Enclosed Space Risk Monitoring With Sensor-Driven Interventions
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Solution Overview
Problem
Existing systems for managing health risks in enclosed spaces are limited in their effectiveness, failing to adequately address factors such as elevated levels of pathogens, carbon dioxide, humidity, particulate matter, and other environmental conditions that impact human health and well-being.
Innovation Solution
A system comprising a processor unit with a rules engine and workflow engine, environmental sensors, and a database that processes sensor signals to determine elevated risks and selects appropriate interventions from an infrastructure library, utilizing machine learning and AI to adapt and improve risk mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If basic sensor systems are used to monitor temperature and carbon monoxide, then implementation cost is low, but health risk mitigation effectiveness is insufficient
Solution Approach 1:
The system segments health risk monitoring into multiple independent sensor modules, each dedicated to detecting specific risk factors (pathogens, CO2, humidity, particulate matter, VOCs, temperature). This modular segmentation allows comprehensive monitoring while maintaining manageable system complexity through standardized integration interfaces.
Solution Approach 2:
The processor unit serves multiple functions: it processes data from all sensor types, evaluates combined risk factors, generates healthiness ratings, and triggers appropriate interventions. This multi-functionality consolidates what could be separate complex systems into a single integrated platform, improving effectiveness without proportionally increasing complexity.
2Measurement precision
If comprehensive environmental monitoring is implemented, then health risk detection accuracy improves, but system cost increases
Solution Approach 1:
Multiple sensors detecting different risk factors (pathogens, CO2, humidity, particulate matter, VOCs, temperature) are merged into a single integrated monitoring system. The processor unit combines data from all sensors to generate an overall healthiness rating, achieving comprehensive detection accuracy while avoiding the redundancy and cost of separate independent systems.
Solution Approach 2:
The system automatically processes sensor data, evaluates risk levels, and triggers interventions without requiring manual analysis or separate monitoring systems. The processor unit self-manages the integration and interpretation of data from multiple sensors, reducing the need for additional human resources or parallel systems.
3Reliability
If reactive intervention systems are used, then system simplicity is maintained, but health risk reduction effectiveness is limited
Solution Approach 1:
The system continuously monitors environmental parameters and proactively triggers interventions before health risks reach critical levels. By detecting early signs of poor air quality, high pathogen presence, or uncomfortable conditions, the system takes preliminary action to maintain healthy environments, rather than merely reacting to established problems.
Solution Approach 2:
The system implements closed-loop feedback by continuously measuring environmental parameters, evaluating them against health standards, and automatically adjusting conditions through triggered interventions. This feedback mechanism ensures sustained health risk reduction effectiveness by dynamically responding to changing conditions rather than relying on static, manual adjustments.
4Reliability
If energy-intensive interventions are deployed, then health risk mitigation is maximized, but energy consumption increases
Solution Approach 1:
The system applies interventions selectively based on the specific risk factors detected and their severity levels. Rather than continuously maximizing all interventions, the processor unit triggers only the necessary partial actions required to address identified risks, optimizing the balance between health protection and energy consumption by avoiding excessive intervention when risks are minimal.
Data Source
AI summary
A system for mitigating risk in an enclosed space comprises: a processor unit having a rules engine and a workflow engine; an infrastructure library accessible by the processor unit and holding a plurality of available interventions which can be enacted by the system; a plurality of environmental sensors, each arranged to supply the processor unit with sensor signals indicating a sensed risk factor of the enclosed space; a workflow interface arranged to instruct the operation of a workflow which enacts an intervention; wherein the rules engine is configured to process the sensor signals from the plurality of sensors against a set of rules to determine if the risk is elevated to a level where mitigating action is required; wherein the workflow engine is configured to select one or more of the available interventions from the infrastructure library when intervention is required; wherein the processor unit is configured to generate a workflow instruction signal when a workflow has been selected and to send that workflow instruction signal to the workflow interface;