航天器环境工程2025，Vol.42Issue(5)：494-503,10.DOI:10.12126/see.2025092
基于K-means聚类算法和贝叶斯网络模型的沿海地区温湿度数据挖掘
Temperature and humidity data mining in coastal areas using K-means clustering and Bayesian network models
摘要
Abstract
Spacecraft to be launched at sea are exposed to harsh,hot,and humid corrosive environments for extended periods.These regional climatic conditions significantly constrain their operational performance and storage reliability.However,the correlation between test profiles derived from existing environmental testing methods and actual environmental data remains insufficient.In this study,the coastal area of Wanning(in Hainan province,China)was selected as the research site.A distributed monitoring system was used to collect real-time temperature and humidity data from three types of sites:outdoor exposed areas,sheds,and storage rooms.The K-means clustering and K2 Bayesian network structure learning algorithms were applied to characterize the intrinsic structures of the temperature and humidity data and to explore their underlying patterns.The results showed that the temperature and humidity distributions could be clustered into three typical patterns:moderate temperature with high humidity,high temperature with low humidity,and low temperature with low humidity.A Bayesian network model was established to reveal the probabilistic relationships among months,sites,and temperature-humidity patterns,enabling the generation of probabilistic environmental profiles based on actual mission parameters.The proposed method demonstrates excellent adaptability in identifying coastal environmental characteristics and offers a viable methodology for the design of spacecraft adaptability to the hot and humid marine environment,and the customized design of test conditions.关键词
K-means聚类/贝叶斯网络模型/海洋环境/温度-湿度/数据挖掘Key words
K-means clustering/Bayesian network/marine environment/temperature-humidity/data mining分类
航空航天引用本文复制引用
任浩源,金晶,王艳艳,常汉江,史泰龙..基于K-means聚类算法和贝叶斯网络模型的沿海地区温湿度数据挖掘[J].航天器环境工程,2025,42(5):494-503,10.基金项目
国家国防科技工业局技术基础科研项目(编号:JSHS2020209A001) （编号:JSHS2020209A001）
国家自然科学基金项目(编号:11902363) （编号:11902363）
