The Application of Intelligent Computing and Machine Learning in OPGW Temperature Monitoring and Early Warning System

The construction scale of transmission lines continues to expand, and the safety, stability and reliability of their operation have become particularly important. This article proposes a temperature monitoring and early warning system based on Internet of Things technology to address the temperature problem in OPGW (Optical Power Grounded Wave) lines. This article uses intelligent computing and machine learning technology to monitor and provide early warning of OPGW temperature data, and applies Savitzky-Golay filtering, Bayesian and other algorithms to process and analyze the temperature data, thereby achieving temperature monitoring and early warning. In the experiment, different solutions were used to test the OPGW temperature data. Experimental data shows that as the temperature increases, its Brillouin frequency value changes in proportion to the difference between 0.01 and 0.02, respectively, and then increases. The system uses advanced temperature sensors, wireless communication technology and big data analysis methods to achieve real-time temperature monitoring and early warning of the OPGW line.