Facility Fire Spread Prediction With Zoned Event Data
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Solution Overview
Problem
Existing fire prediction systems in facilities lack the ability to accurately determine the spread of fire, direction, and safe/unsafe areas for firefighters, leading to inadequate response planning.
Innovation Solution
A computing device receives data from event devices within a facility, sorts them into priority levels based on proximity, location, and detected variables, and provides real-time graphical information on fire spread, including safe and unsafe areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data from all event devices is processed to predict fire spread, then prediction accuracy is improved, but computational complexity and data processing time increase
Solution Approach 1:
The system segments the facility into multiple zones and divides event devices into groups based on their spatial location and functional characteristics. This segmentation allows the computing device to process data from specific zones independently, reducing the overall computational complexity while maintaining comprehensive fire spread prediction coverage across the entire facility.
Solution Approach 2:
The system applies local quality by prioritizing data processing for event devices located in zones closer to the detected fire or zones with higher fire risk characteristics. The computing device dynamically adjusts processing intensity and resource allocation based on the specific local conditions of different facility zones, optimizing prediction accuracy where it matters most while reducing unnecessary processing in low-risk areas.
2Measurement precision
If data from all event devices is collected and processed, then fire spread prediction accuracy is improved, but response time may be delayed
Solution Approach 1:
The system performs preliminary actions by pre-calculating fire spread models, pre-segmenting facility zones, and pre-organizing event device data structures before a fire event occurs. When fire is detected, the computing device can immediately apply pre-prepared algorithms and data frameworks to specific zones, dramatically reducing the time required for real-time prediction while maintaining high accuracy.
Solution Approach 2:
The system implements partial action by initially processing data only from critical zones most likely to be affected by fire spread, rather than simultaneously processing data from all event devices in the facility. This selective processing approach provides timely prediction results for the most urgent areas first, with the option to expand processing to additional zones as needed.
3Loss of information
If the system provides detailed graphical information on fire spread, then incident commander decision-making is improved, but information transmission complexity increases
Solution Approach 1:
The system transforms complex multi-dimensional fire spread data into intuitive two-dimensional graphical representations displayed on a map or floor plan view. The computing device projects fire spread predictions, zone classifications, and event device statuses onto a visual spatial framework, allowing incident commanders to comprehend comprehensive information at a glance without being overwhelmed by raw data complexity.
Solution Approach 2:
The system uses color changes to encode different fire spread risk levels, zone classifications, and event device statuses in the graphical information. Different colors represent different prediction outcomes (e.g., high risk, medium risk, low risk zones), enabling incident commanders to rapidly assess the situation and prioritize responses based on visual cues rather than interpreting complex numerical data.
Data Source
AI summary
Devices, systems, and methods for predicting fire spread in a facility are described herein. In some examples, one or more embodiments include a computing device comprising a processor and a memory having instructions stored thereon which, when executed by the processor, cause the processor to receive an indication of a fire detected by an event device of a plurality of event devices installed in a facility, sort the plurality of event devices into a plurality of categories based on a determined likelihood of fire spread to each of the plurality of event devices, receive data collected by the plurality of event devices during the fire, and provide, via an interface, graphical information descriptive of a spread of the fire based on the received data.