Autonomous Fire Suppression With Targeted Agent Selection
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Solution Overview
Problem
Existing fire suppression systems cause collateral damage to valuable equipment and inventory due to indiscriminate deployment of firefighting agents, and lack the ability to preemptively detect and respond to fire threats.
Innovation Solution
A system equipped with sensor packages, computational resources, and robotic nozzle assemblies that autonomously predict, detect, classify, and respond to fire threats by selectively deploying firefighting agents, using machine learning and AI to characterize normal space activity and assess threat probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed firefighting systems are used to protect overall space, then fire protection coverage is improved, but collateral damage to electronics, paper, and food inventories increases
Solution Approach 1:
The system transitions from uniform space-wide fire suppression to localized targeted suppression. Sensors detect fire events at specific locations, and the control system directs firefighting agents only to those precise locations, applying different suppression strategies based on local conditions rather than flooding the entire space.
Solution Approach 2:
The system divides the protected space into multiple zones with individual detection and suppression capabilities. Rather than a single centralized system affecting the whole space, multiple localized suppression points can operate independently, allowing selective application of agents only where needed.
2Reliability
If large amounts of water, chemical, or foam agents are deployed, then fire extinguishing effectiveness is improved, but damage to electronic equipment, paper supplies, and food inventories increases
Solution Approach 1:
The system applies the minimum necessary amount of firefighting agents required to suppress the fire, rather than deploying excessive amounts that would guarantee fire extinction but also guarantee collateral damage. The control system modulates agent delivery to match the actual fire threat level.
Solution Approach 2:
The system selects and adjusts parameters of firefighting agents (type, amount, delivery rate, temperature, pressure) based on the specific fire conditions detected. Different agent types and delivery parameters are chosen for different fire classes and locations to maximize effectiveness while minimizing damage to surrounding materials.
3Measurement precision
If autonomous robotic nozzle assemblies are used, then response precision is improved, but system complexity increases
Solution Approach 1:
The system incorporates autonomous decision-making capabilities where the control system automatically processes sensor data, identifies fire events, selects appropriate suppression strategies, and activates robotic nozzles without human intervention. The system serves itself by integrating detection, analysis, and response functions into a self-regulating autonomous system.
Solution Approach 2:
The system combines multiple previously separate functions (fire detection, threat assessment, agent selection, nozzle control, and delivery management) into an integrated autonomous system. This merging reduces the need for separate manual systems while achieving higher precision through coordinated operation of combined components.
Data Source
AI summary
The present invention generally provides systems and methods for autonomous space characterization, data analysis, anomaly detection, hazard probability assessment, and profile deviance assessment and incident response action, and firefighting employing robotically controlled firefighting equipment, autonomous selection and release of firefighting agent, and the autonomous prediction, detection, classification, and location of existing and impending threats and fire events.