Dynamic Risk Assessment in Intelligent Buildings
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Solution Overview
Problem
Existing methods for assessing and predicting the risk of contagious airborne diseases in intelligent buildings are limited by their focus on primary or secondary data, often resulting in inaccurate and economically inefficient solutions that are invasive, qualitative, or specific to the COVID-19 scenario, failing to account for the dynamic nature of disease spread in populated urban areas.
Innovation Solution
A dynamic, cloud-based, IoT-enabled two-phase risk assessment and prediction system that utilizes real-time primary data and historical secondary data to classify sub-regions within intelligent buildings as high, medium, or low-risk through a three-layered architecture (edge, fog, and cloud layers) and a SARIMA model for future risk prediction, integrating primary and secondary risk factors to provide a comprehensive and non-invasive assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a risk assessment system focuses only on primary data, then the system complexity is reduced, but the measurement precision and reliability of risk assessment deteriorates
Solution Approach 1:
The patent combines primary data (real-time sensor data from IoT devices monitoring temperature, humidity, occupancy, air quality) with secondary data (historical infection data, demographic information, building layout data) to create a comprehensive risk assessment model. This merging of multiple data sources resolves the contradiction by achieving high measurement precision without excessive system complexity, as the integration is managed through a structured two-phase approach.
Solution Approach 2:
The risk assessment system is segmented into two distinct phases: Phase 1 processes primary real-time data for immediate risk detection, while Phase 2 integrates secondary historical and contextual data for refined assessment. This segmentation allows the system to maintain manageable complexity at each stage while achieving comprehensive accuracy through the combination of both phases.
2Ease of operation
If a risk assessment system uses only qualitative methods, then the ease of operation is improved, but the measurement precision and reliability deteriorates
Solution Approach 1:
The patent replaces qualitative manual assessment methods with a quantitative computational model that processes sensor data, historical records, and environmental parameters through mathematical algorithms. This substitution maintains ease of operation through automated cloud-based processing while dramatically improving measurement precision by calculating specific risk scores based on multiple measured parameters rather than subjective qualitative judgments.
3Device complexity
If a risk assessment system is designed for a specific disease scenario, then the device complexity is reduced, but the adaptability deteriorates
Solution Approach 1:
The patent designs a universal risk assessment framework that can be applied to multiple contagious diseases (COVID-19, influenza, tuberculosis, etc.) by using disease-specific parameters as configurable inputs. The core system architecture remains the same, but the model can be adapted to different diseases by adjusting the weightings and thresholds of relevant risk factors, thereby achieving high adaptability without increasing fundamental system complexity.
4Object-affected harmful factors
If entry restrictions and social distancing are implemented in common areas, then the risk of disease spread is reduced, but the ease of operation and accessibility deteriorates
Solution Approach 1:
The patent implements a feedback-based dynamic risk management system that continuously monitors real-time conditions (occupancy levels, air quality, temperature) and provides feedback through notifications and alerts. Instead of imposing strict entry restrictions, the system dynamically adjusts recommendations based on current risk levels, allowing free access when risk is low while suggesting precautions when risk increases, thereby maintaining accessibility while reducing disease spread risk.
Data Source
AI summary
A cloud-based, three-layered IoT-enabled, dynamic assessment and prediction system RAIB (Risk Assessment in Intelligent Building) in an intelligent building (IB) is disclosed, comprising of Risk assessment module I (RAIB-I), capable of assessing the risk based on the primary risk factors (fp1, fp2, fp3, . . . fpn) obtained from multiple users (q1, q2, q3, . . . qn) in the said building, and Risk assessment module II (RAIB-II), capable of assessing the risk factors based on the secondary risk factors (fs1, fs2 . . . fsn) of the said multiple users (q1, q2, q3, . . . qn). Prediction of future risk is done by prediction-based model using the historical risk assessment data. The Cloud layer (CL) deployed with RAIB and risk prediction module is used for performing the two-phase risk assessment, RAIB-I and RAIB-II and risk prediction from the stored historical data (HD).