Off-Grid AI Firebrand Tracking for Targeted Suppressant Discharge
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Solution Overview
Problem
Existing fire prevention systems require extensive installation times, large water storage, and are inefficient in resource utilization, often failing to completely extinguish fires due to their complexity and indiscriminate spraying.
Innovation Solution
A robotic fire prevention system with area fire prevention units equipped with directable nozzles, communicatively coupled to a computing device that detects firebrands, predicts their trajectories, and precisely discharges fire suppressant using machine learning algorithms to target and extinguish firebrands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rooftop fire prevention systems discharge high density water over the entirety of a home, then fire coverage is improved, but water storage requirements and system complexity increase significantly
Solution Approach 1:
The system transitions from uniform water distribution across the entire property to localized suppression at specific fire hazard points. Sensors detect firebrands and the system directs suppressant precisely to those locations, eliminating the need for massive water storage while maintaining effective fire coverage.
Solution Approach 2:
The invention extracts the water storage component from the traditional rooftop system design. Instead of requiring large on-site water tanks, the system uses a centralized water source and delivers suppressant on-demand through targeted delivery mechanisms, dramatically reducing local water storage requirements.
2Reliability
If rooftop systems are designed to the perfect square dimensions of every home, then fire protection effectiveness is improved, but manufacturing time and installation complexity increase
Solution Approach 1:
The system employs standardized modular units that can be deployed across different property configurations without custom manufacturing. The sensor-networked suppressant delivery system uses universal interfaces and standardized protocols, enabling rapid deployment while maintaining effective fire protection across various home layouts.
Solution Approach 2:
The fire prevention system is divided into independent modular units that can be deployed separately and scaled as needed. Each unit operates autonomously but communicates with the network, allowing for quick installation without requiring complete system redesign for each property.
3Reliability
If mobile fire prevention systems use large storage tanks of water, then fire suppression capacity is improved, but the system still exhausts water supply before complete extinguishment
Solution Approach 1:
The system continuously monitors firebrand positions and suppressant delivery effectiveness through sensor feedback. This real-time data allows the control system to adjust delivery parameters dynamically, ensuring sufficient suppressant is delivered to extinguish fires completely without exhausting the water supply unnecessarily.
Solution Approach 2:
The system dynamically adjusts suppressant delivery parameters based on real-time fire conditions. Instead of using fixed storage tank capacities, the system adapts the amount and timing of suppressant delivery to match the actual fire suppression needs, extending the effective operation duration.
4Reliability
If fire prevention systems spray water indiscriminately, then fire coverage is improved, but resource utilization efficiency and environmental impact worsen
Solution Approach 1:
The system delivers fire suppressant precisely to localized fire hazard points identified by sensors, rather than spraying indiscriminately across the entire property. This targeted approach dramatically improves resource utilization efficiency while maintaining effective fire coverage at critical locations.
Solution Approach 2:
The system replaces traditional mechanical spray systems with sensor-networked intelligent delivery mechanisms. Instead of relying on broad-area mechanical spraying, the system uses sensors to detect firebrands and electronically controls targeted suppressant delivery, improving both precision and resource efficiency.
Data Source
AI summary
A system and method for protecting an area from fire having one or more area fire prevention units capable of discharging fire suppressant via a directable nozzle, each fire prevention unit being communicatively coupled to a computing device which detects airborne firebrands, predicts their trajectories and final landing positions, and directs one or of the fire prevention units to discharge fire suppressant toward the firebrand at its final landing position. Depending on configuration, the system may further use wind data, GPS, and terrain models to calculate the trajectory and final position of the firebrand. Also depending on configuration, the system may calculate a spread and distance of suppressant discharge, a nozzle aperture, and an amount of suppressant to discharge. Some embodiments may use trained machine learning algorithms to make one or more of the system's calculations.