Method of determining replacement time of filter and air conditioner that determines replacement time of filter
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Solution Overview
Problem
Air conditioners currently determine filter replacement time based on fixed criteria, neglecting actual usage and pollution levels, leading to inaccurate predictions and inefficient resource allocation.
Innovation Solution
An air conditioner equipped with cameras to monitor filter pollution levels, a controller to analyze images, and a deep neural network model to predict replacement time based on usage data and environmental factors, including air pollution levels, allowing for accurate determination of filter replacement needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If filter replacement is determined by fixed cycle, then device complexity is reduced, but measurement precision of filter pollution level is insufficient
Solution Approach 1:
The patent replaces mechanical/fixed-time filter replacement systems with optical sensing systems (infrared sensors, cameras) that detect pollution levels directly. This substitution enables precise measurement of filter state without complex mechanical monitoring mechanisms, resolving the contradiction between simplicity and precision.
Solution Approach 2:
The patent introduces infrared sensors and cameras as intermediary devices that indirectly measure filter pollution levels by detecting infrared radiation or visual characteristics of polluted filters. These intermediaries provide accurate pollution level data without requiring direct contact with or complex interaction with the filter structure.
2Device complexity
If filter replacement is determined by indirect dust concentration measurement, then device complexity is reduced, but measurement precision of filter pollution level deteriorates
Solution Approach 1:
The patent extracts the pollution detection function from indirect environmental sensing and places it directly at the filter location using infrared sensors and cameras mounted within the air conditioner. This extraction enables direct observation of filter pollution state, improving measurement precision while keeping the overall system relatively simple.
Solution Approach 2:
The patent uses cameras to create visual copies/images of the filter surface, which are then analyzed to determine pollution levels. This copying approach allows for detailed inspection of filter condition without physical contact or complex sensing mechanisms, achieving high precision with simple implementation.
3Device complexity
If filter replacement is determined by infrared light transmission amount, then device complexity is reduced, but measurement precision of filter pollution level deteriorates
Solution Approach 1:
The patent segments the filter inspection process into multiple independent sensing channels (infrared sensors at different positions, camera-based visual inspection) rather than relying on a single infrared transmission measurement. This segmentation provides multiple data points for comprehensive pollution assessment, improving precision while maintaining system simplicity.
Solution Approach 2:
The patent employs infrared sensors and cameras that serve multiple functions: detecting filter pollution levels, monitoring air quality, and providing diagnostic information. This multi-functionality improves measurement precision for filter pollution while avoiding the need for dedicated specialized devices, thus maintaining system 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
Enables precise timing for filter replacement, reducing waste and resource consumption by considering actual usage and pollution levels, improving air conditioner performance and user efficiency.
Implementation Method 1
a first camera disposed so as to monitor the state of the filter
Implementation Method 2
a deep neural network model to predict replacement time based on usage data and environmental factors
Data Source
AI summary
Disclosed herein is an air conditioner that determines the replacement time of a filter. The air conditioner may include a filter for filtering particles in air that is suctioned, a first camera disposed so as to monitor the state of the filter, and a controller for analyzing an image of the filter acquired from the first camera through a deep neural network model trained using machine learning in order to determine a pollution level of the filter. The controller may be configured to determine the time at which the filter is to be replaced based on at least the pollution level of the filter, and may be further configured to use information collected from other electronic devices in an Internet-of-Things environment connected as a 5G communication environment in order to determine the time at which the filter is to be replaced.