Multi-Sensor Hazard Risk Scoring With AI Data Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing hazard risk assessment methods, such as FEMA maps, are outdated and inaccurate, failing to consider real-time data and climate change, and do not account for individual property vulnerabilities, leading to underestimated risks and lack of comprehensive vulnerability analysis for future climate impacts.
Innovation Solution
A system utilizing a distributed sensor network and data fusion techniques to generate real-time hazard risk scores based on property and environmental data, incorporating machine learning for accurate risk assessment and mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hazard risk assessment methods (FEMA maps) are used, then the assessment process is simple and widely available, but the accuracy and real-time capability are insufficient
Solution Approach 1:
The patent combines multiple data sources including distributed sensor networks, satellite imagery, climate models, and vulnerability databases into a unified risk assessment system. This integration enables comprehensive real-time hazard risk assessment by merging diverse data streams and analytical methods, directly addressing the accuracy limitation of conventional single-source assessment methods.
Solution Approach 2:
The system segments the risk assessment process into distinct functional modules: data collection from multiple sources, real-time processing units, vulnerability analysis components, and risk scoring engines. This segmentation allows each module to specialize in specific tasks, improving overall assessment accuracy while maintaining manageable system complexity through modular architecture.
2Speed
If real-time distributed sensor networks are deployed, then real-time hazard detection capability is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The distributed sensor network is designed with multi-functional sensors that can detect multiple hazard types (floods, wildfires, extreme heat) using the same infrastructure. This universality reduces network complexity by eliminating the need for separate specialized sensor systems for each hazard type, while maintaining real-time detection capabilities across diverse environmental threats.
Solution Approach 2:
The patent introduces intermediary data processing layers and fusion algorithms that aggregate and synthesize data from numerous distributed sensors. These intermediaries simplify the complexity of raw sensor networks by transforming multiple individual sensor inputs into consolidated real-time hazard detections, reducing the burden on individual sensors and communication infrastructure.
3Measurement precision
If comprehensive vulnerability data collection is performed, then the vulnerability assessment accuracy is improved, but the data collection burden and time requirements increase
Solution Approach 1:
The system performs preliminary vulnerability assessments using existing databases, historical data, and automated property information systems before actual hazard events occur. This preliminary action pre-populates vulnerability profiles for properties and infrastructure, so that during real-time hazard assessment, only updates and adjustments are needed rather than complete data collection, significantly reducing time loss while maintaining comprehensive accuracy.
Solution Approach 2:
The vulnerability assessment system incorporates feedback mechanisms where actual hazard impact data from sensor networks and satellite observations continuously refine and update vulnerability profiles. This feedback loop improves assessment accuracy over time by learning from real-world outcomes, while reducing future data collection requirements as the system becomes better at predicting vulnerabilities based on accumulated knowledge.
Data Source
AI summary
Disclosed are systems and methods for community-based multi-sensor fused data processing to determine natural hazard risk. In one embodiment, a system comprises one or more memory units storing instructions and one or more processors configured to execute the instructions to receive localized data from a distributed multi-sensor network, the distributed multi-sensor network including a plurality of sensor devices associated with a community, receive property data, community infrastructure data and environmental data from at least one external repository, generate combined data using data fusion, the combined data being based on the localized data from the distributed multi-sensor network and at least one of the property data, community infrastructure data or the environmental data, determine a community risk score for a natural hazard by implementing a machine learning method on the combined data, and perform a mitigating action based on the community risk score.