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（exclusive OR, XOR）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0.015 6，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0.302 8，在非桥梁数据集上压缩率0.662 8，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。

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, compression schemes were explored for bridge monitoring data. Differential compression was investigated based on the arithmetic progression properties of bridge monitoring timestamps and floating-point exclusive OR(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 added 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 0.015 6 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 0.302 8 for bridge data and 0.662 8 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.