficient accuracy and generalization ability in sprinkler blockage prediction in existing technologies, and achieves high-precision cross-scenario prediction and optimization design.
Patent Information
- Application Number
- CN202511830738.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-06
- Publication Date
- 2026-03-03
AI Technical Summary
Existing methods for predicting sprinkler blockage suffer from high computational costs, difficulty in simulating long-term multi-mechanism coupled blockage processes, and the inability of traditional statistical models to capture the nonlinear relationships between water quality, structure, and operating conditions, resulting in insufficient prediction accuracy and generalization ability.
A database covering various irrigation water sources and emitter structure types was constructed. Multiple machine learning algorithms were used for model training and hyperparameter optimization. Combined with SHAP interpretability analysis, the contribution of each input feature to the prediction results was quantified, key driving factors were identified, and nonlinear interactions were analyzed through SHAP dependency graph analysis. Specific design criteria for emitter structure optimization were proposed.
It significantly improves the accuracy and cross-scenario generalization ability of predicting the anti-clogging life of water emitters. By revealing the physical mechanism through SHAP analysis, it provides quantitative guidance for the anti-clogging optimization design of water emitters, solving the pain point that existing technologies cannot effectively guide product iteration and upgrades.
Smart Images
Figure CN121598792A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of agricultural technology, specifically to a machine learning-based method for predicting and optimizing the anti-clogging lifespan of irrigation systems. Background Technology
[0002] Drip irrigation technology is an important way of efficient water use in modern agriculture, but clogging of the irrigation emitters is a core problem restricting their performance and lifespan. Existing prediction methods, such as simulations based on computational fluid dynamics (CFD) or traditional statistical regression models, have obvious limitations: CFD methods are computationally expensive and difficult to simulate long-term multi-mechanism coupled clogging processes; traditional statistical models cannot capture the complex nonlinear relationships between multiple factors such as water quality, structure, and operating conditions, and have poor generalization ability.
[0003] Emitter clogging is a core factor limiting the performance and lifespan of drip irrigation systems, primarily caused by the combined effects of sediment, chemical deposits, and biofilm. However, existing prediction methods have significant limitations: a lack of systematic comparisons of different machine learning models and a comprehensive assessment of the coupling effect between water quality parameters and emitter structural characteristics leads to insufficient prediction accuracy and generalization ability under complex and variable operating conditions.
[0004] In recent years, machine learning (ML) has been introduced into this field, but its "black box" nature severely hinders the transformation of prediction results into physical mechanisms, making it difficult to guide the anti-clogging optimization design of irrigation systems. Therefore, there is an urgent need for a new method that can achieve high-precision predictions while revealing the underlying physical mechanisms and providing quantitative evidence for optimization design. Summary of the Invention
[0005] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a machine learning-based method for predicting and optimizing the anti-clogging lifespan of irrigation systems, thus resolving the technical deficiencies in the prior art. Technical solution
[0006] To achieve the above objectives, the present invention provides the following technical solution: a machine learning-based method for predicting and optimizing the anti-clogging lifespan of irrigation emitters, comprising: S1: Construct a comprehensive database covering multiple irrigation water sources and various emitter structure types. Each sample in the database includes water quality parameters and emitter structure parameters as input features, and emitter anti-clogging life as the target feature; S2: Preprocess the database, including data normalization and training / test set partitioning; S3: Employ multiple machine learning algorithms for model training and hyperparameter optimization, and select the model that performs best in cross-scene prediction through systematic performance comparison; S4: Perform SHAP interpretability analysis on the optimal model, quantify the contribution of each input feature to the prediction results, identify key driving factors, and analyze the nonlinear interaction between key features through SHAP dependency graph. S5: The lifespan of the new emitters is predicted using the trained optimal model. More importantly, based on the physical mechanisms revealed by SHAP analysis, including the threshold regulation effect of channel depth D on the clogging effect of sediment concentration (SC), specific structural optimization design criteria for the emitters are proposed, thereby fundamentally improving the anti-clogging performance of the emitters. Preferably, in S1, the water quality parameters include at least the suspended solids concentration for characterizing physical clogging risk, pH value and total hardness for characterizing chemical clogging risk, and total organic carbon content for characterizing biological clogging risk; the emitter structural parameters include at least the minimum cross-sectional area of the channel, the tortuosity of the channel, and the inlet filtration area.
[0007] Preferably, after step S2, the method further includes constructing a comprehensive clogging risk index Iccr for the irrigation water source based on the water quality parameters. The comprehensive clogging risk index Iccr is calculated using the following logic: the comprehensive clogging risk index equals the suspended solids concentration multiplied by the physical risk weighting coefficient Wp, plus the combined function value of pH and total hardness multiplied by the chemical risk weighting coefficient Wc, plus the total organic carbon content multiplied by the biological risk weighting coefficient Wb, and finally plus a first correction constant K1. Preferably, after step S2, the method further includes constructing a structural anti-clogging capability index Isac for the irrigation emitter based on the emitter's structural parameters. The structural anti-clogging capability index Isac is calculated using the following logic: The structural anti-clogging index is equal to the minimum cross-sectional area of the flow channel multiplied by the cross-sectional weighting coefficient Wa, plus the flow channel tortuosity multiplied by the tortuosity weighting coefficient Wt, plus the inlet filtration area multiplied by the filtration weighting coefficient Wf, and finally the second correction constant K2.
[0008] Preferably, before step S3, the comprehensive congestion risk index Iccr and the structural anti-congestion capability index Isac are added as new composite input features and input together into the machine learning algorithm for model training. The goal of model training is to establish a nonlinear mapping relationship between the composite input features, the original input features, and the anti-clogging life of the emitter. Preferably, in step S4, performing SHAP interpretability analysis on the optimal model further includes calculating the global average absolute value of the SHAP for each input feature in relation to the prediction results, and ranking all input features by importance based on the magnitude of this absolute value to identify the top N key driving factors that have the most significant impact on the anti-clogging life of the emitter.
[0009] Preferably, in step S5, based on the key driving factor, an optimization potential assessment value Vop is generated for the emitter to be optimized. The optimization potential assessment value Vop is calculated using logic expressed in the following language: The optimization potential assessment value is equal to the absolute value of the difference between the current value and the theoretical optimal value of each key driving factor, multiplied by the absolute value of the global average SHAP corresponding to that factor, then the calculation results of all key driving factors are weighted and summed, and finally the third correction constant K3 is added.
[0010] Preferably, the optimization potential assessment value Vop is compared with a preset optimization threshold To to determine whether to start the structural optimization design process, specifically as follows: if the optimization potential assessment value Vop is greater than the optimization threshold To, it is determined that the current irrigation device design has significant optimization value, and the subsequent structural optimization design process is started. If the optimization potential assessment value Vop is not greater than the optimization threshold To, it is determined that the current irrigation device design is close to optimal and no significant adjustment to the structural parameters is required.
[0011] Preferably, in step S5, proposing specific design criteria for optimizing the structure of the irrigation emitter specifically includes: Using the optimal model, a sensitivity analysis curve is generated for the impact of a key driving factor, including the flow channel depth, on the predicted lifetime under different values. Based on the curve, one or more performance inflection points are determined, and the parameter value ranges corresponding to these inflection points are defined as the recommended intervals for optimization design, thereby forming an engineering design criterion containing specific numerical suggestions.
[0012] Preferably, the method further includes calculating and outputting a prediction confidence score Spc when predicting the lifespan of a new sprinkler; the prediction confidence score Spc is calculated as follows: the Euclidean distance between the input feature vector of the new sprinkler and the feature vectors of all training samples in the comprehensive database is calculated, and the K closest samples are selected, and the prediction confidence score is inversely proportional to the average of these K distances; When the prediction confidence score is lower than the preset confidence threshold Tc, the system will mark the prediction result as having low reliability and suggest conducting physical experiments for verification.
[0013] Beneficial effects This invention provides a machine learning-based method for predicting and optimizing the anti-clogging lifespan of irrigation systems. It offers the following advantages: (1) This machine learning-based method for predicting and optimizing the anti-clogging lifespan of irrigation emitters constructs a comprehensive clogging risk index Iccr that includes physical, chemical, and biological clogging risk characteristics and a structural anti-clogging capability index Isac that quantifies the quality of flow channel design. These two composite features are then input into the optimal machine learning model selected through systematic comparison for training. This method can more deeply explore the coupling effect between water quality and structure on lifespan. This method not only effectively captures the complex nonlinear relationship between multiple factors and significantly improves the accuracy and cross-scenario generalization ability of the prediction model, but also provides a reliability assessment of the prediction results of new irrigation emitters by introducing the calculation of the prediction confidence score Spc, thereby overcoming the technical difficulties of poor generalization ability and unstable prediction results under variable working conditions of traditional models. (2) This machine learning-based method This machine learning-based method for predicting and optimizing the anti-clogging lifespan of irrigation emitters utilizes SHAP interpretability Analysis technology. It not only identifies the top N key driving factors affecting emitter lifespan but also innovatively proposes a quantitative process to transform model interpretation results into engineering design guidance. By calculating the optimization potential assessment value Vop and comparing it with a preset optimization threshold To, it provides an objective basis for deciding whether structural optimization is necessary. Furthermore, by generating sensitivity analysis curves, it determines performance inflection points and provides specific recommended ranges for optimization design. This series of methods successfully opens the "black box" of machine learning models, deeply linking prediction results with physical mechanisms. It provides unprecedented quantitative and data-driven guidance for the anti-clogging optimization design of irrigation emitters, fundamentally solving the pain point that existing technologies cannot effectively guide product iteration and upgrades. Attached Figure Description
[0014] Figure 1 This is a schematic diagram of the process structure of a machine learning-based method for predicting and optimizing the anti-clogging lifespan of water emitters according to the present invention. Detailed Implementation
[0015] 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 embodiments of the present invention, and not all embodiments. 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.
[0016] Example 1 Please see Figure 1 This invention provides a machine learning-based method for predicting and optimizing the anti-clogging lifespan of irrigation systems, comprising: S1: Construct a comprehensive database covering multiple irrigation water sources and various emitter structure types. Each sample in the database includes water quality parameters and emitter structure parameters as input features, and emitter anti-clogging life as the target feature; S2: Preprocess the database, including data normalization and training / test set partitioning; S3: Employ multiple machine learning algorithms for model training and hyperparameter optimization, and select the model that performs best in cross-scenario prediction through systematic performance comparison; S4: Perform SHAP interpretability analysis on the optimal model, quantify the contribution of each input feature to the prediction result, identify key driving factors, and analyze the nonlinear interaction between key features through SHAP dependency graph. S5: The lifespan of the new emitters is predicted using the trained optimal model. More importantly, based on the physical mechanisms revealed by SHAP analysis, including the threshold regulation effect of channel depth D on the clogging effect of sediment concentration SC, specific structural optimization design criteria for emitters are proposed, thereby fundamentally improving the anti-clogging performance of the emitters.
[0017] In this embodiment, the 'water emitter anti-clogging life' is used as a target feature, and its specific definition and acquisition method are as follows.
[0018] First, a clear quantitative definition is given for 'irrigator anti-clogging life': it refers to the cumulative continuous operating time, in hours (h), experienced when the average outflow rate of a single irrigation emitter decreases from the initial rated flow rate to 70% under standard accelerated clogging test conditions; second, the standard accelerated clogging test is conducted through a specially constructed test platform. The platform mainly includes: 1) A water supply unit: consisting of a centrifugal pump with a rated head of 30 meters and a flow rate of 5 cubic meters per hour, and a clean water storage tank with a capacity of 1000 liters; 2) A water quality preparation unit: including a 500-liter mother liquor tank with a stirrer, used to prepare high-concentration turbid water, and injecting the turbid water into the main water supply pipeline through a precision metering pump; in this embodiment, the injected turbid water is prepared using loess with an average particle size of 0.05 mm, so that the suspended solids concentration at the inlet of the test pipeline is kept constant at 500 mg / L; 3) A test pipeline unit: 20 test branch pipes are set up in parallel, each branch pipe is equipped with a water injector to be tested, and is equipped with an independent pressure sensor (range 0-0.5 MPa, accuracy 0.5%) and an electromagnetic flow meter (range 0-20 liters per hour, accuracy 1%). 4) Data Acquisition and Control Unit: A Siemens S7-1200 programmable logic controller (PLC) is used in conjunction with LabVIEW software to precisely control the outlet pressure to remain constant at 0.1 MPa. It automatically acquires and records data from all flow meters and pressure sensors at 1-minute intervals. Furthermore, the specific experimental procedure for obtaining the 'anti-clogging life of the emitter' is as follows: Step 1: After installing a new emitter, it is first run under clean water conditions for 10 minutes, and the stable average flow rate during this period is recorded as the 'initial calibration flow rate' of the emitter. Step 2: The water quality adjustment unit is activated to inject high-concentration turbid water into the main pipeline to begin the accelerated clogging experiment, and the data acquisition unit starts timing. Step 3: The PLC system monitors the instantaneous flow rate of each emitter in real time and calculates its ratio to its respective 'initial calibration flow rate', i.e., the real-time flow rate decay rate. Step four: When the real-time flow rate decay rate of any emitter first reaches 30%, that is, when the instantaneous flow rate equals 70% of the 'initial calibration flow rate', the system determines that the emitter has reached the end of its lifespan, immediately stops supplying water to the branch pipe, and records the total time from step two to this point. This time is the 'anti-clogging lifespan' of the emitter sample. Finally, by testing emitters with various structural parameters under the above platform and process, a series of 'emitter anti-clogging lifespan' data with clear physical meaning and consistency standards can be obtained, which are used to construct the target feature column in the comprehensive database. In this embodiment, the anti-clogging lifespan of a certain type A emitter is set to 15.2 hours under the conditions of this embodiment, while that of a type B emitter is 28.6 hours.
[0019] Example 2 In S1, the water quality parameters include at least the suspended solids concentration for characterizing the risk of physical blockage, the pH value and total hardness for characterizing the risk of chemical blockage, and the total organic carbon content for characterizing the risk of biological blockage; the water emitter structural parameters include at least the minimum cross-sectional area of the flow channel, the tortuosity of the flow channel, and the filtration area of the inlet.
[0020] Following S2, the process further includes constructing a comprehensive clogging risk index Iccr for irrigation water sources based on the water quality parameters. The comprehensive clogging risk index Iccr is calculated using the following logic: the comprehensive clogging risk index equals the suspended solids concentration multiplied by the physical risk weighting coefficient Wp, plus the combined function value of pH value and total hardness multiplied by the chemical risk weighting coefficient Wc, plus the total organic carbon content multiplied by the biological risk weighting coefficient Wb, and finally plus the first correction constant K1. The significance of this calculation is that it integrates multiple dimensions of water quality parameters into a single, quantifiable risk assessment index to assess the potential severity of clogging caused by irrigation water sources.
[0021] Following S2, the process further includes constructing a structural anti-clogging index (Isac) for the emitter based on its structural parameters. The structural anti-clogging index (Isac) is calculated using the following logic: the structural anti-clogging index equals the minimum cross-sectional area of the flow channel multiplied by the cross-sectional weighting coefficient Wa, plus the flow channel tortuosity multiplied by the tortuosity weighting coefficient Wt, plus the inlet filtration area multiplied by the filtration weighting coefficient Wf, and finally, the second correction constant K2. The significance of this calculation is to quantitatively evaluate the geometric and physical design of the emitter itself to measure its inherent ability to resist different types of clogging.
[0022] Before step S3, the comprehensive congestion risk index Iccr and the structural anti-congestion capability index Isac are added as new composite input features and input together into the machine learning algorithm for model training. The goal of model training is to establish a nonlinear mapping relationship between the composite input features, the original input features, and the anti-clogging life of the water emitter.
[0023] In step S4, the SHAP interpretability analysis of the optimal model includes calculating the global average absolute value of the SHAP for each input feature in relation to the prediction result, and ranking all input features by importance based on the magnitude of this absolute value to identify the top N key driving factors that have the most significant impact on the anti-clogging life of the emitter. In step S5, based on the key driving factors, an optimization potential assessment value Vop is generated for the emitter to be optimized. The optimization potential assessment value Vop is calculated using logic expressed in the following language: The optimization potential assessment value is equal to the absolute value of the difference between the current value and the theoretical optimal value of each key driving factor, multiplied by the absolute value of the global average SHAP corresponding to that factor, and then the calculation results of all key driving factors are weighted and summed, and finally the third correction constant K3 is added; the significance of this calculation is to quantitatively evaluate the expected life improvement space that an existing irrigation emitter design can obtain by adjusting key structural parameters.
[0024] The optimization potential assessment value Vop is compared with the preset optimization threshold To to determine whether to initiate the structural optimization design process, as follows: If the optimization potential assessment value Vop is greater than the optimization threshold To, the current emitter design is determined to have significant optimization value, and the subsequent structural optimization design process is initiated; if the optimization potential assessment value Vop is not greater than the optimization threshold To, the current emitter design is determined to be close to optimal, and no significant adjustment to the structural parameters is required.
[0025] In step S5, the specific design criteria for optimizing the structure of the irrigation emitter include: Using the optimal model, a sensitivity analysis curve is generated for the impact of a key driving factor, including the flow channel depth, on the predicted lifetime under different values. Based on the curve, one or more performance inflection points are determined, and the parameter value ranges corresponding to these inflection points are defined as the recommended intervals for optimization design, thereby forming an engineering design criterion containing specific numerical suggestions.
[0026] The method further includes calculating and outputting a prediction confidence score Spc when predicting the lifespan of a new emitter; the prediction confidence score Spc is calculated as follows: The Euclidean distance is calculated between the input feature vector of the new irrigator and the feature vectors of all training samples in the comprehensive database. The K closest samples are selected, and the prediction confidence score is inversely proportional to the average of these K distances. When the prediction confidence score is lower than the preset confidence threshold Tc, the system will mark the prediction result as having low reliability and suggest conducting physical experiments for verification.
[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A machine learning-based method for predicting and optimizing the anti-clogging lifespan of water emitters, characterized in that: S1: Construct a comprehensive database covering multiple irrigation water sources and various emitter structure types. Each sample in the database includes water quality parameters and emitter structure parameters as input features, and emitter anti-clogging life as the target feature; S2: Preprocess the database, including data normalization and training / test set partitioning; S3: Employ multiple machine learning algorithms for model training and hyperparameter optimization, and select the model that performs best in cross-scene prediction through systematic performance comparison; S4: Perform SHAP interpretability analysis on the optimal model, quantify the contribution of each input feature to the prediction results, identify key driving factors, and analyze the nonlinear interaction between key features through SHAP dependency graph. S5: The lifespan of the new emitters is predicted using the trained optimal model. More importantly, based on the physical mechanisms revealed by SHAP analysis, including the threshold regulation effect of channel depth D on the clogging effect of sediment concentration SC, specific structural optimization design criteria for emitters are proposed, thereby fundamentally improving the anti-clogging performance of the emitters.
2. The method for predicting and optimizing the anti-clogging lifespan of water emitters based on machine learning according to claim 1, characterized in that: In S1, the water quality parameters include at least the suspended solids concentration for characterizing the risk of physical blockage, the pH value and total hardness for characterizing the risk of chemical blockage, and the total organic carbon content for characterizing the risk of biological blockage; the water emitter structural parameters include at least the minimum cross-sectional area of the flow channel, the tortuosity of the flow channel, and the filtration area of the inlet.
3. The method for predicting and optimizing the anti-clogging lifespan of an irrigation system based on machine learning according to claim 2, characterized in that: Following S2, the process further includes constructing a comprehensive clogging risk index Iccr for the irrigation water source based on the water quality parameters. The comprehensive clogging risk index Iccr is calculated using the following logic: the comprehensive clogging risk index equals the suspended solids concentration multiplied by the physical risk weighting coefficient Wp, plus the combined function value of pH value and total hardness multiplied by the chemical risk weighting coefficient Wc, plus the total organic carbon content multiplied by the biological risk weighting coefficient Wb, and finally plus the first correction constant K1.
4. The method for predicting and optimizing the anti-clogging lifespan of water emitters based on machine learning according to claim 2, characterized in that: Following S2, the process further includes constructing a structural anti-clogging index (Isac) for the emitter based on its structural parameters. The structural anti-clogging index Isac is calculated using logic expressed in the following language: The structural anti-clogging index is equal to the minimum cross-sectional area of the flow channel multiplied by the cross-sectional weighting coefficient Wa, plus the flow channel tortuosity multiplied by the tortuosity weighting coefficient Wt, plus the inlet filtration area multiplied by the filtration weighting coefficient Wf, and finally the second correction constant K2.
5. The machine learning-based method for predicting and optimizing the anti-clogging lifespan of irrigation systems according to claims 3 and 4, characterized in that: Before step S3, the comprehensive congestion risk index Iccr and the structural anti-congestion capability index Isac are added as new composite input features and input together into the machine learning algorithm for model training. The goal of model training is to establish a nonlinear mapping relationship between the composite input features, the original input features, and the anti-clogging life of the water emitter.
6. The method for predicting and optimizing the anti-clogging lifespan of an irrigation system based on machine learning according to claim 1, characterized in that: In step S4, the SHAP interpretability analysis of the optimal model also includes calculating the global average SHAP absolute value of each input feature on the prediction result, and ranking all input features by importance based on the magnitude of the absolute value, so as to identify the top N key driving factors that have the most significant impact on the anti-clogging life of the water emitter.
7. The method for predicting and optimizing the anti-clogging lifespan of an irrigation system based on machine learning according to claim 6, characterized in that: In step S5, based on the key driving factors, an optimization potential assessment value Vop is generated for the emitter to be optimized. The optimization potential assessment value Vop is calculated using logic expressed in the following language: The optimization potential assessment value is equal to the absolute value of the difference between the current value and the theoretical optimal value of each key driving factor, multiplied by the absolute value of the global average SHAP corresponding to that factor, then the calculation results of all key driving factors are weighted and summed, and finally the third correction constant K3 is added.
8. The method for predicting and optimizing the anti-clogging lifespan of a water emitter based on machine learning according to claim 7, characterized in that: The optimization potential assessment value Vop is compared with the preset optimization threshold To to determine whether to initiate the structural optimization design process, as follows: If the optimization potential assessment value Vop is greater than the optimization threshold To, the current water emitter design is determined to have significant optimization value, and the subsequent structural optimization design process is initiated. If the optimization potential assessment value Vop is not greater than the optimization threshold To, it is determined that the current irrigation device design is close to optimal and no significant adjustment to the structural parameters is required.
9. The method for predicting and optimizing the anti-clogging lifespan of an irrigation system based on machine learning according to claim 1, characterized in that: In step S5, the specific design criteria for optimizing the structure of the irrigation device include: Using the optimal model, a sensitivity analysis curve is generated for the impact of a key driving factor, including the flow channel depth, on the predicted lifetime under different values. Based on the curve, one or more performance inflection points are determined, and the parameter value ranges corresponding to these inflection points are defined as the recommended intervals for optimization design, thereby forming an engineering design criterion containing specific numerical suggestions.
10. The method for predicting and optimizing the anti-clogging lifespan of an irrigation system based on machine learning according to claim 1, characterized in that: The method further includes calculating and outputting a prediction confidence score Spc when predicting the lifespan of a new emitter; the prediction confidence score Spc is calculated as follows: The Euclidean distance is calculated between the input feature vector of the new irrigator and the feature vectors of all training samples in the comprehensive database. The K closest samples are selected, and the prediction confidence score is inversely proportional to the average of these K distances. When the prediction confidence score is lower than the preset confidence threshold Tc, the system will mark the prediction result as having low reliability and suggest conducting physical experiments for verification.