Mixed Reality Fire Investigation With AI 3D Scene Modeling
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Solution Overview
Problem
Modern fire investigation techniques face challenges due to the scarcity of experienced investigators, lengthy analysis times, and the individualized nature of data recording, which hampers reproducibility and efficiency, while posing safety risks to investigators.
Innovation Solution
A Mixed Reality (MR) and Artificial Intelligence (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 efficient and standardized data collection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experienced fire investigators conduct investigations manually, then accurate determination of fire origin and cause is achieved, but the investigation process is time-consuming and the rate of processing fire-damaged scenes is low
Solution Approach 1:
The system creates a digital 3-D copy of the fire-damaged scene using depth sensors and cameras. This digital replica preserves all spatial and visual information while enabling rapid analysis without requiring repeated physical site visits, thus maintaining accuracy while improving processing speed.
Solution Approach 2:
The patent replaces manual mechanical measurement and documentation methods with automated sensor-based capture systems. Depth sensors, cameras, and processors automatically record and analyze scene data, eliminating the time-consuming manual processes while maintaining or improving determination accuracy.
2Adaptability or versatility
If individual fire investigators record data according to their own processes, then their personal expertise is utilized, but post-recordation analysis by other analysts is severely hampered
Solution Approach 1:
The system employs a standardized data capture framework that works across all investigation scenarios. The 3-D digital model and associated metadata are created in a universal format that can be accessed and analyzed by any authorized personnel, eliminating the problems of individualized recording processes while preserving investigative flexibility.
Solution Approach 2:
By creating a comprehensive digital replica of the scene with all spatial relationships and visual evidence preserved, the system ensures that any analyst can review the exact same data without loss or distortion, enabling consistent post-recordation analysis regardless of who conducted the investigation.
3Reliability
If lengthy on-site investigation and analysis visits are conducted, then thorough data collection is achieved, but the time required for analysis increases
Solution Approach 1:
The system performs comprehensive data capture in advance during a single on-site visit, creating a complete 3-D digital model that preserves all scene information. This preliminary action eliminates the need for repeated site visits and extended analysis periods, as all necessary data is collected and preserved in the digital replica.
Solution Approach 2:
Automated sensor systems and processing algorithms replace lengthy manual data collection and analysis processes. The system rapidly captures comprehensive scene data using depth sensors and cameras, then automatically processes this information to generate the 3-D model and identification results, significantly reducing both on-site time and analysis time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system allows for rapid, standardized data capture and analysis, reducing the need for specialized personnel, minimizing safety risks, and decreasing the time and cost of fire investigations.
Implementation Method 1
acquire, by a time of flight sensor, data descriptive of first distances from a mixed reality device to a first plurality of surface points
Data Source
AI summary
A Mixed Reality (MR) and Artificial Intelligence (AI)-enhanced fire 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 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.