MR Fire Scene Reconstruction With AI Safety and Remote Guidance
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Solution Overview
Problem
Modern fire investigation techniques face challenges due to the scarcity of experienced investigators, lengthy analysis times, inefficiencies, and the difficulty in reproducing individualized records, while also posing safety risks to investigators.
Innovation Solution
An MR/AI-enhanced fire investigation system utilizing wearable computer hardware and sensors to capture and process 3-D data, generate wire mesh models, and provide safety alerts, enabling untrained users to conduct investigations with remote guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fire investigation methods are used, then detailed photographic data and notes can be recorded, but the analysis time becomes lengthy and the rate of processing fire-damaged scenes decreases
Solution Approach 1:
The patent replaces manual mechanical recording methods (photographs and notes taken by investigators) with automated sensor-based systems. Sensors automatically capture spatial data, thermal information, and environmental parameters, eliminating the time-consuming manual documentation process while maintaining comprehensive data recording capabilities.
Solution Approach 2:
The investigation system performs self-service by automatically capturing, processing, and analyzing fire scene data without requiring extensive manual intervention. The system independently records spatial relationships, identifies objects, and generates reports, reducing the time investigators need to spend on data collection and analysis.
2Reliability
If experienced fire investigators conduct investigations, then accurate determinations can be made, but the scarcity of such personnel limits availability and increases time required to process scenes
Solution Approach 1:
The patent introduces AI algorithms and automated systems as intermediaries between the fire scene and the investigation process. These intermediaries perform data collection, analysis, and preliminary determinations, reducing reliance on scarce experienced investigators while maintaining accuracy through sophisticated computational methods.
Solution Approach 2:
The system creates digital copies and representations of fire scenes through sensor data, 3D modeling, and virtual reconstructions. These digital replicas can be analyzed repeatedly without time loss, allowing multiple investigators to review the same data simultaneously and accelerating the overall processing rate while maintaining analytical accuracy.
3Ease of operation
If individual fire investigators record data according to their own processes, then their personal expertise is applied, but post-recordation analysis by other analysts is severely hampered
Solution Approach 1:
The patent standardizes data collection by changing from investigator-specific recording methods to uniform sensor-based parameter capture. All data are collected using consistent sensor protocols, creating standardized digital formats that can be easily processed and analyzed by different analysts regardless of their individual expertise or the original investigator's methodology.
4Quantity of substance
If investigators spend extensive time at fire scenes, then comprehensive data can be collected, but safety risks increase due to prolonged exposure to hazardous environments
Solution Approach 1:
The patent substitutes human investigators with automated sensor systems that can collect comprehensive data without physical presence in hazardous environments. Sensors capture thermal, spatial, and environmental data remotely, eliminating safety risks associated with prolonged investigator exposure to fire-damaged scenes while maintaining complete data collection.
Data Source
AI summary
A Mixed Reality (MR) and Artificial Intelligence (AI)-enhanced fire investigation system and method that analyzes data descriptive of fire-damaged locations, identifies objects of the location, creates a 3-D model of the location, automatically analyzes the location utilizing an AI fire investigation model, automatically analyzes the location utilizing an AI safety evaluation model, and automatically embeds and provides layered access to data assigned to the 3-D model.