CN110889869A - Water dispenser water yield control method and device, water dispenser and storage medium - Google Patents

Water dispenser water yield control method and device, water dispenser and storage medium Download PDF

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CN110889869A
CN110889869A CN201911174374.4A CN201911174374A CN110889869A CN 110889869 A CN110889869 A CN 110889869A CN 201911174374 A CN201911174374 A CN 201911174374A CN 110889869 A CN110889869 A CN 110889869A
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water
cup
image
frame image
rectangular frame
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黄智刚
宋德超
陈翀
陈向文
肖文轩
魏文应
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Gree Electric Appliances Inc of Zhuhai
Zhuhai Lianyun Technology Co Ltd
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Gree Electric Appliances Inc of Zhuhai
Zhuhai Lianyun Technology Co Ltd
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Abstract

The disclosure relates to the technical field of water dispensers, in particular to a method and a device for controlling water yield of a water dispenser and a storage medium, which are used for solving the technical problem that the water yield of the water dispenser needs to be controlled by manual operation in the related art. The control method of the water outlet quantity of the water dispenser comprises the following steps: acquiring an image of a water cup, wherein the water cup in the image comprises a cup opening and a cup body; inputting the image of the water cup into the trained neural network model to output a first rectangular frame image corresponding to the cup mouth and a second rectangular frame image corresponding to the cup body; calculating to obtain the volume of the water cup according to the pixel values of the first rectangular frame image and the second rectangular frame image; and controlling the water yield of the water dispenser according to the volume of the water cup.

Description

Water dispenser water yield control method and device, water dispenser and storage medium
Technical Field
The disclosure relates to the technical field of water dispensers, in particular to a method and a device for controlling water yield of a water dispenser and a storage medium.
Background
The drinking machine is a device which heats or cools the barreled purified water (or mineral water) and is convenient for people to drink. The barreled water is placed above the machine and is matched with the barreled water for use. The water dispensers are classified into warm, ice-hot and ice-hot types, and the ice-hot machines are classified into semiconductor refrigeration water dispensers and compressor type refrigeration water dispensers.
At present, the output control of all water dispensers on the requirements of users (water temperature/water quantity) requires the users to trigger the machines to realize the required functions by manually operating mechanical devices (handles/buttons and the like). With the development of science and technology, people continuously pursue humanization of operation and fluency of experience for the requirements of products.
Disclosure of Invention
The present disclosure provides a method, a device and a storage medium for controlling water output of a water dispenser, so as to solve the technical problem in the related art that the water output of the water dispenser needs to be controlled by manual operation.
In order to achieve the above object, in a first aspect of the embodiments of the present disclosure, a method for controlling water output of a water dispenser is provided, where the method includes:
acquiring an image of a water cup, wherein the water cup in the image comprises a cup opening and a cup body;
inputting the image of the water cup into the trained neural network model to output a first rectangular frame image corresponding to the cup mouth and a second rectangular frame image corresponding to the cup body;
calculating to obtain the volume of the water cup according to the pixel values of the first rectangular frame image and the second rectangular frame image;
and controlling the water yield of the water dispenser according to the volume of the water cup.
Optionally, before acquiring the image of the cup, the method further includes:
and confirming that the water cup is positioned at the water receiving platform in the water dispenser.
Optionally, acquire the image of drinking cup, the drinking cup in the image includes rim of a cup and cup body, includes:
the image of the water cup is shot through a fixed-focus-distance camera arranged above the water receiving table, and the water cup in the image comprises a cup opening and a cup body.
Optionally, calculating to obtain the volume of the water cup according to the pixel values of the first rectangular frame image and the second rectangular frame image, including:
and calculating to obtain the volume of the water cup by taking the pixel width of the first rectangular frame image as the pixel diameter of the cup opening and the pixel height of the second rectangular frame image as the pixel height value of the cup body and the K value of the fixed-focus-distance camera.
Optionally, the volume of the cup is obtained by calculating according to the following equation:
V＝πR2H，H＝Kh，R＝kd/2；
wherein V is the volume of the cup, R is the actual radius of the cup, H is the actual height of the cup, H is the pixel height value of the cup body, and d is the pixel diameter of the cup rim.
Optionally, controlling the water output of the water dispenser according to the volume of the water cup comprises:
outputting selection information to prompt selection of the type of the drinking water;
and controlling the water outlet quantity of the water dispenser according to the selected type of the drinking water, wherein the water outlet quantity of the water dispenser is smaller than the volume of the water cup.
Optionally, the neural network model is a model using Fast RCNN network; the method further comprises the following steps:
collecting water cup photos and photographing data in a network;
and training the Fast RCNN according to the water cup pictures and the photographing data to obtain a trained neural network model.
In a second aspect of the embodiments of the present disclosure, a device for controlling the water output of a water dispenser is provided, the device comprising:
the acquisition module is configured to acquire an image of a cup, wherein the cup in the image comprises a cup opening and a cup body;
a recognition module configured to input an image of the water cup into the trained neural network model to output a first rectangular-frame image corresponding to the cup mouth and a second rectangular-frame image corresponding to the cup body;
the calculation module is configured to calculate and obtain the volume of the water cup according to the pixel values of the first rectangular frame image and the second rectangular frame image;
and the control module is configured to control the water outlet quantity of the water dispenser according to the volume of the water cup.
In a third aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the method of any one of the above first aspects.
In a fourth aspect of the embodiments of the present disclosure, a device for controlling the water output of a water dispenser is provided, which includes:
a memory having a computer program stored thereon; and
a processor for executing the computer program in the memory to implement the steps of the method of any of the first aspects above.
By adopting the technical scheme, the following technical effects can be at least achieved:
according to the water dispenser, the image of the water cup is acquired, the image is input into the trained neural network model, so that a first rectangular frame image corresponding to the cup mouth and a second rectangular frame image corresponding to the cup body are output, then the volume of the water cup can be obtained according to the pixel values of the first rectangular frame image and the second rectangular frame image, and the water dispenser can be controlled to release the water amount corresponding to the volume of the water cup, so that the purposes of intelligent water taking and operation simplification are achieved, a user does not need to press water or wait for water outlet, the combination of an artificial intelligence technology and a household appliance product is achieved, and the technical problem that the water outlet amount of the water dispenser needs to be controlled through manual operation in the related technology is solved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
fig. 1 is a flowchart illustrating a method for controlling the water output of a water dispenser according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flow chart of another method for controlling the water output of a water dispenser according to an exemplary embodiment of the present disclosure.
Fig. 3 is a block diagram illustrating a structure of a fast RCNN network according to an exemplary embodiment of the present disclosure.
FIG. 4 is a flowchart illustrating a training test of the Faster RCNN network according to an exemplary embodiment of the present disclosure.
Fig. 5 is a block diagram of a device for controlling the water output of a water dispenser according to an exemplary embodiment of the present disclosure.
Fig. 6 is a block diagram of a water dispenser shown in an exemplary embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and examples, so that how to apply technical means to solve technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and various features in the embodiments of the present application can be combined with each other without conflict, and the formed technical solutions are all within the protection scope of the present disclosure.
The inventor of the present disclosure finds that, in the water dispenser in the related art, generally, the keys are worn out after long-time operation due to mechanical key or man-machine contact touch, and the function keys cannot be identified; or the key is damaged, the function is invalid; and the general water dispenser only has the function of getting water, has low intelligent degree of operation, and can not meet the form of modern multifunctional requirements.
Example one
Fig. 1 is a flowchart of a method for controlling water output of a water dispenser according to an exemplary embodiment of the present disclosure, so as to solve the technical problem in the related art that the water output of the water dispenser needs to be manually controlled. As shown in fig. 1, the method for controlling the water output of the water dispenser may include the following steps:
s11, acquiring an image of the water cup, wherein the water cup in the image comprises a cup opening and a cup body.
S12, inputting the image of the water cup into the trained neural network model to output a first rectangular frame image corresponding to the cup mouth and a second rectangular frame image corresponding to the cup body.
And S13, calculating and obtaining the volume of the water cup according to the pixel values of the first rectangular frame image and the second rectangular frame image.
And S14, controlling the water yield of the water dispenser according to the volume of the water cup.
In step S11, the image of the cup may be obtained by capturing an image of the cup through a fixed-focus camera disposed above the water receiving platform, where the captured image of the cup needs to include a cup opening and a cup body. Certainly, in other embodiments, the user may also upload the cup image through the client corresponding to the water dispenser after shooting the cup image through the mobile terminal (such as a mobile phone).
After the image of the cup is obtained, step S12 is executed to input the image of the cup into the trained neural network model to output a first rectangular-frame image corresponding to the cup mouth and a second rectangular-frame image corresponding to the cup body. The neural network model splits an input image into two images, namely a first rectangular-frame image and a second rectangular-frame image. The first rectangular frame image only comprises a cup opening image, and the pixel width of the first rectangular frame image corresponds to the diameter of the cup opening; the size of the pixel height of the second rectangular frame image corresponds to the size of the cup body height. That is, after the neural network model outputs a first rectangular frame image corresponding to the cup rim and a second rectangular frame image corresponding to the cup body, the volume of the cup can be calculated and obtained according to the pixel values of the first rectangular frame image and the second rectangular frame image.
Taking an image of a cup shot by a fixed-focus camera as an example, taking the pixel width of the first rectangular frame image as the pixel diameter of the cup opening, the pixel height of the second rectangular frame image as the pixel height value of the cup body and the K value (the ratio of the actual length to the pixel length, unit mm/pixel) of the fixed-focus camera, the volume of the cup can be obtained by a cylindrical volume calculation formula.
The cylinder volume calculation is as follows: v ═ pi R2And H, wherein H is Kh, and R is kd/2. In the above formula, V is the volume of the cup, R is the actual radius of the cup, H is the actual height of the cup, H is the pixel height value of the cup body, and d is the pixel diameter of the cup rim.
And (5) after the volume of the water cup is obtained through the calculation of the formula, executing step S14, and controlling the water yield of the water dispenser according to the volume of the water cup. The water outlet quantity of the water dispenser can be set as the volume of the water cup; the volume of the cup can be set, so that water is prevented from overflowing when the user holds the cup filled with water.
Before the water dispenser is controlled to discharge water, selection information can be output through a display screen to enable a user to select the type of drinking water, such as warm water, ice water or hot water. When the user selects warm water, the water dispenser is controlled to release the warm water into the water cup, and the released water yield is smaller than the volume of the water cup.
Before step S12 is executed, it is necessary to determine whether the cup is located at the water receiving platform of the water dispenser. The pressure sensor can be arranged at the bottom of the water receiving platform, and when the water cup is arranged at the water receiving platform, the pressure sensor can detect the pressure value, so that the water cup can be confirmed to be positioned at the water receiving platform in the water dispenser; when the pressure sensor does not detect the pressure value or the detected pressure value is smaller than the threshold value, the cup is confirmed not to be positioned at the water receiving platform in the water dispenser, and the steps S11 and S12 are not required to be executed.
Optionally, the neural network model is a model using Fast RCNN network, and a large number of cup images are required to be trained before using the neural network model. Therefore, the water cup pictures and the photographing data in the network can be collected, and the photographing data can be the width pixel value of the cup opening in the water cup image and the height pixel value of the cup body in the water cup image. And then training the Fast RCNN according to the water cup pictures and the photographing data to obtain a trained neural network model.
According to the water dispenser, the image of the water cup is acquired, the image is input into the trained neural network model, so that a first rectangular frame image corresponding to the cup mouth and a second rectangular frame image corresponding to the cup body are output, then the volume of the water cup can be obtained according to the pixel values of the first rectangular frame image and the second rectangular frame image, and the water dispenser can be controlled to release the water amount corresponding to the volume of the water cup, so that the purposes of intelligent water taking and operation simplification are achieved, a user does not need to press water or wait for water outlet, the combination of an artificial intelligence technology and a household appliance product is achieved, and the technical problem that the water outlet amount of the water dispenser needs to be controlled through manual operation in the related technology is solved.
It should be noted that the method embodiment shown in fig. 1 is described as a series of acts or combinations for simplicity of description, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts or steps described. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required in order to implement the disclosure.
Example two
Fig. 2 is a flowchart of another method for controlling the water outlet amount of a water dispenser according to an exemplary embodiment of the present disclosure, so as to solve the technical problem in the related art that the water outlet amount of the water dispenser needs to be controlled by manual operation. As shown in fig. 2, the method for controlling the water output of the water dispenser may include the following steps:
firstly, it is determined that the cup is placed at a fixed position, and in this embodiment, the position of the water dispenser cup is fixed by the placing table, that is, the position of the cup is fixed, so as to perform actual correspondence (correspondence between a pixel value and a real value) in the subsequent calculation of the height and the top surface area of the cup.
And then, acquiring an image of the water dispenser after the cup is placed in front of the water dispenser through the camera. The camera adopts a fixed focal length camera, and is obliquely arranged above the maximum height of the water cup at the position of the water dispenser, so that the top surface and the side surface (namely the cup opening and the cup body) of the water cup can be shot;
then, identifying the water cup through a Faster-RCNN network, outputting a network identification result into two rectangular frames, wherein one rectangular frame is an integral frame of the water cup, and the pixel height of the frame in the image is the pixel height value h of the water cup; the second is a detection frame of the cup mouth of the water cup, and the width of a pixel in the image of the frame is the pixel diameter d of the top surface of the water cup. Because a camera with a fixed focal length is adopted and the position of the water cup is fixed, the K value (the ratio of the actual length to the pixel length, unit mm/pixel) of the camera is easy to know, and the actual radius R and the actual height H of the water cup are calculated according to the following formula: and H is Kh, and R is kd/2. Whereby the formula V ═ pi R is calculated by the cylinder volume2H, the volume of the water cup can be obtained, so that the water yield of the current water cup is obtained, and the water dispenser is used for carrying out water outlet operation and stopping the water outlet operation. The volume can be equal to the water yield, the water outlet of the water dispenser is constant, the flow rate is constant, the water yield in unit time is constant, the control can be realized through time, and the water outlet time is volume/water yield in unit time.
Fig. 2 is a flow chart of another method for controlling the water output of a water dispenser according to an exemplary embodiment of the present disclosure. When the camera collects the image data in the camera, the Faster RCNN network sends the image data as an input end to the VGG convolutional neural network for convolution calculation. The VGG network convolves the original image to obtain a convolution characteristic image Feature map (characteristic map). After the convolution characteristic image is input into an RPN network for screening, the RPN network outputs a batch of rectangular candidate regions, and the rectangular candidate regions are input into an ROI POOLING POOLING layer together with Feature map for classification processing. And finally, the data stream enters an FC full link layer, type recognition is carried out through a softmax function in the FC full link layer, and a result is output.
Fig. 3 is a block diagram illustrating a structure of a fast RCNN network according to an exemplary embodiment of the present disclosure. The training library of the neural network in the present disclosure can be divided into two types, one is a network library, and the other is a self-built library. The network library is a standard library for the deep learning network training at present and is used for carrying out initialization setting on weight parameters of the whole network and the VGG network; the self-built library is used for identifying an actual water cup, and various water cup pictures which are marked and are commonly used in the market and photographing data from a user are contained in the self-built library. And dividing a part of samples in the self-built library into test sets for testing the network training result. And inputting the test set into an RPN and a VGG network for training, and after the training is finished, training the whole Fast RCNN network. At this time, the weights of the networks in each layer are distributed roughly, and then the training is completed by training and fine-tuning the RPN network and the Fast RCNN network. And finally, inputting a test set to test the whole network, so that the method can be used for identifying the water cup of the water dispenser.
The invention discloses an intelligent water dispenser control method based on deep learning, which is characterized in that a cup in front of a water dispenser is scanned and identified by training a fast RCNN neural network, the volume of the cup is calculated by an identification result, and the water dispenser is reasonably controlled, so that the purposes of intelligent water taking and simplified operation are achieved, a user does not need to press water or wait for water outlet, and the combination of an artificial intelligence technology and household appliances is realized.
EXAMPLE III
Fig. 5 is a device for controlling the water outlet amount of a water dispenser according to an exemplary embodiment of the present disclosure, so as to solve the technical problem in the related art that the water outlet amount of the water dispenser needs to be controlled by manual operation. As shown in fig. 5, the control device 300 for controlling the water output of the water dispenser includes:
an obtaining module 310 configured to obtain an image of a cup, wherein the cup in the image includes a rim and a body of the cup;
a recognition module 320 configured to input an image of the cup into the trained neural network model to output a first rectangular-frame image corresponding to the cup rim and a second rectangular-frame image corresponding to the cup body;
a calculating module 330 configured to calculate and obtain a volume of the cup according to pixel values of the first rectangular frame image and the second rectangular frame image;
and the control module 340 is configured to control the water outlet quantity of the water dispenser according to the volume of the water cup.
The present disclosure also provides another preferred embodiment of a water outlet amount control device of a water dispenser, in this embodiment, the water outlet amount control device of the water dispenser includes: a processor, wherein the processor is configured to execute the following program modules stored in the memory: the acquisition module is configured to acquire an image of a water cup, wherein the image of the water cup comprises a cup mouth and a cup body of the water cup; a recognition module configured to input an image of the water cup into the trained neural network model to output a first rectangular-frame image corresponding to the cup mouth and a second rectangular-frame image corresponding to the cup body; the calculation module is configured to calculate and obtain the volume of the water cup according to the pixel values of the first rectangular frame image and the second rectangular frame image; and the control module is configured to control the water outlet quantity of the water dispenser according to the volume of the water cup.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Example four
The present disclosure also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any of the alternative embodiments described above.
The method implemented when the computer program running on the processor is executed can refer to the specific embodiment of the method for controlling the water outlet quantity of the water dispenser of the present disclosure, and details are not repeated here.
The processor may be an integrated circuit chip having information processing capabilities. The processor may be a general-purpose processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like.
EXAMPLE five
The present disclosure also provides a water dispenser, comprising:
a memory having a computer program stored thereon; and
a processor for executing the computer program in the memory to perform the method steps of any of the alternative embodiments described above.
Fig. 6 is a block diagram of a water dispenser 400 according to an exemplary embodiment. As shown in fig. 6, the water dispenser 400 may include: a processor 401, a memory 402, a multimedia component 403, an input/output (I/O) interface 404, and a communication component 405.
The processor 401 is configured to control the overall operation of the water dispenser 400, so as to complete all or part of the steps in the above-mentioned method for controlling the water output of the water dispenser. The memory 402 is used to store various types of data to support operation at the water dispenser 400, which may include, for example, instructions for any application or method operating on the water dispenser 400, as well as application-related data. The Memory 402 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 403 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 402 or transmitted through the communication component 405. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 404 provides an interface between the processor 401 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 405 is used for wired or wireless communication between the water dispenser 400 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding Communication component 405 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the water dispenser 400 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to perform the above-mentioned method for controlling the water output of the water dispenser.
In another exemplary embodiment, a computer readable storage medium, such as a memory 402, comprising program instructions executable by a processor 401 of the water dispenser 400 to perform the above-described method of controlling the water output of the water dispenser is also provided.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.

Claims (10)

1. A method for controlling the water outlet quantity of a water dispenser is characterized by comprising the following steps:
acquiring an image of a water cup, wherein the water cup in the image comprises a cup opening and a cup body;
inputting the image of the water cup into the trained neural network model to output a first rectangular frame image corresponding to the cup mouth and a second rectangular frame image corresponding to the cup body;
calculating to obtain the volume of the water cup according to the pixel values of the first rectangular frame image and the second rectangular frame image;
and controlling the water yield of the water dispenser according to the volume of the water cup.
2. The method of claim 1, wherein prior to acquiring the image of the cup, further comprising:
and confirming that the water cup is positioned at the water receiving platform in the water dispenser.
3. The method of claim 2, wherein acquiring an image of a cup, the cup in the image including a rim and a body, comprises:
the image of the water cup is shot through a fixed-focus-distance camera arranged above the water receiving table, and the water cup in the image comprises a cup opening and a cup body.
4. The method of claim 3, wherein calculating the volume of the cup according to the pixel values of the first rectangular frame image and the second rectangular frame image comprises:
and calculating to obtain the volume of the water cup by taking the pixel width of the first rectangular frame image as the pixel diameter of the cup opening and the pixel height of the second rectangular frame image as the pixel height value of the cup body and the K value of the fixed-focus-distance camera.
5. The method of claim 4, wherein the volume of the cup is obtained by the following equation:
V＝πR2H，H＝Kh，R＝kd/2；
wherein V is the volume of the cup, R is the actual radius of the cup, H is the actual height of the cup, H is the pixel height value of the cup body, and d is the pixel diameter of the cup rim.
6. The method of claim 1, wherein controlling the water output of the water dispenser based on the volume of the cup comprises:
outputting selection information to prompt selection of the type of the drinking water;
and controlling the water outlet quantity of the water dispenser according to the selected type of the drinking water, wherein the water outlet quantity of the water dispenser is smaller than the volume of the water cup.
7. The method of claim 1, wherein the neural network model is a model employing Fast RCNN network; the method further comprises the following steps:
collecting water cup photos and photographing data in a network;
and training the Fast RCNN according to the water cup pictures and the photographing data to obtain a trained neural network model.
8. A water outlet quantity control device of a water dispenser is characterized by comprising:
the acquisition module is configured to acquire an image of a water cup, wherein the image of the water cup comprises a cup mouth and a cup body of the water cup;
a recognition module configured to input an image of the water cup into the trained neural network model to output a first rectangular-frame image corresponding to the cup mouth and a second rectangular-frame image corresponding to the cup body;
the calculation module is configured to calculate and obtain the volume of the water cup according to the pixel values of the first rectangular frame image and the second rectangular frame image;
and the control module is configured to control the water outlet quantity of the water dispenser according to the volume of the water cup.
9. A water dispenser, characterized in that it comprises:
a memory having a computer program stored thereon; and
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
10. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
CN201911174374.4A 2019-11-26 2019-11-26 Water dispenser water yield control method and device, water dispenser and storage medium Pending CN110889869A (en)

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