基于北斗GNSS的边坡自动化实时监测数据处理及变形预测方法

Method of Data Processing and Deformation Prediction for Slope Automatic Real-time Monitoring Based on Beidou GNSS

目前边坡实时自动化监测技术已得到较为广泛的应用,但在复杂条件下监测精度有所下降,获取的数据存在不同程度误差。以某边坡为例,运用北斗全球导航卫星系统(GNSS)实现自动化采集边坡实时监测数据,采用莱茵达准则识别监测数据序列中的异常值以剔除粗大误差,同时使用插值算法对缺失数据进行填补;构建GM(1,1)灰色模型对边坡变形监测数据进行处理及预测,以判断该边坡监测点的变形发展趋势及安全状态。工程应用表明,该方法的数据处理结果较为理想,可以有效区分异常值与正常值;模型对原数据的拟合程度较好,预测结果较为可信。

The real-time automatic monitoring technology of slope has been widely used currently, but its monitoring accuracy has decreased under complex conditions, and the obtained data appear different degrees of error. This paper takes a slope as the research object, and real-time monitoring data of slope was collected by the Beidou Global Navigation Satellite System(GNSS). Rhineda criteria were used to identify the abnormal values in the monitoring data, then eliminating the gross errors. The interpolation algorithm was used to fill in the missing data. The GM(1,1) grey model was established to process and predict the slope deformation monitoring data so as to judge the deformation development trend and safety state of the slope monitoring point. The engineering application results show that the data processing method can effectively distinguish the abnormal values from normal values. The proposed model fits well with the original data, and the predicted results are reliable.