Multi-source information fusion fire prediction method based on dynamic integrated neural network

Abstract

The invention relates to a multi-source information fusion fire prediction method based on a dynamic integrated neural network. Innovative logic design, establishing a fire prediction model based on amulti-source information fusion method; various fire characteristic signals pass through an information layer, a characteristic layer and a decision-making layer in sequence; in a feature layer, LSTMand RBF-BP neural networks in deep learning are used as sub-networks to carry out adaptive learning on multi-source fire feature signals; according to the fire prediction method, the output result issubjected to integrated analysis, and fire prediction is completed through the decision-making layer, so that the problems of time-varying characteristics and nonlinear characteristics of the fire signal and high missing report rate and false alarm rate of the single-characteristic signal fire prediction method are solved, and the recognition accuracy of the fire prediction system is effectivelyimproved. The method has high expandability, a complete prediction model can be established only by providing a data set again after a detection environment is changed, and the method has high adaptability.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a multi-source information fusion fire prediction method based on a dynamic integrated neural network. Background technique [0002] With the continuous advancement of science and technology and the continuous improvement of people's living standards, smart home has become a trend. Comfort is no longer the only criterion for home life, and people pay more and more attention to safety and intelligence. The frequency and scale of modern home fires are increasing, causing great loss of life and property, so the research on modern home fire prediction intelligent systems is of great significance. [0003] Most of the traditional fire early warning methods use a variety of sensors (smoke sensor, carbon monoxide sensor and temperature sensor) to obtain the smoke concentration, carbon monoxide concentration and temperature information in the air, and the final result is...

AI Technical Summary

Problems solved by technology

However, due to the cause of the fire and the difference in the environment, the fire characteristic information may have different performances. Using a single characteristic information as the detection object will lead to the phenomenon of missed or false alarms in the traditional fire early warning system, resulting in a large number of personal and property damages. loss and resource depletion to society

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