Similarity data clustering method for dam safety monitoring data

Abstract

The invention discloses a similarity data clustering method for dam safety monitoring data. The method comprises the following steps of separating a single measuring point sequence trend term from thehigh-frequency noise by utilizing an EMD algorithm, detecting the time sequence change points by adopting an inflection point detection method of a cumulative sum control graph, and splitting to obtain all subsequence sets; adopting a DTW distance measurement method for calculating the distance problem of the subsequence, and calculating the distance minimum value between the two pieces of subsequence data dynamically; and clustering the mined sub-time sequences by using hierarchical clustering, and dynamically analyzing the time sequence clustering distribution condition under different clustering numbers through the obtained tree-shaped clustering graph. According to the method, the similarity of the monitoring data is reasonably analyzed, the correlation of the same monitoring point inthe time sequence can be mined, and meanwhile the correlation between the safety monitoring data can be quantified. And the monitoring data subjected to similarity analysis processing can accuratelyreflect the change trend of the dam in the time dimension, and the subsequent monitoring data mining difficulty can be effectively reduced in combination with the change trend rule.

Description

technical field [0001] The invention belongs to the technical field of time-space sequence prediction for dam safety monitoring, in particular to a similarity data clustering method for dam safety monitoring data. Background technique [0002] The dam safety monitoring data is the overall performance of the dam’s operating status. Under the influence of the same or similar external factors, there must be correlation between the data in each region. This correlation is mainly manifested in the similarity of time series trend changes or spatial trends. Changes are similar. For example, the displacement along the river of the dam has a similar change trend with the pressure and strain of each dam section, and there is a correlation between the temperature and the stress in each area of ​​the dam, etc. Reasonably analyze the similarity of monitoring data. Through similarity analysis, we can not only find out which monitoring variables are correlated, but also quantify the corre...

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Problems solved by technology

[0004] Purpose of the invention: In order to overcome the problem of single data processing in the traditional structural mechanics model in the prior art, which is limited by the engineering cognition and professional knowledge level of technicians, and the quality and efficiency of abnormal monitoring and screening are low, the present invention provides a The similarity data clustering method for dam safety monitoring data can reasonably analyze the similarity of monitoring data, which can not only dig out which monitoring variables are correlated, but also quantify the correlation between safety monitoring data; after similarity analysis The processed monitoring data can accurately reflect the change trend of the dam in the time dimension, and combined with the change trend law can effectively reduce the difficulty of subsequent monitoring data mining

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