CN112131740A - Method for predicting service life of filter element of water purifier - Google Patents
Method for predicting service life of filter element of water purifier Download PDFInfo
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- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
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Abstract
The invention provides a method for predicting the service life of a filter element of a water purifier, which belongs to the technical field of water purifiers and comprises the following steps: judging whether the early-warning water purifier filter element is available; calculating to obtain the actual service life of the filter element of the water purifier; extracting the actual service life of the filter element of the water purifier and the characteristic value of the information about whether the filter element of the water purifier is available or not, and unifying the data dimension of the characteristic value; training a multiple linear regression model; optimizing the trained multiple linear regression model by using a gradient descent method; and predicting the service life of all current water purifier filter elements which do not reach the early warning threshold value. According to the invention, a big data and artificial intelligence mode is adopted, regression analysis is reversely carried out on the filter element use data in a result data driving mode, and the difference between the actual filter element use data and the analysis data is found out and calibrated, so that the purposes of individuation in use and accurate data are achieved, and the problem of potential safety hazard of drinking water caused by inaccurate monitoring of the service life of the filter element in the actual use process of the water purifying equipment is solved.
Description
Technical Field
The invention belongs to the technical field of water purifiers, and particularly relates to a method for predicting the service life of a filter element of a water purifier.
Background
In recent years, along with the development of industry, there are many reports on water pollution all over the country, and people pay more attention to the health and safety of drinking water, and the sales of water purifiers and other related water purifying equipment are increasing year by year. The fundamental reason is that the tap water pipe network is built in a long time, rust deposits in the water pipe and secondary water supply of a high-rise building are caused, and the water outlet at the tail end of drinking water is worried by uncontrollable factors of a plurality of links.
In the existing water purification equipment, the water purification equipment is distinguished according to functions, and the most common water purification equipment is an ultrafiltration water purifier and a reverse osmosis water purifier. Regardless of the type of water purifier, the filter element types generally include a PP cotton filter element, an activated carbon filter element, a reverse osmosis membrane filter element, a nanofiltration reverse osmosis membrane filter element, an ion exchange resin filter element, and the like. From the perspective of the filtration and adsorption mechanism of the filter element, the physical and chemical characteristics, namely, the technologies of pore size filtration, physical adsorption, charge repulsion, ion exchange and the like, are utilized to achieve the purposes of filtration, adsorption and separation, but the functions and the flow rate are not unchanged, and the functions or the flow rate can be reduced along with the prolonging of the service time and the increasing of the purified water amount, namely, the service life is consumed.
At present, in the industry, for defining the service life of a filter element, the common method is that manual judgment is carried out, only the initial state is evaluated, the difference of the consumption degree of the filter element caused by regional water quality difference is not considered, and meanwhile, the real use condition is not automatically analyzed and calibrated by utilizing big data and an artificial intelligence technology. The prior art has the defects that: in the current water purification industry and the actual use process of water purification equipment, the service life of a filter element is monitored or evaluated mainly through two modes, namely a countdown mode, namely, countdown is started from the installation of the filter element, and the expired service life is ended; and the second mode is a flow mode, namely, the water passing amount is calculated from the installation of the filter element, and the service life is ended when the water passing amount reaches a certain limit value. The two modes show that no matter which mode is adopted, the water quality difference of each region is not taken into calculation, the water quality of one region is not invariable, but a simple and rough mode is adopted, so that the problem of accurate evaluation of the service life of the filter element in the actual use process cannot be fundamentally solved, the efficiency is low by manual measurement, and the scale cannot be realized.
Disclosure of Invention
Aiming at the defects in the prior art, the method for predicting the service life of the filter element of the water purifier provided by the invention solves the problems that the service life of the filter element is inaccurate in monitoring and the potential safety hazard of drinking water is caused in the actual use process of water purifying equipment, and the service life detection at present usually depends on a manual judgment mode and cannot realize large-scale data analysis.
In order to achieve the above purpose, the invention adopts the technical scheme that:
the scheme provides a method for predicting the service life of a filter element of a water purifier, which comprises the following steps:
s1, judging whether the pre-warning water purifier filter element is available;
s2, performing labeling treatment on the available filter element of the water purifier to obtain the actual service life of the filter element of the water purifier;
s3, extracting characteristic values of the actual service life of the filter element of the water purifier and the information about whether the filter element of the water purifier is available or not, and unifying the data dimensions of the characteristic values;
s4, training a multivariate linear regression model by using the characteristic values with unified dimensionality;
s5, optimizing the trained multiple linear regression model by using a gradient descent method;
and S6, predicting the service life of all current water purifier filter elements which do not reach the early warning threshold value by using the optimized multiple linear regression model, and completing the prediction of the service life of the water purifier filter elements.
Further, the feature values in step S3 include: the characteristic value extracted aiming at the PP cotton filter element, the characteristic value extracted aiming at the front active carbon filter element, the characteristic value extracted aiming at the rear active carbon filter element and the characteristic value extracted aiming at the RO membrane filter element.
Still further, the characteristic values extracted for the PP cotton filter element include: the number of days the PP cotton filter element is used and the actual measured service life, the current available state, the current residual service life of the filter element, the water quality turbidity and the total flow of water;
the characteristic values extracted aiming at the front activated carbon filter element comprise: the number of days of use of the preposed active carbon filter element, the actual measured service life, the current available state, the current residual service life of the filter element, the residual chlorine content and the total flow of water;
the characteristic values extracted aiming at the rear activated carbon filter element comprise: the number of days of used post-positioned active carbon filter elements and the actual measured service life, the current available state, the current residual service life of the filter elements and the total flow of water;
the characteristic values extracted for the RO membrane cartridge include: the number of days the RO membrane filter has been in service and the actual measured life, the current state of availability, the current remaining life of the filter, the TDS mean value of the source water and the total flow of water.
Still further, the expression of the number of days the PP cotton filter element has been used and the actual measured life is as follows:
Lp＝Tb*Dq
wherein Lp represents the number of days the PP cotton filter element has been used and the actual measured service life, Tb represents the turbidity of the water, and Dq represents the flow difference between the current flow accumulated value and the previous value;
the expression of the used days and the actual measured service life of the preposed active carbon filter element is as follows:
Lc＝C1*Dq
wherein Lc represents the number of days the front activated carbon filter element has been used and the actual measurementLife time, C1Representing the residual chlorine information of the water quality acquired by a residual chlorine sensor;
the expression of the number of days the RO membrane cartridge has been used and the actual measured life is as follows:
Lk＝C1*R*Dq*TDS
wherein Lk represents the number of days the RO membrane filter has been used and the actual measured life, R represents the ratio of the permeate water yield to the water inflow, and TDS represents the total solid dissolved amount of water quality acquired by the TDS sensor.
Still further, the expression of the optimized multiple linear regression model in step S5 is as follows:
ak+1＝ak+ρks-(k)
wherein, ak+1Represents a multiple linear regression model optimized in the gradient direction, akRepresenting the current value, p, of a multiple linear regression model calculationkDenotes the search step in the gradient direction, s-(k)Indicating the negative direction of the gradient.
Still further, the expression of the prediction function of the multiple linear regression model in step S6 is as follows:
y＝β0+β1x1+β2x2+...+βpxp+
where y represents the prediction function of the multiple linear regression model, represents the random error, βpDenotes partial and regression constants, xpRepresenting the input feature value.
The invention has the beneficial effects that:
(1) according to the invention, a big data and artificial intelligence mode is adopted, regression analysis is reversely carried out on the filter element use data in a result data driving mode, and the difference between the actual filter element use data and the analysis data is found out and calibrated, so that the purposes of individuation in use and accurate data are achieved, and the problem of potential safety hazard of drinking water caused by inaccurate monitoring of the service life of the filter element in the actual use process of the water purifying equipment is solved.
(2) The calibration of the filter element service life is based on the actual water consumption data of the user and the water source water quality data of the actual position, so that the filter element service life of the user is reduced by ' thousands of people ' and thousands of faces ', the purpose of personalized use is achieved, and a foundation is laid for subsequent service expansion and business models.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Examples
The invention takes the data generated in the actual use process of the filter element life of the water purifier as the basis, leads the consumption data of the filter element life to become more accurate through big data and artificial intelligence, and fundamentally ensures the continuous drinking water safety of customers, as shown in figure 1, the invention discloses a method for predicting the filter element life of the water purifier, which comprises the following steps:
s1, judging whether the pre-warning water purifier filter element is available;
in this embodiment, make preliminary judgement to the recovery filter core life-span that uses the early warning threshold value, judge promptly whether the filter core is available and the life-span value of consumption.
S2, performing labeling treatment on the available filter element of the water purifier to obtain the actual service life of the filter element of the water purifier;
in this embodiment, the filter core that does not use the early warning threshold value will be tentatively judged and the survey is added the mark, survey filter core surplus practical life result.
S3, extracting characteristic values of the actual service life of the filter element of the water purifier and the information about whether the filter element of the water purifier is available or not, and unifying the data dimensions of the characteristic values;
in this embodiment, the characteristic values extracted for the PP cotton filter element include: the number of days the PP cotton filter element is used and the actual measured service life, the current available state, the current residual service life of the filter element, the water quality turbidity and the total flow of water; the characteristic values extracted aiming at the front activated carbon filter element comprise: the number of days of use of the preposed active carbon filter element, the actual measured service life, the current available state, the current residual service life of the filter element, the residual chlorine content and the total flow of water; the characteristic values extracted aiming at the rear active carbon filter element comprise: the number of days of used post-positioned active carbon filter elements and the actual measured service life, the current available state, the current residual service life of the filter elements and the total flow of water; the characteristic values extracted for the RO membrane cartridge include: the number of days the RO membrane filter has been in service and the actual measured life, the current state of availability, the current remaining life of the filter, the TDS mean value of the source water and the total flow of water.
In this embodiment, the expression of the number of days the PP cotton filter element has been used and the actual measured life is as follows:
Lp＝Tb*Dq
wherein Lp represents the number of days the PP cotton filter element has been used and the actual measured service life, Tb represents the turbidity of the water, and Dq represents the flow difference between the current flow accumulated value and the previous value;
the expression of the used days and the actual measured service life of the preposed active carbon filter element is as follows:
Lc＝C1*Dq
wherein Lc represents the number of days the front activated carbon filter element has been used and the actual measured service life, C1Representing the residual chlorine information of the water quality acquired by a residual chlorine sensor;
the expression of the number of days the RO membrane cartridge has been used and the actual measured life is as follows:
Lk＝C1*R*Dq*TDS
wherein Lk represents the number of days the RO membrane filter has been used and the actual measured life, R represents the ratio of the permeate water yield to the water inflow, and TDS represents the total solid dissolved amount of water quality acquired by the TDS sensor.
In this embodiment, since the labeling experiment is not necessarily performed on all the recovered filter elements, the actual measured life parameters cannot be obtained for the filter elements that have not undergone the labeling experiment. In this case, the "actual measured life" is equal to the "current remaining life of the filter element", and the "current availability status" is equal to 1(1 represents available, and 2 represents unavailable). No matter whether through the experiment of adding mark, other parameters including filter core days of having used, residual chlorine content, water turbidity, source water TDS mean value, water purification TDS mean value, with water total flow all need do the completion operation as training data after acquireing from the data warehouse.
In this embodiment, the feature scaling is performed by dividing all feature values by the maximum value of the feature to obtain a normalized mean value, so that the numerical value becomes a number between 0 and 1:
Rx＝(xp-μi)/Si
wherein Rx represents the mean value after normalization, μiRepresenting a characteristic value xpAverage value of (1), SiRepresenting a range of characteristic values.
S4, training a multiple linear regression model by using the characteristic values after the unified dimensionality:
in this embodiment, parameters are set based on the linear regression method of the linear model in skleran, the cut-off value is set to True, and the normalized value is set to false. Starting regression calculation by using a fit (training) method and adding training set data as input, performing predictive analysis by using a predict method and adding verification set data as input, performing model evaluation by using a score (evaluation) method, stopping training when the evaluation score of the model is greater than a preset score (0.8), and storing the training model. The process is repeated every day according to newly-recycled filter elements and filter element service life data triggering early warning, and model evaluation scores are continuously improved.
S5, optimizing the trained multiple linear regression model by using a gradient descent method:
ak+1＝ak+ρks-(k)
wherein, ak+1Represents a multiple linear regression model optimized in the gradient direction, akRepresenting the current value, p, of a multiple linear regression model calculationkDenotes the search step in the gradient direction, s-(k)Indicating the negative direction of the gradient.
And S6, predicting the service life of all current water purifier filter elements which do not reach the early warning threshold value by using the optimized multiple linear regression model, and completing the prediction of the service life of the water purifier filter elements.
The expression of the prediction function of the multiple linear regression model is as follows:
y＝β0+β1x1+β2x2+...+βpxp+
where y represents the prediction function of the multiple linear regression model, represents the random error, βpDenotes partial and regression constants, xpRepresenting the input feature value.
In the embodiment, the trained and optimized multivariate linear regression model is used for calibrating life data of other water purifier filter elements which do not reach the early warning threshold value, and the prediction result is more accurate.
Through the design, the purposes of individuation in use and accurate data are achieved, and the problem of potential safety hazards of drinking water caused by inaccurate monitoring of the service life of the filter element in the actual use process of the water purifying equipment is solved.
Claims (6)
1. A method for predicting the service life of a filter element of a water purifier is characterized by comprising the following steps:
s1, judging whether the pre-warning water purifier filter element is available;
s2, performing labeling treatment on the available filter element of the water purifier to obtain the actual service life of the filter element of the water purifier;
s3, extracting characteristic values of the actual service life of the filter element of the water purifier and the information about whether the filter element of the water purifier is available or not, and unifying the data dimensions of the characteristic values;
s4, training a multivariate linear regression model by using the characteristic values with unified dimensionality;
s5, optimizing the trained multiple linear regression model by using a gradient descent method;
and S6, predicting the service life of all current water purifier filter elements which do not reach the early warning threshold value by using the optimized multiple linear regression model, and completing the prediction of the service life of the water purifier filter elements.
2. The method for predicting the lifetime of a filter element of a water purifier as recited in claim 1, wherein the characteristic value in step S3 includes: the characteristic value extracted aiming at the PP cotton filter element, the characteristic value extracted aiming at the front active carbon filter element, the characteristic value extracted aiming at the rear active carbon filter element and the characteristic value extracted aiming at the RO membrane filter element.
3. The method for predicting the service life of the filter element of the water purifier as recited in claim 2, wherein the characteristic values extracted for the PP cotton filter element comprise: the number of days the PP cotton filter element is used and the actual measured service life, the current available state, the current residual service life of the filter element, the water quality turbidity and the total flow of water;
the characteristic values extracted aiming at the front activated carbon filter element comprise: the number of days of use of the preposed active carbon filter element, the actual measured service life, the current available state, the current residual service life of the filter element, the residual chlorine content and the total flow of water;
the characteristic values extracted aiming at the rear activated carbon filter element comprise: the number of days of used post-positioned active carbon filter elements and the actual measured service life, the current available state, the current residual service life of the filter elements and the total flow of water;
the characteristic values extracted for the RO membrane cartridge include: the number of days the RO membrane filter has been in service and the actual measured life, the current state of availability, the current remaining life of the filter, the TDS mean value of the source water and the total flow of water.
4. The method for predicting the life of a filter element of a water purifier as recited in claim 3, wherein the expression of the number of days the PP cotton filter element has been used and the actual measured life is as follows:
Lp＝Tb*Dq
wherein Lp represents the number of days the PP cotton filter element has been used and the actual measured service life, Tb represents the turbidity of the water, and Dq represents the flow difference between the current flow accumulated value and the previous value;
the expression of the used days and the actual measured service life of the preposed active carbon filter element is as follows:
Lc＝C1*Dq
wherein Lc represents the number of days the front activated carbon filter element has been used and the actual measured service life, C1Representing the residual chlorine information of the water quality acquired by a residual chlorine sensor;
the expression of the number of days the RO membrane cartridge has been used and the actual measured life is as follows:
Lk＝C1*R*Dq*TDS
wherein Lk represents the number of days the RO membrane filter has been used and the actual measured life, R represents the ratio of the permeate water yield to the water inflow, and TDS represents the total solid dissolved amount of water quality acquired by the TDS sensor.
5. The method for predicting the lifetime of a filter element of a water purifier as recited in claim 1, wherein the expression of the optimized multiple linear regression model in the step S5 is as follows:
ak+1＝ak+ρks-(k)
wherein, ak+1Represents a multiple linear regression model optimized in the gradient direction, akRepresenting the current value, p, of a multiple linear regression model calculationkDenotes the search step in the gradient direction, s-(k)Indicating the negative direction of the gradient.
6. The method for predicting the lifetime of a filter element of a water purifier as recited in claim 1, wherein the expression of the prediction function of the multiple linear regression model in step S6 is as follows:
y＝β0+β1x1+β2x2+...+βpxp+
where y represents the prediction function of the multiple linear regression model, represents the random error, βpDenotes partial and regression constants, xpRepresenting the input feature value.
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