Closed graph similarity search method based on time sequence complexity difference

Abstract

The invention provides a closed graph similarity search method based on time sequence complexity difference, which comprises the following steps: S1, observing a closed graph to obtain a time sequenceof the closed graph; S2, setting the complexity difference CO of the two time sequences; S3, setting a complexity time measurement standard TSD; S4, finishing neighbor search on the complexity time measurement standard in the S3 by adopting an exhaustion method; S5, correcting the distance value by adopting a triangular inequality; S6, obtaining a closed position of the two-dimensional relative graph in the serialized index data structure; the problem that an existing graph similarity search method cannot achieve similarity search of two-dimensional graphs is solved. Meanwhile, the time dimension of a time sequence is greatly solved, the effectiveness of graph rotation in the measurement process is ensured, measurement standards are given for time sequences with different complexity degrees, and finally similarity search of two-dimensional closed graphs is achieved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence search methods, in particular to a closed graph similarity search method based on time series complexity differences. Background technique [0002] Similarity search is an effective method for shape matching of two-dimensional closed graphs. Similarity search was first applied in the process of time series search. Firstly, the time series is mapped to a single-dimensional space through indexing, secondly, Euclidean distance is used for measurement, and finally the final matching result is obtained on the basis of sequential scanning and other methods. The limitation of the similarity search method is that changing the sample length of the query will get faster search results, but will lead to lower precision. Whether the similarity sample sequence of the two-dimensional closed graph is representative depends on the high trust in the sampling method and the calculation method of th...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a closed graph similarity search method based on time series complexity differences to solve the existing graph similarity search method proposed in the above background technology. The similarity search of two-dimensional graphics cannot be realized. At the same time, the time dimension of time series has been solved, so the effectiveness of the rotation method cannot be guaranteed, and the complexity time metric cannot be given, which affects the determination of the time dimension or attributes of two-dimensional graphics.

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