Gross error elimination method for dam displacement data

Abstract

The invention discloses a gross error elimination method for dam displacement data. The gross error elimination method comprises the steps of setting weight coefficients for four off-group point judging criteria, namely a Dixon criterion, a Grubbs criterion, a Chauvenet criterion, and a quartile criterion, and setting a gross error judging threshold value; then, for each measured value, carrying out judgment respectively by using above four criteria, and if a judgment result under a certain criterion is a suspected gross error, accumulating the weight coefficient corresponding to the criterion; and at last, obtaining a weight coefficient sum of the measured value, and comparing with the gross error judging threshold value, wherein the measured value with the weight coefficient sum up to or exceeding the gross error judging threshold value is judged as a gross error. According to the gross error elimination method disclosed by the invention, multiple off-group point data identification methods are considered comprehensively, the possibilities of misjudgment and judgment omission are weighed, and a scientific and reasonable method is provided to eliminate the gross error in the dam displacement data.

Description

technical field [0001] The invention relates to a gross error elimination method for dam displacement data, belonging to the technical field of dam safety monitoring. Background technique [0002] At present, my country's dam safety monitoring is generally in a special stage. Many old dams are about to reach the design reference period, and a large number of new dams are being put into operation. Therefore, it is necessary to predict the dam safety monitoring indicators to provide safety monitoring decision-making assistance. . The displacement of the dam is one of the main indicators of dam safety monitoring, which plays an important role in the overall performance evaluation of the dam, and its abnormal measurement value should be paid special attention and analysis. The outliers caused by factors such as measurement system and human operation should be regarded as gross errors and eliminated to avoid interfering with subsequent analysis and evaluation operations. [0003...

AI Technical Summary

Problems solved by technology

The expert experience method mainly relies on the engineering experience of hydraulic safety monitoring experts to determine the gross error, which is more dependent on subjective cognition; the statistical model method mainly relies on a certain strategy, such as based on the extreme value, amplitude, and fitting value. The size of the residual error, etc., the use of data is not comprehensive and in-depth, there is a large risk of misjudgment and missed judgment

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