In response to issues such as inadequate real-time monitoring, delayed risk assessment, and suboptimal maintenance strategies in oil and gas pipeline safety management, this paper designs and implements an oil and gas pipeline integrity intelligent management and prediction system. The system is based on a microservices distributed architecture, utilizing an improved BP neural network model for corrosion depth prediction, and integrating Monte Carlo simulation to construct a pipeline failure risk assessment model. The system achieves core functions including real-time data acquisition, intelligent risk assessment, and predictive maintenance. Test results demonstrate significant improvements over existing systems in response time, data processing capability, alert accuracy, and operational efficiency. In the future, the system will further integrate new technologies such as digital twins, edge computing, blockchain, 5G, and IoT to continuously enhance prediction accuracy and decision support, providing robust support for the intelligent operation and digital transformation of oil and gas pipeline industries.
Tang et al. (Wed,) studied this question.