Water Sensor Placement Using ML for Structure-Specific Leak Detection
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Solution Overview
Problem
Conventional water sensor placement techniques are ineffective due to generic recommendations that fail to account for unique structural considerations, leading to suboptimal placement and reduced effectiveness in preventing water damage.
Innovation Solution
A computer-implemented method using machine learning to determine optimal water sensor placement by analyzing structure information, historical water damage claims data, and building code requirements, providing recommendations for placement locations corresponding to potential sources of water damage, and utilizing augmented reality and chatbots for visualization and guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic recommendations are used for water sensor placement, then installation is simple and quick, but placement effectiveness is reduced due to failure to account for unique structural considerations
Solution Approach 1:
The system performs preliminary analysis of structure information, historical water damage claims data, and building code requirements before providing sensor placement recommendations. The machine learning model pre-processes and evaluates multiple factors to identify optimal locations, so that when sensors are installed, they are already positioned for maximum effectiveness without requiring complex on-site analysis during installation
2Reliability
If machine learning analysis is used to determine optimal sensor placement, then detection effectiveness is improved by identifying specific high-risk areas, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning system that acts as a mediator between raw data (structure information, historical claims, building codes) and the final sensor placement recommendations. This intermediary layer automatically processes and synthesizes multiple data sources, providing expert-level analysis without requiring the end user to directly manage the complexity of the underlying machine learning model or data processing pipelines
Data Source
AI summary
Systems and methods disclosed herein relate to determining optimal placement location of one or more water sensors proximate a structure using machine learning. The machine learning model may be provided structure information for a structure at which the water sensors are to be placed, and generate an indication of the optimal placement location of the one or more water sensors proximate the structure. The optimal placement location of the water sensors may correspond to potential sources of water damage. A user device may receive the indication of the optimal placement location of the one or more water sensors proximate the structure to a user device.