Correlated Sensor Group for Particle Concentration Data Quality Control
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Solution Overview
Problem
Existing automated particle concentration monitoring systems, such as those using beta gauges and tapered element oscillating microbalances (TEOM), suffer from poor data quality due to inefficiencies and inaccuracies in existing methods for data verification and improvement, including manual reviews and statistical methods with simple thresholds.
Innovation Solution
A method and system for data quality control using a correlated sensor group, where a pair of inexpensive light scattering sensors are selected based on uniformity and correlation with reference stations, generating a particle concentration model to calculate deviations and recognize outliers, thereby enhancing data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated particle concentration monitoring systems use beta gauges or TEOM for mass measurement, then particle concentration can be monitored automatically, but data quality is poor due to inefficiencies and inaccuracies in verification methods
Solution Approach 1:
The patent introduces light scattering sensors as intermediary devices that correlate with reference station data to verify and improve data quality from automated monitoring systems. These sensors act as mediators between the automated monitoring system and the reference standards, enabling automated outlier detection without sacrificing measurement precision
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing sensor data with reference station data, generating quality control metrics that feed back into the monitoring system to identify and flag outliers, thereby improving data quality through automated feedback loops
2Measurement precision
If manual review methods are used to verify data quality, then data accuracy can be improved, but the process is very inefficient and time-consuming
Solution Approach 1:
The system enables self-service quality control by automating the data verification process using light scattering sensors and statistical algorithms that automatically detect outliers and assess data quality without requiring manual review, thereby maintaining data accuracy while dramatically improving verification efficiency
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated electronic system using light scattering sensors, computational algorithms, and statistical analysis to perform quality control functions that previously required human intervention, substituting mechanical human labor with automated sensing and processing
3Productivity
If statistical methods with simple thresholds are used for data verification, then the process is efficient, but accuracy is poor
Solution Approach 1:
The system changes the parameters used for verification by moving from simple threshold-based statistical methods to multi-parameter correlation analysis involving light scattering sensors, reference station data, and dynamic quality control metrics, thereby improving verification accuracy while maintaining efficiency through automated computation
Solution Approach 2:
The patent creates a composite verification approach by combining multiple data sources (light scattering sensors, reference station measurements) and multiple analysis methods (correlation analysis, outlier detection, quality control metrics) into an integrated system that achieves both efficiency and accuracy, analogous to creating composite materials that combine properties of individual components
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
This approach improves data quality by identifying and eliminating outliers, providing consistent trends between light scattering sensors and standard particle monitoring stations, thus enhancing the efficiency and accuracy of particle concentration monitoring.
Implementation Method 1
data quality control using light scattering based sensors
Data Source
AI summary
A system, a computer readable storage medium, and a method for data quality control using a correlated sensor group includes selecting a sensor group, by at least one processor, according to a uniformity and a correlation with air pollution concentration data from a reference station, selecting a time series based on the correlation, generating a particle concentration model between the sensor group and the reference station, calculating a deviation in the particle concentration data from the particle concentration model, and recognizing outliers from the deviation as unacceptable data.