Mobile Air Cleaner Using Robot Dust Mapping for Targeted Purification
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Solution Overview
Problem
Conventional air cleaners that move to search for areas with poor air quality for purification face limitations in sensing range, as they can only detect air quality in their immediate vicinity and may miss areas requiring the most purification, leading to inefficient targeting of purification efforts.
Innovation Solution
An air cleaner equipped with a communication unit to receive status information from a moving agent, allowing it to determine zones requiring purification and move accordingly, expanding its sensing range without the need for multiple sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single air cleaner performs both dust detection and air purification, then the device can be simplified, but the sensing range becomes narrow and cannot detect air quality in other areas
Solution Approach 1:
The system divides the air quality monitoring function into two separate components: a moving agent (robot vacuum) that collects dust and status information from multiple areas, and a stationary air cleaner that receives this information via communication unit and performs purification. This segmentation allows the sensing range to be expanded through the moving agent's movement while the air cleaner remains stationary and simplified.
Solution Approach 2:
The communication unit acts as an intermediary between the moving agent and the air cleaner. The moving agent transmits collected status information (air quality, dust levels) to the air cleaner, enabling the air cleaner to make informed decisions about where to direct purification efforts without needing its own sensors in multiple locations.
2Productivity
If the air cleaner moves to search for poor air quality areas, then it can perform purification, but it takes time to locate the worst areas and may miss them
Solution Approach 1:
The moving agent performs preliminary action by collecting dust and status information from multiple areas before the air cleaner needs to purify. This advance data collection allows the air cleaner to immediately identify and navigate to the worst air quality areas without searching, significantly reducing the time to locate target areas.
Solution Approach 2:
The system implements feedback through the communication unit, which continuously provides the air cleaner with real-time status information from the moving agent about air quality and dust levels in various areas. This feedback loop enables the air cleaner to dynamically adjust its purification targets based on current conditions, maximizing purification efficiency.
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 targeted air purification by identifying areas with the worst air quality and prioritizing them, reducing the need for extensive sensor placement and improving the efficiency of air cleaner movement.
Implementation Method 1
a fan motor configured to generate an air flow
Implementation Method 2
a purifying unit installed inside the blowing device to purify air
Data Source
AI summary
Disclosed herein is an air cleaner disposed in an indoor space. An air cleaner according to an embodiment includes a blowing device including a suction port and a discharge port, a fan motor configured to generate an air flow, a purifying unit installed inside the blowing device to purify air, a driving portion configured to move the air cleaner, a communication unit configured to communicate with a moving agent moving in the indoor space, and a processor configured to receive status information including at least one of air quality information and dust occurrence information collected by the moving agent, determine a specific zone in which air purification is to be performed using the status information collected by the moving agent, and perform air purification in the specific zone.