Research on Online Monitoring Method of Multi-Environmental Factors of Underground Cable Pipe Gallery Based on NB-IoT

This paper presents a new approach to real-time monitoring of underground cable corridors using Internet of Things (NB-IoT) technology. The system integrates advanced sensor technologies and wireless communication modules for collecting and transmitting environmental data, including temperature, humidity, and harmful gas levels. The proposed system addresses critical safety issues and improves the management efficiency of underground cable grids, especially in response to extreme weather events such as flooding. The architecture of the system focuses on providing a visual early warning system for potential risks within cable corridors, ensuring effective operational monitoring and management in high stress environments. The paper also outlines predictive models for dealing with data loss in IoT communications due to factors such as electromagnetic interference, cyber-attacks and human error. By combining a hybrid prediction model based on a Long Short-Term Memory (LSTM) grid and a Multi-Layer Perceptron (MLP) error correction method, the system achieves reliable prediction of incomplete industrial IoT communication data. Finally, the practical application of this monitoring system is evaluated, emphasizing its importance in maintaining the safety and stability of underground cable tunnels under extreme natural conditions.