Application of Voice Recognition Interaction and Big Data Internet of Things in Urban Fire Fighting
Authors: Xianchun Sunl, Kui Cai, Bingjing Chen, Jingyu Zha, Gang Zhou
Abstract: With the continuous development of science and technology, especially computer technology, people need a more convenient and natural way to communicate with the machine. Language can provide people with convenient and efficient information, and speech recognition technology makes this convenience extended to the field of science and deep into human daily life. In this paper, based on human-computer speech recognition interaction system, using big data Internet of things as technical support, the contribution of intelligent social service robot to urban fire protection is studied. In this system, the user can control the action of the service robot through voice command, and the user can also realize voice interaction with the robot. Because of the continuous expansion of information technology and computer technology, human beings have entered the era of information overload, and big data technology has become a hot spot in people’s production and life. The integration of big data and Internet of things technology will make the intelligence of human society to a new level, and its development has unlimited possibilities in the future. In recent years, China’s urbanization process continues to accelerate, and the land price and house price of cities begin to rise rapidly. In order to meet people’s need, a large number of high-rise, super high-rise and underground buildings continue to increase, which not only provides us with convenience, but also makes fire safety a hot concern of the whole society. Fire fighting plays an increasingly important role in the life of urban residents. In order to greatly reduce the lack of fire safety monitoring ability, this paper uses speech recognition technology to design a city fire safety management service platform based on big data Internet of things.
International Journal of Mathematical Models and Methods in Applied Sciences, E-ISSN: 1998-0140, Volume 16, 2022, Art. #17
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