Life correction method, system, electronic device and storage medium for water purification filter element

Abstract

The present invention relates to a life correction method, system, electronic device and storage medium for a water purification filter element. The method comprises the following steps: obtaining a water efficiency grade of a water purification system based on the water flow rate thereof; judging whether it is necessary to obtain spectral data of influent water according to the water efficiency grade; preprocessing the spectral data after obtaining the spectral data of influent water to obtain first spectral data of tap water; inputting the first spectral data into a pre-trained water quality prediction model to predict the water quality of purified water to obtain a first water quality data set; obtaining spectral data of purified water after purification and preprocessing it as a second water quality data set; comparing the first water quality data set with the second water quality data set; and correcting the water quality prediction model according to the comparison result to update the life of the filter element of the water purification system.

Description

Technical Field

[0001] The present invention relates to the technical field of water purification system equipment, and in particular to a life correction method, system, electronic equipment and storage medium for a water purification filter element. Background Art

[0002] At present, water purification systems in the prior art usually use fixed time and water flow rate to warn about the life of the filter element. Using a fixed time dimension to remind filter element replacement is too mechanical and simplistic, and it often happens that the installed water purification system reminds filter element replacement before it is used. In order to more accurately evaluate the life of water purification filter elements, most of the existing technologies evaluate the purification effect of the filter element by real-time monitoring of water quality parameters before and after purification, such as pH value, hardness, soluble solids content, etc., but they fail to consider the influence of water temperature on water quality analysis and filter element performance, as well as the solubility of dissolved substances in water, chemical reaction rate, and microbial activity, which in turn affects water quality and the purification efficiency of the filter element.

[0003] As recorded in the patent with publication number CN117454648A, the life prediction method, device, water purifier and storage medium of the water purifier filter element discloses that a remaining life prediction model is obtained based on the water flow rate of the filter element and the filter element usage time in the time dimension. According to the remaining life prediction model, the time required for the attenuation of the water flow rate of the filter element to reach the attenuation threshold is determined, and the time is used as the remaining usage time of the filter element, thereby predicting the life of the filter element. However, it directly performs model prediction, ignoring the errors that may be caused by factors such as water temperature fluctuations in tap water quality data. If this error is not effectively corrected, it will directly affect the accuracy of water quality prediction and the reliability of filter element life assessment. Therefore, how to develop a water purification filter element life correction method that can monitor, accurately predict and correct the life of a water purification filter element in real time has become an urgent problem to be solved.

[0004] The present application aims to establish a life correction method, system, electronic device and storage medium for a water purification filter element to solve the above-mentioned problems. Summary of the invention

[0005] In order to achieve the above-mentioned purpose and other advantages according to the present invention, the first purpose of the present invention is to provide a life correction method for a water purification filter element, comprising the following steps:

[0006] Obtain the water efficiency level of the water purification system based on the water flow rate;

[0007] Determining whether it is necessary to obtain the spectral data of the incoming water according to the water efficiency level;

[0008] After obtaining the spectral data of the influent water, the spectral data is preprocessed to obtain first spectral data of the tap water;

[0009] Inputting the first spectral data into a pre-trained water quality prediction model to predict the water quality of the purified water to obtain a first water quality data set;

[0010] Acquiring spectral data of purified water after purification and performing preprocessing as a second water quality data set;

[0011] comparing the first water quality data set and the second water quality data set;

[0012] According to the comparison results, the water quality prediction model is modified to update the filter life of the water purification system.

[0013] Further, judging whether it is necessary to obtain the spectral data of the influent water according to the water efficiency level comprises the following steps:

[0014] Determining the use stage of the water purification filter element of the water purification system according to the water efficiency level;

[0015] If the water purification filter element is in the middle or late use stage, collecting spectral data of the incoming water;

[0016] Comparing the spectral data of the influent with the preset spectral data of the influent threshold value;

[0017] According to the comparison results, determine whether the water intake standard is met;

[0018] If the inlet water standard is not met, the spectral data of the inlet water is obtained and preprocessed.

[0019] Furthermore, before the spectral data is preprocessed to obtain the first spectral data of tap water, the following steps are also included:

[0020] Acquiring the water temperature of the inlet water based on the spectral data of the inlet water;

[0021] Determining whether the water temperature of the inlet water falls within a threshold water temperature range;

[0022] If yes, preprocessing the spectral data of the influent water;

[0023] If it does not, the water temperature of the inlet water is corrected until it reaches the threshold water temperature range, and the spectral data of the inlet water is collected again and pre-processed.

[0024] Furthermore, the first water quality data set includes one or more of the pH value index, water temperature index, turbidity index, TDS index, COD index, and TOC water quality index of the purified water predicted by the water quality prediction model; the second water quality data set includes one or more of the pH value index, water temperature index, turbidity index, TDS index, COD index, and TOC water quality index of the purified water tested after purification.

[0025] Furthermore, comparing the first water quality data set with the second water quality data set comprises the following steps:

[0026] Comparing each indicator in the first water quality data set with the corresponding indicator in the second water quality data set;

[0027] Calculate the difference between each indicator separately;

[0028] When all the differences do not exceed the corresponding first indicator threshold, the comparison result is that the water purification standard is met;

[0029] When at least one of the differences exceeds the corresponding first indicator threshold, the comparison result is that the water purification standard is not met.

[0030] Furthermore, the water quality prediction model is corrected according to the comparison result to update the filter life of the water purification system, including the following steps:

[0031] When the comparison result shows that the water purification standard is not met, the second water quality data set is input into the water quality prediction model to cover its corresponding first water quality data set, and iterative training is performed to complete the correction of the model, thereby updating the remaining life of the filter element.

[0032] Furthermore, it also includes building a water quality prediction model, including the following steps:

[0033] Use deep learning network models to build water quality prediction models;

[0034] Initialize network parameters;

[0035] The water quality prediction model is iteratively trained to obtain a pre-trained water quality prediction model.

[0036] Furthermore, the initializing network parameters comprises the following steps:

[0037] Input layer, where a number of neurons are set to match the dimensions of the input water quality dataset;

[0038] Convolutional layer: three convolutional layers are set to extract feature information from the water quality dataset, and the convolution kernels are 32, 64, and 128 respectively;

[0039] Activation layer, set the Relu function as the activation function;

[0040] A fully connected layer maps the extracted feature information to a water quality dataset;

[0041] The output layer sets the number of neurons to be the same as the dimension of the water quality dataset, and uses the Softmax function as the activation function to output the prediction results.

[0042] Furthermore, the iterative training of the water quality prediction model to obtain a pre-trained water quality prediction model comprises the following steps:

[0043] Collecting spectral data of tap water, wherein the spectral data of tap water is obtained by a multi-parameter water quality sensor;

[0044] Purifying the tap water through a water purification filter and simultaneously collecting spectral data of the purified water, wherein the spectral data of the purified water is obtained through a multi-parameter water quality sensor;

[0045] Preprocessing the spectral data of the tap water and the spectral data of the purified water to obtain a tap water quality data set and a preprocessed purified water quality data set;

[0046] Marking water quality index categories on the tap water quality dataset and the purified water quality dataset, and classifying the tap water quality dataset and the purified water quality dataset item by item according to the marked water quality index categories to form an input dataset;

[0047] The input data set is iteratively trained in the water quality prediction model, and a loss function and an Adam optimizer are defined to iteratively optimize the model parameters. When the loss of the water quality prediction model no longer decreases significantly during continuous iterations or reaches a preset number of iterations, the iteration output model parameters are stopped to update the water quality prediction model to obtain a pre-trained water quality prediction model.

[0048] Furthermore, the spectral data of the tap water includes spectral data of tap water sampled multiple times when the water purification filter element is in a mid-use stage and a late-use stage within a threshold water temperature range.

[0049] Furthermore, the method of obtaining the water efficiency level of the water purification system based on the water flow rate thereof comprises the following steps:

[0050] During the use of the water purification system, the total water intake Q of the tap water is obtained respectively. f , the total amount of purified water Q p , the total concentrated water volume of wastewater Q r ;

[0051] Calculating a water volume measurement error rate, and performing flow calibration according to the water volume measurement error rate;

[0052] Determine whether the flow verification is passed, and when the flow verification is passed, calculate the water purification rate Y, and evaluate the water efficiency level of the water purification system according to the water purification rate Y;

[0053] When the flow rate verification fails, an abnormal flow meter or a water purification system leakage warning is prompted.

[0054] Further, the evaluating the water efficiency level of the water purification system according to the water purification rate Y comprises the following steps:

[0055] When the water purification rate Y is greater than the first water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 1, and it is determined that the water purification filter element is in the initial use stage;

[0056] When the water purification rate Y is less than the first water efficiency threshold and greater than or equal to the second water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 2, and it is determined that the water purification filter element is in the mid-term use stage;

[0057] When the water purification rate Y is less than the second water efficiency threshold and greater than or equal to the third water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 3, and it is determined that the water purification filter element is in the later use stage;

[0058] When the water purification rate Y is less than the third water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 4, and it is determined that the water purification filter element is in the scrap stage.

[0059] The second object of the present invention is to provide a life correction system for a water purification filter element, comprising the following modules:

[0060] A water efficiency rating assessment module is used to obtain the water efficiency rating of the water purification system based on the water flow rate;

[0061] A prediction condition determination module, used to determine whether it is necessary to obtain the spectral data of the inlet water according to the water efficiency level;

[0062] A preprocessing module, used for preprocessing the spectral data of the influent water after acquiring the spectral data of the influent water, so as to obtain the first spectral data of the tap water;

[0063] A water quality prediction module, used for inputting the first spectral data of the tap water into a pre-trained water quality prediction model, predicting the water quality of the tap water after purification, and obtaining a first water quality data set;

[0064] A water quality acquisition module, used to obtain spectral data of purified water after purification and perform preprocessing as a second water quality data set;

[0065] a water purification filter life correction module, used for comparing the first water quality data set with the second water quality data set;

[0066] According to the comparison results, the water quality prediction model is modified to update the filter life of the water purification system.

[0067] The third object of the present invention is to provide an electronic device, comprising: a processor and a memory, wherein the memory is used to store one or more programs; when the one or more programs are executed by the processor, a method for correcting the life of a water purification filter element is implemented.

[0068] A fourth object of the present invention is to provide a readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, a method for correcting the life of a water purification filter element is implemented.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention relates to a life correction method, system, electronic device and storage medium for a water purification filter element. By evaluating the water efficiency level of the water purification system, it is determined whether it is necessary to obtain the spectral data of tap water, and then the pre-trained water quality prediction model is called to predict the water quality of the tap water after purification, and the prediction result is compared with the real-time purified water quality data to achieve the evaluation of the accuracy of the water quality prediction model, and then the correction of the water quality prediction model and the update of the life of the water purification filter element are achieved, which not only improves the accuracy and real-time performance of the water purification filter element life monitoring, but also effectively brings users a more convenient, comfortable and safe drinking water experience.

[0071] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0073] Figure 1 The life correction method process of the water purification filter element of this application Figure 1 ;

[0074] Figure 2 This is a flow chart for judging whether it is necessary to obtain the spectral data of the influent water according to the water efficiency level described in Example 1;

[0075] Figure 3 The life correction method of the water purification filter element described in Example 1 is as follows Figure 2 ;

[0076] Figure 4 The flow chart of the water quality prediction model constructed in Example 1;

[0077] Figure 5 It is a flow chart of iteratively training the water quality prediction model described in Example 1 to obtain a pre-trained water quality prediction model;

[0078] Figure 6 A flow chart for obtaining the water efficiency level of the water purification system based on the water flow rate described in Example 1;

[0079] Figure 7 Schematic diagram of the life correction system of the water purification filter in Example 2

[0080] Figure 8 is a schematic diagram of an electronic device in Example 3;

[0081] Fig. 9 Schematic diagram of the life correction device of the water purification filter element in Example 3;

[0082] Fig.10 This is a schematic diagram of a computer-readable storage medium in Example 4. DETAILED DESCRIPTION

[0083] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0084] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0085] Example 1

[0086] The present invention provides a method for correcting the life of a water purification filter element, such as Figure 1 As shown, the specific steps include:

[0087] S101, obtaining a water efficiency level of the water purification system based on the water flow rate thereof;

[0088] S102, judging whether it is necessary to obtain the spectral data of the inlet water according to the water efficiency level;

[0089] Among them, the spectral data in step S102 is obtained through a multi-parameter water quality sensor, including an inlet water quality data set. It should be understood that the inlet water quality data set includes one or more of the first pH value index, the first water temperature index, the first turbidity index, the first TDS index, the first COD index, and the first TOC water quality index obtained in the tap water detection source.

[0090] S103, after acquiring the spectral data of the inlet water, preprocessing the spectral data to obtain first spectral data of the tap water;

[0091] S104, inputting the first spectral data into a pre-trained water quality prediction model to predict the water quality of the purified water to obtain a first water quality data set;

[0092] Among them, the first spectral data of the tap water in step S104 is obtained by a multi-parameter water quality sensor, and the first water quality data set is obtained by inputting the spectral data of the tap water into a pre-trained water quality prediction model for prediction, including one or more of the fifth pH value index, the fifth water temperature index, the fifth turbidity index, the fifth TDS index, the fifth COD index, and the fifth TOC water quality index predicted by the tap water (i.e., incoming water) detection source after purification by the purification filter element.

[0093] S105, obtaining spectral data of the purified water and preprocessing it as a second water quality data set;

[0094] Among them, the spectral data of the purified water described in step S105 is obtained by a multi-parameter water quality sensor, including a water quality data set of purified water actually obtained (i.e., a second water quality data set). It should be understood that the second water quality data set includes one or more of the third pH value index, third water temperature index, third turbidity index, third TDS index, third COD index, and third TOC water quality index detected in the purified water detection source after the tap water (i.e., incoming water) detection source is actually purified by the water purification filter.

[0095] S106, comparing the first water quality data set and the second water quality data set;

[0096] According to the comparison results, the water quality prediction model is modified to update the filter life of the water purification system.

[0097] In some embodiments, step S106 specifically includes:

[0098] Compare each of the fifth indicators in the first water quality data set with the corresponding third indicators in the second water quality data set, and calculate the difference between each of the fifth indicators and the corresponding third indicators;

[0099] When all the differences are less than the corresponding first indicator threshold, the comparison result is that the water purification standard is met, and there is no need to correct the water quality prediction model. At this time, the life of the water purification filter element is also normal, and no correction or update is required;

[0100] When at least one of the differences is greater than or equal to the corresponding first indicator threshold, the judgment result is that the water purification standard is not met, and the water quality prediction model needs to be corrected to update the filter life of the water purification system.

[0101] Specifically, when the comparison result is that the water purification standard is not met, the second water quality data set is input into the water quality prediction model to cover its corresponding first water quality data set, and iterative training is performed to complete the correction of the model, thereby updating the remaining life of the filter element.

[0102] Among them, it should be understood that the first indicator thresholds corresponding to each item can be pre-set or adjusted according to the water inlet environment, the installation environment of the water purification equipment and the actual situation, and this application is no longer limited here.

[0103] For example, this application uses TDS indicator data for illustration, as follows:

[0104] Assume that the remaining life of the water filter element currently displayed is 12 months;

[0105] The TDS index is set to ±10ppm in the first index threshold;

[0106] Using a multi-parameter water quality sensor to obtain spectral data of tap water (influent water) and then pre-processing it to obtain first spectral data of tap water;

[0107] The first spectral data is input into a pre-trained water quality prediction model to obtain a first water quality data set, which includes a predicted fifth TDS index after purification, and the predicted value is 50 ppm.

[0108] Using a multi-parameter water quality sensor to obtain the spectral data of the purified water after the tap water (inlet water) is purified by the water purification filter element and pre-processing is performed, and the actual third TDS index is extracted from the spectral data of the purified water, and the measured value is 60ppm;

[0109] Compare the predicted fifth TDS value (50 ppm) with the actual third TDS value (60 ppm) and calculate the difference between the two: 60 ppm - 50 ppm = 10 ppm.

[0110] The comparison result is that the difference is equal to the first indicator threshold value, therefore, the determination result is that the water purification standard is not met, and the water quality prediction model should be corrected to update the filter element life of the water purification system;

[0111] Specifically, the third TDS index of 60 ppm measured in the second water quality data set is input into the water quality prediction model, covering the TDS index of the corresponding first water quality data set (i.e., the original prediction value is 50 ppm) in the water quality prediction model, and iterative training is performed to complete the correction of the water quality prediction model to optimize the prediction accuracy of the model;

[0112] The filter life of the water purification system is corrected to 10 months through display devices such as display platforms / display screens.

[0113] In the existing water quality detection of water purification systems, most of them can only detect single water quality data of a single water channel, which leads to low detection efficiency and inaccurate detection results, which greatly affects the water quality status assessment of the water purification system and the user experience. This application realizes the detection of multiple water quality indicators of 2 channels (tap water inlet water channel and purified water water channel) by setting a multi-parameter water quality sensor. When monitoring water quality, this application detects the above seven water quality indicators separately, rather than being limited to a single indicator, to obtain more accurate and comprehensive real-time water quality status assessment results, so that the final water quality detection results of the water purification system are more accurate, ensuring the safety of users' drinking water.

[0114] Among them, the pH value index refers to the negative logarithm of the hydrogen ion concentration in the water body, that is, the acidity and alkalinity of the water.

[0115] Temperature index refers to the temperature of the water body. As an important physical index in water quality testing, the physical and chemical properties of water are closely related to temperature. The solubility of dissolved gases in water, the activity of microorganisms, pH and salinity, and the water purification rate of the water purification system are all related to temperature changes.

[0116] The turbidity index refers to the clarity of water by measuring the degree of scattering of light penetrating the water.

[0117] The TDS index refers to total dissolved solids, that is, the total amount of dissolved solids in water, Total Dissolved Solids, abbreviated as TDS, which indicates how many milligrams of dissolved solids are dissolved in one liter of water, in mg / L. The higher the value, the higher the dissolved solids content in the water.

[0118] COD refers to Chemical Oxygen Demand, abbreviated as COD, which is a chemical method to measure the amount of reducing substances that need to be oxidized in water samples, the oxygen equivalent of substances (generally organic matter) that can be oxidized by strong oxidants in wastewater, wastewater treatment plant effluent and polluted water; the unit is mg / L. It reflects the degree of pollution of water by reducing substances. This indicator is also used as one of the comprehensive indicators of the relative content of organic matter.

[0119] TOC index refers to the content of total organic carbon, abbreviated as TOC. TOC is the total amount of carbon contained in organic matter in water, with the unit of mg / L, which can fully reflect the degree of pollution of organic matter to water body.

[0120] In a preferred embodiment, the outer layer of the multi-parameter water quality sensor described in the present application adopts an IP68 waterproof grade design to ensure normal operation in a humid water environment. At the same time, a shock absorbing device is provided in the sensor to reduce the impact of water flow fluctuations on the sensor.

[0121] In a preferred embodiment, the multi-parameter water quality sensor described in the present application is provided with a wireless communication module, including but not limited to LoRa, NB-IoT, 4G / 5G / WIFI and other data networks to transmit the collected spectral data to the water quality prediction model and system.

[0122] In the prior art, methods for correcting the life of a water purification filter in a water purification system mostly rely on fixed usage time or water output, and cannot accurately reflect the actual life of the water purification filter under different water quality and usage conditions, resulting in problems such as premature replacement or excessive use of the water purification filter, and difficulty in achieving real-time monitoring and accurate prediction, which not only affects the user experience, but also increases the user's financial burden and may also threaten water quality safety.

[0123] Therefore, in order to solve this problem, the present application collects and analyzes the spectral data of the water purification system during operation at various stages, combines it with a deep learning algorithm, and establishes and pre-trains a water quality prediction model. By combining the water efficiency level of the water purification system and the use stage status of the water purification filter element, the working status of the water purification filter element and its remaining life can be more accurately evaluated. When the water purification filter element is in the mid-term or late-stage use, by detecting the water quality of the tap water and judging whether it meets the preset standards, the rapid loss of the filter element due to poor water quality can be avoided to a certain extent, which helps to extend the actual service life of the filter element and reduce unnecessary replacement costs. At the same time, by using the pre-trained water quality prediction model to predict the quality of the purified water and compare it with the water quality data obtained in real time, the water quality changes can be discovered in time, thereby more accurately adjusting the predicted value of the filter element life and reducing the energy consumption and operating costs of the water purification system.

[0124] In some embodiments, the step S102 determines whether it is necessary to obtain the spectral data of the incoming water according to the water efficiency level, such as Figure 2 As shown, the specific steps include:

[0125] S121, determining the use stage of the water purification filter element of the water purification system according to the water efficiency level;

[0126] If the water purification filter element is in the middle or late use stage, collecting spectral data of the incoming water;

[0127] If the water purification filter element is in the initial use stage, the water purification work is performed normally;

[0128] If the water purification filter element is in the scrapping stage, the water purification system is switched to a deactivated state and a scrapping warning is issued;

[0129] S122, comparing the spectral data of the inlet water with preset spectral data of the inlet water threshold;

[0130] S123, judging whether the water inlet standard is met according to the comparison result;

[0131] If the water inlet standard is not met, the spectral data of the tap water is obtained and preprocessed.

[0132] If the water inlet standard is met, the water inlet work will proceed normally.

[0133] In a preferred embodiment, when the scrap warning is issued in step S121, a scrap warning signal can be generated. Through the scrap warning signal, the maintenance personnel of the water purification system can be notified in time so as to perform timely maintenance on the water purification system.

[0134] Specifically, the alarm signal can be uploaded to a remote water purification system, and can also be uploaded to a user terminal (such as a small program in the user terminal), so that remote supervisors and / or users can timely grasp the status of the water purification equipment. The alarm signal can be in the form of a text message or a pop-up window, etc., and this application does not limit this.

[0135] In a preferred embodiment, it should be understood that after determining the use stage of the water purification filter element in step S121, it should be determined whether it is necessary to perform the related operations of S122-S123 according to the stage of the water purification filter element. If the water purification filter element is in the initial use stage, it means that the current water purification system is a newly activated water purification system, the life of the water purification filter element is still within the normal qualified range, the water purification efficiency and the water quality of the purified water are also in line with the standards, and there is no need to correct the life of the water purification filter element for the time being; if the water purification filter element is in the scrapped state, stage, it means that the life of the water purification filter in the current water purification system has exceeded the limit, and the water purification filter cannot complete the water purification work, so the water purification system needs to be switched to the disabled state and issue a scrapping warning; if the water purification filter is in the mid-term or late-term use stage, it means that the water purification system has been working for a period of time, and it is necessary to combine the spectral data of the current tap water to determine whether the water inlet standard is met. Therefore, steps S122-S123 are only executed after the spectral data of tap water is collected when the water purification filter is in the mid-term or late-term use stage.

[0136] In a preferred embodiment, the step S122 of comparing the spectral data of the incoming water with the preset spectral data of the incoming water threshold value specifically includes:

[0137] Acquire a water quality data set of the tap water according to the spectral data of the tap water;

[0138] Comparing the water quality data set of the tap water with the preset inlet water quality threshold data set item by item to determine whether the water quality standard is met;

[0139] Specifically, it should be understood that the preset inlet water quality threshold data set includes one or more of the preset second pH value index, second water temperature index, second turbidity index, second TDS index, and second COD index;

[0140] The step of comparing the tap water quality data set with the preset inlet water quality threshold data set item by item to determine whether the water quality standard is met specifically includes:

[0141] Compare each of the first indicators in the tap water quality data set with the corresponding second indicators in the preset inlet water quality threshold data set,

[0142] When all the differences do not exceed the corresponding second indicator threshold, the judgment result is that the water quality standard is met, and it is determined that the water purification system does not meet the prediction condition;

[0143] When at least one of all the differences exceeds the corresponding second indicator threshold, the judgment result is that the water inlet standard is not met, and it is determined that the water purification system meets the prediction conditions.

[0144] In another preferred embodiment, the present application corrects the life of the water purification filter element even if it meets the standard after the backwashing procedure of the water purification system. The present application can not only correct the life of the filter element during the normal operation of the water purification system by judging whether the water quality at the time of water inlet meets the standard, but also effectively measure the water quality when the filter element is backwashed, and update and correct the life of the water purification filter element, thereby avoiding not updating the life of the filter element due to the backwashing water quality meeting the standard, thereby affecting the evaluation of the water purification filter element, and then affecting the user experience.

[0145] For example, this application uses TOC indicator data for illustration, as follows:

[0146] The second TOC indicator data is set to 2.0 mg / L in the second indicator threshold;

[0147] The first TOC index in the water quality data set of the tap water obtained by a multi-parameter water quality sensor is 1.5 mg / L;

[0148] The comparison result is that the first TOC index does not exceed the second TOC index (assuming that other first indicators do not exceed the second indicator threshold). At this time, it is considered that the water quality meets the water inlet standard and the water purification system does not meet the prediction conditions, that is, the water purification system can perform water purification work normally without the need for additional water purification function prediction. On the contrary, when any at least one first indicator exceeds its corresponding second indicator threshold, it is considered that the water quality is not up to standard, that is, the water purification system needs to perform water purification function prediction.

[0149] The present application can clearly and intuitively compare the water quality difference between the inlet water quality of the currently collected tap water and the preset inlet water quality by comparing each first indicator with the corresponding second indicator. By comparing each first indicator with the corresponding indicator threshold, the water quality difference between the inlet water quality of the currently collected tap water and the preset inlet water quality threshold can be determined. When a certain first indicator exceeds the corresponding indicator threshold after comparison, it means that the inlet water quality of the currently collected tap water does not meet the inlet water standard. At this time, the water purification system is overloaded with water purification, and therefore, the prediction conditions are met. When all indicators are normal, it means that the inlet water quality of the currently collected tap water has reached the inlet water standard. At this time, the water purification system can be purified normally, and the water purification work can be performed normally without the need to predict the water purification function, that is, the prediction conditions are not met.

[0150] Among them, it should be understood that the corresponding second indicator thresholds of each item can be pre-set or adjusted according to the water inlet environment, the installation environment of the water purification equipment and the actual situation, and this application is no longer limited here.

[0151] In some embodiments, before step S103, Figure 3 As shown, the following steps are also included:

[0152] S131, obtaining the water temperature of the inlet water based on the spectral data of the inlet water;

[0153] S132, determining whether the water temperature of the inlet water belongs to a threshold water temperature range;

[0154] S1321, if yes, preprocessing the spectrum data of the influent water;

[0155] S1322: If it does not belong to the above, the water temperature of the inlet water is corrected until it reaches the threshold water temperature range, and the spectral data of the inlet water is collected again.

[0156] In a preferred embodiment, the water temperature correction can be adjusted by a commonly used method of heating / cooling the incoming water (tap water) detection source, and the present application does not impose any limitation thereto.

[0157] During the correction of the life of the water purification filter, it is crucial to ensure that the inlet water temperature (i.e., the tap water temperature) is within the set threshold range. Water temperature is an important physical indicator in water quality testing. Changes in water temperature will directly affect the pH value results in water quality testing; increased water temperature may accelerate the movement of suspended matter or colloidal particles in the water, resulting in increased light scattering, changes in water density and optical properties, and thus higher turbidity measurement results, which in turn affect turbidity measurement results; COD detection involves oxidation reactions, and the chemical reaction rate usually increases with increasing temperature. Therefore, changes in water temperature may also affect COD test results; increased water temperature will lead to the decomposition of organic matter and increased solubility, resulting in low TOC values, which will affect TOC measurement results; changes in water temperature will lead to direct changes in water production, thereby affecting the speed of water purification filtration.

[0158] Therefore, in order to solve this problem, before preprocessing the spectral data of the inlet water (i.e., tap water), the present application preferentially obtains the water temperature of the inlet water (i.e., tap water) through the spectral data of the tap water. When the water temperature of the inlet water (i.e., tap water) belongs to the threshold water temperature interval, the spectral data of the inlet water (i.e., tap water) is preprocessed; when the water temperature of the inlet water (i.e., tap water) does not belong to the threshold water temperature interval, the water temperature of the inlet water (i.e., tap water) is corrected and reaches the threshold interval, and the spectral data of the inlet water (i.e., tap water) is collected again. The present application controls the stability of the water temperature, improves the accuracy and consistency of the spectral data, and thus enhances the reliability of the water quality prediction model. It can not only protect the prediction model from the influence of abnormal data, but also optimize the prediction accuracy of the filter element life, ensuring that the water purification system can provide stable and standard water quality under different environments, thereby greatly improving the user experience and meeting the strict requirements of various specific application scenarios for water quality and water temperature.

[0159] Specifically, the threshold water temperature range can be adaptively adjusted according to the performance of the water quality prediction model, the sensitivity of the spectral data, and the actual application scenario of the water purification system, and this application does not limit this.

[0160] In a preferred embodiment, the threshold water temperature range set in the present application is 20°C~30°C. Within this temperature range, the physical and chemical properties of the water sample are relatively stable, which is conducive to the accuracy and reliability of the detection results.

[0161] In some embodiments, the spectral data of the incoming water is preprocessed in step S103 to obtain the first spectral data of tap water, specifically: the spectral data of the incoming water obtained in step S102 is uniformly subjected to preprocessing operations such as cleaning, denoising and baseline calibration to improve the data quality of the spectral data.

[0162] In some embodiments, before step S104, Figure 4 As shown, it also includes building a water quality prediction model, which specifically includes the following steps:

[0163] S141, building a water quality prediction model using a deep learning network model;

[0164] S142, initializing network parameters;

[0165] S143, iteratively training the water quality prediction model to obtain a pre-trained water quality prediction model.

[0166] In a preferred embodiment, the initialization of network parameters in step S142 specifically includes:

[0167] Input layer, where a number of neurons are set to match the dimensions of the input water quality dataset;

[0168] Convolutional layer: three convolutional layers are set to extract feature information from the water quality dataset, and the convolution kernels are 32, 64, and 128 respectively;

[0169] Activation layer, set the Relu function as the activation function;

[0170] A fully connected layer maps the extracted feature information to a water quality dataset;

[0171] The output layer sets the number of neurons to be the same as the dimension of the water quality dataset, and uses the Softmax function as the activation function to output the prediction results.

[0172] In a preferred embodiment, the water quality prediction model is iteratively trained in step S143 to obtain a pre-trained water quality prediction model, such as Figure 5 As shown, the specific steps include:

[0173] S151, collecting spectral data of tap water, wherein the spectral data of tap water is obtained by a multi-parameter water quality sensor;

[0174] S152, purifying the tap water through a water purification filter and synchronously collecting spectral data of the purified water, wherein the spectral data of the purified water is obtained by a multi-parameter water quality sensor;

[0175] S153, preprocessing the spectral data of the tap water and the spectral data of the purified water to obtain a tap water quality data set and a preprocessed purified water quality data set;

[0176] S154, marking water quality index categories on the tap water quality dataset and the purified water quality dataset, and classifying the tap water quality dataset and the purified water quality dataset item by item according to the marked water quality index categories to form an input dataset;

[0177] S155, iteratively train the input data set in the water quality prediction model, define the loss function and Adam optimizer to iteratively optimize the model parameters, and when the loss of the water quality prediction model no longer decreases significantly during continuous iterations or reaches a preset number of iterations, stop iterating and output model parameters to update the water quality prediction model to obtain a pre-trained water quality prediction model.

[0178] In a preferred embodiment, the spectral data of the tap water pre-includes spectral data of tap water sampled multiple times within a threshold water temperature range when the water purification filter is in the initial use stage, mid-use stage, and late use stage, so as to construct a rich and comprehensive tap water quality data set.

[0179] Specifically, it should be understood that the preprocessing of the spectral data of the tap water and the spectral data of the purified water in step S153 is consistent with the preprocessing step described in step S103, which will not be repeated here; the tap water quality data set described in step S153 includes the third pH value indicator, the third water temperature indicator, the third turbidity indicator, the third TDS indicator, the third COD indicator, and the third TOC water quality indicator collected from the tap water detection source; the purified water quality data set described in step S154 includes the fourth pH value indicator, the fourth water temperature indicator, the fourth turbidity indicator, the fourth TDS indicator, the fourth COD indicator, and the fourth TOC water quality indicator that correspond one by one to the collected tap water detection source after purification by the water purification filter element, and they are classified item by item according to different indicator categories to form an input data set.

[0180] In some embodiments, a water quality prediction model is obtained by selecting and constructing a suitable deep learning network to train the input data set. Deep learning network models including but not limited to convolutional neural network CNN, recurrent neural network RNN ​​or LSTM, GRU, etc. can be selected as deep learning network models. In a preferred embodiment, the present application adopts a CNN network model for training to obtain a water quality prediction model. Specifically, the maximum number of iterations set in the present application is 60 times. When the model training reaches the maximum number of iterations or the accuracy does not change for 4 consecutive rounds, the training of the CNN network model is completed to obtain a water quality prediction model.

[0181] In some embodiments, the water efficiency level of the water purification system is obtained based on the water flow rate of the water purification system in step S101, such as Figure 6 As shown, the specific steps include:

[0182] S161, during the use of the water purification system, respectively obtain the total water intake Q of the tap water f , the total amount of purified water Q p , the total concentrated water volume of wastewater Q r ;

[0183] S162, calculating a water volume measurement error rate, and performing flow calibration according to the water volume measurement error rate;

[0184] S163, determining whether the flow verification is passed, and when the flow verification is passed, calculating the water purification rate Y, and evaluating the water efficiency level of the water purification system according to the water purification rate Y;

[0185] When the flow rate verification fails, an abnormal flow meter or a water purification system leakage warning is prompted.

[0186] In a preferred embodiment, Fig. 9 As shown, the present application sets a first flow meter 1 between the tap water inlet waterway and the water purification filter element to obtain the total purified water volume Q of the tap water. f The unit is liter (L). A second flow meter 2 is set between the water purification filter and the purified water waterway to obtain the total purified water volume Q of the purified water. p The unit is liter (L). A third flow meter 3 is set between the water purification filter and the wastewater waterway to obtain the total concentrated water volume Q of the wastewater. r The unit is liter (L).

[0187] In a preferred embodiment, after a new water purification system is started or a water purification filter element is replaced, the total purified water volume Q of the tap water is f , the total net water volume Q of the purified water p , the total concentrated water volume Q of the wastewater r The value should be set to zero; the data refresh cycle and flow verification time can be set according to user habits. The cycle can be 1 day, 5 days, 10 days or 1 month. This application does not limit this. In order to obtain water purification data more accurately, this application sets the data refresh cycle to 1 day, the daily refresh node and flow verification time to 0 o'clock every day. In a preferred embodiment, the water volume measurement error rate Q calculated in step S162 MER Calculate using the following formula:

[0188] Q MER =(Q p +Q r -Q f ) / Q f *100%

[0189] Among them, Q f Indicates the total water intake of the tap water, Q p Indicates the total amount of purified water, Qr Indicates the total concentrated water volume of wastewater.

[0190] In a preferred embodiment, when the water metering error rate Q MER If the absolute value is ≤5%, it means that the flow verification has passed; otherwise, it means that the flow verification has failed, indicating that the flow of the water purification system is abnormal, indicating that the flow meter is abnormal, or there is a water leakage warning in the water purification system.

[0191] In a preferred embodiment, the water purification rate Y in step S163 is calculated by the following formula:

[0192] Y = Q p / (Q p +Q r )*100%

[0193] Among them, Q p Indicates the total amount of purified water, Q r Indicates the total concentrated water volume of wastewater.

[0194] This application sets up multiple flow meters to accurately measure the water volume data of each water channel, and combines it with the water purification rate calculation to objectively and accurately evaluate the water efficiency level of the water purification system, providing users with an accurate reference basis while promptly discovering potential abnormalities such as flow meter abnormalities or water leakage in the water purification system during the flow verification process, thereby avoiding waste of water resources and further damage to the water purification equipment, prompting users to understand the operating status of the water purification system in a timely manner, and improving the user experience.

[0195] In a preferred embodiment, the step S163 of evaluating the water efficiency level of the water purification system according to the water purification rate Y specifically includes the following steps:

[0196] When the water purification rate Y is greater than the first water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 1, and it is determined that the water purification filter element is in the initial use stage;

[0197] When the water purification rate Y is less than the first water efficiency threshold and greater than or equal to the second water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 2, and it is determined that the water purification filter element is in the mid-term use stage;

[0198] When the water purification rate Y is less than the second water efficiency threshold and greater than or equal to the third water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 3, and it is determined that the water purification filter element is in the later use stage;

[0199] When the water purification rate Y is less than the third water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 4, and it is determined that the water purification filter element is in the scrap stage.

[0200] In a preferred embodiment, the first water efficiency threshold is set to 65%, the second water efficiency threshold is set to 55%, and the third water efficiency threshold is 45%.

[0201] This application collects spectral data of tap water and purified water at different use stages within the threshold water temperature range of the water purification filter for preprocessing, and marks the water quality index categories for classification training, so that the trained water quality prediction model has stronger generalization ability, can cope with water quality prediction tasks in more actual scenarios, and can more accurately reflect the impact of filter performance changes on water quality, thereby improving the accuracy of the prediction. Based on the prediction results of the water quality prediction model, combined with the comparison with the real-time acquired water quality data set, in order to evaluate the accuracy of the water quality prediction model, and then realize the correction of the water quality prediction model and the update of the life of the water purification filter, a more scientific and reasonable filter replacement strategy can be formulated. When it is predicted that the water quality is about to exceed the safety standard, the user can be reminded to replace the filter in time to ensure the safety and health of water use.

[0202] The life correction method of the water purification filter provided in this application can be applied to the life correction system of the water purification filter to meet the user's real-time monitoring and correction needs for water quality and water purification filter life. For a detailed description of the life correction system of the water purification filter, please refer to the corresponding description in Example 2 of the present invention, which will not be repeated here.

[0203] The life correction method of the water purification filter provided in this application can be applied to an electronic device, which can be configured as a life correction device for a water purification filter and a water purifier. For a detailed description of the life correction device for a water purification filter and the water purifier, please refer to the corresponding description in Example 3 of the present invention, which will not be repeated here.

[0204] Example 2

[0205] The present invention provides a life correction system for a water purification filter element, such as Figure 7 As shown, it includes the following modules:

[0206] A water efficiency rating assessment module is used to obtain the water efficiency rating of the water purification system based on the water flow rate;

[0207] A prediction condition determination module, used to determine whether it is necessary to obtain the spectral data of the inlet water according to the water efficiency level;

[0208] A preprocessing module, used for preprocessing the spectral data of the influent water after acquiring the spectral data of the influent water, so as to obtain the first spectral data of the tap water;

[0209] A water quality prediction module, used for inputting the first spectral data of the tap water into a pre-trained water quality prediction model, predicting the water quality of the tap water after purification, and obtaining a first water quality data set;

[0210] A water quality acquisition module, used to obtain spectral data of purified water after purification and perform preprocessing as a second water quality data set;

[0211] The water purification filter life correction module is used to compare the first water quality data set and the second water quality data set; according to the comparison result, the water quality prediction model is corrected to update the filter life of the water purification system.

[0212] In some embodiments, the life correction system of the water purification filter element further includes the following modules:

[0213] A water temperature correction module, used for obtaining the water temperature of the inlet water based on the spectral data of the tap water;

[0214] Determining whether the water temperature of the inlet water falls within a threshold water temperature range;

[0215] If yes, preprocessing the spectral data of the influent water;

[0216] If it does not, the water temperature of the inlet water is corrected until it reaches the threshold water temperature range, and the spectral data of the inlet water is collected again.

[0217] The present application provides a life correction system for a water purification filter element, and the system mainly includes a water efficiency rating evaluation module, a prediction condition determination module, a pretreatment module, a water quality prediction module, a water purification filter element life update module, and a water temperature correction module. First, the present application obtains the water flow data of the water purification system in real time through the water efficiency rating evaluation module, and uses the corresponding algorithm to analyze and process these data, so as to accurately evaluate the water efficiency rating of the water purification system, so as to understand the operating efficiency of the water purification system, and provide an important reference for subsequent water quality prediction and filter element life evaluation. Secondly, based on the water efficiency rating evaluation of the water purification filter element use stage, the current use status of the water purification filter element is further analyzed. In terms of water quality prediction, the system described in the present application is equipped with a pre-trained water quality prediction model, which can accurately predict the water quality of the current tap water after purification in the future period of time according to multi-dimensional information such as historical water quality data and environmental factors, and generate a first water quality data set to predict the purification trend of water quality. At the same time, a water purification filter element life correction module is set to obtain the purified water quality data set (i.e., the second water quality data set) in real time, and compare it with the predicted water quality data set item by item. Through comparative analysis, the system can promptly detect deviations in the water quality prediction model and automatically make corrections to ensure the accuracy of the prediction results. At the same time, the system will also update the remaining life of the water purification filter according to the real-time water quality data, reminding users to replace the filter at the appropriate time to ensure the continued safety and health of household water use. In addition, the system involved in this application can also automatically compare and analyze the water quality data set of tap water with the preset water quality threshold data set to quickly determine whether the water quality meets the standard. Once it is found that the water quality does not meet the standard, the system will immediately issue a reminder or warning signal to notify the user to take appropriate measures to improve the water quality. This function effectively avoids the inconvenience and risks brought to users' lives due to water quality problems, improves the user experience, and has good application prospects.

[0218] Example 3

[0219] An embodiment of the present invention further provides an electronic device, as shown in FIG8 , comprising: a processor and a memory, wherein the memory is used to store one or more programs; when the one or more programs are executed by the processor, the life correction method of the water purification filter element as described in the above-mentioned embodiment 1 is implemented.

[0220] In a preferred embodiment, the electronic device involved in the present application can be equipped with a life correction device for a water purification filter element, as shown in the schematic diagram Fig. 9 As shown, it includes a first flow meter 1, a second flow meter 2, a third flow meter 3, a tap water inlet waterway 4, a water purification filter 5, a wastewater waterway 6, a purified water waterway 7, a one-way valve 8, a multi-parameter water quality sensor 9, a wastewater waterway 10, and a one-way valve 11.

[0221] In a preferred embodiment, the electronic device involved in the present application can be configured as a water purifier, including: a machine body, a life correction device for a water purification filter element, which is arranged in the machine body; and a life correction system for the water purification filter element as described in Example 2, which is arranged in the machine body and is communicatively connected to the life correction device for the water purification filter element.

[0222] Example 4

[0223] The embodiment of the present invention also provides a computer readable storage medium. Fig.10 As shown, program instructions are stored thereon, and when the program instructions are executed, the life correction method of the water purification filter element as recorded in the above-mentioned embodiment 1 is implemented.

[0224] Among them, the program instructions are stored in a computer-readable storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, including a number of computer program instructions to enable a computing device (which can be a personal computer, server, or network device, etc.) to execute the above method according to the implementation mode of the present application.

[0225] Through the description of the above implementation modes, it is easy for those skilled in the art to understand that the example implementation modes described here can be implemented by software, or by software combined with necessary hardware. Although the implementation modes of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, other modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the legends shown and described here.

[0226] The apparatus, electronic device, non-volatile computer storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, electronic device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, electronic device and non-volatile computer storage medium will not be repeated here.

[0227] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0228] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0229] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0230] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0231] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0232] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0233] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0234] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0235] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0236] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0237] The specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0238] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0239] The above description is only an embodiment of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification.

Claims

1. A method for correcting the life of a water purification filter element, characterized in that: The specific steps include: Obtain the water efficiency level of the water purification system based on the water flow rate; Determining whether it is necessary to obtain the spectral data of the incoming water according to the water efficiency level; After obtaining the spectral data of the influent water, the spectral data is preprocessed to obtain first spectral data of the tap water; Inputting the first spectral data into a pre-trained water quality prediction model to predict the water quality of the purified water to obtain a first water quality data set; Acquiring spectral data of purified water after purification and performing preprocessing as a second water quality data set; comparing the first water quality data set and the second water quality data set; According to the comparison results, the water quality prediction model is modified to update the filter life of the water purification system; Wherein, judging whether it is necessary to obtain the spectral data of the influent water according to the water efficiency level comprises the following steps: Determining the use stage of the water purification filter element of the water purification system according to the water efficiency level; If the water purification filter element is in the middle or late use stage, collecting spectral data of the incoming water; Comparing the spectral data of the influent with the preset spectral data of the influent threshold value; According to the comparison results, determine whether the water intake standard is met; If the inlet water standard is not met, the spectral data of the inlet water is obtained and preprocessed.

2. The life correction method of the water purification filter element according to claim 1, characterized in that: Before the spectral data is preprocessed to obtain the first spectral data of tap water, the following steps are also included: Acquiring the water temperature of the inlet water based on the spectral data of the inlet water; Determining whether the water temperature of the inlet water falls within a threshold water temperature range; If yes, preprocessing the spectral data of the influent water; If it does not, the water temperature of the inlet water is corrected until it reaches the threshold water temperature range, and the spectral data of the inlet water is collected again and pre-processed.

3. The life correction method of the water purification filter element according to claim 1, characterized in that: The first water quality data set includes one or more of the pH value index, water temperature index, turbidity index, TDS index, COD index, and TOC water quality index of the purified water predicted by the water quality prediction model; the second water quality data set includes one or more of the pH value index, water temperature index, turbidity index, TDS index, COD index, and TOC water quality index of the purified water tested after purification.

4. The life correction method of a water purification filter element according to claim 1, characterized in that: The comparing the first water quality data set with the second water quality data set comprises the following steps: Comparing each indicator in the first water quality data set with the corresponding indicator in the second water quality data set; Calculate the difference between each indicator separately; When all the differences do not exceed the corresponding first indicator threshold, the comparison result is that the water purification standard is met; When at least one of the differences exceeds the corresponding first indicator threshold, the comparison result is that the water purification standard is not met.

5. The life correction method of a water purification filter element according to claim 1, characterized in that: According to the comparison result, the water quality prediction model is corrected to update the filter element life of the water purification system, including the following steps: When the comparison result shows that the water purification standard is not met, the second water quality data set is input into the water quality prediction model to cover its corresponding first water quality data set, and iterative training is performed to complete the correction of the model, thereby updating the remaining life of the filter element.

6. The life correction method of a water purification filter element according to claim 1, characterized in that: It also includes building a water quality prediction model, including the following steps: Use deep learning network models to build water quality prediction models; Initialize network parameters; The water quality prediction model is iteratively trained to obtain a pre-trained water quality prediction model.

7. The life correction method of a water purification filter element according to claim 6, characterized in that: The initialization of network parameters includes the following steps: Input layer, where a number of neurons are set to match the dimensions of the input water quality dataset; Convolutional layer: three convolutional layers are set to extract feature information from the water quality dataset, and the convolution kernels are 32, 64, and 128 respectively; Activation layer, set the Relu function as the activation function; A fully connected layer maps the extracted feature information to a water quality dataset; The output layer sets the number of neurons to be the same as the dimension of the water quality dataset, and uses the Softmax function as the activation function to output the prediction results.

8. The method for correcting the life of a water purification filter element according to claim 6, characterized in that: The iterative training of the water quality prediction model to obtain a pre-trained water quality prediction model comprises the following steps: Collecting spectral data of tap water, wherein the spectral data of tap water is obtained by a multi-parameter water quality sensor; Purifying the tap water through a water purification filter and simultaneously collecting spectral data of the purified water, wherein the spectral data of the purified water is obtained through a multi-parameter water quality sensor; Preprocessing the spectral data of the tap water and the spectral data of the purified water to obtain a tap water quality data set and a preprocessed purified water quality data set; Marking water quality index categories on the tap water quality dataset and the purified water quality dataset, and classifying the tap water quality dataset and the purified water quality dataset item by item according to the marked water quality index categories to form an input dataset; The input data set is iteratively trained in the water quality prediction model, and a loss function and an Adam optimizer are defined to iteratively optimize the model parameters. When the loss of the water quality prediction model no longer decreases significantly during continuous iterations or reaches a preset number of iterations, the iteration output model parameters are stopped to update the water quality prediction model to obtain a pre-trained water quality prediction model.

9. The method for correcting the life of a water purification filter element according to claim 8, characterized in that: The spectral data of the tap water includes spectral data of tap water sampled multiple times when the water purification filter element is in the middle use stage and the late use stage within the threshold water temperature range.

10. The life correction method of a water purification filter element according to claim 1, characterized in that: The method of obtaining the water efficiency grade of the water purification system based on the water flow rate thereof comprises the following steps: During the use of the water purification system, the total water intake Q of the tap water is obtained respectively. f , the total amount of purified water Q p , the total concentrated water volume of wastewater Q r ; Calculating a water volume measurement error rate, and performing flow calibration according to the water volume measurement error rate; Determine whether the flow verification is passed, and when the flow verification is passed, calculate the water purification rate Y, and evaluate the water efficiency level of the water purification system according to the water purification rate Y; When the flow rate verification fails, an abnormal flow meter or a water purification system leakage warning is prompted.

11. The life correction method of a water purification filter element according to claim 10, characterized in that: The step of evaluating the water efficiency level of the water purification system according to the water purification rate Y comprises the following steps: When the water purification rate Y is greater than the first water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 1, and it is determined that the water purification filter element is in the initial use stage; When the water purification rate Y is less than the first water efficiency threshold and greater than or equal to the second water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 2, and it is determined that the water purification filter element is in the mid-term use stage; When the water purification rate Y is less than the second water efficiency threshold and greater than or equal to the third water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 3, and it is determined that the water purification filter element is in the later use stage; When the water purification rate Y is less than the third water efficiency threshold, the water efficiency level of the water purification system is evaluated to be level 4, and it is determined that the water purification filter element is in the scrap stage.

12. A life correction system for a water purification filter element, characterized in that: Includes the following modules: A water efficiency rating assessment module is used to obtain the water efficiency rating of the water purification system based on the water flow rate; A prediction condition determination module, used to determine whether it is necessary to obtain the spectral data of the inlet water according to the water efficiency level; A preprocessing module, used for preprocessing the spectral data of the influent water after acquiring the spectral data of the influent water, so as to obtain the first spectral data of the tap water; A water quality prediction module, used for inputting the first spectral data of the tap water into a pre-trained water quality prediction model, predicting the water quality of the tap water after purification, and obtaining a first water quality data set; A water quality acquisition module, used to obtain spectral data of purified water after purification and perform preprocessing as a second water quality data set; a water purification filter life correction module, used for comparing the first water quality data set with the second water quality data set; According to the comparison results, the water quality prediction model is modified to update the filter life of the water purification system; Wherein, judging whether it is necessary to obtain the spectral data of the inlet water according to the water efficiency level in the prediction condition determination module includes the following steps: Determining the use stage of the water purification filter element of the water purification system according to the water efficiency level; If the water purification filter element is in the middle or late use stage, collecting spectral data of the incoming water; Comparing the spectral data of the influent with the preset spectral data of the influent threshold value; According to the comparison results, determine whether the water intake standard is met; If the inlet water standard is not met, the spectral data of the inlet water is obtained and preprocessed.

13. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store one or more programs; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 11 is implemented.

14. A readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by the processor as a method for correcting the life of a water purification filter element as described in any one of claims 1-11.

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