AI Washing Machine Time Estimation Using Feedback and Self-Learning
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Solution Overview
Problem
Conventional washing machines provide inaccurate estimated washing times due to deviations in actual use environments, causing user inconvenience and inefficiency.
Innovation Solution
An artificial intelligence washing machine equipped with a processor, weight sensor, and communication units that estimate water supply, drainage, and spin-drying times based on previous settings and user input, using machine learning algorithms to adjust for environmental factors and detect abnormal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard water supply time and drainage time are set in advance, then the washing machine can provide an estimated time to the user, but the actual washing time deviates significantly from the estimated time due to various causes in the actual use environment
Solution Approach 1:
The washing machine monitors actual washing process time and compares it with the estimated time. When a deviation is detected, the system automatically adjusts the estimated time for subsequent operations based on the monitored actual time, creating a closed-loop feedback mechanism that continuously improves time estimation accuracy.
Solution Approach 2:
The system performs preliminary monitoring of washing process parameters (water supply time, drainage time, washing time) during initial operations. This preliminary data collection enables the system to establish baseline values that are used to refine future time estimates before the user even starts a washing cycle.
2Ease of operation
If the washing machine uses fixed standard times for water supply, drainage, and washing, then the operation is simple, but it causes user inconvenience due to significant difference between estimated and actual washing time
Solution Approach 1:
The washing machine automatically monitors its own operation parameters (water supply time, drainage time, washing time) and uses this self-collected data to adjust and refine its own time estimates. The system serves itself by continuously learning from its operational history without requiring external intervention or complex user input.
Solution Approach 2:
The system replaces fixed mechanical time settings with an intelligent estimation mechanism that uses processors to calculate and adjust time based on monitored actual performance. This substitution of static mechanical parameters with dynamic computational estimation enables the system to adapt to varying operational conditions while maintaining ease of use.
3Reliability
If the washing machine monitors long-term washing process time using artificial intelligence, then it can classify and recognize abnormal conditions, but the device complexity increases
Solution Approach 1:
The processor performs multiple functions: it monitors washing process time, compares actual time with estimated time, detects deviations, classifies abnormal conditions, and adjusts future time estimates. By consolidating these diverse functions into a single processing unit, the system achieves comprehensive monitoring and diagnostic capability without proportionally increasing overall system complexity.
Solution Approach 2:
The system merges the time monitoring function, abnormal condition detection function, and time estimation adjustment function into an integrated process. The processor combines multiple monitoring tasks (water supply time, drainage time, washing time) and analysis functions into a unified operational framework, reducing the need for separate dedicated components for each function.
Data Source
AI summary
The present invention relates to an artificial intelligence washing machine and an operation method thereof. A method of operating a washing machine may comprise estimating, based on a previous setting of a previous washing operation, an estimated setting for a current washing operation, wherein the estimated setting includes a water supply time, a drainage time, and a spin-drying time, obtaining a user input, obtaining an amount of laundry from a weight sensor of the washing machine, determining, based on the estimated setting, the user input, and the amount of laundry, a washing time for the current washing operation and displaying the determined washing time to the user. Accordingly, the estimated washing time is provided with the reflection of data of the washing machine which has been accumulated by the increase of the number of uses, thereby reducing a difference between an actual operating time and the estimated washing time and minimizing the user inconvenience.