IoT Damage Assessment Using Constructed Data for Missing Sensors
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Solution Overview
Problem
Existing damage assessment systems struggle to accurately determine damage levels in properties due to missing or incomplete data from Internet of Things (IoT) devices, leading to inefficient damage detection and increased insurance claims.
Innovation Solution
A damage assessment system that utilizes machine learning algorithms and clustering techniques to analyze IoT data, generate constructed data to remedy missing information, and integrate external and historical data to provide real-time damage assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional damage assessment systems rely on complete IoT device data, then measurement precision is improved, but device complexity and data collection requirements increase
Solution Approach 1:
The patent introduces machine learning models as intermediary components that mediate between incomplete IoT sensor data and damage assessment outcomes. These models process and interpret the available data, filling gaps through pattern recognition and prediction, thereby maintaining high measurement precision without requiring complete data sets from all IoT devices
Solution Approach 2:
The system creates virtual copies or representations of missing data through machine learning-generated synthetic data and digital twins of property conditions. By generating constructed data that replicates expected sensor readings, the system compensates for missing IoT device data while maintaining assessment accuracy without increasing physical device complexity
2Loss of information
If the system collects data from all IoT devices continuously, then loss of information is reduced, but energy consumption and data processing load increase
Solution Approach 1:
The system implements partial data collection by selectively activating only the subset of IoT devices and data streams necessary for the specific damage assessment task at hand. Machine learning algorithms determine which sensors are relevant based on the type of damage being assessed, collecting only sufficient rather than complete data, thereby reducing energy consumption while preventing information loss for the specific assessment objective
Solution Approach 2:
The system employs periodic or event-triggered data collection from IoT devices rather than continuous monitoring. Data is collected at intervals or when specific conditions are met, reducing overall energy consumption while ensuring that critical damage-related information is captured. Machine learning models process these periodic data streams to maintain comprehensive assessment capability
3Measurement precision
If machine learning algorithms generate constructed data to remedy missing information, then measurement precision is maintained, but device complexity increases
Solution Approach 1:
The patent merges multiple data sources and processing functions into an integrated machine learning framework. By combining IoT sensor data, historical property data, environmental data, and predictive models into a unified system, the complexity is consolidated and managed efficiently. This integration maintains measurement precision through comprehensive data synthesis while avoiding the need for separate complex systems for each data type
Data Source
AI summary
A damage assessment system includes one or more processors and memory storing instructions executable by the one or more processors to cause the one or more processors to receive IOT data from one or more IOT devices at a property. The instructions are executable by the one or more processors to cause the one or more processors to analyze the IOT data to identify missing data that is expected to be received from the one or more IOT devices at the property, generate constructed data to remedy the missing data based on the IOT data using machine learning algorithms, determine characteristics of damage present at the property based on the IOT data and the constructed data, and output a damage assessment report to indicate the characteristics of the damage present at the property.