CN114108232B - Foam amount prediction method, device, storage medium and washing equipment - Google Patents
Foam amount prediction method, device, storage medium and washing equipment Download PDFInfo
- Publication number
- CN114108232B CN114108232B CN202111463309.0A CN202111463309A CN114108232B CN 114108232 B CN114108232 B CN 114108232B CN 202111463309 A CN202111463309 A CN 202111463309A CN 114108232 B CN114108232 B CN 114108232B
- Authority
- CN
- China
- Prior art keywords
- washing
- foam
- parameter
- correlation
- parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
- D06F33/00—Control of operations performed in washing machines or washer-dryers
- D06F33/30—Control of washing machines characterised by the purpose or target of the control
- D06F33/32—Control of operational steps, e.g. optimisation or improvement of operational steps depending on the condition of the laundry
-
- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
- D06F34/00—Details of control systems for washing machines, washer-dryers or laundry dryers
- D06F34/04—Signal transfer or data transmission arrangements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Textile Engineering (AREA)
- Computer Networks & Wireless Communication (AREA)
- Control Of Washing Machine And Dryer (AREA)
Abstract
The application provides a foam amount prediction method, a device, a storage medium and washing equipment, wherein the foam amount prediction method comprises the following steps: acquiring a plurality of washing parameters of the washing equipment; calculating the correlation of each washing parameter with the amount of foam; determining target washing parameters with correlation meeting preset conditions from the plurality of washing parameters; and predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and the foam prediction model. According to the method, the correlation between each washing parameter and the foam amount is calculated, and the foam amount generated in the washing process of the washing equipment is predicted by the target washing parameters with the correlation meeting the preset conditions through the foam prediction model. The accuracy of foam quantity prediction of the washing equipment is improved.
Description
Technical Field
The application belongs to the technical field of washing equipment, and particularly relates to a foam quantity prediction method and device and washing equipment.
Background
During the washing process, foam may be generated due to friction of the detergent with laundry, and the amount of foam may become an important factor affecting the laundry effect. If the foam quantity is too much, the pressure of the inner cylinder of the washing machine can be increased, and the full foam can lead the motor of the washing machine to rotate forcefully, thereby influencing the service life of the washing machine. The prediction of the amount of foam is therefore very important, and the accuracy of the prediction of the amount of foam is currently low.
Disclosure of Invention
The embodiment of the application provides a foam amount prediction method, a foam amount prediction device, a storage medium and washing equipment, which can improve the accuracy of predicting the foam amount generated in the washing process of the washing equipment.
The embodiment of the application provides a foam amount prediction method, which comprises the following steps:
acquiring a plurality of washing parameters of the washing equipment;
calculating the correlation of each washing parameter with the amount of foam;
determining target washing parameters with correlation meeting preset conditions from the plurality of washing parameters;
and predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and the foam prediction model.
Optionally, said calculating the correlation of each wash parameter with the amount of suds comprises:
calculating a correlation value between each washing parameter and the foam amount by normalizing the mutual information;
the determining the target washing parameters with the correlation meeting the preset conditions from the plurality of washing parameters comprises the following steps:
and determining a target washing parameter with a correlation value larger than a correlation threshold value from the plurality of washing parameters.
Optionally, the determining, from the plurality of washing parameters, a target washing parameter with a correlation value greater than a correlation threshold value includes:
calculating a correlation mean value according to the correlation value of each washing parameter;
and determining a target washing parameter with a correlation value larger than the correlation mean value from the plurality of washing parameters.
Optionally, said calculating a correlation value between each wash parameter and the foam volume by normalizing the mutual information comprises:
calculating the entropy of each washing parameter and the entropy of the foam quantity;
calculating the joint entropy of each washing parameter and the foam quantity;
calculating mutual information of the entropy and the joint entropy;
normalizing the mutual information to obtain a correlation value between each washing parameter and the foam quantity.
Optionally, the method further comprises:
by the formulaCalculating the entropy of each washing parameter by the formula +.>Calculating the entropy of the foam quantity, wherein p (x k ) Is x k Probability density, p (y) l ) Is y l Probability density of (c);
by the formulaCalculating the joint entropy of each washing parameter and the foam quantity, wherein p (x k ,y l ) Is x k And y l Is a joint probability density function of (1);
calculating mutual information of the entropy and the joint entropy by the formula I (X, Y) =h (X) +h (Y) -H (X, Y);
by the formulaA correlation value between each washing parameter and the amount of foam was obtained.
Optionally, the method further comprises:
acquiring a plurality of historical washing parameters of the washing equipment;
calculating the correlation of each historical washing parameter and the historical foam quantity;
determining target historical washing parameters with correlation meeting preset conditions from the historical washing parameters;
and constructing the foam prediction model according to the target historical washing parameters and a support vector regression model.
Optionally, the constructing the foam prediction model according to the target historical washing parameters and a support vector regression model includes:
by the formulaConstructing the foam prediction model, wherein +.>Is Lagrangian multiplier, b is bias variable, k (x i ,x j ) As a kernel function, the target historical washing parameter is taken as an input parameter of the kernel function, and f (x) is output as a predicted foam amount.
The embodiment of the application also provides a foam quantity prediction device, which comprises:
an acquisition module for acquiring a plurality of washing parameters of the washing device;
a calculation module for calculating the correlation of each washing parameter with the amount of foam;
the determining module is used for determining target washing parameters with correlation meeting preset conditions from the plurality of washing parameters;
and the prediction module is used for predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and the foam prediction model.
Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the foam amount prediction method as described above.
Embodiments of the present application also provide a washing apparatus comprising a processor, a memory and a computer program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the foam amount prediction method as described above.
According to the foam quantity prediction method, the correlation between each washing parameter and the foam quantity is calculated, and the foam quantity generated in the washing process of the washing equipment is predicted by the target washing parameters with the correlation meeting the preset conditions through the foam prediction model. The accuracy of the foam quantity prediction of the washing apparatus can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort to a person skilled in the art.
For a more complete understanding of the present application and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings. Wherein like reference numerals refer to like parts throughout the following description.
Fig. 1 is a schematic flow chart of a foam amount prediction method according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a second flow chart of a foam amount prediction method according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a foam amount predicting device according to an embodiment of the present application.
Fig. 4 is a schematic structural view of a washing apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a foam amount prediction method according to an embodiment of the present application. The washing machine is applied to washing equipment, and the washing equipment can be a pulsator washing machine, a drum washing machine, a stirring washing machine, a jet washing machine and the like. The foam amount prediction method comprises the following steps:
101, a plurality of washing parameters of the washing apparatus are acquired.
The washing parameter of the washing apparatus may be a parameter generated by the washing apparatus during the washing process, for example, the washing parameter of the washing apparatus may be a water temperature parameter, a detergent amount parameter, a water inflow parameter, a washing pattern parameter, a load weight parameter, a capacity parameter of the washing tub, a type parameter of the washing apparatus, a water inflow speed parameter of the washing apparatus, etc. It should be noted that the washing parameters may also include other parameters, and the washing parameters generated in the washing process all belong to the washing parameters.
102, the correlation of each wash parameter to the amount of foam is calculated.
The correlation between the water temperature of the washing water and the amount of generated foam, the correlation between the amount of the washing agent and the amount of generated foam, the correlation between the amount of the water inflow and the amount of generated foam, the correlation between the type of the washing pattern and the amount of generated foam, the correlation between the amount of load weight and the amount of generated foam, the correlation between the size of the capacity of the washing tub and the amount of generated foam, the correlation between the type of the washing apparatus and the amount of generated foam, the speed of water inflow of the washing apparatus and the amount of generated foam, and the like are calculated for each of the correlation between the washing parameter and the amount of foam, the correlation between the amount of the washing agent parameter and the amount of foam, the correlation between the amount of water inflow parameter and the amount of foam, the correlation between the amount of load weight parameter and the amount of foam, the correlation between the capacity parameter of the washing tub and the amount of foam, the type parameter of the washing apparatus and the amount of foam, the correlation between the speed of water inflow parameter of the washing apparatus and the amount of foam, and the like, respectively.
And 103, determining target washing parameters with correlation meeting preset conditions from the plurality of washing parameters.
And determining a correlation parameter meeting a preset condition from the correlations of the washing parameters and the foam amount, and determining a target washing parameter according to the correlation parameter meeting the preset condition. It is understood that the washing parameter strongly correlated with the amount of foam generation is marked as the target washing parameter.
104, predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and the foam prediction model.
According to the foam quantity prediction method, the correlation between each washing parameter and the foam quantity is calculated, the target washing parameter with the correlation meeting the preset condition is used as the input parameter of the foam prediction model, the foam prediction model outputs the foam quantity, and compared with the method that all the washing parameters are used as the input parameter of the foam prediction model, the washing parameters with the correlation meeting the preset condition are screened out in advance, so that the accuracy of foam quantity prediction of washing equipment can be improved.
With continued reference to fig. 2, fig. 2 is a schematic diagram illustrating a second flow chart of the foam amount prediction method according to the embodiment of the present application. The foam amount prediction method comprises the following steps:
a plurality of washing parameters of the washing apparatus are acquired 201.
The washing parameter of the washing apparatus may be a parameter generated by the washing apparatus during the washing process, for example, the washing parameter of the washing apparatus may be a water temperature parameter, a detergent amount parameter, a water inflow parameter, a washing pattern parameter, a load weight parameter, a capacity parameter of the washing tub, a type parameter of the washing apparatus, a water inflow speed parameter of the washing apparatus, etc. It should be noted that the washing parameters may also include other parameters, and the washing parameters generated in the washing process all belong to the washing parameters.
The water temperature parameter of the washing equipment can be obtained through a temperature sensor, the detergent quantity parameter can be obtained through a detergent liquid level sensor, the water inflow parameter can be obtained through a water level sensor, the washing mode parameter selected by a user can be obtained through a main control board, the load weight parameter can be obtained through a weight sensor of the washing barrel, the capacity parameter of the washing barrel and the type parameter of the washing equipment can be obtained through the preset specification parameter of the washing equipment, the water inflow speed parameter can be obtained through a sensor of a water inlet valve, and the like. It should be noted that the method for obtaining the above washing parameters is merely exemplary, and the above washing parameters may be obtained in other manners.
And 202, calculating a correlation value between each washing parameter and the foam amount by normalizing the mutual information.
Calculating a correlation value between each wash parameter and the suds amount by normalizing the mutual information may comprise:
calculating the entropy of each washing parameter and the entropy of the foam quantity;
calculating the joint entropy of each washing parameter and the foam quantity;
calculating mutual information of entropy and joint entropy;
normalizing the mutual information to obtain a correlation value between each washing parameter and the foam quantity.
Further, the formula can be obtainedCalculating the entropy of each washing parameter by the formula +.>Calculating the entropy of the foam quantity, wherein p (x k ) Is x k Probability density, p (y) l ) Is y l Is a probability density of (c).
Can be expressed by the formulaCalculating the joint entropy of each washing parameter and the foam quantity, wherein p (x x ,y l ) Is x k And y l Is a joint probability density function of (a).
Mutual information of the entropy and the joint entropy can be calculated by the formula I (X, Y) =h (X) +h (Y) -H (X, Y);
can be expressed by the formulaA correlation value between each washing parameter and the amount of foam was obtained.
The normalized mutual information NMI is obtained by scaling the mutual information to be between 0 and 1, so that not only the influence of different dimensions and value ranges is counteracted, but also the relation existing between original samples is reserved, and the correlation between each washing parameter and the generated foam amount can be obtained by normalizing the mutual information.
And 203, calculating a correlation mean value according to the correlation value of each washing parameter.
The correlation mean value corresponding to each washing parameter is added and divided by the number of the washing parameters to obtain the correlation mean value, and the correlation mean value is used as a correlation threshold value and can be calculated by a formulaCalculating a correlation mean value and NMI i NMI for each washing parameter corresponding correlation value d Is the average of the correlation.
204, determining a target washing parameter with a correlation value larger than the correlation mean value from the plurality of washing parameters.
And selecting a washing parameter with a correlation value larger than the correlation mean value as a target washing parameter.
205, predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and the foam prediction model.
Wherein, the foam prediction model can be trained by the following ways:
a plurality of historical washing parameters of the washing apparatus are obtained, wherein the historical washing parameters may be historical washing parameters generated by the washing apparatus during the historical washing process. The historical washing parameter of the washing apparatus may be a parameter generated by the washing apparatus during the historical washing process, for example, the historical washing parameter may be a historical water temperature parameter, a historical detergent amount parameter, a historical water inflow parameter, a historical washing pattern parameter, a historical load weight parameter, a capacity parameter of the washing tub, a type parameter of the washing apparatus, a water inflow speed parameter of the historical washing apparatus, and the like. It should be noted that the historical washing parameters may also include other parameters, and the washing parameters generated in the washing process all belong to the washing parameters. The method comprises the steps of acquiring historical water temperature parameters of washing equipment through a temperature sensor, acquiring historical detergent quantity parameters through a detergent liquid level sensor, acquiring historical water inflow parameters through a water level sensor, acquiring historical washing mode parameters selected by a user through a main control board, acquiring historical load weight parameters through a weight sensor of the washing barrel, acquiring capacity parameters of the washing barrel and type parameters of the washing equipment from preset specification parameters of the washing equipment, and acquiring historical water inflow speed parameters and the like through a sensor of a water inlet valve. It should be noted that the method for obtaining the above washing parameters is merely exemplary, and the above washing parameters may be obtained in other manners.
The correlation of each historical washing parameter with the historical foam amount is calculated, and the correlation value between each washing parameter and the historical foam amount can be calculated by normalizing the mutual information. Wherein, the entropy of each historical washing parameter and the entropy of the historical foam amount can be calculated; calculating the joint entropy of each historical washing parameter and the historical foam quantity; calculating mutual information of entropy and joint entropy; normalizing the mutual information to obtain a correlation value between each historical washing parameter and the historical foam quantity.
And determining a target historical washing parameter with the correlation meeting the preset condition from the plurality of historical washing parameters, and marking the washing parameter with the correlation value larger than the correlation threshold value as the target historical washing parameter.
And constructing a foam prediction model according to the target historical washing parameters and the support vector regression model. The support vector regression (Support Vector Regression, SVR) is an application of SVM (support vector machine support vector machine) to regression, well solves the construction problem of a high-dimensional model with a limited number of samples, and the constructed model has good prediction performance.
Constructing the foam prediction model from the target historical washing parameters and the support vector regression model may include:
by the formulaConstructing an untrained foam prediction model, wherein +.>Is Lagrangian multiplier, b is bias variable, k (x i ,x j ) As a kernel function, the target historical washing parameter is taken as an input parameter of the kernel function, and f (x) is output as a predicted foam amount. The kernel function may be a gaussian function, although in some embodiments, the kernel function may be another function.
And retraining the obtained untrained foam prediction model on a training set, finally constructing to obtain a foam prediction model value, and finally constructing to obtain a foam prediction model which is a trained foam prediction model, wherein in practical application, a target washing parameter is used as an input parameter of the foam prediction model to calculate to obtain a predicted foam quantity.
In some embodiments, the foam prediction model may be built and trained without building and training within the washing apparatus, and may be built and trained on a server or other electronic device, such as at PC (Personal Computer) end of support vector regression calculation software.
The embodiment of the application provides a foam amount prediction method. Compared with the problem that the washing quality is affected because the random of predicting the foam quantity by manually selecting the washing parameters or the foam quantity generated in the washing process cannot be accurately predicted due to the fact that all the washing parameters are taken as the model input parameters through a neural network model, the foam quantity is predicted by taking parameters with higher correlation with the foam quantity as support vector regression model input variables through normalization mutual information, and the parameters with higher correlation with the foam quantity are selected as model input and the foam quantity is accurately predicted under the condition of limited data.
In some embodiments, after obtaining the predicted foam amount, the following steps may also be performed:
calculating to obtain defoaming time according to the foam quantity;
updating the displayed washing time according to the defoaming time comprises the following steps:
determining a load for executing the defoaming action according to the foam quantity and the execution time length when the load executes the defoaming action;
and determining the defoaming time according to the execution time.
In some embodiments, after obtaining the predicted foam amount, the following steps may also be performed:
determining a corresponding defoaming program according to the foam quantity;
if the foam amount is within a first preset range, defoaming is performed through a first defoaming program;
if the foam amount is within a second preset range, defoaming is performed through a second defoaming program;
if the foam amount is within a third preset range, defoaming is performed through a third defoaming program;
the minimum value of the first preset range is larger than the maximum value of the second preset range, the minimum value of the second preset range is larger than the maximum value of the third preset range, the defoaming time of the first defoaming program is larger than the defoaming time of the second defoaming program, and the defoaming time of the second defoaming program is larger than the defoaming time of the third defoaming program.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a foam amount predicting device according to an embodiment of the present application. The embodiment of the application also provides a foam quantity prediction device, which comprises:
an acquisition module for acquiring a plurality of washing parameters of the washing device;
a calculation module for calculating the correlation of each washing parameter with the amount of foam;
the determining module is used for determining target washing parameters with correlation meeting preset conditions from the plurality of washing parameters;
and the prediction module is used for predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and the foam prediction model.
In some implementations, the computing module is further to: calculating a correlation value between each washing parameter and the foam amount by normalizing the mutual information;
the determination module is also for: and determining a target washing parameter with a correlation value larger than a correlation threshold value from the plurality of washing parameters.
In some embodiments, the determining module is further to:
calculating a correlation mean value according to the correlation value of each washing parameter;
and determining a target washing parameter with a correlation value larger than the correlation mean value from the plurality of washing parameters.
In some implementations, the computing module is further to:
calculating the entropy of each washing parameter and the entropy of the foam quantity;
calculating the joint entropy of each washing parameter and the foam quantity;
calculating mutual information of the entropy and the joint entropy;
normalizing the mutual information to obtain a correlation value between each washing parameter and the foam quantity.
In some embodiments, the computing module is further to:
by the formulaCalculating the entropy of each washing parameter by the formula +.>Calculating the entropy of the foam quantity, wherein p (x k ) Is x k Probability density, p (y) l ) Is y l Is a probability density of (c).
By the formulaCalculating the joint entropy of each washing parameter and the foam quantity, wherein p (x k ,y l ) Is x k And y l Is a joint probability density function of (a).
Calculating mutual information of the entropy and the joint entropy by the formula I (X, Y) =h (X) +h (Y) -H (X, Y);
by the formulaA correlation value between each washing parameter and the amount of foam was obtained.
In some embodiments, the foam amount prediction apparatus further comprises:
a history washing parameter obtaining module for obtaining a plurality of history washing parameters of the washing equipment;
the historical correlation calculation module is used for calculating the correlation between each historical washing parameter and the historical foam amount;
the target historical washing parameter determining module is used for determining target historical washing parameters with correlation meeting preset conditions from the plurality of historical washing parameters;
and the construction module is used for constructing the foam prediction model according to the target historical washing parameters and the support vector regression model.
In some embodiments, the build module is further to:
by the formulaConstructing the foam prediction model, wherein +.>Is Lagrangian multiplier, b is bias variable, k (x i ,x j ) As a kernel function, the target historical washing parameter is taken as an input parameter of the kernel function, and f (x) is output as a predicted foam amount.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a washing apparatus provided in an embodiment of the present application, and the embodiment of the present application further provides a washing apparatus, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the processor in a circuit of the washing apparatus is configured to perform:
acquiring a plurality of washing parameters of the washing equipment;
calculating the correlation of each washing parameter with the amount of foam;
determining target washing parameters with correlation meeting preset conditions from the plurality of washing parameters;
and predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and the foam prediction model.
In some embodiments, in said calculating the correlation of each wash parameter to the amount of suds, the processor is further configured to:
calculating a correlation value between each washing parameter and the foam amount by normalizing the mutual information;
the determining the target washing parameters with the correlation meeting the preset conditions from the plurality of washing parameters comprises the following steps:
and determining a target washing parameter with a correlation value larger than a correlation threshold value from the plurality of washing parameters.
In some embodiments, when the target washing parameter having a correlation value greater than a correlation threshold is determined from the plurality of washing parameters, the process is further configured to perform:
calculating a correlation mean value according to the correlation value of each washing parameter;
and determining a target washing parameter with a correlation value larger than the correlation mean value from the plurality of washing parameters.
In some embodiments, in said calculating a correlation value between each wash parameter and the suds volume by normalizing the mutual information, the processor is further configured to perform:
calculating the entropy of each washing parameter and the entropy of the foam quantity;
calculating the joint entropy of each washing parameter and the foam quantity;
calculating mutual information of the entropy and the joint entropy;
normalizing the mutual information to obtain a correlation value between each washing parameter and the foam quantity.
In some implementations, the processor is further configured to perform:
by the formulaCalculating the entropy of each washing parameter by the formula +.>Calculating the entropy of the foam quantity, wherein p (x k ) Is x k Probability density, p (y) l ) Is y l Is a probability density of (c).
By the formulaCalculating the joint entropy of each washing parameter and the foam quantity, wherein p (x k ,y l ) Is x k And y l Is a joint probability density function of (a).
Calculating mutual information of the entropy and the joint entropy by the formula I (X, Y) =h (X) +h (Y) -H (X, Y);
by the formulaObtaining each washCorrelation value between the polyester parameter and the amount of foam.
In some implementations, the processor is further configured to perform:
acquiring a plurality of historical washing parameters of the washing equipment;
calculating the correlation of each historical washing parameter and the historical foam quantity;
determining target historical washing parameters with correlation meeting preset conditions from the historical washing parameters;
and constructing the foam prediction model according to the target historical washing parameters and a support vector regression model.
In some embodiments, in said building said foam prediction model from said target historical washing parameters and a support vector regression model, the processor is further configured to perform:
by the formulaConstructing the foam prediction model, wherein +.>Is Lagrangian multiplier, b is bias variable, k (x i ,x j ) As a kernel function, the target historical washing parameter is taken as an input parameter of the kernel function, and f (x) is output as a predicted foam amount.
It will be appreciated that in some embodiments, the construction and training of the foam prediction model may not be constructed and trained within the washing apparatus, and may be constructed and trained on a server or other electronic device, such as at the PC (Personal Computer) end of the support vector regression calculation software.
In some embodiments, the washing apparatus may further include a drying device, a driving device, an intelligent module, etc., the drying device may dry the dehydrated laundry, the intelligent module may be coupled with the driving device, the water inlet and outlet device, the heating device, the drying device, etc., and the driving device, the water inlet and outlet device, the heating device, and the drying device may be driven according to a user's demand, wherein the intelligent module may further include a voice module, and each functional device of the washing machine may be operated by a voice signal.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by way of a computer program, which may be stored in a computer readable storage medium, which may include, but is not limited to: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more features.
The above description of the foam amount prediction method, device and washing equipment provided in the embodiments of the present application has been provided in detail, and specific examples are applied herein to illustrate the principles and embodiments of the present application, and the above description of the examples is only used to help understand the method and core idea of the present application; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the ideas of the present application, the contents of the present specification should not be construed as limiting the present application in summary.
Claims (10)
1. A foam amount prediction method applied to a washing apparatus, characterized by comprising:
acquiring a plurality of washing parameters of the washing equipment;
calculating the correlation of each washing parameter with the amount of foam;
determining target washing parameters with correlation meeting preset conditions from the plurality of washing parameters;
predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and a foam prediction model, wherein the foam prediction model is prepared by a formulaConstruction of->Is Lagrangian multiplier, b is bias variable, k (x i ,x j ) As a kernel function, the f (x) output is the predicted foam quantity.
2. The foam amount prediction method according to claim 1, wherein the calculating of the correlation of each washing parameter with the foam amount comprises:
calculating a correlation value between each washing parameter and the foam amount by normalizing the mutual information;
the determining the target washing parameters with the correlation meeting the preset conditions from the plurality of washing parameters comprises the following steps:
and determining a target washing parameter with a correlation value larger than a correlation threshold value from the plurality of washing parameters.
3. The foam amount prediction method according to claim 2, wherein the determining a target washing parameter having a correlation value greater than a correlation threshold value from the plurality of washing parameters includes:
calculating a correlation mean value according to the correlation value of each washing parameter;
and determining a target washing parameter with a correlation value larger than the correlation mean value from the plurality of washing parameters.
4. The foam amount prediction method according to claim 2, wherein the calculating a correlation value between each washing parameter and the foam amount by normalizing mutual information includes:
calculating the entropy of each washing parameter and the entropy of the foam quantity;
calculating the joint entropy of each washing parameter and the foam quantity;
calculating mutual information of the entropy and the joint entropy;
normalizing the mutual information to obtain a correlation value between each washing parameter and the foam quantity.
5. The foam amount prediction method according to claim 4, characterized in that the method further comprises:
by the formulaCalculating the entropy of each washing parameter by the formula +.>Calculating the entropy of the foam quantity, wherein p (x k ) Is x k Probability density, p (y) l ) Is y l Probability density of (c);
by the formulaCalculating the joint entropy of each washing parameter and the foam quantity, wherein p (x k ,y l ) Is x k And y l Is a joint probability density function of (1);
calculating mutual information of the entropy and the joint entropy by a formula I (X, Y) =h (X) +h (T) -H (X, Y);
by the formulaA correlation value between each washing parameter and the amount of foam was obtained.
6. The foam amount prediction method according to claim 1, characterized in that the method further comprises:
acquiring a plurality of historical washing parameters of the washing equipment;
calculating the correlation of each historical washing parameter and the historical foam quantity;
determining target historical washing parameters with correlation meeting preset conditions from the historical washing parameters;
and constructing the foam prediction model according to the target historical washing parameters and a support vector regression model.
7. The method of claim 6, wherein constructing the foam prediction model from the target historical washing parameters and a support vector regression model comprises:
the target historical washing parameter is taken as an input parameter of the kernel function, and f (x) is output as a predicted foam amount.
8. A foam amount predicting apparatus applied to a washing device, characterized by comprising:
an acquisition module for acquiring a plurality of washing parameters of the washing device;
a calculation module for calculating the correlation of each washing parameter with the amount of foam;
the determining module is used for determining target washing parameters with correlation meeting preset conditions from the plurality of washing parameters;
a prediction module for predicting the foam amount in the washing process of the washing equipment according to the target washing parameter and a foam prediction model, wherein the foam prediction model is prepared by a formula Construction of->Is Lagrangian multiplier, b is bias variable, k (x i ,x j ) As a kernel function, () output is the predicted foam amount.
9. A computer storage medium, having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the foam amount prediction method of any one of claims 1 to 7.
10. A washing appliance comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the foam amount prediction method of any one of claims 1 to 7 when the program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111463309.0A CN114108232B (en) | 2021-12-02 | 2021-12-02 | Foam amount prediction method, device, storage medium and washing equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111463309.0A CN114108232B (en) | 2021-12-02 | 2021-12-02 | Foam amount prediction method, device, storage medium and washing equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114108232A CN114108232A (en) | 2022-03-01 |
| CN114108232B true CN114108232B (en) | 2024-03-12 |
Family
ID=80366842
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111463309.0A Active CN114108232B (en) | 2021-12-02 | 2021-12-02 | Foam amount prediction method, device, storage medium and washing equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114108232B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115613272B (en) * | 2022-10-11 | 2026-01-23 | 珠海格力电器股份有限公司 | Washing control method and device, washing machine and storage medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0657576A1 (en) * | 1993-12-10 | 1995-06-14 | Bosch-Siemens Hausgeräte GmbH | Automatically controlled washing machine |
| CN1137077A (en) * | 1995-05-31 | 1996-12-04 | 博施-西门子家用器具有限公司 | automatic washing machine |
| JP2001120878A (en) * | 1999-10-22 | 2001-05-08 | Toshiba Corp | Washing machine |
| US6269506B1 (en) * | 1998-09-30 | 2001-08-07 | Siemens Aktiengesellschaft | Method and arrangement for computer-assisted determination of clusters for the recognition of foaming in a washing machine as well as method and arrangement for recognizing foaming in a washing machine |
| JP2006346255A (en) * | 2005-06-17 | 2006-12-28 | Hitachi Appliances Inc | Electric washing machine |
| CN101466884A (en) * | 2006-06-12 | 2009-06-24 | Bsh博世和西门子家用器具有限公司 | Method of removing scum in a drum washing machine, and drum washing machine suitable for this purpose |
| KR20090114602A (en) * | 2008-04-30 | 2009-11-04 | 엘지전자 주식회사 | Drum Washing Machine and Control Method |
| EP2418315A2 (en) * | 2010-07-29 | 2012-02-15 | BSH Bosch und Siemens Hausgeräte GmbH | Household device with fill level sensor and method for determining a status parameter in a household appliance |
| EP2609845A1 (en) * | 2011-12-30 | 2013-07-03 | Indesit Company S.p.A. | Dishwasher and method for detecting malfunctions thereof |
| JP2014054403A (en) * | 2012-09-13 | 2014-03-27 | Panasonic Corp | Washing machine |
| DE202016104343U1 (en) * | 2016-08-05 | 2017-08-08 | Edmund Pötsch | Analysis device for determining foam |
| CN112941806A (en) * | 2021-01-29 | 2021-06-11 | 珠海格力电器股份有限公司 | Washing machine, foam quantity prediction method and device thereof and electronic equipment |
-
2021
- 2021-12-02 CN CN202111463309.0A patent/CN114108232B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0657576A1 (en) * | 1993-12-10 | 1995-06-14 | Bosch-Siemens Hausgeräte GmbH | Automatically controlled washing machine |
| CN1137077A (en) * | 1995-05-31 | 1996-12-04 | 博施-西门子家用器具有限公司 | automatic washing machine |
| US6269506B1 (en) * | 1998-09-30 | 2001-08-07 | Siemens Aktiengesellschaft | Method and arrangement for computer-assisted determination of clusters for the recognition of foaming in a washing machine as well as method and arrangement for recognizing foaming in a washing machine |
| JP2001120878A (en) * | 1999-10-22 | 2001-05-08 | Toshiba Corp | Washing machine |
| JP2006346255A (en) * | 2005-06-17 | 2006-12-28 | Hitachi Appliances Inc | Electric washing machine |
| CN101466884A (en) * | 2006-06-12 | 2009-06-24 | Bsh博世和西门子家用器具有限公司 | Method of removing scum in a drum washing machine, and drum washing machine suitable for this purpose |
| KR20090114602A (en) * | 2008-04-30 | 2009-11-04 | 엘지전자 주식회사 | Drum Washing Machine and Control Method |
| EP2418315A2 (en) * | 2010-07-29 | 2012-02-15 | BSH Bosch und Siemens Hausgeräte GmbH | Household device with fill level sensor and method for determining a status parameter in a household appliance |
| EP2609845A1 (en) * | 2011-12-30 | 2013-07-03 | Indesit Company S.p.A. | Dishwasher and method for detecting malfunctions thereof |
| JP2014054403A (en) * | 2012-09-13 | 2014-03-27 | Panasonic Corp | Washing machine |
| DE202016104343U1 (en) * | 2016-08-05 | 2017-08-08 | Edmund Pötsch | Analysis device for determining foam |
| CN112941806A (en) * | 2021-01-29 | 2021-06-11 | 珠海格力电器股份有限公司 | Washing machine, foam quantity prediction method and device thereof and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114108232A (en) | 2022-03-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8811743B2 (en) | Resource-aware computer vision | |
| CN116562156B (en) | Training method, device, equipment and storage medium for control decision model | |
| CN114108232B (en) | Foam amount prediction method, device, storage medium and washing equipment | |
| JP2001101154A (en) | Deviated value degree calculation device, probability density estimation device to be used for the calculation device and forgetting type histogram calculation device | |
| CN113408622A (en) | Non-invasive load identification method and system considering characteristic quantity information expression difference | |
| CN113822144B (en) | Target detection method, device, computer equipment and storage medium | |
| CN116795198A (en) | Data center energy consumption optimization methods, devices and storage media | |
| CN107609583A (en) | Parameter optimization method, device, computer equipment and the storage medium of disaggregated model | |
| CN114118432B (en) | Equipment parameter adjusting method and system | |
| CN118860572A (en) | A method, device and electronic device for elastic scaling in container cloud environment | |
| CN110241550A (en) | Control method, control device and the recording medium for having recorded control program of washing machine | |
| CN115946820A (en) | Training method of floating environment data prediction model, floating control method and equipment | |
| CN113268822A (en) | Centrifugal pump performance prediction method based on small sample nuclear machine learning | |
| CN116050506A (en) | A backdoor attack defense method, device, electronic equipment and storage medium | |
| CN119397921A (en) | A method for predicting drag reduction of fuselage turbulent micro-ribs based on Bayesian probability model | |
| JP6233432B2 (en) | Method and apparatus for selecting mixed model | |
| CN117976000A (en) | Valve cooling equipment fault detection method, device, computer equipment and storage medium | |
| CN113408692A (en) | Network structure searching method, device, equipment and storage medium | |
| CN115125695A (en) | Drainage method of washing machine and washing machine | |
| CN116227332A (en) | A transformer mixed bit quantization method and system | |
| CN114896601A (en) | Smart contract vulnerability detection method, device and related equipment based on reinforcement learning | |
| CN114737354B (en) | Water inlet control method, water inlet control device, storage medium and washing machine | |
| CN114836942A (en) | Eccentricity correction method, device and computer readable storage medium | |
| CN114657741B (en) | Washing device control method, washing device control device, washing device and storage medium | |
| Hossain et al. | Hpc application parameter autotuning on edge devices: A bandit learning approach |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |