Speaker
Description
The Large High-Altitude Air Shower Observatory (LHAASO) generates massive multi-dimensional operational data in the course of continuous operation, posing prominent challenges to efficient data management and rapid analytical decision-making. To address these challenges, this paper proposes a comprehensive framework integrating data orchestration, intelligent processing, and interactive diagnosis. It classifies LHAASO data by defining the core concepts of "event" and "parameter," realizing the structured organization of heterogeneous data. A hybrid architecture combining persistent storage and stream processing is designed to ensure the reliable integration, high-capacity storage, and low-latency retrieval of TB-level data, with a minimum response time reaching the millisecond level. Based on this, a large model-driven intelligent analysis module is developed: users input natural language requirements via a web interface, and the system can automatically generate data processing code, execute tasks, and visualize results, effectively lowering the technical threshold for non-experts. Experiments demonstrate that the framework can process data efficiently, the large model can enhance analytical convenience, and the system can complete anomaly detection and rapid positioning within seconds, providing an intelligent solution for LHAASO and a reference for the management, analysis, and intelligent development of massive data in large scientific facilities.
| Minioral | Yes |
|---|---|
| IEEE Member | Yes |
| Are you a student? | No |