Artificial intelligence laundry treatment apparatus
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Solution Overview
Problem
Users find it difficult to accurately assess the contamination level inside a laundry treatment apparatus and remember to perform the necessary cleaning cycles, leading to reduced cleanliness and potential odor issues.
Innovation Solution
An AI-powered laundry treatment apparatus equipped with door and gasket imaging sensors, which classify the state of the door and determine the contamination level of the gasket, using deep learning algorithms to automatically detect contamination and prompt users for cleaning when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If imaging sensors and AI algorithms are added to automatically detect contamination, then measurement precision of contamination level is improved, but device complexity increases
Solution Approach 1:
The laundry treatment apparatus performs self-diagnosis by automatically capturing images of its internal components (door, gasket, drum) using integrated imaging sensors, processing the images through AI algorithms to detect contamination, and generating cleaning notifications without user intervention. This resolves the contradiction by enabling the system to autonomously monitor its own state, improving measurement precision while the added complexity is justified by the automated self-service capability.
Solution Approach 2:
The patent replaces manual visual inspection and mechanical contamination assessment with optical imaging sensors and AI-based image processing. The imaging sensors capture visual data, and deep learning algorithms automatically analyze the images to determine contamination levels, substituting mechanical/manual methods with optical and computational approaches to achieve higher measurement precision.
2Ease of operation
If the apparatus automatically monitors and notifies for cleaning, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system implements a feedback loop where imaging sensors continuously monitor the internal components, AI algorithms process the images to assess contamination levels, and the control unit generates notifications when cleaning is required. This automated feedback mechanism improves ease of operation by eliminating the need for users to manually check contamination, while the complexity is managed through integrated control logic that coordinates sensing, processing, and notification functions.
3Measurement precision
If multiple imaging sensors are installed for comprehensive monitoring, then measurement precision is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The monitoring system is segmented into multiple specialized imaging sensors, each positioned to capture specific components (door glass, gasket, drum interior). This segmentation allows comprehensive contamination assessment of different areas using targeted sensors, improving measurement precision while enabling modular manufacturing where each sensor module can be independently installed based on manufacturing requirements.
Data Source
AI summary
An artificial intelligent laundry treatment apparatus according to an embodiment of the present invention includes: a door including an external cover and an internal glass and configured to open and close a laundry entrance; a gasket formed on an inner circumferential surface of the laundry entrance; a door imaging sensor disposed to face the internal glass and configured to acquire a door image; a gasket imaging sensor configured to acquire a gasket image including a region of the gasket; and a processor configured to classify a state of the door on the basis of the door image, to acquire a gasket contamination degree on the basis of the gasket image, and to determine whether inside cleansing is required for a region including an inside of a drum on the basis of at least one of the classification result of the state of the door or the acquired gasket contamination degree.