Pollution Sensing With Multifactor Data for Pollutant Classification
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Solution Overview
Problem
Current air quality sensors cannot identify or distinguish types of particulate pollutants and do not actively combine data from hardware environmental sensors with remotely sourced contextual data from the Internet in real-time to provide specific pollutant identification.
Innovation Solution
A pollution sensing system that integrates local sensor data with remote data sources, employing machine learning and multifactor analysis to classify pollutants by combining local particulate measurements with contextual data from IoT devices and the Internet, using high-frequency light interaction measurements and pulse analysis to identify particle characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current air quality sensors are used to measure pollutant metrics, then general pollutant concentration can be obtained, but specific pollutant type identification is not provided
Solution Approach 1:
The patent combines multiple data sources including local particle sensor data, chemical sensor data, environmental sensor data, and remotely sourced contextual data from IoT devices and the Internet into a unified analysis system. This merging of diverse data streams enables specific pollutant type identification without requiring a single complex specialized sensor for each pollutant type.
Solution Approach 2:
The patent introduces an intermediary data processing and analysis system that acts as a mediator between raw sensor data and pollutant identification. This intermediary system uses machine learning algorithms and multifactor analysis to translate general sensor measurements into specific pollutant type classifications, resolving the contradiction between measurement precision and device complexity.
2Loss of information
If only local sensor data is used for pollutant measurement, then real-time local monitoring is achieved, but contextual environmental information is missing
Solution Approach 1:
The patent creates a multi-functional system that not only monitors local pollutant levels but also integrates remote contextual data from IoT devices and Internet sources. This universal system performs multiple functions including local sensing, remote data acquisition, data correlation, and contextual analysis, eliminating information loss while managing complexity through integrated design.
Solution Approach 2:
The patent adds another dimension to local sensor monitoring by incorporating spatial and temporal contextual data from remote sources. This dimensional expansion transforms single-point local measurements into multi-dimensional environmental analysis, providing comprehensive contextual information while using distributed data sources rather than centralizing all functionality.
3Adaptability or versatility
If general pollutant measurement is used, then device simplicity is maintained, but targeted air treatment responses cannot be implemented
Solution Approach 1:
The patent implements a feedback loop where pollutant identification results from multifactor analysis are fed back to control air treatment devices. This feedback mechanism enables targeted responses by continuously monitoring pollutant types and adjusting treatment device operation accordingly, achieving both measurement precision and adaptability through the closed-loop control system.
Solution Approach 2:
The patent creates a dynamic system where the air treatment response is not fixed but adapts in real-time based on identified pollutant types and levels. The system dynamically adjusts treatment device activation and intensity according to the specific pollution conditions detected, providing versatile targeted responses while maintaining measurement precision through continuous monitoring and adaptation.
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 identification and classification of pollutant types, allowing for targeted responses through connected air treatment devices and user interfaces, enhancing air quality management.
Implementation Method 1
high-frequency light interaction measurements
Implementation Method 2
photodetector that reacts to incident light and produces an electrical response
Data Source
AI summary
Systems and methods classify pollutants based on multifactor analysis of data from sensors in a monitored area and contextual data from remote or local sources. Classifying a pollutant in air at a monitored area may include operating a particulate matter sensor to produce raw data representing measurements of particulate matter in the air, evaluating pulses in the raw data to determine a pulse width and a maximum for each pulse, and identifying a type for the pollutant in the air using a classification model and data including the pulse widths and the maxima of the pulses. The data use in classification may further include non-particulate measurements from local chemical or environmental sensors and contextual data from the cloud or from local user devices.