房屋安全问题愈发受到重视，对于老旧房屋的自动化健康监测十分必要。在此背景下，优化自动化监测数据的预处理流程，使用Python丰富的“库”资源构建预处理程序。程序使用灰色关联法对数据进行可信度评估筛选数据，利用箱形图筛选异常数据，并对异常数据和缺失数据进行插补替换，最后使用卡尔曼滤波平滑数据以降低数据离散度；自动化监测设备因自身特点易受到周围环境的影响，为此提出了一种利用人工监测数据校核自动化监测数据的数据比对方法，以保证自动化监测数据的准确性和可靠度；最后使用拟合曲线实现变形监测数据的短期预测，预测精度达到0.1%，有效预测时间约10 d，最终的结果满足程序设计需求。

With increasing attention paid to house safety, automatic health monitoring of old buildings becomes a necessity. In this context, the preprocessor of automatic monitoring data was optimized and a preprocessor was built leveraging Python's abundant "library" resources. The data was screened according to the credibility through Grey Relational Analysis. The abnormal data was screened through Box-plot. Then, the abnormal data and missing data were interpolated and replaced. Finally, the data was smoothed by Kalman filtering so that the dispersion was reduced. Given automatic monitoring equipment was sensitive to surrounding environment, a data comparison method to check automatic monitoring data with manual monitoring data was proposed. This method was aimed at ensuring the accuracy and reliability of automatic monitoring data. The short-term deformation monitoring data was predicted with fitted curve. The prediction accuracy reaches 0.1% and the effective prediction time is about 10 days. This result can meet the need of researching.