Indoor fire prediction method based on radial basis function neural network and system thereof

Abstract

The invention relates to an indoor fire prediction method based on a radial basis function neural network and a system thereof. The method comprises the following steps: (a) building an indoor fire probability prediction model which is positioned in a host computer; (b) setting needed monitoring nodes in a room, wherein the monitoring nodes collect indoor environmental parameters in real time and transmit the collected indoor environmental parameters to the host computer; (c) the host computer inputs the received indoor environmental parameters into the indoor fire probability prediction model so as to obtain the corresponding fire probability value of the current indoor environment; and (d) when the host computer obtains the corresponding indoor environmental judgment as a flame or a smoldering fire through the indoor fire probability prediction model, the host computer transmits alarm information to the monitoring nodes and carries out alarm prompting through the monitoring nodes. According to the method and the system, the indoor fire hidden trouble can be found timely, and the method and the system have the advantages of good real-time performance, high reliability and strong stability.

Description

technical field [0001] The invention relates to an indoor fire prediction method and system, in particular to an indoor fire prediction method and system based on a radial basis neural network, belonging to the technical field of indoor fire prediction. Background technique [0002] With the development of society and the improvement of people's living standards, the density of people in shopping and entertainment places such as commercial buildings, hotels, and guesthouses has also increased. These places are often difficult to evacuate due to complex fire factors, many combustible materials, and dense property distribution. Once a fire breaks out, the casualties and property losses will be even greater. In recent years, the continuous large and small fires have exposed a variety of indoor fire safety hazards, which has sounded the alarm for people. [0003] Most of the existing data fusion processing models established by choosing neural networks use BP networks, but BP (...

AI Technical Summary

Problems solved by technology

If the location cannot be accurately located, due to the complex structure of the indoor area, the trapped people in a coma cannot make a sound for help or the children hide in a narrow space that is not easy to be found, which will lead to the inability of firefighters to rescue the trapped people quickly and effectively

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