Washing apparatus capable of adjusting operation based on additional laundry and method of controlling the same

Patent metadata

Field	Value
Publication number	US-11795597-B2
Application number	US-202016850539-A
Country	US
Kind code	B2
Filing date	Apr 16, 2020
Priority date	Dec 19, 2019
Publication date	Oct 24, 2023
Grant date	Oct 24, 2023

Abstract

Provided are a washing apparatus that is capable of being operated in an Internet of Things (IoT) environment over a 5G communication network and estimating additional laundry that is introduced and the amount of detergent that is additionally introduced based thereon through a neural network model generated based on machine learning, and a method of controlling the same.

First claim

What is claimed is:

1. A method comprising:
providing detergent into a tub of a washing apparatus and performing a first washing cycle based on detecting, by a weight sensor, an initial amount of laundry in the tub;
determining, by the weight sensor, an amount of additional laundry added into the tub during the performing of the first washing cycle;
adding detergent into the tub based on the determined amount of the additional laundry;
measuring a conductivity and a turbidity in order to determine a degree of contamination of washing water in the tub due to the additional laundry;
measuring a temperature of the washing water;
correcting the measured conductivity and the measured turbidity based on the measured temperature of the washing water;
determining the degree of contamination of the washing water based on the corrected conductivity and the corrected turbidity; and
setting a second washing cycle based on the determined degree of contamination,
wherein setting the second washing cycle comprises changing an operating condition of the first washing cycle based on the determined degree of contamination being less than a predetermined threshold value, and
wherein changing the operating condition of the first washing cycle comprises changing a motion intensity of the tub while maintaining an operation time, wherein the operation time includes at least one of a washing operation time, a rinsing operation time, or a spinning operation time of the first washing cycle.

2. The method of claim 1 , wherein setting the second washing cycle further comprises resetting the first washing cycle based on the determined degree of contamination being equal to or greater than the predetermined threshold value,
wherein resetting the first washing cycle comprises changing a sequence of the washing operation, the rinsing operation, or the spinning operation in the first washing cycle and adding an additional at least one washing operation or spinning operation.

3. The method of claim 2 , further comprising inputting the determined amount of the additional laundry to a first neural network for estimating the amount of the detergent to be added,
wherein the first neural network model is trained based on inputted amounts of additional laundry comprising amounts of additional laundry labeled with corresponding amounts of the detergent to be added.

4. The method of claim 2 , wherein resetting the first washing cycle further comprises measuring a remainder of a set washing time in the first washing cycle, wherein the set washing time corresponds to a total washing time set when the first washing cycle is performed.

5. The method of claim 4 , further comprising:
after measuring the remainder of the set washing time, providing a user terminal with an extension of the set washing time based on a difference between an estimated washing time and the set washing time of the first washing cycle being greater than a second predetermined threshold value, wherein the estimated washing time is based on an estimated value of the determined degree of contamination.

6. The method of claim 4 , further comprising:
after measuring the remainder of the set washing time, changing the motion intensity of the tub based on a difference between an estimated washing time and the set washing time of the first washing cycle being less than or equal to a second predetermined threshold value, wherein the estimated washing time is based on an estimated value of the determined degree of contamination.

7. The method of claim 1 , further comprising adding additional washing water into the tub when the detergent is added, wherein the additional washing water is based on the detected initial amount of laundry and the determined additional amount of laundry.

8. The method of claim 7 , wherein adding additional washing water comprises inputting the amount of the detergent added to a second neural network model for estimating the amount of the additional washing water to be added,
wherein the second neural network model is trained based on inputted amounts of additional laundry comprising amounts of detergent labeled with corresponding amounts of additional washing water to be added into the tub.

9. The method of claim 7 , wherein adding additional washing water comprises measuring a remainder of a set washing time in the first washing cycle, wherein the set washing time corresponds to a total washing time set when the first washing cycle is performed, and
the method further comprises providing a user terminal with an extension of the set washing time based on a difference between an estimated washing time and the set washing time of the first washing cycle being greater than or equal to a threshold value, wherein the estimated washing time is based on an estimated value of the determined degree of contamination.

Assignees

• Lg Electronics Inc

Inventors

• Cha Seung Chul
• Lee Sang Hyun
• Park Hyunji
• Koo Bonkwon

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• D06F34/22 Primary

  Condition of the washing liquid, e.g. turbidity · CPC title

• D06F33/34

  of water filling · CPC title

• D06F33/36 Primary

  of washing · CPC title