Fire prediction method based on self-organizing neural network

Abstract

The invention discloses a fire prediction method based on a self-organizing neural network, comprising the steps of: establishing a fir probability prediction model based on a self-organizing neural network in a host computer; installing a sensor set at monitoring points to collect environmental parameters and transmit collected real-time data to the host computer through a router; inputting data received by the host computer into the fir probability prediction model to obtain a fir probability value corresponding with current environment and determine existence of a fire; and transmitting fire information to a linkage controller, driving a linkage fire extinguishing device, and furthermore realizing alarm and self-extinguishing. The fire prediction method based on a self-organizing neural network employs a hidden layer node increase-decrease method to realize dynamic adjustment of a network structure, can timely discover and control fire hazards, and has the characteristics of high instantaneity, great reliability and sufficient stability.

Description

technical field [0001] The invention belongs to the technical field of fire early warning, and in particular relates to a fire prediction method based on self-organizing neural network. Background technique [0002] With the continuous development of the economy and the improvement of people's living standards, the flow of people in commercial buildings, hotels, guesthouses, KTV and other large entertainment venues is increasing. If a fire breaks out, the consequences will be disastrous. However, the current fire monitoring system has the following deficiencies: ①The sensor used is single, with a high rate of false alarms and false alarms, and it cannot detect changes in environmental parameters in the initial stage of a fire in time; ②Most connection methods are wired connections, which are prone to aging and corrosion , not easy to repair and replace; ③ Most of the prediction models used are based on static networks, but the static learning algorithms have weak computing p...

AI Technical Summary

Problems solved by technology

However, the current fire monitoring system has the following deficiencies: ① the sensor used is single, the rate of false alarms and false alarms is high, and the change of environmental parameters in the early stage of the fire cannot be detected in time; ② most of the connection methods are wired connections, which are easy to age and corrode , not easy to repair and replace; ③ Most of the prediction models used are based on static networks, but the static learning algorithms have weak computing power, poor real-time performance, low accuracy, and cannot satisfy nonlinear functions, which will affect the overall performance of the system; ④ Most of the systems are single The early warning system or control system does not realize the combination of early warning and linkage control well, which reduces the control level of the fire control ability

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