Water Sensor Placement Using ML Chatbots for Damage 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 through a system that includes processors, sensors, and user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic recommendations are used for water sensor placement, then installation simplicity is improved, but placement effectiveness deteriorates
Solution Approach 1:
The system changes the parameters of sensor placement from generic fixed locations to dynamic locations determined by multiple structure-specific parameters including layout configuration, appliance types, building codes, and historical water damage data. The machine learning model processes these parameters to generate optimized placement recommendations that adapt to each unique structure.
Solution Approach 2:
The system performs preliminary analysis of structure information, building codes, and historical water damage claims data before determining sensor placement locations. This advance preparation allows the system to identify high-risk areas and recommend optimal placement locations before installation, ensuring both effectiveness and informed decision-making.
2Quantity of substance
If too few water sensors are installed, then installation cost is reduced, but damage detection coverage deteriorates
Solution Approach 1:
The system determines the optimal quantity of sensors by analyzing structure-specific parameters such as size, layout complexity, number of water sources, and historical damage patterns. The machine learning model processes these parameters to recommend a precise number of sensors needed, avoiding both under-installation and over-installation.
Solution Approach 2:
The system identifies specific high-risk locations within the structure where sensors should be placed based on local conditions such as proximity to water sources, historical damage patterns, and building code requirements. Each sensor placement location is optimized for its specific local context rather than using uniform spacing.
3Device complexity
If water sensors are placed in suboptimal locations, then installation complexity is reduced, but detection effectiveness deteriorates
Solution Approach 1:
The system performs preliminary determination of optimal sensor locations by analyzing structure information, building codes, and historical water damage data before installation. This advance planning provides clear, location-specific recommendations that guide installers to the most effective placement spots, ensuring high detection effectiveness without increasing installation complexity.
Solution Approach 2:
The system uses historical water damage claims data as a template or copy of past failures to inform future sensor placements. By analyzing patterns from historical data, the system replicates successful placement strategies adapted to the specific structure's characteristics, improving detection effectiveness based on proven patterns.
Data Source
AI summary
Systems and methods disclosed herein relate to determining an optimal placement location of one or more water sensors proximate a structure using a machine learning (ML) chatbot. The ML chatbot may detect a request to identify the optimal placement location of the water sensors. In response to the request, structure information is provided to a trained ML model to generate an indication of the optimal placement location of the water sensors. The ML chatbot may detect the indication of the optimal placement location of the water sensors. The indication of the optimal placement location of the water sensors is provided to a user device.