An accurate and fast fire smoke detection algorithm is urgently needed to solve the emergency linkage measures to prevent early fire spread and after the fire. In this paper, the depth matrix of motion difference is constructed for background modeling, the moving target is extracted using flame smoke motion characteristics, and the suspected area of flame smoke is obtained through corrosion and expansion. The unique characteristics of flame and smoke are then extracted, and the smoke flame recognition model is built using a BP neural network optimized by a genetic algorithm. The experimental results show that the algorithm can reliably eliminate the interference of moving vehicles and lights in the tunnel, detect flames and smoke, and generate an alarm, and that it can be applied to fire smoke detection in real-world scenes. Finally, the emergency linkage measures in the corresponding area are automatically initiated based on the position of the fire point via information interaction with the fire smoke detection and alarm system.
KEYWORDS: Internet, Data conversion, Data acquisition, Telecommunications, Neurons, Neural networks, Data communications, Data transmission, Electromechanical design, Sensors
With the development of highway information, according to the actual demand of highway electromechanical equipment management, this paper designs a multi-protocol internet of things data acquisition system to realize instant messaging and data sharing of electromechanical equipment. The Kohonen neural network is used to train the adaptive module of the existing input protocol. According to the eigenvalues of the header, the shaping number of the end bytes and the length of a single packet of the protocol, the function of automatically selecting the appropriate protocol is realized. In the later stage, the ability to learn more protocol adaptation independently can be realized by updating the protocol knowledge base on the internet of things platform. The results show that the average processing time of Kohonen network for each protocol data is about 109ms, and the average recognition rate reaches 95.45%. Kohonen network can be applied in traffic engineering field and realize the conversion of various information data of electromechanical equipment with different protocols into unified information data through protocol conversion rules.
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