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Description
The Jiangmen Underground Neutrino Observatory (JUNO) is a multipurpose neutrino experiment featuring the world’s largest liquid scintillator detector. With a 20-kton target mass instrumented with more than 17,000 20-inch and 25,600 3-inch photomultiplier tubes (PMTs), JUNO enables neutrino measurements with unprecedented precision.To ensure the stable operation of this highly complex detector, a robust and scalable distributed Detector Control System (DCS) has been developed. The core acquisition software for subsystem Input/Output Controllers (ASIOCs), implemented using EPICS and Java, adopts distributed algorithms for data partitioning and parallel acquisition. This architecture efficiently manages the massive data flow from approximately 40 million channels and stores the processed information in a centralized database.As a key subsystem of the DCS, the high-voltage control system provides precise regulation of tens of thousands of high-voltage channels, ensuring the safe and stable operation of the PMTs. Each Input/Output Controller (IOC) communicates with the front-end electronics via the IPbus protocol to perform high-voltage control and data readout, while the supervisory computer interacts with the IOCs through the Channel Access (CA) protocol, enabling centralized monitoring and control.By adopting a modular architecture, concurrent multi-channel operation, and asynchronous driving techniques, the system achieves significantly improved real-time performance and operational concurrency. Performance tests demonstrate that the high-voltage control system fully satisfies the design requirements of the JUNO experiment. After ten months of continuous stable operation, the system has proven its reliability, robustness, and scalability, meeting the demands of JUNO’s long-term data-taking program.This paper presents the architecture and functional implementation of the distributed control system, reports performance test results on the EPICS platform, and discusses potential future upgrades incorporating artificial intelligence techniques.