MR Spatial Investigation With AI 3D Fire Scene Analysis
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Solution Overview
Problem
Fire investigation techniques are hindered by the scarcity of experienced investigators, lengthy analysis times, and the individualized and incomplete nature of fire investigation records, leading to inefficiencies and safety risks.
Innovation Solution
Utilizing Mixed Reality (MR) and Artificial Intelligence (AI) enhanced systems with wearable computer hardware and sensors to capture, process, and analyze fire-damaged environments, enabling efficient and standardized data collection and safety hazard detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fire investigation methods are used, then investigator expertise and judgment are utilized, but the process is time-consuming and limited by the scarcity of experienced personnel
Solution Approach 1:
The patent creates a digital twin (3D model) of the fire scene that replicates the physical environment. This virtual copy can be analyzed repeatedly without time loss, allowing multiple analysts to examine the same scene simultaneously and eliminating the time-consuming nature of traditional on-site analysis while preserving investigative accuracy
Solution Approach 2:
The patent replaces manual physical measurement and documentation methods with automated sensor systems (LIDAR, thermal cameras, gas detectors) that objectively capture spatial and environmental data. This substitution eliminates human error and accelerates data collection while maintaining the expertise needed for analysis through integrated AI processing
2Adaptability or versatility
If individualized data collection methods are used, then investigator flexibility is maintained, but post-recordation analysis by other analysts is severely hampered
Solution Approach 1:
The patent applies different sensor types and capture methods to specific locations and objects within the fire scene based on their investigative importance. Each area receives targeted data collection (e.g., thermal imaging on suspected ignition points, gas composition analysis in confined spaces), maintaining flexibility while ensuring comprehensive, standardized data is preserved for future analysis
Solution Approach 2:
The system incorporates real-time feedback mechanisms where sensors continuously monitor environmental conditions and the 3D model dynamically updates as data is collected. This feedback loop ensures that all relevant information is captured and automatically integrated into the digital twin, making the records complete and accessible to any analyst regardless of their background
3Measurement precision
If comprehensive spatial analysis is performed, then investigation thoroughness is improved, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary actions by automatically generating the complete 3D model and measuring all spatial parameters during the initial sensor deployment phase. The system pre-processes data to identify potential ignition sources, burn patterns, and environmental factors before formal analysis begins, so that when analysts review the scene, the foundational spatial data is already prepared and accessible
Solution Approach 2:
The patent transforms 2D photographs and notes into a comprehensive 3D digital twin that captures spatial relationships, distances, and orientations in multiple dimensions. This dimensional transformation allows analysts to visualize and measure the scene from any angle simultaneously, improving measurement precision while reducing analysis time compared to traditional single-view documentation
Data Source
AI summary
A Mixed Reality (MR) and Artificial Intelligence (AI)-enhanced spatial investigation system 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 spatial 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.