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Research On Deep Learning Based Factory Fire Identification And Early Warning System

Posted on:2024-06-24Degree:MasterType:Thesis
Country:ChinaCandidate:N WangFull Text:PDF
GTID:2531307115458134Subject:Communication engineering
Abstract/Summary:PDF Full Text Request
In recent years,China’s industrial economy has developed rapidly,and the environmental safety of factories,as the source of industrial economy,has gradually attracted attention.The introduction of Internet of Things(Io T)technology and deep learning algorithms in the environmental monitoring of factories is an important research direction to solve the safety problems of factories.This paper designs a factory fire identification and early warning system based on Io T technology and artificial intelligence algorithm,including two parts: environmental data monitoring and fire identification and early warning.The goal is to improve the real-time monitoring of the factory environment and the accuracy of fire identification,to achieve timely and accurate identification and early warning at the early stage of fire,to help staff to fully grasp the factory environment and take fire-fighting measures at the early stage of fire to reduce casualties and property damage.The main research content of this paper is as follows:(1)The hardware and software platform of the factory environment monitoring system was built,the selection of relevant components and the design of relevant circuit schemes were carried out,the hardware driver was written,and the hardware of the monitoring system was completed;at the same time,the upper computer platform was developed under the Lab VIEW environment to realize the functions of real-time display,storage and viewing of environmental data.(2)The fire identification and early warning model is designed,which includes the environmental prediction model and the fire identification model,and the results of the two models are combined to identify and warn of fires in the factory.The Bi-LSTM algorithmbased environmental data prediction model is used to predict the environmental data in the factory in the future and determine whether the environmental parameters are in the normal range;A fire recognition model based on CNN-GRU hybrid neural network is proposed by combining CNN and GRU,which takes the temperature,humidity,smoke concentration,carbon monoxide concentration and toxic gas concentration in the environmental data as the input and the two states of no fire and fire as the output.The classification result of the fire recognition model is used as the basis for the system to identify the fire.(3)The system was experimentally tested,firstly on the environmental data acquisition part and the upper computer function,then on the prediction accuracy of the environmental data prediction model and the classification accuracy of the fire identification model.The test results show that the system is able to accurately collect various environmental data in the factory,predict the environmental data in the future and identify whether there is a fire,achieve the purpose of fire identification and early warning,and has certain application value for practical production.
Keywords/Search Tags:IoT, environmental monitoring, deep learning, environmental prediction, fire identification and early warning
PDF Full Text Request
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