Similar hydrologic process searching method using user interaction

Abstract

The invention discloses a similar hydrologic process searching method using user interaction. An Euclidean distance with weight is used as similar measurement, similar searching is conducted on a search sequence designated by a user, the user conducts marking on a search result, and similar level or dissimilar level is set on the search result according to the understanding of the user on a search sequence mode; the similar or dissimilar sequence properties are combined by an algorithm, the weight is regulated, so that a search sequence better meeting requirements of the user is generated, search is conducted circularly until the user terminates the search process. According to the similar hydrologic process searching method using user interaction, the user interaction is used for regulating the search sequence and the weight, the search accuracy is improved, and the accuracy of the hydrologic sequence similar search is improved.

Description

technical field [0001] The invention relates to information processing technology, in particular to a search method for similar hydrological processes utilizing user interaction. Background technique [0002] Time series similarity search is to find and find time series similar to a given pattern in the time series database. The process of finding similar subsequences is often encountered in practical problems. For example, in the human genome project, from DNA gene sequence Find sub-fragments similar to a given gene segment in the given gene segment, and conduct research based on genetic similarity; find out similar product sales patterns based on the sales records of various commodities, and formulate similar sales strategies based on the sales patterns of similar products strategies, etc.; find out the same precursors of natural disasters, so as to conduct decision-making research on natural disasters; in the field of hydrology, find out the historical flood process simil...

AI Technical Summary

Problems solved by technology

In terms of time series similarity search, in 1998, EamonnJ.Keogh et al. proposed a time series exploration framework based on relevant feedback, which can be classified and clustered. The time series is described by piecewise linear fitting (PLR) with weights. Each segment has a weight that describes the importance of the segment. During the retrieval process, the weight is corrected through user interaction, but the PLR ​​calculation complexity is high. At the same time, when calculating the distance between two subsequences, further segmentation and alignment are required. At the same time PLR description cannot be effectively indexed

In 2002, Zheng Binxiang et al. used discrete Fourier transform to reduce the dimensionality of time series, and used R-tree to build an index for similarity retrieval. The user marked the result sequence and gave the importance of each result sequence. The new query sequence It is a linear combination of the old query sequence and all the result sequences with importance as the coefficient. This method cannot consider the importance of different parts of the sequence. Generally, the implicit pattern of a sequence for a period of time is often determined by a part of the sequence, while other parts have no influence on the pattern of the sequence. relatively small impact

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