Method and apparatus for drying laundry using intelligent washer
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Solution Overview
Problem
Current laundry drying technologies fail to accurately predict the time required for complete dryness, leading to either under-dried or over-dried laundry, which can result in damage or inefficiency.
Innovation Solution
An intelligent washer system that uses a time prediction model trained with data from sensors monitoring internal washer states and operations, such as humidity, temperature, and RPM, to determine the remaining dry time and adjust the drying cycle accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the dry cycle time is extended to ensure complete drying, then the laundry dryness is improved, but the energy consumption and time cost increase
Solution Approach 1:
The system continuously monitors washer state information (humidity, temperature, RPM) during the drying cycle and feeds this data back to the prediction model. The model dynamically adjusts the remaining time prediction based on real-time feedback, allowing the system to terminate the drying cycle precisely when the laundry is fully dry, avoiding both under-drying and over-drying.
Solution Approach 2:
The prediction model is trained in advance using historical drying data to learn the relationship between washer state information and drying time. This preliminary training enables the system to make accurate predictions without requiring extensive real-time computation, allowing for optimized drying cycle termination decisions.
2Reliability
If the dry cycle time is extended to ensure complete drying, then the laundry dryness is improved, but the energy consumption increases
Solution Approach 1:
The system uses real-time feedback from sensors monitoring humidity, temperature, and drum RPM to continuously update the prediction model. This allows the system to determine the precise moment when drying is complete and terminate the cycle, preventing unnecessary energy consumption from over-drying while ensuring thorough drying through continuous monitoring.
Solution Approach 2:
The prediction model analyzes changes in washer state parameters (humidity levels, temperature variations, RPM changes) to determine drying completion. By monitoring parameter changes rather than relying on fixed time schedules, the system can adapt to varying laundry loads and environmental conditions, optimizing energy usage while ensuring complete drying.
3Ease of operation
If a fixed drying cycle time is used, then the operation simplicity is maintained, but the drying precision deteriorates
Solution Approach 1:
The system automatically performs drying cycle optimization without requiring user intervention. The prediction model self-adjusts based on real-time washer state information, automatically determining the optimal drying time for each load. This maintains ease of operation while achieving high drying precision, as the system handles the complex prediction and adjustment processes autonomously.
Solution Approach 2:
The system incorporates continuous feedback from sensors that monitor drying progress. This feedback enables the prediction model to dynamically adjust the drying cycle duration based on actual drying conditions, achieving high precision without requiring complex user input or manual adjustments, thus maintaining operational simplicity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Precisely predicts the time needed for complete dryness, preventing under-drying or over-drying and ensuring optimal laundry condition.
Implementation Method 1
a first heating temperature of a heater
Implementation Method 2
The spin cycle removes water by the centrifugal force of the inner tub
Implementation Method 3
a first internal humidity of the washer
Implementation Method 4
a first internal temperature distribution
Data Source
AI summary
Disclosed is a method for drying laundry using an intelligent washer. According to an embodiment of the present disclosure, a method for drying laundry trains a time prediction model for obtaining first remaining time information related to complete dryness of the laundry, obtains first washer state information related to a state of a washer for drying the laundry, obtains the first remaining time information by inputting the first washer state information to the trained time prediction model, and dries the washer based on the obtained first remaining time information, thus precisely predicting the time necessary for complete dryness of wet laundry. According to an embodiment, the washer may be related to artificial intelligence (AI) modules, unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.