Abstract：The integrated modeling of massive dynamic data sets is a key basic technology that needs to be solved for digital product twins. The data modeling target is identified by analyzing the characteristics of various data types for the digital product twin. The features of various data storage modes were studied here to develop a digital product twin modeling method for massive dynamic data sets based on a time-series database. The structure, attributes and scale characteristics of the dynamic product data are used to improve the database performance. A case analysis of rolling bearing vibration data sets is used to show how this method works with massive dynamic data sets.
郑孟蕾, 田凌. 基于时序数据库的产品数字孪生模型海量动态数据建模方法[J]. 清华大学学报（自然科学版）, 2021, 61(11): 1281-1288.
ZHENG Menglei, TIAN Ling. Digital product twin modeling of massive dynamic data based on a time-series database. Journal of Tsinghua University(Science and Technology), 2021, 61(11): 1281-1288.
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