Method for detecting cleanliness of water purifier

Abstract

The invention relates to the technical field of water purifier testing, and particularly discloses a water purifier cleanliness detection method which comprises the following steps: acquiring historical data at a water inlet of a water purifier, and constructing a water purifier cleanliness data set according to historical water quality indexes and historical running state data of the water purifier; constructing an initial water purifier cleanliness detection model based on a recurrent neural network, and training the water purifier cleanliness detection model to obtain a completely trained water purifier cleanliness detection model; acquiring a real-time raw water quality index at a water inlet of the water purifier and current real-time running state data of the water purifier; inputting the real-time raw water quality index and the real-time operation state into a water purifier cleanliness detection model to obtain a water purifier cleanliness prediction result; and outputting a water purifier cleanliness adjustment strategy based on the cleanliness prediction result and a preset water purification standard. According to the invention, the limitation of traditional water purification equipment can be effectively overcome, and more intelligent and efficient water purifier cleanliness inspection is realized, so that the safety of drinking water is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of water purifier testing, and particularly to a method for detecting the cleanliness of a water purifier. Background Art

[0002] Water purifiers play a crucial role in our daily lives and directly affect the safety of our drinking water. The main function of a water purifier is to remove contaminants from water to ensure that the treated water meets the health and safety standards for drinking water. By understanding the actual working conditions of the water purifier and the water treatment effect, operation strategies can be adjusted and optimized to improve the efficiency and performance of water treatment. Effective cleanliness management of water purifiers not only helps to safeguard personal health but also meets the requirements of environmental protection and sustainable development, reduces resource waste, and extends the service life of the equipment.

[0003] Existing technologies have some limitations. For example, traditional water purifiers usually operate based on static preset working modes and cannot be dynamically adjusted according to the real-time raw water quality and the operating status of the machine. In addition, they may lack intelligent monitoring capabilities and cannot provide real-time feedback on the water treatment effect or predict possible problems or failures. There are also deficiencies in terms of accuracy and cannot provide sufficient precise water treatment guarantees. With the increasing demand for sustainable development, traditional water purification equipment also faces certain challenges in terms of resource utilization efficiency and environmental impact, and more precise cleanliness control methods are needed to ensure the reliability and stability of water treatment.

[0004] Therefore, it is necessary to propose a method for inspecting the cleanliness of a water purifier, which can effectively overcome the various limitations of traditional water purification equipment, achieve more intelligent, efficient, and sustainable water treatment and management, improve the safety of drinking water, and at the same time provide innovative solutions for future water resource management. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for detecting the cleanliness of a water purifier to solve the following technical problems: (1) The detection of the cleanliness of a water purifier needs to comprehensively consider multiple parameters such as residual chlorine, TDS, pH value, turbidity, and VOCs, etc. Their mutual influence is complex, and it is relatively difficult to handle with traditional methods.

[0006] (2) Traditional methods often have difficulty in making personalized adjustments to the operation of water purifiers according to the water quality differences in specific regions and user usage habits, and cannot provide accurate and efficient water purification services.

[0007] (3) When the built-in cleanliness detection device of the water purifier is damaged or malfunctioning, it is impossible to effectively evaluate the water treatment results based on the historical status and the current operating status.

[0008] The purpose of the present invention can be achieved by the following technical solutions: A method for detecting the cleanliness of a water purifier, comprising the following steps: Obtain the historical raw water quality indicators at the water inlet of the water purifier, the historical operating status data of the water purifier, and the historical purified water quality indicators corresponding to different purification time points at the water outlet, and construct a water purifier cleanliness dataset based on the historical water quality indicators and the historical operating status data of the water purifier; Construct an initial water purifier cleanliness detection model based on a recurrent neural network, and train the water purifier cleanliness detection model according to the water purifier cleanliness dataset to obtain a trained water purifier cleanliness detection model; Obtain the real-time raw water quality indicators at the water inlet of the water purifier and the current real-time operating status data of the water purifier; Input the real-time raw water quality indicators and real-time operating status into the water purifier cleanliness detection model to obtain a water purifier cleanliness prediction result; Output a water purifier cleanliness adjustment strategy based on the cleanliness prediction result and a preset water purification standard; Wherein, the water quality indicators include residual chlorine, TDS, pH value, turbidity, and VOCs, and the operating status data of the water purifier includes the opening and closing status of each control valve of the water purifier.

[0009] As a further solution of the present invention: The construction of the water purifier cleanliness dataset according to the historical water quality indicators and the historical operating status data of the water purifier includes: Represent the historical raw water quality indicators at the water inlet, the historical operating status data of the water purifier, and the historical purified water quality indicators corresponding to different purification time points at the water outlet as a matrix X(t)=(N, T, D) with a feature dimension and a time dimension; Wherein, N represents the number of samples in the dataset, T represents the recorded time points, D represents the feature dimension, that is, the number of features corresponding to each time point, and the feature dimension includes raw water quality indicators, the operating status of the water purifier, and the purified water quality indicators at the water outlet; Perform normalization processing on the feature dimension data. For the feature data D(i) of any dimension, the normalization processing formula for D(i) is: Wherein, D(i) represents the original data, Dmin represents the minimum value in the dataset, Dmax represents the maximum value in the dataset, and Dn(i) represents the normalized data.

[0010] It should be noted that in addition to the above-mentioned preprocessing of the data, preprocessing operations such as removing abnormal data and filling in missing data are also required. The specific methods can adopt conventional methods in the field of data processing and will not be elaborated here.

[0011] As a further solution of the present invention: the cleanliness detection model of the water purifier is constructed based on an RNN network; The hidden layer uses an LSTM network with a dimension of H; the output layer uses a fully connected layer, and Sigmoid is used as the activation function; the outputs of each layer are expressed by formulas: Input layer: ; Hidden layer: Output layer: Wherein, xt represents the input data at time point t; ht represents the hidden state at the current time step, and W is the output layer weight matrix, which is used to convert the final hidden state hT of the LSTM into the form of an output vector Y.

[0012] As a further solution of the present invention: the cleanliness adjustment strategy of the water purifier includes forward cleaning and reverse cleaning of the water purifier; The forward cleaning is used to clean the particles, sediments and dirt on the surface of the water purifier filter element to maintain the stability of water quality and purification efficiency; The reverse cleaning is used to discharge the dirt, particles or pollutants inside the filter element out of the water purifier through reverse water flow or reverse air flow, and discharge the fine particles inside the filter element.

[0013] As a further solution of the present invention: the forward cleaning strategy includes: closing the first solenoid valve arranged at the water outlet of the water purifier, and at the same time opening the second solenoid valve arranged at the sewage outlet of the water purifier, controlling the three-way valve arranged at the water inlet of the water purifier, so that the water inlet is communicated with the sewage outlet, the water inlet is disconnected from the water outlet, and the raw water rushes from the water inlet through the inner side of the filter element conduit to the sewage outlet and flows out, completing the forward cleaning.

[0014] As a further solution of the present invention: the reverse cleaning strategy includes: closing the first solenoid valve arranged at the water outlet of the water purifier, and at the same time opening the second solenoid valve arranged at the sewage outlet of the water purifier, controlling the three-way valve arranged at the water inlet of the water purifier, so that the water inlet is communicated with the water outlet, the raw water enters from the water inlet through the outer side of the filter element conduit and rushes to the inner side and flows out to the sewage outlet, completing the reverse cleaning.

[0015] As a further solution of the present invention: the cleanliness adjustment strategy of the water purifier further includes: Based on the cleanliness prediction result, set the daily self-cleaning time period, execute the forward cleaning strategy at the first moment of each day, and execute the reverse cleaning strategy at the second moment of each day.

[0016] As a further solution of the present invention: the method further includes: Monitor the change trend of the TOC value in the purified water quality within a preset time period, and draw a change trend curve of the TOC value; Calculate the correlation coefficient between the change trend curve and the preset mapping curve; When the correlation coefficient is greater than the preset correlation threshold, issue a prompt to replace the consumables.

[0017] Advantages of the present invention: (1) By establishing an accurate cleanliness detection model, the real-time monitoring and prediction of water quality can be realized, potential water quality problems can be discovered in time, and targeted measures can be taken to ensure that the water quality meets health and safety standards.

[0018] (2) By combining historical and real-time data, the cleanliness detection model of the water purifier can not only make predictions under known conditions, but also adapt and adjust under new or changing environmental conditions to maintain a stable purification effect.

[0019] (3) Through real-time monitoring and cleanliness prediction, problems that may occur in the water purifier can be discovered and solved in time, its service life can be extended and maintenance costs can be reduced, and at the same time, the water quality safety of the home and office environment can be ensured. Brief Description of the Drawings

[0020] The present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 is a flowchart of the detection method for the cleanliness of the water purifier of the present invention; Figure 2 is a schematic diagram of the working process of the forward cleaning strategy of the water purifier of the present invention; Figure 3 is a schematic diagram of the working process of the reverse cleaning strategy of the water purifier of the present invention; Figure 4 is a schematic diagram of the water purification process of the water purifier of the present invention. Detailed Embodiments

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 As shown, the present invention is a detection method for the cleanliness of a water purifier, including the following steps: Step S101: Obtain the historical raw water quality indicators at the water inlet of the water purifier, the historical operating status data of the water purifier, and the historical purified water quality indicators corresponding to different purification time points at the water outlet. Construct a water purifier cleanliness dataset based on the historical water quality indicators and the historical operating status data of the water purifier; Step S102: Construct an initial water purifier cleanliness detection model based on a recurrent neural network. Train the water purifier cleanliness detection model according to the water purifier cleanliness dataset to obtain a trained and complete water purifier cleanliness detection model; Step S103: Obtain the real-time raw water quality indicators at the water inlet of the water purifier and the current real-time operating status data of the water purifier; Step S104: Input the real-time raw water quality indicators and the real-time operating status into the water purifier cleanliness detection model to obtain a water purifier cleanliness prediction result; Step S105: Output a water purifier cleanliness adjustment strategy based on the cleanliness prediction result and a preset water purification standard; Among them, in order to comprehensively evaluate the water quality, the embodiments of the present invention analyze the water quality from multiple dimensions such as physical indicators, chemical indicators, and organic matter content. The water quality indicators include residual chlorine, TDS, pH value, turbidity, and VOCs. The operating status data of the water purifier includes the opening and closing states of each control valve of the water purifier.

[0024] The working principle of the present invention: First, obtain the water quality indicators of the historical raw water from the water inlet of the water purifier, such as residual chlorine, TDS, pH value, turbidity, and VOCs. Obtain the historical operating status data of the water purifier, including the working status of the water purifier at different time points, mainly the opening and closing conditions of the control valves, etc. These data are used to construct a comprehensive water purifier cleanliness dataset for subsequent model training. Secondly, construct an initial water purifier cleanliness detection model based on a recurrent neural network. RNN is suitable for processing sequential data and can consider the influence of historical data on the current state. When the water purifier is running, obtain the real-time raw water quality indicators at the water inlet and the current operating status data in real time. Input these real-time data into the trained water purifier cleanliness detection model for cleanliness prediction. Finally, based on the cleanliness prediction result, compare and analyze it with the preset water purification standard. If the cleanliness of the water purifier is lower than the preset standard, perform corresponding operations according to the preset adjustment strategy, such as increasing the cleaning frequency, adjusting the filter element usage duration, etc., to ensure that the water purifier can always provide water quality that meets the standard.

[0025] In order to provide an accurate and comprehensive data basis for training and optimizing the water purifier cleanliness detection model, it is necessary to preprocess the original data.

[0026] In some embodiments, in step S101, constructing the water purifier cleanliness dataset based on historical water quality indicators and historical operating status data of the water purifier includes: Represent the historical raw water quality indicators at the water inlet, the historical operating status data of the water purifier, and the historical purified water quality indicators corresponding to different purification time points at the water outlet as a matrix X(t)=(N, T, D) with feature dimensions and time dimensions; Where N represents the number of samples in the dataset, T represents the recorded time points, D represents the feature dimension, that is, the number of features corresponding to each time point, and the feature dimension includes raw water quality indicators, water purifier operating status, and purified water quality indicators at the water outlet; Perform normalization processing on the feature dimension data. For the feature data D(i) of any dimension, the normalization formula for D(i) is: Where D(i) represents the original data, Dmin represents the minimum value in the dataset, Dmax represents the maximum value in the dataset, and Dn(i) represents the normalized data.

[0027] The water purifier cleanliness detection model is constructed based on the RNN network; The hidden layer uses the LSTM network with dimension H; the output layer uses the fully connected layer, and Sigmoid is used as the activation function; express the output of each layer with a formula: Input layer: ; Hidden layer: Output layer: Where xt represents the input data at time point t; ht represents the hidden state at the current time step, and W is the output layer weight matrix used to convert the final hidden state hT of the LSTM into the form of the output vector Y.

[0028] Since the LSTM network is particularly suitable for processing time series data and can effectively capture long-term dependencies. Therefore, the hidden layer uses the LSTM (Long Short-Term Memory) network, which can be used to analyze the performance and cleanliness of the water purifier over time. At the same time, the LSTM network effectively controls the transmission and retention of information through gating structures (such as input gates, forget gates, output gates), thus effectively alleviating the problems of gradient disappearance and gradient explosion, enabling the network to train and converge more stably. Analyze the raw water quality and operating status through historical states and current inputs to more accurately predict the cleanliness of the water purifier.

[0029] The fully connected layer can map the final hidden state hT of the LSTM to the output vector Y through the weight matrix W, which can effectively convert the complex features learned by the LSTM into the final cleanliness prediction result. At the same time, the output layer uses the Sigmoid activation function, which can usually limit the output between 0 and 1, representing the probability or score of the water purifier cleanliness. This design helps to intuitively understand the current cleanliness state of the water purifier.

[0030] As a preferred embodiment, the water purifier cleanliness adjustment strategy includes forward cleaning and reverse cleaning of the water purifier; The forward cleaning is used to clean the particles, sediments and dirt on the surface of the water purifier filter element to maintain the stability of water quality and purification efficiency; The reverse cleaning is used to discharge the dirt, particles or pollutants inside the filter element from the water purifier through reverse water flow or reverse air flow, and discharge the fine particles inside the filter element.

[0031] As a preferred embodiment, the forward cleaning strategy includes: closing the first solenoid valve provided at the water outlet of the water purifier, and at the same time opening the second solenoid valve provided at the sewage outlet of the water purifier, controlling the three-way valve provided at the water inlet of the water purifier, so that the water inlet is communicated with the sewage outlet and the water inlet is disconnected from the water outlet, and the raw water rushes from the water inlet through the inner side of the filter element conduit to the sewage outlet and flows out to complete the forward cleaning.

[0032] The following combines Figure 2 for detailed description. As Figure 2 shown, the solenoid valve D1 is energized for 1 minute and the valve closes. The solenoid valve D2 is energized for 1 minute and the valve opens. The three-way valve T is de-energized. The forward flushing passage is opened, and the tap water rushes from the water inlet through the inner side of the filter element conduit to the sewage outlet and flows out to complete the forward flushing for 1 minute.

[0033] The reverse cleaning strategy includes: closing the first solenoid valve provided at the water outlet of the water purifier, and at the same time opening the second solenoid valve provided at the sewage outlet of the water purifier, controlling the three-way valve provided at the water inlet of the water purifier, so that the water inlet is communicated with the water outlet, and the raw water enters from the water inlet through the outer side of the filter element conduit and rushes to the sewage outlet through the inner side to complete the reverse cleaning.

[0034] The following combines Figure 3 for detailed description. As Figure 3 shown, the solenoid valve D1 is energized for 30 seconds and the valve closes. The solenoid valve D2 is energized for 30 seconds and the valve opens. The three-way valve T is energized for 30 seconds, the B port closes, and the A to C passage. The reverse flushing passage is opened, and the tap water enters from the water inlet through the outer side of the filter element conduit and rushes to the sewage outlet through the inner side to complete the reverse flushing for 30 seconds.

[0035] It should be noted here that as Figure 4As shown, when the water purification system is operating normally, solenoid valves D1, D2 and three-way valve T are all de-energized. At this time, the filtration path is open, and when the faucet connected to the water outlet is opened, filtered clean water can flow out.

[0036] As a preferred embodiment, the cleaning degree adjustment strategy of the water purifier further includes: Based on the cleaning degree prediction result, set the daily self-cleaning time period, execute the forward cleaning strategy at the first moment of each day, and execute the reverse cleaning strategy at the second moment of each day.

[0037] Generally, in order to balance water purification performance and the water usage habits of most families, the first moment and the second moment are usually set in the time period from 04:00 to 06:00 every day. From 04:00 to 06:00 in the early morning is the low water usage peak period for most families, and the household water consumption is less at this time. Setting the operation time period of the water purifier in this way can avoid overlapping with the peak water usage period, reduce the impact on water pressure and water flow rate, ensure that the water purifier can operate under relatively stable conditions, improve the purification effect and performance stability. At the same time, the electricity cost is usually lower during the early morning period, and the energy cost consumed by operating the water purifier is relatively less, which can reduce the operating cost and provide a better water usage experience and economic benefits for the family.

[0038] Furthermore, the cleaning degree adjustment strategy of the water purifier also includes increasing the cleaning frequency, extending the cleaning time, adjusting the filter element usage duration, etc., to ensure that the water purifier can always provide water quality that meets the standards.

[0039] In some embodiments, in addition to monitoring the opening and closing state of the valves, the opening degree of the valves can be controlled according to the water quality comparison. By dynamically adjusting the opening degree of the valves, the water purifier can optimize the purification effect according to the real-time water quality changes. For example, when the water quality is poor, the processing time of the water purifier can be increased or the processing channel can be enlarged to ensure more thorough purification; when the water quality is good, the processing time can be appropriately reduced or the processing channel can be narrowed, thereby saving energy and extending the service life of the water purifier.

[0040] As a preferred embodiment, the method further includes: Monitor the change trend of the TOC value in the purified water quality within a preset time period, and draw a change trend curve of the TOC value; Calculate the correlation coefficient between the change trend curve and the preset mapping curve; When the correlation coefficient is greater than the preset correlation threshold, send a reminder to replace the consumables.

[0041] Specifically, TOC refers to the total amount of all organic carbon in water and is an important indicator for evaluating the content of organic pollutants in water quality. Organic substances such as dissolved organic matter (DOM), residual drugs, oils, and other organic pollutants can all affect water quality and the performance of water purification equipment. The performance of a water purifier directly affects its TOC removal effect. Therefore, monitoring TOC changes can help evaluate the operating status of the equipment and the timing of maintenance. Within a preset time period (usually set to 1 month), regularly measure and record the TOC value and plot the change curve of the TOC value over time. Such a curve can visually display the changing trend of water quality, including seasonal changes, emergencies, or long-term cumulative effects, etc.

[0042] When the correlation coefficient between the actual TOC change curve and the preset mapping curve exceeds this threshold, it indicates that the actual changing trend deviates significantly from the expectation. This may suggest a decline in the performance of the water purifier, the need to replace consumables, or the need to adjust the operation strategy.

[0043] The water purifier cleanliness detection method proposed in the present invention realizes real-time monitoring and prediction of water quality by establishing an accurate cleanliness detection model, promptly discovers potential water quality problems, and takes targeted measures to ensure that the water quality meets health and safety standards; through the combination of historical and real-time data, the water purifier cleanliness detection model can not only make predictions under known conditions, but also adapt and adjust under new or changing environmental conditions to maintain a stable purification effect; through real-time monitoring and cleanliness prediction, it can promptly discover and solve problems that may occur in the water purifier, extend its service life and reduce maintenance costs, and ensure the water quality safety of the home and office environment. The present invention not only improves drinking water safety, but also provides an innovative solution for future water resource management.

[0044] The above has described in detail one embodiment of the present invention, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for detecting the cleanliness of a water purifier, characterized in that, Including the following steps: Obtain the historical raw water quality indicators at the water inlet of the water purifier, the historical operating status data of the water purifier, and the historical purified water quality indicators corresponding to different purification time points at the water outlet, and construct a water purifier cleanliness dataset based on the historical water quality indicators and the historical operating status data of the water purifier; Construct an initial water purifier cleanliness detection model based on a recurrent neural network, and train the water purifier cleanliness detection model according to the water purifier cleanliness dataset to obtain a trained and complete water purifier cleanliness detection model; Obtain the real-time raw water quality indicators at the water inlet of the water purifier and the current real-time operating status data of the water purifier; Input the real-time raw water quality indicators and the real-time operating status into the water purifier cleanliness detection model to obtain a water purifier cleanliness prediction result; Output a water purifier cleanliness adjustment strategy based on the cleanliness prediction result and the preset water purification standard; Wherein, the water quality indicators include residual chlorine, TDS, pH value, turbidity and VOCs, and the operating status data of the water purifier includes the opening and closing status of each control valve of the water purifier.

2. The detection method for the cleanliness of a water purifier according to claim 1, characterized in that, The construction of the water purifier cleanliness dataset according to the historical water quality indicators and the historical operating status data of the water purifier includes: Represent the historical raw water quality indicators at the water inlet, the historical operating status data of the water purifier, and the historical purified water quality indicators corresponding to different purification time points at the water outlet as a matrix X(t)=(N, T, D) with a feature dimension and a time dimension; Wherein, N represents the number of samples in the dataset, T represents the recorded time points, D represents the feature dimension, that is, the number of features corresponding to each time point, and the feature dimension includes raw water quality indicators, water purifier operating status and water outlet purified water quality indicators; Perform normalization processing on the feature dimension data. For the feature data D(i) of any dimension, the normalization processing formula for D(i) is: Wherein, D(i) represents the original data, Dmin represents the minimum value in the dataset, Dmax represents the maximum value in the dataset, and Dn(i) represents the normalized data.

3. The detection method for the cleanliness of a water purifier according to claim 1, wherein, The water purifier cleanliness detection model is constructed based on the RNN network; The hidden layer adopts an LSTM network with a dimension of H; the output layer adopts a fully connected layer, and uses Sigmoid as the activation function; the outputs of each layer are expressed by formulas: Input layer: ; Hidden layer: Output layer: Wherein, xt represents the input data at time point t; ht represents the hidden state of the current time step, and W is the output layer weight matrix, which is used to convert the final hidden state hT of the LSTM into the form of an output vector Y.

4. The detection method for the cleanliness of a water purifier according to claim 1, characterized in that, The water purifier cleanliness adjustment strategy includes forward cleaning and reverse cleaning of the water purifier; The forward cleaning is used to clean the particles, sediments and dirt on the surface of the water purifier filter element to maintain the stability of the water quality and the purification efficiency; The reverse cleaning is used to discharge the dirt, particles or pollutants inside the filter element from the water purifier through reverse water flow or reverse air flow, and discharge the fine particles inside the filter element.

5. The detection method for the cleanliness of a water purifier according to claim 4, wherein The forward cleaning strategy includes: closing the first electromagnetic valve provided at the water outlet of the water purifier, and at the same time opening the second electromagnetic valve provided at the sewage outlet of the water purifier, controlling the three-way valve provided at the water inlet of the water purifier to connect the water inlet with the sewage outlet and disconnect the water inlet from the water outlet, and allowing the raw water to flow from the water inlet through the inner side of the filter element conduit towards the sewage outlet for discharge, thereby completing the forward cleaning.

6. The detection method for the cleanliness of a water purifier according to claim 4, characterized in that, The reverse cleaning strategy includes: closing the first electromagnetic valve provided at the water outlet of the water purifier, and at the same time opening the second electromagnetic valve provided at the sewage outlet of the water purifier, controlling the three-way valve provided at the water inlet of the water purifier to connect the water inlet with the water outlet, and allowing the raw water to enter from the water inlet through the outer side of the filter element conduit and flow towards the inner side and then towards the sewage outlet for discharge, thereby completing the reverse cleaning.

7. A method for detecting the cleanliness of a water purifier according to claim 1, characterized in that, The water purifier cleanliness adjustment strategy further includes: Based on the cleanliness prediction result, set the daily self-cleaning time period, execute the forward cleaning strategy at the first moment of each day, and execute the reverse cleaning strategy at the second moment of each day.

8. The detection method for the cleanliness of a water purifier according to claim 1, wherein The method further includes: Monitoring the change trend of the TOC value in the purified water quality within a preset time period, and plotting the change trend curve of the TOC value; Calculating the correlation coefficient between the change trend curve and the preset mapping curve; When the correlation coefficient is greater than the preset correlation threshold, issue a prompt for replacing the consumables.

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