Yellow river diversion drip irrigation grading filtration control method and system

Abstract

The application discloses a kind of methods and systems for filtering control of Yellow River drip irrigation, belong to agricultural water-saving irrigation technical field, obtain the real-time water quality parameter of Yellow River water source, construct water source filtration load index model according to water quality parameter, output filtration load grade;Drip irrigation area is classified according to irrigation grade and crop water requirement characteristics, and a partition filtering response model is constructed;According to filtration load grade and response model matching filtering unit combination strategy, and output control instruction;Control the running state of each unit in multistage filtration system;Continuously update water quality data and adjust control strategy during operation process;If it is detected that the filtering unit is blocked or the pressure difference is abnormal, a backup path switching or backwashing enhancement strategy is executed;The application realizes the dynamic response of the filtration system to water quality changes and irrigation demand, improves water resource utilization efficiency and system operation reliability, and is suitable for drip irrigation engineering application in the Yellow River Basin and similar high-sediment water source irrigation areas.

Description

Technical Field

[0001] This invention relates to the field of agricultural water-saving irrigation technology, specifically to a graded filtration control method and system for drip irrigation from the Yellow River. Background Technology

[0002] With the continuous advancement of agricultural modernization, drip irrigation technology, as an efficient water-saving irrigation method, is widely used in arid and semi-arid regions. In the Yellow River Basin, due to the high content of silt and suspended particles in the water source, direct application to the drip irrigation system can easily lead to dripper clogging and pipe deposition, affecting the stable operation of the irrigation system and the uniform irrigation of crops. Therefore, multi-stage filtration equipment is often used for water pretreatment.

[0003] Existing filtration systems mostly employ fixed filter screens and multi-stage series structures. While these can filter out some impurities to a certain extent, they still suffer from low filtration efficiency, easy filter clogging, and frequent maintenance issues when faced with problems such as large particle size variations and strong water quality fluctuations in the Yellow River water source. Furthermore, traditional control methods lack the ability to respond in stages to the filtration needs of different irrigation areas and cannot be flexibly adjusted according to real-time water quality changes, resulting in high energy consumption and high operating costs for the filtration system.

[0004] Therefore, proposing a method based on dynamic identification of water quality parameters and capable of hierarchical filtration control according to the needs of different irrigation areas has become an important research direction for improving the efficiency and reliability of the Yellow River drip irrigation system. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for graded filtration control of drip irrigation from the Yellow River, in order to address the shortcomings in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for graded filtration control of drip irrigation from the Yellow River, comprising: The real-time water quality parameters of the Yellow River water source are obtained, including suspended particle concentration, particle size distribution and turbidity. A water source filtration load index model is constructed based on water quality parameters to obtain the current filtration load level; The drip irrigation areas are classified according to the irrigation level of the plots and the water requirements of the crops, and a zoned filtration response model is constructed. Based on the filtration load level and the zonal response model, the corresponding filtration unit combination strategy is matched, and filtration control commands are output. Control the operating status of each stage of the filtration unit in the multi-stage filtration system, including start / stop, backwash frequency and flow rate adjustment; During the operation of the filtration system, water quality change data is continuously collected and the filtration load level is updated, and the filtration control strategy is adjusted in real time. If a clogging trend or pressure difference is detected in a certain stage of the filter unit, a backup filter path switching or backwash enhancement strategy will be executed according to the level of abnormality.

[0007] Preferably, the step of constructing the water source filtration load index model based on water quality parameters includes: The collected water quality parameters were subjected to multidimensional normalization to construct a water quality state vector Q, which includes suspended particle concentration, particle size distribution density function and real-time turbidity. The improved fuzzy hierarchical clustering algorithm is used to cluster and identify Q, extract water quality characteristic subclasses representing different filtration load conditions, and construct a filtration load index model M by combining historical filtration performance data. Input the current water quality state vector Q into the model M, calculate its membership degree in each filtration level interval through the fuzzy membership function, and output the filtration load level G corresponding to the maximum membership degree.

[0008] Preferably, clustering and identifying Q based on an improved fuzzy hierarchical clustering algorithm includes: A multidimensional feature matrix of the water quality state vector Q is constructed, and an adaptive membership threshold mechanism is used to compress the weights of highly correlated feature dimensions. A fuzzy hierarchical clustering algorithm with time-series constraint factors is adopted to perform multi-scale clustering operations on the compressed feature matrix to obtain multiple water quality sub-clusters with time-series distribution characteristics; Based on the central membership vector of each water prote sub-cluster, the historical filtration pressure difference change curve and filter cartridge life record are associated and matched to extract the filtration performance factor and construct the water source filtration load index model M. The water source filtration load index model M predicts the filtration load level under dynamic water quality by coupling a time-sensitive clustering structure with a filtration efficiency factor.

[0009] Preferably, the drip irrigation area is classified according to the irrigation level of the plot and the water requirement characteristics of the crop, and a zoned filtration response model is constructed, including: Collect irrigation cycle, water application limit and crop root zone depth of each plot in the drip irrigation area to construct plot attribute set D; Based on the multi-level principal component weighted analysis method, feature compression is performed on D, and the fusion index γ, which characterizes water demand sensitivity, is extracted to form a land parcel water demand intensity sequence. Based on γ, the plots are hierarchically clustered, and crop category weight factor λ is introduced for adjustment, resulting in irrigation level partition Z containing multidimensional regulatory factors; A zoned filtration response model R is constructed based on Z. By mapping the logical matrix between the filtration level G and the irrigation level Z, the filtration capacity can be dynamically allocated in different areas.

[0010] Preferably, based on the filter load level and the zone response model, the corresponding filter unit combination strategy is matched and the filter control command is output, including: Construct a filter unit combination library F containing different filtration precision, backwash frequency and energy consumption parameters, with each combination configuration having a unique identification code; By establishing the interaction mapping matrix between G and R, the joint scheduling weight β of the current water quality level G and each irrigation zone Z is calculated. Based on β, the optimal combination of filtering units in the combination library F is selected to satisfy both the filtering capacity threshold and the area coverage requirement. And calculate its execution priority η by combining historical operating efficiency; Generate a combination of codes The filter control command set U, which includes the start-up timing, backwash cycle, and flow limit, is sent to each filter control node for execution.

[0011] Preferably, during the operation of the filtration system, water quality change data is continuously collected and the filtration load level is updated, and the filtration control strategy is adjusted in real time, including: Distributed water quality sensing nodes N are set up at the inlet of each level of filtration unit and key branches to collect data including turbidity, particle concentration and instantaneous flow velocity, and a continuous data sequence S(t) is constructed through a timestamp synchronization mechanism. The boundary conditions for water quality changes are identified by fitting S(t) to short-term trends and using a fluctuation discriminant function. Re-input the current data segment into the filtered load index model M, and output the updated load level G′. Comparing G′ with the level G corresponding to the control strategy at the previous moment, if there is a strategy deviation that exceeds the dynamic tolerance threshold, the filter unit combination is rematched and an updated control instruction set U′ is generated.

[0012] Preferably, the strategy of switching to a backup filter path or backwashing enhancement based on the clogging trend or pressure difference of a certain stage filter unit includes: Calculate the rate of change of pressure difference per unit time for each filter unit; A multi-factor anomaly identification model based on differential pressure change rate and unit flow velocity decay ratio is set up to extract blockage evolution characteristic curves and generate anomaly level labels L, including first-level anomaly labels, second-level anomaly labels and third-level labels. When the anomaly level is greater than or equal to the level 2 anomaly label, the backup path P′ is switched according to the current filter unit topology, and the backwash enhancement instruction set W is generated simultaneously, including the backwash duration, pressure enhancement factor and execution frequency. Apply the specified W instruction to the fault filtering unit.

[0013] This invention also provides a graded filtration control system for Yellow River drip irrigation, comprising: The data acquisition module acquires real-time water quality parameters of the Yellow River water source, including suspended particle concentration, particle size distribution, and turbidity. The filtration load assessment module constructs a water source filtration load index model based on water quality parameters to obtain the current filtration load level. The irrigation district demand analysis module classifies drip irrigation areas according to plot irrigation level and crop water requirement characteristics, and constructs a zoned filtering response model; The control decision module matches the corresponding filter unit combination strategy according to the filter load level and the partition response model, and outputs filter control commands. The filtration system execution module controls the operating status of each level of the filtration unit in the multi-stage filtration system, including start / stop, backwash frequency, and flow rate adjustment. The strategy feedback module continuously collects water quality change data and updates the filtration load level during the operation of the filtration system, and adjusts the filtration control strategy in real time. If the fault-tolerant response module detects a clogging trend or pressure difference abnormality in a certain level of filter unit, it will execute a backup filter path switching or backwash enhancement strategy according to the level of abnormality.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention constructs a Yellow River drip irrigation filtration control method that integrates water quality sensing, filtration load modeling, crop water demand response analysis, and multi-level control strategies, achieving intelligent and dynamic operation control of the filtration system under complex water source conditions. Compared to traditional fixed-mode filtration systems, this invention possesses highly sensitive identification capabilities for water quality fluctuations and precise response capabilities to regional irrigation differences, significantly improving system filtration efficiency and operational stability.

[0015] 2. This invention effectively reduces the risk of operational interruption caused by filter blockage by introducing a backup path switching and backwashing enhancement mechanism based on anomaly level judgment, extends equipment life, and reduces the frequency of manual maintenance. Combined with a zone scheduling and strategy optimization model, it achieves optimal configuration of filtration resources and energy consumption control, and has good engineering adaptability and promotion value. It is especially suitable for large-scale agricultural water-saving irrigation scenarios in the Yellow River Basin and similar high-sediment-content irrigation areas. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a flowchart of the method of the present invention.

[0018] Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0020] Example 1, please refer to Figure 1 As shown in this embodiment, a graded filtration control method for drip irrigation from the Yellow River includes: The real-time water quality parameters of the Yellow River water source are obtained, including suspended particle concentration, particle size distribution and turbidity. A water source filtration load index model is constructed based on water quality parameters to obtain the current filtration load level; The drip irrigation areas are classified according to the irrigation level of the plots and the water requirements of the crops, and a zoned filtration response model is constructed. Based on the filtration load level and the zonal response model, the corresponding filtration unit combination strategy is matched, and filtration control commands are output. Control the operating status of each stage of the filtration unit in the multi-stage filtration system, including start / stop, backwash frequency and flow rate adjustment; During the operation of the filtration system, water quality change data is continuously collected and the filtration load level is updated, and the filtration control strategy is adjusted in real time. If a clogging trend or pressure difference is detected in a certain stage of the filter unit, a backup filter path switching or backwash enhancement strategy will be executed according to the level of abnormality.

[0021] In this embodiment, in order to obtain real-time water quality parameters of the Yellow River water source, the system is equipped with an integrated online water quality monitoring module at the head section of the water diversion canal. This module integrates an optical turbidity sensor, a laser particle size analyzer, and a particle counter, which are used to collect turbidity, particle size distribution, and suspended particle concentration, respectively.

[0022] Specifically, the turbidity sensor adopts the principle of infrared scattering, with a measurement range of 0 to 1000 NTU and a response time of less than 5 seconds, and can continuously detect the turbidity of raw water under different flow rate conditions; the laser particle size analyzer adopts the dynamic light scattering method (DLS) and supports real-time analysis of particle size in the range of 0.1 to 1000 μm; the particle counter collects the number of suspended solids in a unit volume of water based on the flow cytometry method, and can set particle size thresholds for statistical classification according to size.

[0023] The system uses a 10-minute sampling cycle to package three types of parameters into a standardized water quality data vector Q = [C, E, T], where C is the suspended particulate concentration (mg / L), E is the particle size distribution characteristic (quantile set in μm), and T is the turbidity value (NTU). All data is transmitted in real time to the filter control host via RS485 bus, serving as the basic input for subsequent load index modeling and control strategy decision-making.

[0024] In this embodiment, to achieve accurate prediction and intelligent control of the operating pressure of the filtration system under varying water quality conditions in the Yellow River water source, a technical solution is proposed to construct a water source filtration load index model M based on an improved fuzzy hierarchical clustering algorithm. The model takes the real-time acquired water quality state vector Q as input, and through cluster learning and mapping with historical filtration performance data, outputs the current filtration load level G, which drives the combined scheduling strategy of multi-level filtration units.

[0025] First, an integrated online water quality monitoring module deployed at the water intake continuously collects three core water quality parameters: suspended particle concentration (mg / L), particle size distribution density function (quantile statistical indicators mainly based on D10, D50, and D90, in μm), and optical turbidity (NTU). The system samples every 10 minutes. After the raw data is preprocessed to remove obvious outliers and noise, a water quality state vector Q is formed according to the following steps: The parameters are subjected to multidimensional normalization, and the minimum-maximum normalization algorithm is used to uniformly map each feature value to the [0,1] interval to obtain a standardized vector. The particle size distribution characteristics were fitted with a density function expression, and representative quantiles (such as D10, D50, and D90) were extracted as structural input dimensions. The overall Q-vector structure is as follows: ;in This represents the normalized particle concentration. For particle size distribution quantiles, This is the normalized turbidity value.

[0026] To extract the correlation characteristics of water quality parameters in the filtration system response, this embodiment introduces an improved fuzzy hierarchical clustering algorithm based on time constraints and membership adjustment mechanism to perform cluster analysis on the Q vector and construct the filtration load level determination logic.

[0027] The continuously input water quality state vector Q is used to construct a multidimensional feature matrix Q_matrix in the form of a sliding window (the window size is set to 12 groups of samples, covering water quality over nearly 2 hours). To reduce the interference of redundant dimensions on the clustering results, an adaptive membership threshold mechanism is adopted to calculate the Pearson correlation coefficient matrix R between each feature dimension, and the weight coefficient α of each dimension is dynamically adjusted according to its correlation to form a weighted matrix Q_weighted.

[0028] Based on traditional fuzzy C-means clustering, a time-series constraint adjustment factor λ is introduced to prevent cluster centers from drifting drastically with short-term fluctuations. The clustering function is defined as: Where J is the clustering objective function, n is the number of samples, and c is the number of cluster centers; is the membership degree of the j-th sample to the i-th cluster center; m is the fuzzy factor (generally ranging from 1.5 to 2.5). Let j be the water quality state vector of the j-th sample; This represents the vector of the i-th cluster center. λ represents the square of the Euclidean distance, used to measure the similarity between a sample and the cluster center; λ represents the temporal constraint adjustment factor (a positive real number used to balance the influence of the temporal term and the fuzzy term). This represents the time difference between the j-th sample and the (j-1)-th sample. This indicates the magnitude of changes in water quality status among consecutive samples (used to suppress the impact of short-term drastic fluctuations on cluster centers).

[0029] During the clustering process, several water quality sub-clusters are finally obtained. The center of each cluster represents a typical water quality state and has a stable correspondence with the filtration load.

[0030] The filtration system operation records corresponding to each water quality cluster were extracted from historical operation data, including the inlet and outlet water pressure difference curve ΔP_mean, filter cartridge lifespan, and backwash rate. Filtration performance factors were extracted using weighted linear regression fitting. The final structure of the load index model M is as follows: That is, Q is matched to a certain water proton cluster through membership degree calculation → mapped to its historical performance factor ψ → generating the current filtration load level G.

[0031] During system operation, the real-time collected water quality state vector Q is input into model M, and then processed through fuzzy membership functions. Calculate its membership degree in each cluster center. The cluster with the highest membership degree is the load cluster that best matches the current state. Its corresponding filtration performance factor ψ is used to infer the current filtration load level G.

[0032] The classification is set to five levels (G1 to G5), corresponding to five situations from "extremely low load" to "extremely high load". The control strategies corresponding to each level (such as the number of filter unit combinations to be activated, backwashing frequency, etc.) are preset in the strategy library.

[0033] For example: When G=G1, the system adopts a combination of single-stage gravity filtration and low-frequency backwashing. When G=G4 or G5, the multi-stage filter press + dual-channel parallel + high-frequency backwashing mechanism is automatically activated.

[0034] In this embodiment, to achieve on-demand allocation of filtration resources and differentiated water supply control within the Yellow River drip irrigation system, a zoned filtration response modeling method based on the hierarchical water demand characteristics of land parcels is proposed. This method constructs an irrigation level zone Z through multi-dimensional land parcel attribute data analysis and crop response adjustment mechanisms, and establishes a response mapping relationship between the filtration level G and the irrigation level Z, forming a zoned filtration response model R.

[0035] This embodiment selects 16 management units (plots) within a typical Yellow River irrigation area and obtains the following three types of key plot attributes using an agricultural Internet of Things (IoT) system: Irrigation cycle (P): The number of days between each drip irrigation cycle (unit: days), reflecting the frequency of water supply; Maximum single application volume (V): refers to the maximum water supply capacity per unit area (unit: m³ / mu). Crop root zone depth (H): The depth of the main distribution area of ​​different crop roots (unit: cm), which represents the water use level.

[0036] The attribute vector for each plot of land is constructed using the above indicators, resulting in the following multi-dimensional attribute set: Where n=16 is the number of plots.

[0037] Considering the order-of-magnitude differences and correlations among three-dimensional attributes, this embodiment employs multi-level principal component weighted analysis (MPCA) for feature compression. The processing procedure is as follows: Normalize P, V, and H to the interval [0,1] to form the normalized matrix Dnorm; Calculate the covariance matrix and perform eigenvalue decomposition to extract the top two principal components, PC1 and PC2, whose combined contribution rate exceeds 90%. Based on the pre-defined factor weight set for crop water requirement sensitivity (e.g., V weight is 0.5, H weight is 0.3, and P weight is 0.2), PC1 and PC2 are weighted and merged to obtain the fusion index. , Based on the differences in water demand levels reflected by the fusion index, this embodiment uses the K-means++ clustering algorithm for classification, dividing the plots into three levels by default: high water demand (Z1), medium water demand (Z2), and low water demand (Z3). A crop category weighting factor λ is specifically introduced during the clustering process to adjust for the differences in crop responses to water. For shallow-rooted crops (such as wheat and potatoes), set λ < 1; For deep-rooted, water-intensive crops (such as corn and sugar beets), set λ > 1.

[0038] The specific clustering distance function is defined as follows: ; The clustering results are output as a set of hierarchical partitions: Z={Z1,Z2,Z3}; each set corresponds to a set of plot numbers and average water demand levels.

[0039] To achieve dynamic resource allocation of the Yellow River diversion filtration system within different irrigation level zones, this embodiment further constructs a logical response matrix L between filtration level G and irrigation level Z:

[0040] Among them, F1~F5 are preset filter unit combination schemes, covering different precision, flow rate and backwashing capabilities; Model R matches the corresponding filter strategy by looking up a table based on the current G value and the Z zone where the plot is located.

[0041] The model response mechanism is implemented through the following formula: .

[0042] In the demonstration project, after configuring the irrigation area with graded filtration control using the method of this embodiment, the following results were achieved: Improved system response sensitivity: Average filtering strategy switching time reduced by 38%; Energy consumption optimization: Energy saving rate reaches 21.4% during typical dry seasons; Water resource utilization efficiency improved: the uniformity of drip irrigation increased to over 91%.

[0043] This embodiment proposes a matching method for filter unit combination strategies based on the joint regulation of filter load level G and irrigation level Z, constructing a control instruction set U to achieve intelligent operation control of a multi-level filtration system. The core of this method includes four stages: filter unit combination library construction, joint scheduling weight calculation, optimal combination strategy selection, and instruction generation, forming a complete response closed loop.

[0044] First, preset and register the filter unit combinations that can be called within the system to build a filter unit combination library F. Each filter combination includes different filtration accuracy levels (such as pre-filtration, medium filtration, fine filtration), backwashing mechanisms (such as automatic backwashing, timed backwashing, differential pressure triggered backwashing), and operating energy consumption (pump power, backwash water consumption) parameters.

[0045] Each combination is uniquely identified by a code. The core performance parameters are shown below:

[0046] The structure and parameters of all combinations are pre-tested and evaluated, and are used as candidates in the optimization screening during strategy matching.

[0047] When the system is running, it first obtains the current filtration load level G (e.g., G4: high load) and the irrigation levels Z of each plot output by the drip irrigation area zoning response model R (e.g., Z1: high water demand, Z2: medium water demand, Z3: low water demand), and constructs the interaction mapping matrix L(G,Z) to represent the actual demand intensity of filtration capacity for each area under the current load level.

[0048] The joint scheduling weights are generated through the following calculations. ;in: express Partition area percentage (used to reflect the spatial distribution of scheduling weights); This indicates that at the current G level, The response coefficient of the zone to the filtering capacity (assigned based on the historical performance evaluation database). express The filtering and scheduling intensity requirements of the partition at the current G level.

[0049] Ultimately, the β values ​​of each Z region form the scheduling vector. This serves as the basis for weighting in combinatorial optimization.

[0050] Select the optimal combination from the combination library F that satisfies the following two conditions. : Filtering capacity threshold meets the following conditions: combination The processing capacity must cover The need for weighted filtering; Maximum regional adaptability: combination It has the strongest coverage or partition adjustability among multiple Z zones.

[0051] Candidate combinations will have their execution priority calculated based on the following priority scoring function. ;in: Indicate combination The adaptation ratio across all Z regions; Indicate combination Filtering efficiency per unit of energy consumption in historical execution; express The rated operating power consumption. The combination with the highest score is the current optimal combination. .

[0052] Based on the selected combination The system automatically generates a control instruction set U to drive the actual operation of the multi-stage filtration system. U contains the following structure: Combination encoding: (Specify the enabled filtering configuration); Start-up timing: Start-up and stop delays (in milliseconds) for different Z zones are set based on the β vector; Backwash cycle: Set according to the current G level and Z zone characteristics (e.g., shorten the backwash cycle to 15 minutes in high-load zones); Flow limit: Set the maximum operating flow rate (L / min) for each filter branch.

[0053] The control instruction set U is sent to the PLC controller or edge gateway in real time via an industrial bus protocol (such as Modbus TCP / IP) to perform start / stop control, backwash command triggering, and flow regulation of each filter unit.

[0054] A three-month verification was conducted in a pilot demonstration area of ​​the Yellow River irrigation district, and the results showed that: The efficiency of the filter unit is improved by 29.6%, avoiding the simultaneous activation of high-energy-consuming configurations in multiple areas; Backwashing frequency is reduced by 17.4%, significantly extending filter life; The total energy consumption of the drip irrigation system decreased by 22.1%; The uniformity of irrigation is maintained at over 91%.

[0055] The control strategy of this embodiment enables optimal scheduling, zone control, and precise energy efficiency control of the filtration unit resources, providing robust and intelligent support for drip irrigation systems under conditions of high sand content.

[0056] This embodiment provides a dynamic adjustment and fault self-response control method for a filtration system designed for real-time operation scenarios, used to ensure the long-term, stable, and efficient operation of filtration equipment in the Yellow River water source drip irrigation system. The method comprises two key parts: first, continuously collecting water quality changes and updating the filtration load level through distributed sensing nodes; and second, executing backup path switching and backwashing enhancement strategies when the filtration unit shows signs of clogging or abnormal pressure differential, thus achieving closed-loop operation control.

[0057] In a multi-stage filtration system, distributed water quality sensing nodes N are set at the inlet of each filtration unit and in each main and branch pipe section. Each node integrates the following sensing modules: Turbidity sensor (T-sensor), measurement range: 0~1000 NTU; Particle concentration meter (C-sensor), resolution: 1 mg / L; A flow velocity sensor (V-sensor) based on electromagnetic or ultrasonic methods has a response time of ≤1s.

[0058] All nodes are uniformly sampled through a timestamp synchronization module (such as a GPS clock or a main control PLC synchronization signal) to construct a continuous time series dataset: ; s represents the total number of time points collected; A short-term sliding window analysis (typically 30 minutes) is performed on the continuous data sequence S(t). A combination of linear regression fitting and the standard deviation volatility index method is used to identify abrupt inflection points and high-frequency fluctuation boundaries. A water quality change threshold ΔQ_thresh is defined, and boundaries are determined according to the following rules: If the standard deviation σ of any indicator in three consecutive windows exceeds the set value and the trend slope changes significantly (p<0.05), it is determined to be a sudden change in water quality; otherwise, it is considered a stable section.

[0059] After identifying changes in water quality, the latest data (such as the latest 5 sets of Q vectors) is re-input into the filtration load index model M. The model structure is based on the aforementioned fuzzy clustering + performance factor mapping structure, and the updated filtration load level G′ is output.

[0060] Compare G′ with the level G recorded at the previous time step: like , where δG is the dynamic tolerance threshold (which can be set to level 1 difference). This triggers the filtering strategy update module, which in turn calls the strategy matching module to reselect the filtering combination. And generate control instruction set U′.

[0061] The new instruction set includes an updated anti-wash cycle, combined start-stop logic, and rate limiting strategy, and is sent to the control terminal for synchronous execution.

[0062] To enable early identification of filter unit clogging trends and proactive response to high-pressure anomalies, this embodiment introduces a multi-factor anomaly identification and backup path scheduling mechanism, the specific process of which is as follows: By installing differential pressure sensors (ΔP-sensors) at the inlet and outlet of each filtration unit, the rate of change of differential pressure per unit time is calculated: This is the outlet pressure value. This is the inlet pressure value. For the time of collection, This represents the rate of change of pressure difference.

[0063] Synchronously acquire the rate of change of flow velocity per unit volume during this period. This forms the basis for joint judgment.

[0064] Construct the following multi-factor anomaly level identification logic: Level 1 anomaly (L1): Exceeding the normal value by 1.2 times; Level 2 anomaly (L2): The flow rate decreases by ≥20%; Level 3 anomaly (L3): This is accompanied by localized backwashing failures or repeated blockages in short cycles.

[0065] Model outputs anomaly level labels .

[0066] When L≥L2, the system performs the following operation: Call the filtering network topology diagram to find the backup path P′ (which can be a bypass branch or a backup filtering module) for the corresponding filtering unit. Perform the switching action (solenoid valve actuation, bypass opening) to redirect the main flow to P′; Simultaneously, an enhanced anti-washing strategy instruction set W is generated, including: Backwash duration: Extend to 1.5 times the normal value (e.g., increase from 10 minutes to 15 minutes); Pressure enhancement factor: Increase the upper limit of backwash pressure (e.g., from 0.3 MPa to 0.5 MPa); Execution frequency: Compress the backwash cycle (e.g., from 30 minutes to 15 minutes).

[0067] All abnormal events will be recorded in the operation log in real time and uploaded to the backend model training platform for subsequent optimization of anomaly recognition model parameters (such as ΔP baseline value self-learning) and policy rule iteration.

[0068] In field tests in typical irrigation areas, this control mechanism demonstrated good dynamic response and anomaly self-handling capabilities, mainly as follows: Load level adjustment response time is reduced by approximately 35%; The number of system downtimes due to congestion decreased by 60%; The average filter replacement cycle has been extended by 22%; System energy consumption was reduced by 17% (based on quarterly statistics).

[0069] In summary, this implementation provides a highly integrated, dynamic sensing, and well-developed intelligent filtration control method with a robust self-recovery mechanism, which is particularly suitable for drip irrigation filtration systems with large water quality fluctuations and long operating cycles.

[0070] Example 2, please refer to Figure 2As shown in this embodiment, a graded filtration control system for Yellow River drip irrigation includes: The data acquisition module acquires real-time water quality parameters of the Yellow River water source, including suspended particle concentration, particle size distribution, and turbidity. The filtration load assessment module constructs a water source filtration load index model based on water quality parameters to obtain the current filtration load level. The irrigation district demand analysis module classifies drip irrigation areas according to plot irrigation level and crop water requirement characteristics, and constructs a zoned filtering response model; The control decision module matches the corresponding filter unit combination strategy according to the filter load level and the partition response model, and outputs filter control commands. The filtration system execution module controls the operating status of each level of the filtration unit in the multi-stage filtration system, including start / stop, backwash frequency, and flow rate adjustment. The strategy feedback module continuously collects water quality change data and updates the filtration load level during the operation of the filtration system, and adjusts the filtration control strategy in real time. If the fault-tolerant response module detects a clogging trend or pressure difference abnormality in a certain level of filter unit, it will execute a backup filter path switching or backwash enhancement strategy according to the level of abnormality.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for graded filtration control of drip irrigation from the Yellow River, characterized in that: include: The real-time water quality parameters of the Yellow River water source are obtained, including suspended particle concentration, particle size distribution and turbidity. A water source filtration load index model is constructed based on water quality parameters to obtain the current filtration load level, including: The collected water quality parameters are subjected to multidimensional normalization to construct a water quality state vector Q, which includes suspended particle concentration, particle size distribution density function, and real-time turbidity. Based on an improved fuzzy hierarchical clustering algorithm, Q is clustered to identify water quality feature subclasses representing different filtration load conditions. A filtration load index model M is then constructed by combining historical filtration performance data. The improved fuzzy hierarchical clustering algorithm refers to a fuzzy hierarchical clustering algorithm that incorporates a time-series constraint factor. The current water quality state vector Q is input into model M, and its membership degree in each filtration level interval is calculated using a fuzzy membership function. The filtration load level G corresponding to the maximum membership degree is then output. The drip irrigation areas are classified according to the irrigation level of the plots and the water requirement characteristics of the crops, and a zoned filtering response model is constructed, including: Irrigation cycle, maximum water application rate, and crop root zone depth of each plot within the drip irrigation area are collected to construct a plot attribute set D. Based on multi-level principal component weighted analysis, feature compression is performed on D to extract a fusion index γ representing water demand sensitivity, forming a plot water demand intensity sequence. Plots are then hierarchically clustered according to γ, and a crop category weight factor λ is introduced for adjustment, resulting in an irrigation level partition Z containing multi-dimensional regulatory factors. A partitioned filtering response model R is constructed based on Z, and by mapping the logical matrix between the filtering level G and the irrigation level Z, dynamic allocation of filtering capacity in different regions is achieved. Based on the filter load level and the zone response model, the corresponding filter unit combination strategy is matched, and filter control commands are output, including: A filter unit combination library F is constructed, containing different filtration accuracies, backwash frequencies, and energy consumption parameters. Each combination configuration has a unique identification code. By establishing an interaction mapping matrix between G and R, the joint scheduling weight β of the current water quality level G and each irrigation zone Z is calculated. Based on β, the filter unit combination that best meets the filtration capacity threshold and the regional demand coverage is selected from the combination library F. ; Control the operating status of each stage of the filtration unit in the multi-stage filtration system, including start / stop, backwash frequency and flow rate adjustment; During the operation of the filtration system, water quality change data is continuously collected and the filtration load level is updated, and the filtration control strategy is adjusted in real time. If a clogging trend or pressure difference is detected in a certain stage of the filter unit, a backup filter path switching or backwash enhancement strategy will be executed according to the level of abnormality.

2. The method for graded filtration control of drip irrigation from the Yellow River according to claim 1, characterized in that: Clustering identification of Q based on an improved fuzzy hierarchical clustering algorithm includes: A multidimensional feature matrix of the water quality state vector Q is constructed, and an adaptive membership threshold mechanism is used to compress the weights of highly correlated feature dimensions. A fuzzy hierarchical clustering algorithm with time-series constraint factors is adopted to perform multi-scale clustering operations on the compressed feature matrix to obtain multiple water quality sub-clusters with time-series distribution characteristics; Based on the central membership vector of each water prote sub-cluster, the historical filtration pressure difference change curve and filter cartridge life record are associated and matched to extract the filtration performance factor and construct the water source filtration load index model M. The water source filtration load index model M predicts the filtration load level under dynamic water quality by coupling a time-sensitive clustering structure with a filtration efficiency factor.

3. The method for graded filtration control of drip irrigation from the Yellow River according to claim 1, characterized in that: Output filter control commands include: Filter unit combination calculated based on historical operating efficiency The execution priority η; generating a combination of codes. The filter control command set U, which includes the start-up timing, backwash cycle, and flow limit, is sent to each filter control node for execution.

4. The method for graded filtration control of Yellow River drip irrigation according to claim 1, characterized in that: During the operation of the filtration system, water quality change data is continuously collected and the filtration load level is updated. The filtration control strategy is adjusted in real time, including: Distributed water quality sensing nodes N are set up at the inlet of each level of filtration unit and key branches to collect data including turbidity, particle concentration and instantaneous flow velocity, and a continuous data sequence S(t) is constructed through a timestamp synchronization mechanism. The boundary conditions for water quality changes are identified by fitting S(t) to short-term trends and using a fluctuation discriminant function. Re-input the current data segment into the filtered load index model M, and output the updated load level G′. Comparing G′ with the level G corresponding to the control strategy at the previous moment, if there is a strategy deviation that exceeds the dynamic tolerance threshold, the filter unit combination is rematched and an updated control instruction set U′ is generated.

5. The method for graded filtration control of drip irrigation from the Yellow River according to claim 1, characterized in that: Based on the clogging trend or pressure difference of a certain stage of filter unit, the following strategies are implemented to switch to a backup filter path or enhance backwashing: Calculate the rate of change of pressure difference per unit time for each filter unit; A multi-factor anomaly identification model based on differential pressure change rate and unit flow velocity decay ratio is set up to extract blockage evolution characteristic curves and generate anomaly level labels L, including first-level anomaly labels, second-level anomaly labels and third-level labels. When the anomaly level is greater than or equal to the level 2 anomaly label, the backup path P′ is switched according to the current filter unit topology, and the backwash enhancement instruction set W is generated simultaneously, including the backwash duration, pressure enhancement factor and execution frequency. Apply the specified W instruction to the fault filtering unit.

6. A graded filtration control system for Yellow River drip irrigation, used to implement the graded filtration control method for Yellow River drip irrigation as described in any one of claims 1-5, characterized in that: include: The data acquisition module acquires real-time water quality parameters of the Yellow River water source, including suspended particle concentration, particle size distribution, and turbidity. The filtration load assessment module constructs a water source filtration load index model based on water quality parameters to obtain the current filtration load level. The irrigation district demand analysis module classifies drip irrigation areas according to plot irrigation level and crop water requirement characteristics, and constructs a zoned filtering response model; The control decision module matches the corresponding filter unit combination strategy according to the filter load level and the partition response model, and outputs filter control commands. The filtration system execution module controls the operating status of each level of the filtration unit in the multi-stage filtration system, including start / stop, backwash frequency, and flow rate adjustment. The strategy feedback module continuously collects water quality change data and updates the filtration load level during the operation of the filtration system, and adjusts the filtration control strategy in real time. If the fault-tolerant response module detects a clogging trend or pressure difference abnormality in a certain level of filter unit, it will execute a backup filter path switching or backwash enhancement strategy according to the level of abnormality.

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