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.