AI Laundry Tub Cleaning for Microorganism-Specific Treatment
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Solution Overview
Problem
Conventional laundry treatment devices detect mold or bacteria but perform generic tub cleaning without differentiation based on the type or proliferation rate of microorganisms, leading to inadequate cleaning.
Innovation Solution
A laundry treatment device equipped with sensors and an artificial neural network that predicts the type and proliferation rate of microorganisms, providing tailored laundry guide information for specific washing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic tub cleaning is performed without differentiation, then device complexity is reduced, but cleaning effectiveness deteriorates
Solution Approach 1:
The patent applies parameter changes by adjusting cleaning parameters (temperature, time, detergent type) based on the detected microorganism type and proliferation rate. The control unit modifies cleaning operation parameters dynamically according to sensing data, transforming a static generic cleaning process into a dynamic adaptive process that resolves the contradiction between cleaning effectiveness and operational simplicity.
Solution Approach 2:
The system implements self-service through automatic detection and decision-making. The sensing unit continuously monitors the tub for microorganisms, and the control unit automatically determines and executes appropriate cleaning operations without user intervention. This maintains ease of operation while achieving targeted effective cleaning through AI-based microorganism identification.
2Measurement precision
If AI-based microorganism detection is implemented, then cleaning precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary AI model that acts as a mediator between the sensing unit and control unit. The sensing unit collects raw data about microorganisms, the AI model processes this data to identify microorganism types and proliferation rates, and the control unit executes cleaning operations based on AI predictions. This intermediary layer enables precise detection while managing system complexity through modular architecture.
Solution Approach 2:
The system replaces complex mechanical or chemical analysis methods with AI-based prediction models. Instead of using sophisticated laboratory equipment for microorganism identification, the patent employs machine learning algorithms that analyze sensing data to predict microorganism types and proliferation rates, achieving high measurement precision with reduced physical complexity.
3Reliability
If targeted cleaning operations are performed, then cleaning effectiveness is improved, but energy consumption increases
Solution Approach 1:
The patent applies partial action by performing cleaning operations only when and where needed based on AI-predicted microorganism locations and proliferation rates. Instead of uniformly cleaning the entire tub, the system concentrates cleaning resources on contaminated areas, achieving effective cleaning while reducing overall energy consumption through selective targeted operations.
Data Source
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AI summary
A laundry treatment device (300) includes a wireless communication unit, at least one sensor, and a processor (180) configured to apply a learning model learned through a supervised learning algorithm to sensing information including a sensing value collected from the at least one sensor and a measurement time of the sensing value, to acquire microorganism information including a type of microorganism and a proliferation rate of the microorganism, to acquire laundry guide information based on the acquired microorganism information, and to transmit the acquired laundry guide information to a terminal through the wireless communication unit.