CN111102745A - Control method and device of water heater - Google Patents

Control method and device of water heater Download PDF

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Publication number
CN111102745A
CN111102745A CN201811251160.8A CN201811251160A CN111102745A CN 111102745 A CN111102745 A CN 111102745A CN 201811251160 A CN201811251160 A CN 201811251160A CN 111102745 A CN111102745 A CN 111102745A
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CN
China
Prior art keywords
user
characteristic information
water heater
information
water
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.)
Pending
Application number
CN201811251160.8A
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Chinese (zh)
Inventor
张龙
文旷瑜
连园园
宋德超
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Gree Electric Appliances Inc of Zhuhai
Original Assignee
Gree Electric Appliances Inc of Zhuhai
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Gree Electric Appliances Inc of Zhuhai filed Critical Gree Electric Appliances Inc of Zhuhai
Priority to CN201811251160.8A priority Critical patent/CN111102745A/en
Publication of CN111102745A publication Critical patent/CN111102745A/en
Pending legal-status Critical Current

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Classifications

    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24HFLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT GENERATING MEANS, IN GENERAL
    • F24H9/00Details
    • F24H9/20Arrangement or mounting of control or safety devices or methods
    • F24H9/2007Arrangement or mounting of control or safety devices or methods for water heaters

Abstract

The invention discloses a control method and a control device for a water heater. Wherein, the method comprises the following steps: detecting characteristic information of a user; inputting the characteristic information into a machine learning model for analysis to obtain a target outlet water temperature of the water heater; and controlling the water heater to supply water according to the target outlet water temperature. The invention solves the technical problems that a user determines whether the water temperature meets the requirement or not by manually and repeatedly adjusting the valve, more time is wasted, and the user experience is poorer.

Description

Control method and device of water heater
Technical Field
The invention relates to the field of intelligent control, in particular to a control method and device of a water heater.
Background
In the prior art, when a user uses a water heater, the proportional valve for mixing cold water and hot water needs to be manually adjusted, so that the proportional valve is determined to be properly stopped to be adjusted according to the sensed water temperature; however, water in the water tank or the water heater flows out of the water heater to the skin of a user, the water pipe or the spray head is needed, the user repeatedly adjusts the proportional valve according to the sensed temperature, time is wasted, and user experience is poor.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a control method and a control device of a water heater, and aims to at least solve the technical problems that a user determines whether the water temperature meets the requirement or not by manually and repeatedly adjusting a valve, the time waste is more, and the user experience is poorer.
According to an aspect of an embodiment of the present invention, there is provided a control method of a water heater, including: detecting characteristic information of a user; inputting the characteristic information into a machine learning model for analysis to obtain the target outlet water temperature of the water heater; and controlling the water heater to supply water according to the target outlet water temperature.
Optionally, the machine learning model is trained by: acquiring sample characteristic information of a plurality of users; cleaning the sample characteristic information to obtain first type sample characteristic information; and training the preset learning model according to the first type of sample characteristic information and the water outlet temperature corresponding to the first type of sample characteristic information to obtain a machine learning model.
Optionally, before obtaining the sample feature information of the plurality of users, the method further includes: acquiring second type sample characteristic information, wherein the second type sample characteristic information is cleaned sample characteristic information; training the preset learning model according to the first characteristic information and the water outlet temperature corresponding to the first characteristic information comprises the following steps: repeatedly executing the following steps until the model parameters of the preset learning model meet the training end conditions: inputting the first type of characteristic information into a preset learning model to obtain a prediction result; adjusting model parameters of the preset learning model according to the prediction result to obtain the adjusted preset learning model; and when the adjusted model parameters do not meet the preset conditions, training the adjusted preset learning model by adopting the second type of sample characteristic information.
Optionally, the model parameters include at least one of: weights in the neural network, support vectors in the support vector machine, and coefficients in a linear or logistic regression.
Optionally, after detecting the feature information of the user, the method further includes: detecting behavior information of a user; when the behavior information is a designated behavior, triggering to start the water heater, wherein the behavior information of the user comprises at least one of the following: gesture information of the user, voice information of the user.
Optionally, before triggering the water heater to be turned on, the method further comprises: detecting a relative distance between a user and the water heater; comparing the relative distance with a preset threshold value; and determining whether to trigger the water heater to be started or not according to the comparison result, wherein when the comparison result indicates that the relative distance is smaller than a preset threshold value, the water heater is triggered to be started.
Optionally, detecting feature information of the user includes: collecting image information of a user; carrying out binarization processing on the image information to obtain a binarized image; and extracting characteristic information from the binary image.
Optionally, before controlling the water heater to supply water according to the target outlet water temperature, the method further includes: displaying the target outlet water temperature to a user; and detecting a confirmation instruction of a user, and controlling the water heater to supply water according to the target outlet water temperature when the confirmation instruction is detected.
According to an aspect of an embodiment of the present invention, there is provided a control apparatus of a water heater, including: the detection module is used for detecting the characteristic information of the user; the analysis module is used for inputting the characteristic information into the machine learning model for analysis to obtain the target outlet water temperature of the water heater; and the control module is used for controlling the water heater to supply water according to the target outlet water temperature.
According to an aspect of the embodiments of the present invention, there is provided a storage medium including a stored program, wherein when the program is executed, a device in which the storage medium is located is controlled to execute the control method of the water heater described above.
According to an aspect of the embodiments of the present invention, there is provided a processor for executing a program, wherein the program executes the control method of the water heater.
In the embodiment of the invention, the characteristic information of a user is detected; inputting the characteristic information into a machine learning model for analysis to obtain the target outlet water temperature of the water heater; the mode that the water heater supplies water according to the target outlet water temperature is controlled, the collected characteristic information of the user is input into the machine model for analysis, the target outlet water temperature of the water heater is determined, the purpose that the target outlet water temperature of the corresponding user is automatically determined according to the characteristic information of different users is achieved, the outlet water temperature required by the user is automatically determined according to the characteristic information of the user, the time of the user is saved, the technical effect of user experience is improved, and the technical problems that the user determines whether the water temperature meets the requirements or not through manually and repeatedly adjusting a valve, the waste time is more, and the user experience is poorer are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic flow chart diagram of an alternative method of controlling a water heater according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a control device of an alternative water heater according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for controlling a water heater, wherein the steps illustrated in the flowchart of the figure may be performed in a computer system, such as a set of computer executable instructions, and wherein, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that illustrated herein.
Fig. 1 is a control method of a water heater according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, detecting characteristic information of a user;
in an alternative embodiment of the present application, the feature information of the detected user may be detected by a photographing device installed in the spatial region according to the photographed image information of the user; the characteristic information of the user can be the height of the user, the sex of the user, the age of the user and the like, and the identity of the user or the characteristic information of the user group can be determined.
Wherein, the water heater is used for bathing in a bathroom or other equipment for supplying hot water; the spatial region may be a room or an outdoor spatial region.
In an optional embodiment of the present application, after the image information of the user is acquired by the shooting device, the image information of the user may be sent to the intelligent device, so that the intelligent device analyzes the image information of the user to determine the feature information of the user; in addition, the shooting device can also analyze the image information of the user to determine the characteristic information of the user;
in an optional embodiment of the present application, when the photographing device detects the feature information of the user according to the photographed image information of the user, the image is processed and detected, so that the edge extraction can be performed on the image, the RGB value can be analyzed, and the dressing color, or the height information, and the gender information of the user can be determined to determine the feature information of the user.
In some optional embodiments of the present application, detecting the characteristic information of the user is implemented by the following steps S1021-S1025:
step S1021, collecting image information of the user;
step S1023, carrying out binarization processing on the image information to obtain a binarized image;
step S1025, extracting the feature information from the binarized image.
In some optional embodiments of the present application, after the image information of the user is collected by the shooting device, when the image is processed, the dressing color or the height information of the user can be confirmed by performing edge extraction on the image through binarization on the image; in addition, the dressing color of the user, or the height information and the sex information are determined by analyzing the RGB value of the image.
Step S104, inputting the characteristic information into a machine learning model for analysis to obtain a target outlet water temperature of the water heater;
in some optional embodiments of the present application, after the characteristic information of the user is detected, the characteristic information is input to the machine learning model for analysis, and the target outlet water temperature of the water heater is obtained.
In some optional embodiments of the present application, the machine learning model is a Deep Neural Network (DNN) model, the DNN is a multi-layer neural network including an input layer, a hidden layer, and an output layer, with connections between adjacent layers, and no connections between the same layer.
In some alternative embodiments of the present application, the machine learning model may be trained by the following steps S1042-1446:
step S1042, obtaining sample characteristic information of a plurality of users;
step S1044, cleaning the sample characteristic information to obtain first type sample characteristic information;
in an optional embodiment, the cleaning of the sample feature information may be performed by filtering sample feature information with incorrect data types in the sample feature information, for example: in the sample characteristic information, data with the age information of the user being letters may exist, and the data needs to be eliminated.
And S1046, training a preset learning model according to the first type of sample characteristic information and the water outlet temperature corresponding to the first type of sample characteristic information to obtain a machine learning model.
In an optional embodiment, the first type sample characteristic information and the outlet water temperature corresponding to the first type sample characteristic information are from historical data in a device with a data storage function.
In addition, after the first type of sample characteristic information is cleaned, the outlet water temperature corresponding to the cleaned sample characteristic information can be removed, and the data with the large numerical fluctuation range of the outlet water temperature can be removed, so that the practicability of the outlet water temperature corresponding to the sample characteristic information can be further improved.
And S106, controlling the water heater to supply water according to the target outlet water temperature.
In one embodiment of the application, after the target outlet water temperature of the water heater is obtained, the intelligent control system controls the water heater to supply water to the current user according to the target outlet water temperature.
In an optional embodiment of the present application, before the step S1042 obtains the sample feature information of multiple users, a step of obtaining second type sample feature information needs to be performed, where the second type sample feature information is cleaned sample feature information;
in an optional embodiment of the present application, the training the preset learning model according to the first feature information and the outlet water temperature corresponding to the first feature information includes: the following steps S1062-S1066 are repeatedly executed until the model parameters of the preset learning model satisfy the training end condition:
step S1062, inputting the first-class characteristic information into a preset learning model to obtain a prediction result;
step S1064, adjusting model parameters of the preset learning model according to the prediction result to obtain the adjusted preset learning model;
and step S1066, when the adjusted model parameters do not meet the preset conditions, training the adjusted preset learning model by adopting the second type sample characteristic information. Therefore, in the embodiment of the application, two types of sample characteristic information are adopted for training, and when the model parameters obtained after training by adopting one type of characteristic information do not meet the preset conditions, the training is carried out by adopting the other type of sample characteristic information, so that the accuracy of the prediction result of the preset learning model can be ensured.
Where the model parameters are configuration variables within the model, the values of which can be estimated using the data. Specifically, the model parameters have the following characteristics: model parameters are needed for model prediction. The model parameter values may define a model function.
The model parameters are obtained by data estimation or data learning. The model parameters are generally not set manually by the practitioner. The model parameters are typically saved as part of the learning model. Model parameters are typically estimated using an optimization algorithm, which is an efficient search for possible values of the parameters.
In an optional embodiment of the present application, the first-class feature information is input into a preset learning model of a preset learning model, when a prediction result is obtained, all the first-class feature information may be input into the preset learning model of the preset learning model, or a part of the first-class feature information may be input, and when the prediction result indicates that the prediction accuracy is lower than a preset threshold, it may be determined that the model parameters of the preset learning model need to be optimized;
model parameters include, but are not limited to: weights in the neural network, support vectors in the support vector machine, and coefficients in a linear or logistic regression.
When the model parameters of the preset learning model need to be optimized, the adjusted preset learning model can be trained by adopting the second type of sample characteristic information. The second type of sample characteristic information may be another group of sample characteristic information and the outlet water temperature corresponding to the second type of sample characteristic information.
In some optional embodiments of the present application, after the detecting the feature information of the user in step S102, the method further includes: the following steps S1022 to S1024;
step S1022 detects behavior information of the user;
step S1024, when the behavior information is the designated behavior, triggering to start the water heater, wherein the behavior information of the user comprises at least one of the following: gesture information of the user, voice information of the user.
In an optional embodiment of the present application, before the triggering of turning on the water heater in the step S1024, the following steps S10222 to S10226 are further performed:
step S10222, detecting the relative distance between the user and the water heater;
in an alternative embodiment of the present application, the relative distance between the user and the water heater refers to the relative distance between the user and the actual water outlet device; for example: the relative distance between the user and the water outlet spray head can also be the relative distance between the user and the water heater. When the water heater is used for bathing by a user, the water heater is triggered to be started when the user actually stands under the spray head;
step S10224, comparing the relative distance with a preset threshold value;
in an alternative embodiment of the present application, the relative distance between the user and the water heater can be detected by a camera or by an infrared sensing device; in addition, the relative distance between the user and the water heater can also be acquired by an infrared sensor arranged below the shower nozzle.
And step S10226, determining whether to trigger the water heater to be turned on or not according to the comparison result, wherein the water heater is triggered to be turned on when the comparison result indicates that the relative distance is smaller than a preset threshold value.
In an optional embodiment of the application, the water heater can be triggered to be opened before a user actually reaches the position right below the spray head, and the water heater needs to empty cold water in the water outlet pipe when supplying water to the user according to the target outlet water temperature for the current user, so that the water heater is opened before the user reaches the position right below the spray head in advance, the user can directly contact water at the target outlet water temperature, and user experience is improved.
The preset threshold value can be a preset threshold value of the distance between the user and the water supply spray head, and can also be a threshold value of the distance between the user and the water heater.
Before controlling the water heater to supply water according to the target outlet water temperature, the following steps are also required to be executed:
displaying the target outlet water temperature to a user;
and detecting a confirmation instruction of a user, and controlling the water heater to supply water according to the target outlet water temperature when the confirmation instruction is detected.
In an alternative embodiment of the present application, a display screen may be installed in a visual location of the user, or the user may be prompted audibly for the current target water temperature. After a confirmation instruction input by a user on a display screen (the display screen can be a touch screen) or a confirmation instruction corresponding to a voice prompt by the user (for example, a voice confirmation instruction of the user) is acquired, controlling the water heater to supply water according to the target outlet water temperature. In an optional embodiment of the present application, after obtaining the target outlet water temperature shown to the user, the user may also adjust the outlet water temperature according to needs, for example, the outlet water temperature may be adjusted by voice.
In the embodiment of the invention, the characteristic information of a user is detected; inputting the characteristic information into a machine learning model for analysis to obtain the target outlet water temperature of the water heater; the mode that the water heater supplies water according to the target outlet water temperature is controlled, the collected characteristic information of the user is input into the machine model for analysis, the target outlet water temperature of the water heater is determined, the purpose that the target outlet water temperature of the corresponding user is automatically determined according to the characteristic information of different users is achieved, the outlet water temperature required by the user is automatically determined according to the characteristic information of the user, the time of the user is saved, the technical effect of user experience is improved, and the technical problems that the user determines whether the water temperature meets the requirements or not through manually and repeatedly adjusting a valve, the waste time is more, and the user experience is poorer are solved.
Fig. 2 is a control device of a water heater according to an embodiment of the present application, as shown in fig. 2, the device at least includes: a detection module 22, an analysis module 24, a control module 26; wherein:
a detection module 22, configured to detect feature information of a user;
in an alternative embodiment of the present application, the feature information of the detected user may be detected by a photographing device installed in the spatial region according to the photographed image information of the user; the characteristic information of the user can be the height of the user, the sex of the user, the age of the user and the like, and the identity of the user or the characteristic information of the user group can be determined.
Wherein, the water heater is used for bathing in a bathroom or other equipment for supplying hot water; the spatial region may be a room or an outdoor spatial region.
In an optional embodiment of the present application, after the image information of the user is acquired by the shooting device, the image information of the user may be sent to the intelligent device, so that the intelligent device analyzes the image information of the user to determine the feature information of the user; in addition, the shooting device can also analyze the image information of the user to determine the characteristic information of the user;
in an optional embodiment of the present application, when the photographing device detects the feature information of the user according to the photographed image information of the user, the image is processed and detected, so that the edge extraction can be performed on the image, the RGB value can be analyzed, and the dressing color, or the height information, and the gender information of the user can be determined to determine the feature information of the user.
In some optional embodiments of the present application, detecting the characteristic information of the user is implemented by:
collecting image information of the user; carrying out binarization processing on the image information to obtain a binarized image; and extracting the characteristic information from the binary image.
In some optional embodiments of the present application, after the image information of the user is collected by the shooting device, when the image is processed, the dressing color or the height information of the user can be confirmed by performing edge extraction on the image through binarization on the image; in addition, the dressing color of the user, or the height information and the sex information are determined by analyzing the RGB value of the image.
The analysis module 24 is used for inputting the characteristic information into the machine learning model for analysis to obtain a target outlet water temperature of the water heater;
and the control module 26 is used for controlling the water heater to supply water according to the target outlet water temperature.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 2, and details are not described here again.
According to another aspect of the embodiments of the present application, there is also provided a storage medium, wherein the storage medium includes a stored program, and when the program runs, the apparatus on which the storage medium is located is controlled to execute the control method of the water heater.
According to another aspect of the embodiment of the application, a processor is further provided, and is characterized in that the processor is used for running a program, wherein the program is run to execute the control method of the water heater.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.

Claims (11)

1. A method of controlling a water heater, comprising:
detecting characteristic information of a user;
inputting the characteristic information into a machine learning model for analysis to obtain a target outlet water temperature of the water heater;
and controlling the water heater to supply water according to the target outlet water temperature.
2. The method of claim 1, wherein the machine learning model is trained by:
acquiring sample characteristic information of a plurality of users;
cleaning the sample characteristic information to obtain first type sample characteristic information;
and training a preset learning model according to the first type of sample characteristic information and the water outlet temperature corresponding to the first type of sample characteristic information to obtain the machine learning model.
3. The method of claim 2,
before obtaining sample feature information of a plurality of users, the method further comprises: acquiring second type sample characteristic information, wherein the second type sample characteristic information is cleaned sample characteristic information;
training a preset learning model according to the first characteristic information and the water outlet temperature corresponding to the first characteristic information comprises the following steps: repeatedly executing the following steps until the model parameters of the preset learning model meet the training end conditions:
inputting the first type of characteristic information into the preset learning model to obtain a prediction result;
adjusting the model parameters of the preset learning model according to the prediction result to obtain an adjusted preset learning model;
and when the adjusted model parameters do not meet the preset conditions, training the adjusted preset learning model by adopting the second type of sample characteristic information.
4. The method of claim 3, wherein the model parameters include at least one of: weights in the neural network, support vectors in the support vector machine, and coefficients in a linear or logistic regression.
5. The method of claim 1, wherein after detecting the characteristic information of the user, the method further comprises:
detecting the behavior information of the user; when the behavior information is a designated behavior, triggering to start the water heater, wherein the behavior information of the user comprises at least one of the following: gesture information of the user, voice information of the user.
6. The method of claim 5, wherein prior to triggering the water heater to turn on, the method further comprises:
detecting a relative distance between the user and the water heater;
comparing the relative distance with a preset threshold value;
and determining whether to trigger the water heater to be started or not according to the comparison result, wherein when the comparison result indicates that the relative distance is smaller than a preset threshold value, the water heater is triggered to be started.
7. The method according to any one of claims 1 to 6, wherein detecting the characteristic information of the user comprises:
collecting image information of the user;
carrying out binarization processing on the image information to obtain a binarized image;
and extracting the characteristic information from the binary image.
8. The method of any one of claims 1 to 6, before controlling the water heater to supply water at the target outlet water temperature, the method further comprising:
displaying the target outlet water temperature to a user;
and detecting a confirmation instruction of a user, and controlling the water heater to supply water according to the target outlet water temperature when the confirmation instruction is detected.
9. A control device for a water heater, comprising:
the detection module is used for detecting the characteristic information of the user;
the analysis module is used for inputting the characteristic information into a machine learning model for analysis to obtain the target outlet water temperature of the water heater;
and the control module is used for controlling the water heater to supply water according to the target outlet water temperature.
10. A storage medium characterized by comprising a stored program, wherein a device in which the storage medium is located is controlled to execute the control method of the water heater according to any one of claims 1 to 8 when the program is executed.
11. A processor, characterized in that it is configured to run a program, wherein the program is configured to execute the control method of the water heater according to any one of claims 1 to 8 when running.
CN201811251160.8A 2018-10-25 2018-10-25 Control method and device of water heater Pending CN111102745A (en)

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Application Number Priority Date Filing Date Title
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