Studying the well-known phenomenon “aurora” plays a pivotal role in investigating the solar–terrestrial coupling mechanism. A special auroral spectrograph in Antarctic Zhongshan Station constitutes a auroral observation joint system with satellite-borne sensors of the Defense Meteorological Satellite Program. Multipoint observation by this system provides more essential information for relevant studies than single observation by each instrument, but also results in a multifold increased volume of data that are difficult to be either stored or transmitted. To address this difficulty, we develop a clustering-based, generic lossless data compression framework that combines the usage of various ultimate compressors with a hierarchical clustering algorithm to exert the strength of all the compressors in data reduction. This framework achieves an always-best compression performance for different-sized datasets with a reasonable time consumption, which promises the design of pipelines using it for real-time data transmission.

研究众所周知的“极光”现象对于研究日地耦合机制具有举足轻重的作用。南极中山站的专用极光光谱仪与国防气象卫星计划的星载传感器构成极光观测联合系统。该系统的多点观测比每个仪器的单点观测为相关研究提供了更多的必要信息，但也导致数据量成倍增加，难以存储或传输。为了解决这个困难，我们开发了一个基于聚类的通用无损数据压缩框架，该框架将各种终极压缩器的使用与分层聚类算法相结合，以发挥所有压缩器在数据缩减方面的优势。