Intelligent fire early warning system and method based on QPSO-BP neural network

Abstract

The invention discloses an intelligent fire early warning system and method based on a QPSO-BP neural network. The system comprises a smoke sensor, a CO concentration sensor, a temperature sensor, an AD conversion module, a Raspberry Pi 4B development board and an Internet of Things device, wherein the temperature sensor is electrically connected with the Raspberry Pi 4B development board and transmits data to the Raspberry Pi 4B development board for storage; the smoke sensor and the CO concentration sensor are electrically connected with the Raspberry Pi 4B development board through the AD conversion module, and the AD conversion module converts analog quantity signals into digital signals and transmits the digital signals to the Raspberry Pi 4B development board for storage; and the Raspberry Pi 4B development board is connected with the Internet of Things equipment through a wireless network for interactive communication, transmits data to the Internet of Things equipment, judges the fire occurrence probability in real time and outputs the fire occurrence probability. According to the system, the QPSO algorithm is adopted to optimize the BP neural network to realize fire early warning, and accuracy of fire early warning is improved.

Description

technical field [0001] The invention relates to the technical field of fire early warning, in particular to an intelligent fire early warning system and method based on a QPSO-BP neural network. Background technique [0002] In recent years, fires have occurred frequently, seriously damaging life and property safety. However, the existing fire warning equipment on the market generally uses a threshold judgment method to detect the occurrence of a fire, and only considers a single factor, such as: temperature, smoke concentration, etc. However, this method has the following defects: 1) Using a single sensor for data processing, in a complex environment, the received information is inaccurate and prone to false alarms; 2) After a fire occurs, the ability to judge and warn of flame images is low ;3) The fire signal transmission mainly adopts traditional cable, which is costly, occupies a large area, and affects the environment; 4) Generally, a single device is used for operati...

AI Technical Summary

Problems solved by technology

However, the existing fire warning equipment on the market generally uses the threshold judgment method to detect the occurrence of fire, and only considers a single factor, such as: temperature, smoke concentration, etc.

However, this method has the following defects: 1) Using a single sensor for data processing, in a complex environment, the received information is inaccurate and prone to false alarms; 2) After a fire occurs, the ability to judge and warn of flame images is low ;3) The fire signal transmission mainly adopts traditional cable, which is costly, occupies a large area, and affects the environment; 4) Generally, a single device is used for operation without a supporting APP, and the delay in receiving fire signals is long and the scalability is low.

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