Domicile indoor air quality analysis system with occupancy detection
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Solution Overview
Problem
Conventional techniques for determining occupancy in a domicile based on indoor air quality are inefficient and lack effective solutions for identifying security threats or anomalies in air quality conditions.
Innovation Solution
An air quality analysis system that utilizes sensors to monitor CO2 and particulate levels, applies machine learning to analyze these metrics, and detects occupancy by comparing actual occupancy to expected occupancy, triggering responses from HVAC and security systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to determine occupancy, then the system structure remains simple, but the measurement precision and reliability of occupancy detection are insufficient
Solution Approach 1:
The patent applies multi-functionality by enabling existing air quality sensors to serve dual purposes: monitoring air quality parameters (CO2, particulate matter) and detecting occupancy status. This eliminates the need for separate occupancy sensors, thereby improving measurement precision without significantly increasing device complexity.
Solution Approach 2:
The system detects occupancy by analyzing changes in air quality parameters (CO2 levels, particulate matter concentrations) that occur when occupants are present. Machine learning models process these parameter variations to infer occupancy status, achieving accurate detection through parameter analysis rather than direct sensing.
2Measurement precision
If additional dedicated occupancy sensors are installed, then the occupancy detection precision improves, but the device complexity and cost increase
Solution Approach 1:
The patent makes existing air quality sensors multi-functional by programming them to detect both air quality metrics and occupancy indicators. The sensors continue their primary air quality monitoring function while simultaneously capturing data patterns that indicate occupancy, eliminating the need for additional dedicated occupancy sensors.
Solution Approach 2:
The system allows air quality sensors to serve themselves by processing their own output data through machine learning algorithms that identify occupancy patterns. The existing sensor infrastructure automatically provides occupancy detection capability without requiring external additions, achieving self-service functionality.
3Device complexity
If air quality sensors are used for occupancy detection, then the system complexity is reduced, but the measurement precision may be affected by air quality variations
Solution Approach 1:
The system incorporates feedback mechanisms where machine learning models continuously analyze air quality parameter patterns and adjust occupancy detection algorithms based on learned relationships. The system learns to distinguish between air quality changes caused by environmental factors versus those caused by occupancy, improving precision while maintaining simplicity.
Solution Approach 2:
The patent transforms air quality parameter data into occupancy information through machine learning processing. By analyzing multiple parameters (CO2, particulate matter) simultaneously and identifying characteristic patterns, the system converts air quality measurements into reliable occupancy indicators, maintaining precision despite the indirect measurement approach.
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
Enhances security and air quality management by accurately detecting occupancy and triggering appropriate responses, reducing the need for dedicated occupancy sensors and optimizing resource usage.
Implementation Method 1
the sensors may monitor levels of a particulate, of carbon dioxide (CO2), etc.
Implementation Method 2
the sensors may monitor levels of a particulate, of carbon dioxide (CO2), etc.
Data Source
AI summary
A system for detecting an occupancy within a domicile may (1) receive air quality metrics for one or more spaces of the domicile from one or more sensors; (2) analyze the air quality metrics for the one or more spaces of the domicile; and/or (3) detect, based upon the analysis of the air quality metrics, the occupancy of the one or more spaces of the domicile. The system may, in response to the detected occupancy of the one or more spaces of the domicile, (4) update a profile corresponding to at least one space of the domicile; (5) trigger a response from one or more devices in or associated with the domicile, the one or more devices configured to control one or more characteristics of the domicile; and/or (6) adjust at least one of an air quality improvement device or a climate control device.