Online Monitoring of Self-elevating Leveling Ship Based on Edge Computing

The development of Internet of Things technologies has provided potential for monitoring industrial equipment. The self-elevating leveling ship collects operating data through Internet of Things to monitor and predict the production process. However, at present, the monitoring data of leveling ships is multi-source, heterogeneous, with a large amount of data and limited network resources. It is difficult to realize realtime processing. To address this problem, an online monitoring system based on edge computing is proposed. The system places data stream processing tasks at edge nodes, and stream processing technology is used to support the collection and processing of monitoring data. By defining a unified data model and XML expressions for complex event processing rules, the stream processing process is standardized. In addition, the system customizes the stream processing strategy and designs a custom window operator to recalculate the delay data. The results show that the system has a large throughput between applications on the stream processing platform and a shorter data stream processing time. The system can monitor and process of leveling ship’s operation data in real time.