面向桥梁健康监测的数据压缩算法研究
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1.四川数字交通科技股份有限公司;2.西南交通大学综合交通大数据应用技术国家工程实验室;3.西南交通大学信息科学与技术学院

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TP391

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四川省科技创新基地(平台)和人才计划项目(2022JDR0356);四川省科技计划项目(软科学项目)(2021JDR0101);宜宾市双城市校协议专项科研经费科技项目(SWJTU2021020005);国重:公路交通系统全息感知与数字孪生技术及应用示范(2022ZD0115600)


CHEN Ken1, ZHONG Ai-ping1, WEN Yu-xuan2,3, YANG Yang1, LI Wei1, ZENG Shan2,3, WANG Jun1, TAN Qu-shan, YANG Liu2,3 *(1. Sichuan Digital Transporation Tech Co., Ltd, Chengdu 610095,China;
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SICHUAN DIGITAL TRANSPORTATION TECH CO., LTD

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    摘要:

    基于传感器数据采集的桥梁健康监测系统已经成为新建桥梁的标配,在这种场景下所带来的是海量监测数据难以存储的问题。因此,针对桥梁监测数据的时间序列特性,研究了桥梁监测数据的压缩方案,该方案研究了基于桥梁监测时间戳数据等差数列性质的差量压缩法和基于监测值数据变化频率不大的浮点数异或压缩法。与Gorilla时序数据库的算法相比,增加了异或压缩法的控制位,避免了压缩结果的恶化。通过实验分析得出,两算法对比常用压缩器有不同程度的优势,时间戳序列差量压缩法在压缩率上优于常用压缩器,针对符合等差数列特性的时间戳序列,压缩率0.0 156,接近压缩极限值,压缩解压速度位居中上,并且对监测类型不敏感。而异或压缩法在变化频率不大的数据集上表现较好,压缩率0.3 028,在非桥梁数据集上压缩率0.6 628,表明异或压缩法对监测类型比较敏感。在桥梁监测的实际应用场景中,可以根据桥梁监测数据集的特点选择合适的压缩存储方案。

    Abstract:

    The bridge health monitoring system based on sensor data acquisition has become standard for new bridge construction. However, this scenario presents challenges due to the massive volume of monitoring data that is difficult to store. Therefore, focusing on the time-series characteristics of bridge monitoring data, this study explores compression schemes for bridge monitoring data. The study investigates differential compression based on the arithmetic progression properties of bridge monitoring timestamps and floating-point XOR compression based on the low frequency of changes in monitoring value data. Compared to the Gorilla time series database algorithm, the XOR compression method adds control bits to avoid degradation of compression results. Experimental analysis reveals that both algorithms exhibit varying degrees of superiority over common compressors. The differential compression of timestamp sequences demonstrates superior compression rates compared to common compressors, achieving a compression rate of 0.0 156 for timestamp sequences that conform to arithmetic progression characteristics, approaching the compression limit value. Compression and decompression speeds are above average, and the method is insensitive to monitoring type. On the other hand, the XOR compression method performs well on datasets with low frequency of change, achieving compression rates of 0.3 028 for bridge data and 0.6 628 for non-bridge data, indicating sensitivity of the XOR compression method to monitoring type. In practical applications of bridge monitoring, suitable compression storage schemes can be selected based on the characteristics of the bridge monitoring dataset.

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陈垦,钟爱平,文煜轩,等. 面向桥梁健康监测的数据压缩算法研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-02-20
  • 最后修改日期:2024-05-10
  • 录用日期:2024-05-21
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