Speaker
Description
Superconducting tokamak fusion facilities continuously generate large volumes of plant data that must be archived reliably and queried efficiently to support system monitoring, fault analysis, and long-term operation. Currently, fusion devices widely adopt the EPICS (Experimental Physics and Industrial Control System) architecture, and plant data are typically archived in files or relational databases. However, these approaches exhibit limited query performance when handling large-scale time-series plant data, making it essential to archive plant data into TSDB (Time Series Databases), which have been demonstrated to deliver excellent query performance when storing massive time-series plant data. To remedy this problem, we propose the EPICS–TSDB Real-Time Archiving Framework (ETRA). Specifically, our approach archives EPICS process variables (PVs) into the TSDB backend in real time. By leveraging TSDB-native data organization and indexing mechanisms, it significantly improves query efficiency for large-scale plant data, alleviating the access performance limitations of relational databases and file-based formats. Furthermore, the ETRA provides an AI model interface. This enables the archived plant data to be used directly for machine-learning inference without moving the data to a separate ML service platform, thus accelerating data processing. ETRA has been fully designed and functionally implemented, and it is planned for deployment on EAST (Experimental Advanced Superconducting Tokamak).
| Minioral | Yes |
|---|---|
| IEEE Member | No |
| Are you a student? | Yes |