Building Air Quality Monitoring with Multi-Sensor Performance Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems struggle to effectively monitor and analyze large amounts of sensor data from multiple sensors in building environments, failing to identify long-term trends in indoor air quality and provide actionable insights for improving occupant health and comfort.
Innovation Solution
A method involving the use of performance indices calculated from sensor data, combined with threshold values and time windows, to classify and score indoor air quality, allowing for real-time monitoring and visualization of spatial and temporal patterns, and enabling data quality checks to ensure accurate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are installed to monitor building conditions in real-time, then measurement precision and data availability are improved, but device complexity and data processing burden increase
Solution Approach 1:
The patent segments the complex sensor data processing task into distinct functional modules: data collection from multiple sensors, data quality assessment, feature extraction, trend analysis, and reporting. This modular approach manages complexity by handling each aspect separately while maintaining real-time monitoring capabilities across multiple parameters (CO2, PM2.5, temperature, humidity).
Solution Approach 2:
The patent introduces an intermediary processing layer between the sensors and the final output that performs data quality assessment and feature extraction. This intermediary layer filters, validates, and prepares sensor data before trend analysis, reducing the burden on the overall system while maintaining measurement precision from multiple sensors.
2Device complexity
If one-time spot measurements are used to simplify data processing, then device complexity is reduced, but the ability to identify long-term trends is lost
Solution Approach 1:
The patent implements continuous monitoring and analysis of sensor data over time windows, maintaining uninterrupted data collection and processing. This continuous action enables the system to identify long-term trends in indoor air quality while managing complexity through efficient data processing algorithms that operate continuously without requiring excessive computational resources.
Solution Approach 2:
The patent employs dynamic time-window analysis where the system adapts its analysis period based on detected patterns and environmental conditions. This dynamic approach allows the system to capture long-term trends when necessary while reducing processing intensity during stable periods, balancing information completeness with system complexity.
3Ease of operation
If raw sensor data are used directly without additional processing, then ease of operation is improved, but data interpretation accuracy decreases
Solution Approach 1:
The patent performs preliminary data quality assessment and feature extraction automatically before presenting results to users. This preliminary processing includes validating sensor readings, detecting anomalies, and pre-computing relevant features, so that users receive ready-to-interpret information without manual processing while maintaining high accuracy through systematic data preparation.
Solution Approach 2:
The patent implements feedback loops where the system continuously monitors data quality metrics and adjusts its processing accordingly. This feedback mechanism ensures that raw sensor data are appropriately validated and processed to maintain interpretation accuracy, while the automated nature of this feedback preserves ease of operation by eliminating manual intervention requirements.
Data Source
AI summary
A method includes receiving data characterizing a time-dependent first sensor data detected by a first sensor, a time-dependent second sensor data detected by a second sensor, a time-dependent third sensor data detected by a third sensor, a first set of threshold values associated with the first sensor, a second set of threshold values associated with the second sensor, and a third set of threshold values associated with the third sensor and a time window. The first, second, and third sensors are located in a first space of a building. The method further includes calculating a first performance index, a second performance index, and a third performance index. The method also includes classifying the first performance index, the second performance index, and the third performance index into one of a plurality of performance indicators. The method further includes assigning a performance rating score for a space based on the classification.