Influence Factors of Powerline Communication by Artificial Intelligence Approach based on Rock Electric Signal

Abstract

The field of powerline communication (PLC) has been around for more than a century and uses the power line as a means of information transport. Because it utilizes the current system to transfer data using electrical power, it is advantageous because it prevents the need for new installations. The impact of coal and rock electric signals is examined using the established test methodology, taking into account the coal and rock temperature, characteristics, friction, loading speed, and load size. The main findings can be summed up as follows: Depending on the intensity of the electrical signals produced, the change rule of electrical signals in the coal rock rising with temperature can be separated into three stages. The electrical signals produced by the uniaxial compression of coal-rock mass with varying properties have distinctly different amplitudes. The friction between coal and rock can produce electrical impulses. As loading speed increases, the electrical signal that indicates when the coal rock is prepared to fracture tends to get stronger. The information bottleneck perspective is then used to study information theory and artificial intelligence. In conclusion, we go through a few concepts about the deep integration of AI methods with wireless communication networks. The fundamental aspects of AI that are pertinent to various uses have been briefly addressed at the outset of the paper.