Laundry scheduling apparatus and method
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Solution Overview
Problem
Existing laundry scheduling systems do not provide an integrated schedule that considers user preferences and the characteristics of multiple washing machines, nor do they effectively account for soil levels of laundry items, leading to inefficient laundry operations.
Innovation Solution
A laundry scheduling apparatus and method that uses machine learning or deep learning algorithms to analyze user preferences and washing machine features, generating an integrated schedule for distributing laundry items based on priority, and adjusting schedules based on soil levels and user availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a user possesses various washing machines or washing auxiliary machines, then the laundry processing capability is enhanced, but an organically integrated laundry schedule in consideration of characteristics of the various washing machines is not provided
Solution Approach 1:
The system segments the laundry scheduling problem into multiple independent components: user preference analysis, washing machine characteristic evaluation, laundry item classification, and schedule generation. Each component is processed separately through dedicated modules, allowing complex multi-machine scheduling to be broken down into manageable segments that can be independently optimized and then integrated.
Solution Approach 2:
The system introduces an intermediary scheduling module that acts as a mediator between the user's laundry needs and the available washing machines. This intermediary analyzes various factors including user preferences, machine characteristics, and laundry requirements to generate an optimized schedule that coordinates multiple machines effectively, transforming a complex integration problem into a series of coordinated decisions.
2Reliability
If machine learning or deep learning algorithms are used to analyze user preferences and generate schedules, then the laundry schedule suitability is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary analysis of user preferences and washing machine characteristics before actual laundry scheduling is needed. By pre-processing and storing this information in structured formats, the system reduces the computational burden during real-time schedule generation, allowing machine learning models to work with pre-prepared data rather than raw information, thus reducing processing time while maintaining high reliability.
Solution Approach 2:
The system implements a two-level scheduling approach where a basic schedule is generated using simplified rules for immediate needs, and more comprehensive machine learning-based optimization is applied selectively for complex scenarios or periodic re-optimization. This partial application of complex algorithms reduces overall processing time while still achieving high schedule suitability when needed.
3Manufacturing precision
If the laundry schedule is adjusted based on soil levels of laundry items, then the soil removal effectiveness is improved, but the scheduling complexity increases
Solution Approach 1:
The system applies local quality by differentiating scheduling criteria based on the specific characteristics of each laundry item, particularly its soil level. Instead of using a uniform scheduling approach, the system tailors the schedule to match the cleaning requirements of each item type, assigning soiled items to machines and time slots optimized for heavy cleaning, while less soiled items receive gentler or faster cycles, thereby improving soil removal effectiveness without requiring complete schedule reconfiguration.
Data Source
AI summary
Disclosed is a laundry scheduling apparatus. The apparatus includes a communication unit, an output unit, and a processor configured to pair with at least one washing machine via the communication unit, obtain laundry preference parameters of a user generated by learning based on at least one of a deep learning algorithm or a machine learning algorithm, using at least one of a laundry log of the user or laundry satisfaction information of the user as input data, generate laundry scheduling information by using washing machine information about the paired at least one washing machine, the laundry preference parameters, and laundry item information obtained via at least one of a user input unit, an interface unit, or a sensor, and cause the output unit to output the laundry scheduling information.