CN112150774B - Washing machine abnormity detection processing method and device - Google Patents
Washing machine abnormity detection processing method and device Download PDFInfo
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Abstract
The invention provides a washing machine abnormity detection processing method and device, wherein the method comprises the following steps: acquiring a target image of the washing machine in the washing process, which is acquired by a camera; determining a target recognition result in the washing machine barrel based on the target image, wherein the target recognition result comprises whether an abnormal condition exists; the target identification result is that the abnormal condition exists, the alarm is initiated, the problem that in the related technology, foreign matters in the washing process are detected through an infrared detector, manual determination is needed, and therefore user experience is poor can be solved, whether the abnormal condition exists in the washing machine barrel or not is determined directly through images collected in the washing process, if the abnormal condition exists, a user is prompted in an alarm mode, the abnormal condition in the washing machine barrel can be detected without manual participation, and the user experience is improved.
Description
Technical Field
The invention relates to the field of smart home, in particular to a washing machine abnormity detection processing method and device.
Background
With the progress of science and technology and the development of artificial intelligence, intelligent algorithms are also increasingly applied to daily life, especially for household appliances, the intelligent development of the household appliances is very important, and the most key problem of intelligence lies in the urgent need of intelligent solution users.
A method for detecting foreign matter in a washing machine is proposed in the related art, which includes the steps of: starting a washing control switch of the washing machine, and detecting the sound of the washing machine when a machine barrel shakes; if the sound exceeds a set threshold value, an infrared detector is started to detect whether foreign matters exist in the washing machine barrel, if so, the infrared detector detects whether the foreign matters are organisms, and if so, a camera in the washing machine barrel is started to shoot images of the foreign matters; the image is transmitted to the intelligent terminal through a network, a user checks the image through the intelligent terminal, and if the foreign matter is confirmed to be a living body, an instruction is sent to a washing control switch and a drainage switch of the washing machine; the washing control switch of the washing machine is closed, and the drainage switch is started. Foreign matters except clothes in the machine barrel of the washing machine can be found in time during washing, and the safety of the washing machine is improved. However, the infrared detector is used, and a user is required to determine whether the organism exists through the image, only the organism in the washing machine can be detected, the organism cannot be detected, artificial determination is required, intelligence is insufficient, and user experience is poor.
Aiming at the problem that foreign matters in the washing process are detected through an infrared detector in the related art, the foreign matters need to be determined manually, and the user experience is poor, a solution is not provided.
Disclosure of Invention
The embodiment of the invention provides a washing machine abnormity detection processing method and device, which at least solve the problem that the user experience is poor due to the fact that foreign matters in the washing process are detected through an infrared detector in the related art and need to be determined artificially.
According to an embodiment of the present invention, there is provided a washing machine abnormality detection processing method including:
acquiring a target image of the washing machine in the washing process, which is acquired by a camera;
determining a target recognition result in the washing machine barrel based on the target image, wherein the target recognition result comprises whether an abnormal condition exists;
and initiating an alarm under the condition that the target identification result indicates that the abnormal condition exists.
Optionally, determining a target recognition result within the washing machine cartridge based on the target image comprises:
comparing the target image with a plurality of pre-stored sample images marked as abnormal conditions to obtain a comparison result;
and determining a target identification result in the washing machine barrel according to the comparison result.
Optionally, comparing the target image with a plurality of pre-stored sample images, and obtaining the comparison result includes:
acquiring target characteristic information of the target image;
and respectively determining the similarity of the target characteristic information and the image characteristics of the plurality of sample images to obtain the comparison result comprising a plurality of similarities.
Optionally, determining whether an abnormal condition exists in the drum of the washing machine according to the comparison result comprises:
determining that the target identification result is that the abnormal condition exists under the condition that at least one target similarity which is larger than a first preset threshold exists in the multiple similarities;
and determining that the target identification result is that the abnormal condition does not exist under the condition that the similarity degrees are all smaller than the first preset threshold value.
Optionally, determining a target recognition result within the washing machine barrel based on the target image comprises:
inputting the target image into a pre-trained target neural network model to obtain the probability of each recognition result corresponding to the target image output by the target neural network model, wherein the recognition result with the probability larger than a second preset threshold is determined as the target recognition result.
Optionally, determining a target recognition result within the washing machine barrel based on the target image comprises:
and determining whether the target recognition result is that foreign matters and/or color fading conditions exist in the drum of the washing machine or not based on the target image.
Optionally, in the case that the target identification result indicates that the abnormal condition exists, initiating an alarm includes:
if the target identification result is that foreign matters exist, initiating first alarm information;
if the target identification result is that the color fading condition exists, initiating second alarm information;
and if the target identification result indicates that foreign matters and the color fading condition exist, initiating third alarm information.
According to another embodiment of the present invention, there is also provided a washing machine abnormality detection processing apparatus including:
the acquisition module is used for acquiring a target image of the washing machine in the washing process, which is acquired by the camera;
a determination module for determining a target recognition result in the washing machine barrel based on the target image, wherein the target recognition result comprises whether an abnormal condition exists;
and the alarm module is used for initiating an alarm under the condition that the target identification result indicates that the abnormal condition exists.
Optionally, the determining module includes:
the comparison sub-module is used for comparing the target image with a plurality of pre-stored sample images marked as abnormal conditions to obtain a comparison result;
and the first determining submodule is used for determining a target recognition result in the washing machine barrel according to the comparison result.
Optionally, the comparison sub-module comprises:
an acquisition unit configured to acquire target feature information of the target image;
a first determining unit, configured to determine similarities between the target feature information and image features of the plurality of sample images, respectively, and obtain the comparison result including a plurality of similarities.
Optionally, the first determining sub-module includes:
a second determining unit, configured to determine that the target identification result is that the abnormal condition exists, when at least one target similarity greater than a first preset threshold exists in the multiple similarities;
a third determining unit, configured to determine that the target identification result is that the abnormal condition does not exist when all of the plurality of similarities are smaller than the first preset threshold.
Optionally, the determining module includes:
and the input submodule is used for inputting the target image into a pre-trained target neural network model to obtain the probability of each recognition result corresponding to the target image output by the target neural network model, wherein the recognition result of which the probability is greater than a second preset threshold value is determined as the target recognition result.
Optionally, the determining module includes:
a second determining sub-module, configured to determine whether the target recognition result is that foreign matter and/or a color fading condition exists in the drum of the washing machine based on the target image.
Optionally, the alarm module includes:
the first warning submodule is used for initiating first warning information if the target identification result is that foreign matters exist;
the second warning submodule is used for initiating second warning information if the target identification result indicates that the color is faded;
and the third alarm submodule is used for initiating third alarm information if the target identification result indicates that foreign matters and the fading condition exist.
According to a further embodiment of the present invention, a computer-readable storage medium is also provided, in which a computer program is stored, wherein the computer program is configured to perform the steps of any of the above-described method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, comprising a memory in which a computer program is stored and a processor configured to run the computer program to perform the steps of any of the method embodiments described above.
According to the invention, the target image of the washing machine in the washing process, which is acquired by the camera, is obtained; determining a target recognition result in the washing machine barrel based on the target image, wherein the target recognition result comprises whether an abnormal condition exists; the target recognition result is for having under the condition of abnormal situation, initiate and report an emergency and ask for help or increased vigilance, can solve among the correlation technique and detect the foreign matter in the washing process through infrared detector, need artificial the confirming, lead to the not good problem of user experience, whether the image of direct collection through the washing process exists abnormal situation in confirming the washing machine bucket, if there is abnormal situation, through the mode suggestion user of reporting an emergency and asking for help or increased vigilance, need not artificial participation alright detect the washing machine bucket in abnormal situation.
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 block diagram of a hardware configuration of a mobile terminal of a washing machine abnormality detection processing method according to an embodiment of the present invention;
fig. 2 is a flowchart of a washing machine abnormality detection processing method according to an embodiment of the present invention;
fig. 3 is a block diagram of an abnormality detection processing device of a washing machine according to an embodiment of the present invention;
fig. 4 is a first block diagram of an abnormality detection processing device of a washing machine in accordance with a preferred embodiment of the present invention;
fig. 5 is a second block diagram of an abnormality detection processing device of a washing machine in accordance with a preferred embodiment of the present invention;
fig. 6 is a block diagram three of an abnormality detection processing device of a washing machine in accordance with a preferred embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the drawings and embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
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.
Example 1
The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking a mobile terminal as an example, fig. 1 is a hardware structure block diagram of a mobile terminal of the washing machine abnormality detection processing method according to the embodiment of the present invention, as shown in fig. 1, the mobile terminal may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, and optionally, the mobile terminal may further include a transmission device 106 for a communication function and an input/output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to the method for processing abnormality detection of a washing machine in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmitting device 106 may be a Radio FrequeNcy (RF) module, which is used to communicate with the internet via wireless.
Based on the above mobile terminal or network architecture, in the present embodiment, a washing machine abnormality detection processing method is provided, and fig. 2 is a flowchart of the washing machine abnormality detection processing method according to the embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, acquiring a target image of the washing machine in the washing process, which is acquired by a camera;
in the embodiment of the invention, the camera is arranged in the washing machine and can acquire the image and/or the video of the washing process in the washing machine barrel, and if the acquired video is acquired, the target image is acquired from the video.
Step S204, determining a target recognition result in the washing machine barrel based on the target image, wherein the target recognition result comprises whether an abnormal condition exists or not;
step S206, when the target identification result is that the abnormal condition exists, an alarm is initiated.
In an embodiment of the present invention, the step S206 may specifically include: if the target identification result is that foreign matters exist, initiating first alarm information; if the target identification result is that the color fading condition exists, initiating second alarm information; if the target identification result indicates that foreign matters and fading conditions exist, third alarm information is initiated, the first alarm information, the second alarm information and the third alarm information are different alarm information, and different abnormalities of the user are prompted in different alarm modes, so that the user can know what the abnormality occurs when the user hears the alarm, and the user can conveniently and quickly solve the abnormality.
In an optional embodiment, the step S204 may specifically include:
comparing the target image with a plurality of pre-stored sample images marked as abnormal conditions to obtain a comparison result, specifically, obtaining target characteristic information of the target image, respectively determining similarity of the target characteristic information and image characteristics of the plurality of sample images to obtain the comparison result comprising a plurality of similarities;
and determining a target recognition result in the washing machine barrel according to the comparison result, specifically, determining that the abnormal condition exists in the target recognition result when at least one target similarity greater than a first preset threshold exists in the plurality of similarities, and determining that the abnormal condition does not exist in the target recognition result when the plurality of similarities are less than the first preset threshold.
In another optional embodiment, the step S204 may specifically further include: inputting the target image into a pre-trained target neural network model to obtain the probability of each recognition result corresponding to the target image output by the target neural network model, wherein the recognition result with the probability larger than a second preset threshold is determined as the target recognition result.
In an embodiment of the present invention, determining the target recognition result in the washing machine barrel specifically includes: and determining whether the target recognition result is that foreign matters and/or color fading conditions exist in the drum of the washing machine or not based on the target image.
The washing machine in the embodiment of the invention is provided with the camera, and intelligent functions of networking, voice recognition, binding of user intelligent screen equipment and the like are supported. When a user starts to wash clothes, the camera is started, the water quality condition in the washing machine is monitored in real time, pictures can be shot at fixed time intervals, or frames are not regularly drawn after real-time video recording. The Haier intelligent washing machine uploads the collected pictures to the server. And the server is responsible for receiving the pictures uploaded by the user and carrying out abnormity detection. The server maintains sample data of thousands of abnormal pictures, such as pictures containing toilet paper in water, pictures of water quality changed into blue, and the like. After the intelligent washing machine automatically uploads the pictures, the server respectively compares the pictures with the pictures in the server sample library, if the degree of identity between the pictures uploaded by the user and a certain abnormal picture in the sample library reaches a threshold value (for example, 80%, the degree of identity can be changed by setting), the hall server sends warning information (for example, "toilet paper is contained in water of the washing machine," and "color-losing clothes are possibly contained in the washing machine") to the intelligent washing machine of the user, and sends the two pictures to a screen of the intelligent washing machine of the user, or a mobile phone bound with the intelligent washing machine, and other intelligent devices with screens. And if the degree of identity between the picture uploaded by the user and one abnormal picture in the sample library does not reach a threshold value, the Haier server does not respond. If the washing machine receives the alarm information of the server, an alarm sound can be sent out, the washing machine stops operating for a period of time, and the specific time can be set by a user or the user selects to ignore the alarm information of all the servers.
According to the embodiment of the invention, foreign matters such as toilet paper and the like in water are automatically detected through an intelligent clothes washing method, or the clothes are detected to be seriously faded, so that a user is timely reminded, and more serious clothes washing accidents are avoided.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
In this embodiment, a washing machine abnormality detection processing device is further provided, and the device is used for implementing the above embodiments and preferred embodiments, and the description of the device is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a block diagram of an abnormality detection processing apparatus of a washing machine according to an embodiment of the present invention, as shown in fig. 3, including:
the acquisition module 32 is used for acquiring a target image of the washing machine acquired by the camera in the washing process;
a determination module 34 for determining a target recognition result within the washing machine barrel based on the target image, wherein the target recognition result includes whether an abnormal condition exists;
and an alarm module 36, configured to initiate an alarm when the target identification result indicates that the abnormal condition exists.
Fig. 4 is a block diagram of an abnormality detection processing device for a washing machine according to a preferred embodiment of the present invention, and as shown in fig. 4, the determination module 34 includes:
the comparison submodule 42 is configured to compare the target image with a plurality of pre-stored sample images marked as abnormal conditions to obtain a comparison result;
and a first determining submodule 44 for determining a target recognition result in the washing machine tank according to the comparison result.
Optionally, the comparison sub-module 42 includes:
an acquisition unit configured to acquire target feature information of the target image;
a first determining unit, configured to determine similarities between the target feature information and image features of the plurality of sample images, respectively, and obtain the comparison result including a plurality of similarities.
Optionally, the first determining submodule 44 includes:
a second determining unit, configured to determine that the target identification result is that the abnormal condition exists when at least one target similarity greater than a first preset threshold exists in the multiple similarities;
a third determining unit, configured to determine that the target identification result is that the abnormal condition does not exist when all of the plurality of similarities are smaller than the first preset threshold.
Fig. 5 is a block diagram ii of the abnormality detection processing device of the washing machine in accordance with the preferred embodiment of the present invention, and as shown in fig. 5, the determination module 34 includes:
the input submodule 52 is configured to input the target image into a pre-trained target neural network model, and obtain a probability that each recognition result corresponds to the target image output by the target neural network model, where a recognition result with the probability being greater than a second preset threshold is determined as the target recognition result.
Fig. 6 is a third block diagram of the washing machine abnormality detection processing apparatus according to the preferred embodiment of the present invention, and as shown in fig. 6, the determination module 34 includes:
a second determining sub-module 62 for determining whether the target recognition result is the existence of foreign matter and/or color loss in the drum of the washing machine based on the target image.
Optionally, the alarm module 36 includes:
the first warning submodule is used for initiating first warning information if the target identification result is that foreign matters exist;
the second warning submodule is used for initiating second warning information if the target identification result indicates that the color is faded;
and the third alarm submodule is used for initiating third alarm information if the target identification result indicates that foreign matters and the fading condition exist.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Example 3
An embodiment of the present invention further provides a storage medium having a computer program stored therein, wherein the computer program is configured to perform the steps in any of the method embodiments described above when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring a target image of a washing machine in a washing process, which is acquired by a camera;
s2, determining a target recognition result in the washing machine barrel based on the target image, wherein the target recognition result comprises whether an abnormal condition exists or not;
and S3, initiating an alarm under the condition that the target identification result shows that the abnormal condition exists.
Optionally, in this embodiment, the storage medium may include but is not limited to: a usb disk, a Read-ONly Memory (ROM), a RaNdom Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, which can store computer programs.
Example 4
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring a target image of the washing machine in a washing process, which is acquired by a camera;
s2, determining a target recognition result in the washing machine barrel based on the target image, wherein the target recognition result comprises whether an abnormal condition exists or not;
and S3, initiating an alarm under the condition that the target identification result shows that the abnormal condition exists.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention shall be included in the protection scope of the present invention.
Claims (6)
1. An abnormality detection processing method for a washing machine, comprising:
acquiring a target image of the washing machine in the washing process, which is acquired by a camera;
determining a target recognition result within the washing machine barrel based on the target image, wherein the target recognition result includes whether an abnormal condition exists;
initiating an alarm under the condition that the target identification result indicates that the abnormal condition exists;
wherein determining a target recognition result within the washing machine cartridge based on the target image comprises:
acquiring target characteristic information of the target image;
respectively determining the similarity of the target characteristic information and the image characteristics of a plurality of pre-stored sample images marked as abnormal conditions to obtain a comparison result comprising a plurality of similarities;
determining that the target identification result is that the abnormal condition exists under the condition that at least one target similarity which is larger than a first preset threshold exists in the multiple similarities;
and determining that the target identification result is that the abnormal condition does not exist under the condition that the similarity degrees are all smaller than the first preset threshold value.
2. The method of claim 1, wherein determining a target recognition result within the washing machine barrel based on the target image comprises:
and determining whether the target recognition result is that foreign matters and/or color fading conditions exist in the drum of the washing machine or not based on the target image.
3. The method of claim 2, wherein in the case that the target identification result is that the abnormal condition exists, initiating an alarm comprises:
if the target identification result is that foreign matters exist, initiating first alarm information;
if the target identification result is that the color fading condition exists, initiating second alarm information;
and if the target identification result indicates that foreign matters and fading conditions exist, initiating third alarm information.
4. An abnormality detection processing device for a washing machine, comprising:
the acquisition module is used for acquiring a target image of the washing machine acquired by the camera in the washing process;
a determination module for determining a target recognition result in the washing machine barrel based on the target image, wherein the target recognition result comprises whether an abnormal condition exists;
the warning module is used for initiating warning under the condition that the target identification result indicates that the abnormal condition exists;
wherein the determining module comprises:
an acquisition unit configured to acquire target feature information of the target image;
the first determining unit is used for respectively determining the similarity of the target characteristic information and the image characteristics of a plurality of pre-stored sample images marked as abnormal conditions to obtain a comparison result comprising a plurality of similarities;
a second determining unit, configured to determine that the target identification result is that the abnormal condition exists when at least one target similarity greater than a first preset threshold exists in the multiple similarities;
a third determining unit, configured to determine that the target recognition result is that the abnormal condition does not exist if all of the plurality of similarities are smaller than the first preset threshold.
5. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 3 when executed.
6. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 3.
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Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2778752B2 (en) * | 1989-08-31 | 1998-07-23 | 株式会社東芝 | Washing machine |
| JP2002224486A (en) * | 2001-02-01 | 2002-08-13 | Toshiba Corp | Washing machine |
| CN105568630B (en) * | 2016-02-29 | 2017-06-16 | 美的集团股份有限公司 | The detection method and device of washing machine article to be washed |
| CN105887419B (en) * | 2016-06-06 | 2018-03-30 | 美的集团股份有限公司 | The detection method and device of washing machine article to be washed |
| CN106676822B (en) * | 2017-01-22 | 2019-12-17 | 无锡小天鹅电器有限公司 | Washing machine and washing control method and device based on clothes properties |
| CN107545569A (en) * | 2017-08-23 | 2018-01-05 | 北京小米移动软件有限公司 | Method for recognizing impurities and device |
| CN107447442B (en) * | 2017-08-31 | 2020-04-10 | 深圳市盛路物联通讯技术有限公司 | Alarm method based on image recognition technology and intelligent device |
| CN107893308B (en) * | 2017-10-31 | 2020-01-24 | 珠海格力电器股份有限公司 | Washing machine component processing method and device |
| CN108823898A (en) * | 2018-05-30 | 2018-11-16 | 苏州树云网络科技有限公司 | A kind of foreign matter detection system for washing machine |
| CN108796988A (en) * | 2018-05-30 | 2018-11-13 | 苏州树云网络科技有限公司 | A kind of foreign matter detection method |
| CN110738077B (en) * | 2018-07-19 | 2023-08-08 | 杭州海康慧影科技有限公司 | Foreign matter detection method and device |
| JP7065005B2 (en) * | 2018-09-28 | 2022-05-11 | 日立グローバルライフソリューションズ株式会社 | Washing machine abnormality diagnosis system |
| CN109898267B (en) * | 2019-04-09 | 2020-11-06 | 珠海格力电器股份有限公司 | Control method and device of washing machine, storage medium and washing machine |
| CN111020976B (en) * | 2019-12-16 | 2021-07-23 | 珠海格力电器股份有限公司 | Washing machine and control method |
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2020
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