Electronic apparatus and control method thereof
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Solution Overview
Problem
Existing drying devices struggle to accurately determine the dry status of laundry, often leading to unnecessary drying time and energy consumption, as they rely solely on sensor values without considering material type and weight.
Innovation Solution
An electronic apparatus that collects actual use dry information from drying devices, calculates an indicator of dryness based on sensor values and material information, and trains a learning model to predict the dry status, thereby improving the accuracy of dry cycle closure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor values are used alone to determine dry status, then the determination process is simple, but the accuracy of dry status determination is low
Solution Approach 1:
The system performs preliminary actions by collecting actual use data including sensor values, material information, and weight information before determining dry status. This pre-collected data is then used to train a learning model that automatically determines dry status, improving accuracy while maintaining operational simplicity.
Solution Approach 2:
A learning model is introduced as an intermediary between the raw sensor data and the dry status determination. The learning model processes multiple input parameters (sensor values, material type, weight) and outputs the determined dry status, thereby improving measurement precision without requiring the user to directly handle the complexity of multiple parameters.
2Productivity
If drying continues until a fixed time is reached, then the drying process is simple to control, but unnecessary drying time and energy are consumed
Solution Approach 1:
The system implements feedback by continuously monitoring sensor values during the drying process and comparing them against the learned patterns from actual use data. The learning model determines when the dry status is achieved based on this feedback, allowing the drying process to stop precisely when needed, thereby improving productivity and reducing unnecessary drying time and energy consumption.
Solution Approach 2:
The system changes from using a fixed time parameter to using dynamic parameters including sensor values, material information, and weight information. The learning model processes these varying parameters to determine the optimal stopping point, enabling adaptive drying control that improves efficiency while reducing waste.
Data Source
AI summary
An electronic apparatus includes a communication device; a memory storing at least one instruction and storing a learning model determining a dry status; and at least one processor configured to execute the at least one instruction. The one or more instructions, when executed by the at least one processor, cause the electronic apparatus to: based on receiving through the communication device actual use dry information including data about a degree of dryness, obtain an indicator of a degree of dryness based on the data about the degree of dryness included in the actual use dry information; obtain a label of the data about the degree of dryness based on the indicator of the degree of dryness; and train the learning model by using the label of the data about the degree of dryness and the actual use dry information.