Water purification filter element pollution degree intelligent evaluation and replacement early warning method and system

Abstract

The invention relates to the technical field of water treatment, and discloses a water purification filter element pollution degree intelligent evaluation and replacement early warning method and system.The water purification filter element pollution degree intelligent evaluation and replacement early warning method comprises the following steps that S1, multi-parameter real-time collection and pretreatment are conducted, the method comprises the following steps: collecting total dissolved solid concentration (TDin) of inlet water, total dissolved solid concentration (TDout) of outlet water, inlet / outlet pressure difference (delta P) of a filter element, accumulated water production quantity (V), real-time flow (Q) and environment temperature and humidity (T / H) in real time; and carrying out de-noising processing on the collected original data. The actual pollution state of the filter element is directly quantified by collecting TDSin / TDOutt (reflecting the filtering precision of dissolved solids), delta P (reflecting the blockage degree of the filter element), Q (reflecting flux attenuation) and environmental parameters (T / H corrected water quality drift) in real time and combining a machine learning model to calculate a comprehensive pollution index CPI (0-1 dimensionless), and the actual pollution state of the filter element is directly quantified instead of depending on fixed use duration or accumulated flow.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology, specifically to a method and system for intelligent assessment and early warning of the degree of contamination of water purifier filter cartridges. Background Technology

[0002] The intelligent assessment and replacement early warning method and system for water purifier filter cartridge contamination is an intelligent solution based on the Internet of Things (IoT), sensor technology, and artificial intelligence algorithms. It is used to monitor the usage status of water purifier filter cartridges in real time. The system deploys water quality sensors at key locations on the filter cartridge to collect key parameters such as water flow rate, pressure difference, and TDS (Total Dissolved Solids). Combined with data such as usage time and raw water quality, the system uses intelligent algorithms to analyze the filter cartridge's contamination level and remaining lifespan, achieving accurate assessment. When the system detects that the filter cartridge's performance has declined to a set threshold, it will promptly issue a replacement warning to the user via a mobile app, display screen, or alarm device, reminding the user to replace the filter cartridge in a timely manner. This ensures the safety and taste of drinking water, extends the overall lifespan of the water purifier, and improves user experience and convenience.

[0003] With the improvement of people's living standards and the enhancement of health awareness, household and commercial water purification equipment has been widely used. As the core component of water purification equipment, the filtration performance of the water filter directly affects the quality of the output water and the user experience. To ensure the purification effect, the filter needs to be replaced regularly. However, current technologies mainly rely on fixed usage time or simple flow statistics to determine when the filter needs to be replaced, lacking an accurate assessment of the actual degree of filter contamination, resulting in the following shortcomings:

[0004] On the one hand, traditional methods typically remind users to replace filter cartridges based on the manufacturer's recommended fixed replacement cycle (such as every 6 months or a certain cumulative water production). However, this does not take into account factors such as differences in water quality in the actual usage environment, fluctuations in water consumption, and the aging of the filter cartridge itself, which can easily lead to problems of "replacing too early" or "replacing too late." Replacing too early not only increases user costs but also wastes resources; while replacing too late may lead to filter clogging, reduced filtration efficiency, and even secondary pollution, seriously affecting drinking water safety.

[0005] On the other hand, although some existing products have introduced devices such as flow meters and pressure sensors to monitor the status of filter cartridges, they mostly only collect single or a few parameters and lack the support of multi-dimensional data fusion analysis and intelligent algorithms. This makes it difficult to accurately determine the actual degree of pollution and remaining effective life of the filter cartridge, resulting in low warning accuracy and poor reliability, which cannot meet users' needs for refined management and intelligent services.

[0006] In addition, existing filter replacement warning systems generally lack intelligence, have limited user interaction methods, and lack intuitive, real-time information feedback and remote management functions. This makes it difficult for users to keep track of the filter status and make reasonable decisions, affecting the overall user experience and widespread adoption of water purification equipment.

[0007] Therefore, we propose a method and system for intelligent assessment and early warning of water purifier filter cartridge contamination levels. Summary of the Invention

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent assessment and early warning of water purifier filter cartridge contamination level, comprising the following steps:

[0009] S1: Real-time acquisition and preprocessing of multiple parameters: Through sensor modules installed at key locations in the water purifier pipeline and filter cartridge, the total dissolved solids concentration (TDS_in) of the influent, the total dissolved solids concentration (TDS_out) of the effluent, the pressure difference between the filter cartridge inlet / outlet (ΔP), the cumulative water production (V), the real-time flow rate (Q), and the ambient temperature and humidity (T / H) are collected in real time; the collected raw data are denoised, units are standardized, and outliers are removed to obtain preprocessed monitoring data;

[0010] S2: Key Feature Extraction and Dynamic Modeling: Based on the preprocessed data, calculate the filtration efficiency decay rate (η), pressure drop rate (ΔP_rate), flow rate decay rate (Q_rate), and comprehensive pollution index (CPI); where:

[0011] Filter efficiency attenuation rate η = (TDS_in - TDS_out) / TDS_in × 100%;

[0012] The differential pressure growth rate ΔP_rate = (ΔP_t - ΔP_0) / V_t (ΔP_0 is the initial differential pressure, ΔP_t is the current differential pressure, and V_t is the current cumulative water production).

[0013] Flow rate attenuation rate Q_rate=(Q_rated-Q_t) / Q_rated×100% (Q_rated is the rated flow rate of the filter element, and Q_t is the current actual flow rate);

[0014] The Comprehensive Pollution Index (CPI) is a dimensionless index (0-1) that characterizes the overall pollution level of the filter element by taking the filtration efficiency decay rate, pressure difference growth rate, flow rate decay rate and environmental parameters (T / H) as inputs through a machine learning model.

[0015] S3: Personalized pollution model training and adaptive correction: Based on the factory parameters of the filter cartridge model and the actual operating data of the previous N days (N≥30), the initial baseline model of the filter cartridge is trained; according to real-time data and historical trends, the model parameters are dynamically adjusted to adapt to different water qualities and water usage habits;

[0016] S4: Pollution level assessment and replacement early warning decision: Set multi-level early warning thresholds. When CPI∈[0.3,0.5], trigger Level I warning (suggestion for attention); when CPI∈[0.5,0.7], trigger Level II warning (immediate replacement); when CPI≥0.7 or pressure difference ΔP exceeds the safety threshold, trigger Level III warning (emergency replacement).

[0017] S5: Data storage and remote management: Stores raw monitoring data, characteristic values, CPI index and early warning records, supports users to view historical trends and filter life prediction reports through terminal devices, and provides remote filter replacement ordering function.

[0018] Preferably, the sensing module includes: a TDS sensor (installed at the inlet and outlet of the filter element respectively), a differential pressure sensor (installed at the inlet / outlet of the filter element to measure ΔP), a flow sensor (to measure real-time flow rate Q), an ambient temperature and humidity sensor (to monitor T / H), and an optional multi-parameter water quality sensor (to monitor residual chlorine, turbidity, and pH value).

[0019] Preferably, the machine learning model is at least one of the random forest model, XGBoost model, and LSTM neural network model, and its input parameters include filtration efficiency decay rate, pressure difference growth rate, flow decay rate, ambient temperature and humidity, and initial parameters corresponding to the filter model.

[0020] Preferably, the training process of the initial benchmark model includes: based on the factory parameters of the filter element model (such as initial TDS removal rate ≥95%, initial pressure difference ≤0.1MPa) and the actual operating data of the previous N days, fitting the benchmark range of each characteristic parameter under normal operating conditions, and establishing the initial CPI calculation rules.

[0021] Preferably, the dynamic correction process includes: adaptively adjusting the threshold ranges of filtration efficiency decay rate, differential pressure growth rate, and flow rate decay rate based on real-time data and historical trends, in order to compensate for the impact of differences in water hardness, particulate matter concentration, and user water usage frequency in different regions.

[0022] Preferably, when the Level III warning is triggered, the system automatically restricts the water purifier's water production function, allowing only unfiltered raw water to flow out or shutting off the water production function to prevent pipeline pressure buildup.

[0023] A smart water purifier filter cartridge contamination level assessment and replacement early warning system includes:

[0024] Multi-parameter sensing module: installed in key locations of the water purifier pipeline and filter element, used to collect TDS_in, TDS_out, ΔP, V, Q and T / H;

[0025] Main control module: Integrated microprocessor, used to perform data preprocessing, feature calculation, model operation and early warning decision;

[0026] Communication module: Supports Wi-Fi, Bluetooth or NB-IoT wireless communication, used to upload data to cloud servers or local storage, and receive remote commands;

[0027] User interaction module: including display screen (locally displays filter status), mobile APP (pushes early warning information and historical data) and sound and light alarm (emergency alert);

[0028] Filter cartridge identification module: Stores filter cartridge model, initial parameters, and production batch information via RFID tags, NFC chips, or QR codes, allowing the system to identify and match the corresponding contamination assessment model.

[0029] Preferably, the main control module includes a data preprocessing unit (for noise reduction, normalization, and outlier filtering), a feature calculation unit (for calculating η, ΔP_rate, Q_rate, and CPI), a model operation unit (for running machine learning models), and an early warning decision unit (for triggering tiered early warnings based on CPI thresholds). The communication module is also used to synchronize real-time data to a cloud server. The cloud server stores historical data from multiple users and optimizes a general pollution assessment model library. The user interaction module's mobile APP supports viewing historical curves of filter element pollution levels (such as TDS decay trends and differential pressure change curves), predicting remaining lifespan, and submitting filter element replacement orders with one click.

[0030] Compared with the prior art, the present invention provides a method and system for intelligent assessment and early warning of water purifier filter cartridge contamination level, which has the following beneficial effects:

[0031] 1. This intelligent assessment and replacement warning method and system for water purifier filter cartridge contamination levels uses real-time data collection of TDS_in / TDS_out (reflecting dissolved solids filtration accuracy), ΔP (reflecting filter cartridge clogging level), Q (reflecting flux decay), and environmental parameters (T / H corrected for water quality drift). Combined with a machine learning model, it calculates a comprehensive contamination index (CPI) (0-1, dimensionless), directly quantifying the actual contamination status of the filter cartridge, rather than relying solely on fixed usage time or cumulative flow. For example, even if two water purifiers have the same cumulative water production, if one is connected to high-hardness water (contamination occurs faster) and the other to purified water (contamination occurs slower), the system can accurately distinguish them based on the CPI difference, avoiding a "one-size-fits-all" misjudgment (traditional methods might both prompt for replacement).

[0032] 2. This intelligent assessment and replacement early warning method and system for water purifier filter cartridges initially trains a "personalized benchmark model" based on the filter cartridge model's factory parameters (such as initial TDS removal rate and rated flow rate) and actual operating data from the previous 30 days. This determines the normal parameter range for the filter cartridge under specific water quality / water usage habits. Subsequently, through a dynamic correction mechanism, thresholds for characteristics such as filtration efficiency decay rate and differential pressure growth rate are adjusted based on real-time data (e.g., automatically relaxing the ΔP_rate threshold in high-hardness areas) to compensate for differences in water hardness, particulate matter concentration, and users' high / low frequency water usage habits. Differential assessments can be achieved for the same model of filter cartridge in different households, significantly improving the accuracy of early warnings.

[0033] 3. This water purifier filter cartridge features an intelligent assessment and replacement warning system for its contamination level. It employs a three-tiered warning strategy: Level I (light contamination, recommended for attention), Level II (moderate contamination, immediate replacement), and Level III (heavy contamination, urgent replacement), with corresponding intervention measures: Level I sends data details via the app (e.g., "Current TDS removal rate 92%, recommended for attention within 3 months"); Level II triggers a strong mobile phone reminder and recommends replacement within 30 days, while simultaneously reducing the water flow rate to slow contamination accumulation; Level III (CPI ≥ 0.7 or ΔP exceeding 0.3 MPa) immediately triggers an audible and visual alarm and restricts water production (e.g., only raw water flows out or the system shuts down) to prevent secondary contamination or pipeline pressure damage. Users can respond flexibly according to their needs, avoiding the risk of sudden malfunctions.

[0034] 4. The intelligent assessment and replacement early warning method and system for the pollution level of the water purifier filter cartridge can accurately assess the remaining lifespan of the filter cartridge, avoiding premature obsolescence caused by the traditional "fixed periodic replacement" (for example, a filter cartridge that was originally usable for 1 year can actually be used for 1.5 years due to better water quality), reducing the amount of filter cartridge waste by 30% to 50%, reducing user costs and alleviating the environmental burden.

[0035] 5. The intelligent assessment and replacement early warning method and system for the water purifier filter cartridge pollution level stores all monitoring data (original parameters, characteristic values, CPI index, early warning records) locally and synchronizes them to the cloud. Users can view historical trend charts (such as "pressure difference growth curve in the past 6 months" and "TDS removal rate decay trend") and obtain filter cartridge life prediction reports (such as "estimated remaining life of 25 days") through the APP. It also supports remote one-click ordering of filter cartridge replacement (automatic model matching), improving service convenience and user stickiness. Detailed Implementation

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below. 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.

[0037] Example

[0038] An embodiment of a method and system for intelligent assessment and early warning of water purifier filter cartridge contamination level.

[0039] A method for intelligent assessment and early warning of water purifier filter cartridge contamination levels includes the following steps:

[0040] S1: Real-time acquisition and preprocessing of multiple parameters: Through sensor modules installed at key locations in the water purifier pipeline and filter cartridge, the total dissolved solids concentration (TDS_in) of the influent, the total dissolved solids concentration (TDS_out) of the effluent, the pressure difference between the filter cartridge inlet / outlet (ΔP), the cumulative water production (V), the real-time flow rate (Q), and the ambient temperature and humidity (T / H) are collected in real time; the collected raw data are denoised, units are standardized, and outliers are removed to obtain preprocessed monitoring data;

[0041] S2: Key Feature Extraction and Dynamic Modeling: Based on the preprocessed data, calculate the filtration efficiency decay rate (η), pressure drop rate (ΔP_rate), flow rate decay rate (Q_rate), and comprehensive pollution index (CPI); where:

[0042] Filter efficiency attenuation rate η = (TDS_in - TDS_out) / TDS_in × 100%;

[0043] The differential pressure growth rate ΔP_rate = (ΔP_t - ΔP_0) / V_t (ΔP_0 is the initial differential pressure, ΔP_t is the current differential pressure, and V_t is the current cumulative water production).

[0044] Flow rate attenuation rate Q_rate=(Q_rated-Q_t) / Q_rated×100% (Q_rated is the rated flow rate of the filter element, and Q_t is the current actual flow rate);

[0045] The Comprehensive Pollution Index (CPI) is a dimensionless index (0-1) that characterizes the overall pollution level of the filter element by taking the filtration efficiency decay rate, pressure difference growth rate, flow rate decay rate and environmental parameters (T / H) as inputs through a machine learning model.

[0046] S3: Personalized pollution model training and adaptive correction: Based on the factory parameters of the filter cartridge model and the actual operating data of the previous N days (N≥30), the initial baseline model of the filter cartridge is trained; according to real-time data and historical trends, the model parameters are dynamically adjusted to adapt to different water qualities and water usage habits;

[0047] S4: Pollution level assessment and replacement early warning decision: Set multi-level early warning thresholds. When CPI∈[0.3,0.5], trigger Level I warning (suggestion for attention); when CPI∈[0.5,0.7], trigger Level II warning (immediate replacement); when CPI≥0.7 or pressure difference ΔP exceeds the safety threshold, trigger Level III warning (emergency replacement).

[0048] S5: Data storage and remote management: Stores raw monitoring data, characteristic values, CPI index and early warning records, supports users to view historical trends and filter life prediction reports through terminal devices, and provides remote filter replacement ordering function.

[0049] Specifically, the sensing module includes: a TDS sensor (installed at the inlet and outlet of the filter element respectively), a differential pressure sensor (installed at the inlet / outlet of the filter element to measure ΔP), a flow sensor (to measure real-time flow rate Q), an ambient temperature and humidity sensor (to monitor T / H), and an optional multi-parameter water quality sensor (to monitor residual chlorine, turbidity, and pH value).

[0050] Specifically, the machine learning model is at least one of the following: random forest model, XGBoost model, and LSTM neural network model. Its input parameters include filtration efficiency decay rate, pressure difference growth rate, flow decay rate, ambient temperature and humidity, and initial parameters corresponding to the filter model.

[0051] Specifically, the initial benchmark model training process includes: based on the factory parameters of the filter cartridge model (such as initial TDS removal rate ≥95%, initial pressure difference ≤0.1MPa) and the actual operating data of the previous N days, fitting the benchmark range of each characteristic parameter under normal operating conditions, and establishing the initial CPI calculation rules.

[0052] Specifically, the dynamic correction process includes: adaptively adjusting the threshold ranges of filtration efficiency decay rate, differential pressure growth rate, and flow rate decay rate based on real-time data and historical trends, in order to compensate for the impact of differences in water hardness, particulate matter concentration, and user water usage frequency in different regions.

[0053] Specifically, when a Level III warning is triggered, the system automatically restricts the water purifier's water production function, allowing only unfiltered raw water to flow out or shutting off the water production function to prevent pipeline pressure buildup.

[0054] A smart water purifier filter cartridge contamination level assessment and replacement early warning system includes:

[0055] Multi-parameter sensing module: installed in key locations of the water purifier pipeline and filter element, used to collect TDS_in, TDS_out, ΔP, V, Q and T / H;

[0056] Main control module: Integrated microprocessor, used to perform data preprocessing, feature calculation, model operation and early warning decision;

[0057] Communication module: Supports Wi-Fi, Bluetooth or NB-IoT wireless communication, used to upload data to cloud servers or local storage, and receive remote commands;

[0058] User interaction module: including display screen (locally displays filter status), mobile APP (pushes early warning information and historical data) and sound and light alarm (emergency alert);

[0059] Filter cartridge identification module: Stores filter cartridge model, initial parameters, and production batch information via RFID tags, NFC chips, or QR codes, allowing the system to identify and match the corresponding contamination assessment model.

[0060] Specifically, the main control module includes a data preprocessing unit (for noise reduction, normalization, and outlier filtering), a feature calculation unit (for calculating η, ΔP_rate, Q_rate, and CPI), a model operation unit (for running machine learning models), and an early warning decision unit (for triggering tiered early warnings based on CPI thresholds). The communication module is also used to synchronize real-time data to a cloud server. The cloud server stores historical data from multiple users and optimizes a general pollution assessment model library. The user interaction module's mobile app supports viewing historical curves of filter contamination levels (such as TDS decay trends and differential pressure change curves), predicting remaining lifespan, and submitting filter replacement orders with a single click.

[0061] Through the above technical solution, this invention directly quantifies the actual pollution state of the filter cartridge by real-time collection of TDS_in / TDS_out (reflecting dissolved solids filtration accuracy), ΔP (reflecting the degree of filter cartridge clogging), Q (reflecting flux decay), and environmental parameters (T / H corrected for water quality drift), combined with a machine learning model to calculate the comprehensive pollution index CPI (0-1 dimensionless), rather than relying solely on fixed usage time or cumulative flow. For example, even if two water purifiers have the same cumulative water production, if one is connected to high-hardness water (faster pollution) and the other is connected to purified water (slower pollution), the system can accurately distinguish them through the CPI difference, avoiding a "one-size-fits-all" misjudgment (traditional methods might both prompt for replacement). In the initial stage, a "personalized benchmark model" is trained based on the filter cartridge's factory parameters (such as initial TDS removal rate and rated flow rate) and actual operating data from the first 30 days to determine the normal parameter range for the filter cartridge under specific water quality / water usage habits. Subsequently, through a dynamic correction mechanism, thresholds for characteristics such as filtration efficiency decay rate and differential pressure growth rate are adjusted based on real-time data (e.g., automatically relaxing the ΔP_rate threshold in high-hardness areas) to compensate for the impact of differences in water hardness, particulate matter concentration, and users' high / low frequency water usage habits. The same model of filter cartridge can achieve differentiated evaluation in different households, significantly improving the accuracy of early warning. A three-tiered early warning strategy is implemented: Level I (mild pollution, recommended for monitoring), Level II (moderate pollution, replacement recommended soon), and Level III (severe pollution, emergency replacement recommended), with corresponding intervention measures: Level I pushes detailed data via the app (e.g., "Current TDS removal rate 92%, recommended for monitoring within 3 months"); Level II triggers a strong mobile phone reminder and recommends replacement within 30 days, while reducing the water flow rate to slow pollution accumulation; Level III (CPI ≥ 0.7 or ΔP > 0.3 MPa) immediately triggers an audible and visual alarm and restricts water production (e.g., only raw water flows out or the system shuts down) to prevent secondary pollution or pipeline pressure damage. Users can respond flexibly according to their needs, avoiding the risk of sudden malfunctions. By accurately assessing the remaining lifespan of the filter cartridges, premature obsolescence caused by traditional "fixed-cycle replacement" is avoided (e.g., a filter cartridge that could be used for 1 year may actually be used for 1.5 years due to better water quality), reducing filter cartridge waste by 30%–50%, lowering user costs while reducing environmental burden. All monitoring data (raw parameters, characteristic values, CPI index, and early warning records) are stored locally and synchronized to the cloud. Users can view historical trend charts (such as "pressure difference growth curve in the past 6 months" and "TDS removal rate decay trend") and obtain filter life prediction reports (such as "estimated remaining life of 25 days") through the APP. It also supports remote one-click ordering of filter replacement (automatic model matching), improving service convenience and user stickiness.

[0062] Example 1: Intelligent Assessment and Early Warning of Household RO Reverse Osmosis Water Purifiers

[0063] System Deployment:

[0064] Sensing Module: A TDS sensor (range 0-1000mg / L, accuracy ±2mg / L) and a differential pressure sensor (range 0-0.5MPa, accuracy ±0.01MPa) are installed at the inlet of the RO filter cartridge, and a TDS sensor is installed at the outlet (same as the inlet); a flow sensor (range 0-3L / min, accuracy ±0.05L / min) is connected in series in the filter cartridge outlet pipe; an ambient temperature and humidity sensor (range 0-60℃ / 0-100%RH, accuracy ±1℃ / ±5%RH) is installed inside the water purifier; an NFC tag (storing model "RO-500G, initial TDS removal rate ≥97%, rated flow rate 1.5L / min, initial differential pressure ≤0.05MPa") is affixed to the filter cartridge shell.

[0065] Main control module: adopts STM32F407 microprocessor (ARM Cortex-M4 core), integrating data preprocessing (Kalman filter denoising), feature calculation (η / ΔP_rate / Q_rate / CPI) and machine learning model (XGBoost algorithm, input parameters: η, ΔP_rate, Q_rate, T, H and filter model code).

[0066] Communication module: The Wi-Fi module (ESP8266) connects to the home router and synchronizes data to the cloud server (storage period of 1 year). It also supports Bluetooth 4.0 for short-range interaction with mobile APP.

[0067] User interaction module: The water purifier panel is equipped with an OLED display screen (showing the current CPI index, warning level and suggested operation); the mobile APP (iOS / Android) provides real-time curve graphs, historical record queries and "one-click purchase of filter cartridges" function; the sound and light alarm (red LED + buzzer) is used for Level III warning.

[0068] Operation process:

[0069] Initialization phase: After the user installs the filter cartridge for the first time, the main control module reads the model information via NFC, loads the pre-stored "RO-500G initial model" (when the initial TDS_in = 300mg / L, TDS_out should be ≤9mg / L, ΔP_0 = 0.05MPa, Q_rated = 1.5L / min), and records the initial pressure difference ΔP_0 = 0.04MPa.

[0070] Routine monitoring (days 1-30): The system collects data every 10 minutes. For example, on day 10, the following data was measured: TDS_in = 290 mg / L, TDS_out = 8 mg / L (η = (290-8) / 290 ≈ 97.2%), ΔP = 0.06 MPa, V = 300 L, Q = 1.48 L / min, T = 23℃, H = 55%. The main control module calculates: ΔP_rate = (0.06-0.04) / 300 ≈ 6.7 × 10⁻⁴ -5 MPa / L, Q_rate=(1.5-1.48) / 1.5≈1.3%; XGBoost model input {η=97.2%, ΔP_rate=6.7×10 -5 If Q_rate = 1.3%, T = 23℃, H = 55%, and the output CPI = 0.2 (below the Level I threshold), the system will not trigger a warning.

[0071] Mid-term pollution accumulation (days 60-90): With prolonged use, on day 75, the following values ​​were measured: TDS_in = 285 mg / L, TDS_out = 15 mg / L (η = (285-15) / 285 ≈ 94.7%), ΔP = 0.15 MPa, V = 2500 L, Q = 1.3 L / min. The calculated ΔP_rate = (0.15-0.04) / 2500 ≈ 4.4 × 10⁻⁴ -5 MPa / L (increased from the initial level), Q_rate=(1.5-1.3) / 1.5≈13.3%, XGBoost model output CPI=0.45 after inputting the current parameters (triggered Level I warning). Mobile APP push: "Current filter efficiency has dropped to 94.7% (initial 97.2%), pressure difference 0.15MPa (normal range <0.2MPa), it is recommended to monitor water quality changes within 3 months."

[0072] Severe Pollution and Emergency Intervention (Day 120): On Day 120, the following measurements were taken: TDS_out = 28 mg / L (η = (285-28) / 285 ≈ 90.2%), ΔP = 0.28 MPa (close to the safety threshold of 0.3 MPa), Q = 1.1 L / min (Q_rate ≈ 26.7%). The XGBoost model output CPI = 0.78 (triggered Level III warning), and the system immediately executed: ① Audible and visual alarm (red LED flashing + buzzer sounding); ② Strong reminder from the APP: "Filter cartridge severely polluted! TDS removal rate is only 90.2%, pressure difference of 0.28 MPa exceeds the safety value, please replace within 24 hours"; ③ Automatically limit the water production flow rate to 0.8 L / min (by adjusting the solenoid valve opening), allowing only a small amount of raw water to flow out to meet basic needs and prevent high pressure from damaging the pipeline.

[0073] Filter replacement reset: After the user replaces the filter, the main control module identifies the new filter model (same model "RO-500G") via NFC, initializes all parameters (ΔP_0=0.04MPa, Q_rated=1.5L / min), clears historical CPI records and starts a new round of monitoring.

[0074] Example 2: Differentiation Evaluation of Commercial Multi-Stage Composite Filter Systems

[0075] For commercial water purifiers (including three-stage composite filters: PP cotton, activated carbon, and RO membrane), the system is equipped with a separate sensing module for each filter (e.g., PP cotton filters monitor differential pressure and flow rate, while RO membrane filters monitor TDS and differential pressure). The system calculates the CPI index of each filter in parallel using multiple models, enabling tiered early warning systems (e.g., prompting cleaning / replacement when PP cotton CPI ≥ 0.6, and prioritizing handling when RO membrane CPI ≥ 0.5). Simultaneously, a cloud server aggregates data on filter models of the same type from multiple users, optimizing a universal pollution assessment model library (e.g., adjusting TDS attenuation weights for high-fluoride water in a specific region), further enhancing the intelligence level of filter management across the industry.

[0076] 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 method for intelligent assessment and early warning of water purifier filter cartridge contamination level, characterized in that: Includes the following steps: S1: Real-time acquisition and preprocessing of multiple parameters: Through sensor modules installed at key locations in the water purifier pipeline and filter cartridge, the total dissolved solids concentration (TDS_in) of the influent, the total dissolved solids concentration (TDS_out) of the effluent, the pressure difference between the filter cartridge inlet / outlet (ΔP), the cumulative water production (V), the real-time flow rate (Q), and the ambient temperature and humidity (T / H) are collected in real time; the collected raw data are denoised, units are standardized, and outliers are removed to obtain preprocessed monitoring data; S2: Key Feature Extraction and Dynamic Modeling: Based on the preprocessed data, calculate the filtration efficiency decay rate (η), pressure difference growth rate (ΔP_rate), flow rate decay rate (Q_rate), and comprehensive pollution index (CPI); where: Filter efficiency attenuation rate η = (TDS_in - TDS_out) / TDS_in × 100%; The differential pressure growth rate ΔP_rate = (ΔP_t - ΔP_0) / V_t (ΔP_0 is the initial differential pressure, ΔP_t is the current differential pressure, and V_t is the current cumulative water production). Flow rate attenuation rate Q_rate=(Q_rated-Q_t) / Q_rated×100% (Q_rated is the rated flow rate of the filter element, and Q_t is the current actual flow rate); The Comprehensive Pollution Index (CPI) is a dimensionless index (0-1) that characterizes the overall pollution level of the filter element by taking into account the filtration efficiency decay rate, pressure difference growth rate, flow rate decay rate, and environmental parameters (T / H) through a machine learning model. S3: Personalized pollution model training and adaptive correction: Based on the factory parameters of the filter element model and the actual operating data of the previous N days (N≥30), the initial baseline model of the filter element is trained. Based on real-time data and historical trends, the model parameters are dynamically adjusted to adapt to different water qualities and water usage habits. S4: Pollution level assessment and replacement early warning decision: Set multi-level early warning thresholds. When CPI∈[0.3,0.5], trigger Level I warning (suggestion for attention); when CPI∈[0.5,0.7], trigger Level II warning (immediate replacement); when CPI≥0.7 or pressure difference ΔP exceeds the safety threshold, trigger Level III warning (emergency replacement). S5: Data storage and remote management: Stores raw monitoring data, characteristic values, CPI index and early warning records, supports users to view historical trends and filter life prediction reports through terminal devices, and provides remote filter replacement ordering function.

2. The intelligent assessment and replacement early warning method for the degree of contamination of a water purifier filter cartridge according to claim 1, characterized in that: The sensing module includes: a TDS sensor (installed at the inlet and outlet of the filter element respectively), a differential pressure sensor (installed at the inlet / outlet of the filter element to measure ΔP), a flow sensor (to measure real-time flow rate Q), an ambient temperature and humidity sensor (to monitor T / H), and an optional multi-parameter water quality sensor (to monitor residual chlorine, turbidity, and pH value).

3. The intelligent assessment and replacement early warning method for the degree of contamination of a water purifier filter cartridge according to claim 1, characterized in that: The machine learning model is at least one of the random forest model, XGBoost model, and LSTM neural network model. Its input parameters include filtration efficiency decay rate, pressure difference growth rate, flow decay rate, ambient temperature and humidity, and initial parameters corresponding to the filter model.

4. The intelligent assessment and replacement early warning method for the degree of contamination of a water purifier filter cartridge according to claim 1, characterized in that: The training process of the initial benchmark model includes: based on the factory parameters of the filter element model (such as initial TDS removal rate ≥95%, initial pressure difference ≤0.1MPa) and the actual operating data of the previous N days, fitting the benchmark range of each characteristic parameter under normal operating conditions, and establishing the initial CPI calculation rules.

5. The intelligent assessment and replacement early warning method for the degree of contamination of a water purifier filter cartridge according to claim 1, characterized in that: The dynamic correction process includes: adaptively adjusting the threshold ranges of filtration efficiency decay rate, differential pressure growth rate, and flow rate decay rate based on real-time data and historical trends, in order to compensate for the impact of differences in water hardness, particulate matter concentration, and user water usage frequency in different regions.

6. The intelligent assessment and replacement early warning method for the degree of contamination of a water purifier filter cartridge according to claim 1, characterized in that: When the Level III warning is triggered, the system automatically restricts the water purifier's water production function, allowing only unfiltered raw water to flow out or shutting off the water production function to prevent pipeline pressure buildup.

7. A smart system for assessing and issuing early warnings of water purifier filter cartridge contamination levels, characterized in that: include: Multi-parameter sensing module: installed in key locations of the water purifier pipeline and filter element, used to collect TDS_in, TDS_out, ΔP, V, Q and T / H; Main control module: Integrated microprocessor, used to perform data preprocessing, feature calculation, model operation and early warning decision; Communication module: Supports Wi-Fi, Bluetooth or NB-IoT wireless communication, used to upload data to cloud servers or local storage, and receive remote commands; User interaction module: including display screen (locally displays filter status), mobile APP (pushes early warning information and historical data) and sound and light alarm (emergency alert); Filter cartridge identification module: Stores filter cartridge model, initial parameters and production batch information through RFID tags, NFC chips or QR codes, so that the system can identify and match the corresponding pollution assessment model.

8. The intelligent assessment and replacement early warning system for the degree of contamination of a water purifier filter cartridge according to claim 7, characterized in that: The main control module includes a data preprocessing unit (for noise reduction, normalization, and outlier filtering), a feature calculation unit (for calculating η, ΔP_rate, Q_rate, and CPI), a model operation unit (for running machine learning models), and an early warning decision unit (for triggering tiered early warnings based on CPI thresholds). The communication module is also used to synchronize real-time data to a cloud server. The cloud server stores historical data from multiple users and optimizes a general pollution assessment model library. The user interaction module's mobile APP supports viewing historical curves of filter contamination levels (such as TDS decay trends and differential pressure change curves), predicting remaining lifespan, and submitting filter replacement orders with a single click.