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The infrared and smoke sensors widely applied in fire alarm and fire monitoring in the scene of large indoor and outdoor warehouse is vulnerable to sorts of interference noise, it may out of work to cause error fire alarm or fire omission. Based on design of reliable flame recognition algorithm, a practical intelligent fire alarm system based on BP neural network and multiple source information was constructed. The system uses multi-channel time-division multiplexing technology to constitute temperature detector, photoelectric smoke detector and infrared CCD detector into multiple source detector set. The information fusion and BP neural network were used for accurate prediction of fire level, which improves reliability of fire alarm and decrease false alarm rate.

Design on Fire Alarm System Based on BP Neural Network and Multiple Source Information Fusion