Air Quality Anomaly Monitoring for Smoking and Vaping Detection
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Solution Overview
Problem
Current systems fail to effectively monitor and manage air quality in spaces for harmful substances like tobacco smoke and vaping fumes, leading to inconvenience and health risks, particularly in prohibited areas such as hotels and vehicles, due to the difficulty in detecting and tracking these pollutants in real-time.
Innovation Solution
An integrated monitoring and analysis system using a distributed computing paradigm, combining edge and cloud processing, with sensors that detect anomalies in air quality and alert systems to notify appropriate endpoints, allowing for real-time detection and reporting of smoking and vaping events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional smoke detection methods are used, then detection capability is limited, but system complexity and cost increase when implementing comprehensive monitoring
Solution Approach 1:
The system segments detection into two levels: edge devices perform initial anomaly detection using lightweight machine learning models, while cloud-based services perform comprehensive analysis. This segmentation allows precise detection without requiring every device to be complex, resolving the contradiction between detection capability and system complexity.
Solution Approach 2:
The patent introduces an intermediary cloud-based anomaly detection service that receives data from edge devices and performs sophisticated analysis. This intermediary handles the complex detection tasks centrally, allowing edge devices to remain simple while achieving high detection precision through the intermediary's advanced capabilities.
2Measurement precision
If real-time monitoring is implemented across multiple spaces, then detection accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary anomaly detection at the edge device level before data is transmitted to the cloud. This preliminary action filters and pre-processes data in real-time, allowing accurate multi-space monitoring without overwhelming computational resources or causing excessive processing delays.
3Adaptability or versatility
If comprehensive sensor deployment is used to detect all air quality parameters, then detection coverage improves, but device cost and power consumption increase
Solution Approach 1:
The system implements partial monitoring at the edge level, focusing only on detecting anomalies rather than continuously measuring all air quality parameters. This partial action approach provides sufficient detection coverage for safety purposes while significantly reducing power consumption compared to comprehensive continuous monitoring of all sensors.
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
The system provides accurate, scalable, and configurable detection of air quality anomalies, enabling timely alerts and improving the maintenance and service quality in spaces by effectively identifying and mitigating harmful substances, thus enhancing user experience and health safety.
Implementation Method 1
The sensing device senses air quality at the monitored space, collects data, detects anomalies in the data
Implementation Method 2
others that are molecularly specific and sense presence of specific environmental toxins or other chemicals in the air
Data Source
AI summary
Systems and methods monitor a space for environmental pollutants. A sensing device senses and reports air quality anomalies to a cloud detection service. The cloud detection service uses artificial intelligence to analyze raw sensor data from the sensing device, determines whether the raw sensor data indicates an air quality event, and sends an air quality report indicative of the air quality event to a client device associated with the sensing device and/or the monitored space. A sensing device may be deployed in an air extraction vent to detect and report indications of vaping in prohibited spaces. A sensing device may be deployed in a vehicle to detect and report smoking in vehicles where smoking is prohibited. An application running on a mobile device reports detection of a short-range wireless beacon within a serviceable space to a cloud detection service, which tracks servicing of the serviceable space.