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为了有效地判别和定位大坝安全监控数据中的粗差,将数学形态滤波应用于粗差检测,针对大坝监控数据的变化特点研究了其算法和结构元素的选取。首先根据实测数据的特点选择合适的结构元素,然后根据所选的结构元素对数据进行数学形态滤波处理,最后根据实测值与滤波结果之间差值的大小应用四分点法拟定门限来判别粗差。对某混凝土坝裂缝开度数据加入粗差以模拟实际的情况,并采用该方法来检测加入的粗差,滤波得到的结果有效抵挡了粗差的影响,加入的粗差全部被检测出。
In order to effectively identify and locate the gross errors in the dam safety monitoring data, the mathematical morphological filtering is applied to the gross error detection. According to the changing characteristics of dam monitoring data, the selection of algorithms and structural elements is studied. First of all, according to the characteristics of the measured data, select the appropriate structural elements, and then according to the selected structural elements of the mathematical morphology of data filtering, and finally according to the measured value and the difference between the filtered results using the quadrant method to determine the threshold to determine the crude difference. The coarseness difference is added to the crack opening data of a concrete dam to simulate the actual situation. The method is adopted to detect the added gross error, and the result obtained by the filtering effectively resists the influence of the gross error. The added gross errors are all detected.