AI Laundry Course Control Using Life Log and Environment Data
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Solution Overview
Problem
Users face difficulty in selecting an optimal laundry management course for their laundry due to lack of information about external environment factors affecting the laundry, leading to suboptimal cleaning results.
Innovation Solution
An artificial intelligence-based laundry treating apparatus that receives big data on external environment information and life log data to automatically set an optimal laundry management course, combining pre-stored courses based on weighted results to maximize cleaning effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the laundry treating apparatus provides multiple pre-prepared management courses for user selection, then the user has more options to choose from, but the user cannot accurately determine the optimal course due to lack of information about external environment factors affecting the laundry
Solution Approach 1:
The system pre-collects and stores management courses with associated weights before user input. When a user selects automatic course setting, the controller retrieves these pre-prepared courses and their weights, combines them based on the weights, and determines the optimal course. This preliminary preparation enables the system to quickly provide adapted recommendations without requiring real-time complex calculations.
Solution Approach 2:
The system incorporates feedback mechanisms by using user selections and laundry treatment outcomes to adjust and optimize the weights of different management courses over time. This allows the system to learn from actual usage patterns and environmental factors, improving the accuracy of automatic course determination while maintaining adaptability to user needs.
2Reliability
If the system automatically sets the optimal laundry management course based on big data and life log data, then the laundry treating effect is maximized, but the device complexity increases
Solution Approach 1:
The controller acts as an intermediary that receives big data about external environment factors and life log data from various sources, processes this information through weight-based algorithms, and translates it into optimal laundry management course recommendations. This intermediary function simplifies the complexity by using a standardized weight-based combination approach rather than requiring complex real-time decision-making algorithms.
Solution Approach 2:
The system uses parameter changes by adjusting the weights of different management courses based on input factors such as external environment data and user behavior patterns. By changing these weight parameters dynamically, the system can adapt to different conditions and maximize laundry treating effects without requiring complete system redesign for each scenario.
3Measurement precision
If the system combines multiple pre-stored laundry management courses based on weighted results, then personalized laundry management courses are set more accurately, but the calculation and processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing management courses with their associated weights before actual use. When determining the optimal course, the system simply retrieves these pre-prepared courses and combines them based on stored weights, avoiding the need for complex real-time calculations and significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The system applies partial action by focusing on combining only the most relevant management courses based on their weights rather than evaluating all possible combinations. This selective approach achieves sufficient accuracy for personalized course recommendation without requiring exhaustive computation of every possible course combination.
Data Source
AI summary
Disclosed herein is an artificial intelligence-based laundry treating apparatus. The artificial intelligence-based laundry treating apparatus according to an embodiment of the present invention receives big data about information of an external environment capable of affecting laundry and life log data including activity information of a user wearing the laundry and automatically sets an optimal laundry management course for the laundry based on input factors including the big data and the life log data. Further, the artificial intelligence-based laundry treating apparatus may operate based on the optimal laundry management course to clean the laundry.A washing machine of the present invention may be associated with an artificial intelligence module, a drone (Unmanned Aerial Vehicle, UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.