Laundry treatment device and method of determining laundry course thereof
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Solution Overview
Problem
Conventional laundry treatment devices fail to provide an optimal washing course for composite fiber materials with different characteristics, leading to potential damage and inadequate washing, especially when multiple types of laundry are washed together.
Innovation Solution
A laundry treatment device equipped with a camera to capture images of tags on the laundry, a processor to convert acquired information into encoding data, and a deep learning algorithm-based laundry course learning model to determine optimal washing variables, ensuring customized and safe washing cycles for mixed fabrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predetermined laundry course is provided based on user selection or fixed parameters, then the device complexity is low and ease of operation is high, but the adaptability to different laundry materials is poor and laundry damage may occur
Solution Approach 1:
The laundry treatment device automatically identifies laundry materials and determines optimal washing parameters without user intervention. The processor captures images of laundry tags, converts them to encoding data, and uses a learning model to autonomously select washing conditions, eliminating the need for manual material classification by the user.
Solution Approach 2:
The system dynamically adjusts washing parameters (temperature, time, agitation intensity) based on the identified laundry material characteristics. The learning model outputs optimized parameter values that adapt to different material types, enabling precise control tailored to each laundry's specific requirements.
2Reliability
If conventional laundry courses are used for composite fiber materials, then the washing process is simple and fast, but the washing quality is insufficient and laundry damage may occur
Solution Approach 1:
The system performs preliminary identification of laundry materials and pre-determines optimal washing parameters before the actual washing process begins. By capturing tag images and analyzing material composition in advance, the system prepares a customized washing course that ensures high washing quality from the start, avoiding the need for multiple trial washes.
Solution Approach 2:
The learning model continuously improves washing quality by learning from accumulated data about different laundry materials and their optimal washing conditions. The system uses feedback from previous washing results to refine parameter selections, ensuring progressively better washing quality while maintaining efficient timing.
3Ease of operation
If manual parameter setting for each laundry step is required, then the adaptability to specific laundry needs is high, but the ease of operation decreases and user effort increases
Solution Approach 1:
The laundry treatment device autonomously performs material identification and parameter optimization without requiring user expertise or manual configuration. The system self-services by automatically capturing tag information, analyzing materials, and determining washing parameters, completely eliminating the need for users to manually set complex washing parameters.
Solution Approach 2:
The system replaces manual user operations with automated image recognition and machine learning processes. Instead of requiring users to physically examine laundry materials and manually adjust parameters, the system uses camera-based tag recognition and algorithmic parameter determination to automate the entire decision-making process.
4Adaptability or versatility
If deep learning algorithms and image recognition are implemented, then the adaptability and washing quality improve significantly, but the device complexity and processing time increase
Solution Approach 1:
The system uses laundry tags as intermediaries to convey material information to the processing system. Instead of requiring complex analysis of the laundry itself, the tag serves as a simplified intermediary that encodes material composition and care instructions, making the identification process more manageable while maintaining high adaptability.
Solution Approach 2:
The system captures a digital copy (image) of the laundry tag and converts it into encoding data for analysis. By working with a digital representation rather than the physical laundry material itself, the system simplifies the processing complexity while maintaining accurate material identification capabilities through the encoded information.
Data Source
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AI summary
A laundry treatment device includes a washing module configured to perform operation related to washing, a camera configured to capture an image of a tag attached to a laundry, and a processor configured to acquire laundry information of a plurality of laundries, to convert the acquired laundry information into encoding data, and to acquire values of laundry control variables corresponding to the converted encoding data based on a laundry course learning model learned using a plurality of reference data through a deep learning algorithm.