A pipeline corrosion monitoring and assessment system integrating artificial intelligence and sensor networks

This paper designs and implements a pipeline corrosion monitoring and assessment system that integrates artificial intelligence and sensor networks. The system utilizes a distributed sensor network to collect corrosion data from key pipeline locations. A wireless sensor network based on the ZigBee protocol is constructed for data transmission, and a hybrid CNN-LSTM deep learning model is developed for intelligent evaluation and early warning. Field trial results demonstrate that the system achieves high detection accuracy and effective early warning capabilities, significantly improving the automation and efficiency of pipeline corrosion monitoring. This system provides robust technical support for pipeline integrity management.