Indoor Air Hazard Scoring With Sensor-Driven Remediation Control
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Solution Overview
Problem
Current systems lack an effective method to assess and manage indoor environmental health impacts by integrating data from multiple sensors to identify and mitigate environmental hazards, leading to potential health risks for occupants.
Innovation Solution
A computer-based system that receives data from a variety of environmental sensors, computes health impact scores, and uses machine learning to identify hazards and generate recommendations for remediation, which can be transmitted to display devices or environmental control equipment to improve indoor air quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple environmental sensors are integrated to comprehensively monitor indoor air quality, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent combines multiple environmental sensors (ozone, humidity, temperature, carbon dioxide, carbon monoxide, nitrous dioxide, sulfur dioxide, tVOC, and particulate matter sensors) into a single integrated monitoring device. This merging approach enables comprehensive environmental parameter measurement while managing device complexity through unified hardware and software architecture.
Solution Approach 2:
The monitoring device is designed with multi-functionality to measure various environmental parameters simultaneously. Each sensor targets a specific parameter (temperature, humidity, air quality indicators), and the system integrates these functions into a universal platform that provides comprehensive indoor air quality assessment.
2Reliability
If health impact scores are computed from multiple sensor measurements to identify environmental hazards, then reliability of hazard identification improves, but loss of time in data processing increases
Solution Approach 1:
The system pre-establishes health impact scoring algorithms and thresholds for different environmental parameters before actual monitoring begins. By having the computational framework ready in advance, the system can quickly process sensor data and identify hazards without extensive real-time computation delays.
Solution Approach 2:
The system continuously computes health impact scores from sensor measurements and provides feedback when thresholds are exceeded. This feedback mechanism enables rapid hazard identification by comparing current readings against pre-defined safety thresholds, reducing the time needed for complex analysis.
Data Source
AI summary
A system and a method include receiving, by a processor, from environmental sensors, environmental output data measurements. The environmental sensors are located at a site location. An health impact scoring algorithm computes a plurality of health impact scores from the environmental output data measurements. An overall health impact score at the site location is computed from the any of the health impact scores having a lowest value. A machine learning model generates at least one recommendation for remediating at least one verified environmental hazard type. At least one of the overall health impact score, the at least one verified environmental hazard type, or the at least one recommendation are displayed on a computing device. An instruction is sent to environment-controlling equipment located at the site location to change an operational parameter of the environment-controlling equipment to mitigate the at least one verified environmental hazard type.