对变形监测序列进行数据建模分析时，含有误差的监测数据会对模型的建立产生较大的影响。提出一种“事件”模型改变原始观测序列的表达形式，通过对原始观测数据进行转换，对变形监测数据按照某种规则进行分类划分，划分后的数据能够适应Cox模型。根据经验或者实际情况，适当调节“事件”模型参数，在一定程度上可以减弱观测误差带给Cox回归模型的影响，算例分析进一步论证了该模型计算结果的准确度能够进一步地提高计算结果的准确度。

Errors of deformation monitoring data has great impact on the modeling of the data sequences. A new model called Event-Model, which changes the expression of the original observation data, was proposed. First, the deformation rate at each monitoring point in the original data was calculated, and then the rates were divided into several ranges. By converting the original observation data into events, the numerical data were divided into categorical data which were composed of the matrix, named Event Matrix in this paper, which could be fitted to the Cox regression model. Each group of categorical data contained certain ranges. On the basis of experiences and the actual situation such as the average displacement rate calculated previously, parameters in the “Event-Model” were adjusted to avoid or weaken the effect of observation errors. The mean square error of the observation data could be treated as adjusted parameters instead of unknown true errors in practice. The influence of monitoring data containing observation errors on the follow-up Cox regression model can be reduced to some extent and improve the accuracy of the calculation results.