Smart Home Peril Scoring With Real-Time Sensor Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current smart home systems struggle to aggregate and analyze data from various sources to effectively measure and manage risks associated with property perils, lacking the ability to identify relationships between multiple events and provide proactive suggestions, and traditional risk prediction methods rely heavily on historic data, making them inefficient for new peril scenarios.
Innovation Solution
A measuring and control system that integrates sensory devices, a hardware controller, and a digital calibration layer to capture and process real-world data, generating a safety score by categorizing perils into natural, property, and life pattern risks, using machine learning to adapt and optimize risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional risk prediction methods rely heavily on historic data, then the system can maintain simplicity in data collection, but the system becomes inefficient for new peril scenarios and lacks forward-looking capability
Solution Approach 1:
The system performs preliminary actions by continuously collecting and processing real-time data from multiple sensors before peril events occur. The calibration layer establishes baseline relationships between sensor readings and peril conditions in advance, enabling the system to predict new peril scenarios without relying solely on historic peril data. This preliminary data accumulation and relationship calibration allows efficient adaptation to novel situations.
Solution Approach 2:
The system transitions from static historic data analysis to dynamic real-time data processing. The calibration layer continuously adapts to new conditions by processing streaming sensor data, allowing the system to dynamically adjust to new peril scenarios. This dynamic approach enables forward-looking predictions while maintaining reliability across varying conditions.
2Measurement precision
If the system integrates multiple sensory devices and data sources to comprehensively measure perils, then the system can provide accurate peril predictions, but the system complexity increases significantly
Solution Approach 1:
The system segments the complex data processing task into distinct functional layers: a digital raw layer for data collection, a calibration layer for relationship establishment, and a peril prediction layer for outcome generation. Each layer handles specific processing tasks independently, reducing overall system complexity while maintaining comprehensive measurement capabilities across multiple sensor types.
Solution Approach 2:
The calibration layer acts as an intermediary between raw sensor data and peril predictions. It establishes calibration relationships that translate complex multi-sensor readings into meaningful peril indicators, simplifying the connection between diverse data sources and prediction outcomes without losing measurement precision.
3Productivity
If the system processes real-time data from multiple sources to generate safety scores, then the system can provide proactive risk management, but the computational resources and processing time increase
Solution Approach 1:
The calibration layer performs preliminary processing by establishing relationships between sensor readings and peril conditions before actual peril events occur. This pre-calibration allows the system to generate rapid predictions during real-time operation without extensive computational overhead, enabling proactive risk management with minimal processing delay.
Solution Approach 2:
The system uses the collected sensor data to automatically calibrate and improve its own prediction capabilities. The calibration layer continuously refines relationships between sensors and perils using incoming data, enabling the system to self-optimize processing efficiency while maintaining proactive risk management capability without requiring external intervention or excessive computational resources.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Proposed is a measuring and control system (1) and method for individually measuring of a multitude of perils and/or safety scores of a property or smart home (3). The measuring and control system (1) comprises at least one hardware controller and a plurality of sensory devices, wherein the property or smart home (3) is populated with the sensory devices, the at least one hardware controller being in communication with the plurality of sensory devices, and the sensory devices transmitting sensory signals to the hardware controller.