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Operation data prediction algorithm of information system based on discrete second-order difference

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Abstract

This study proposes an algorithm for predicting the running data of information systems based on discrete second-order difference clustering. The wide stationary time series model of information system operation data is established, and the association rules mining and feature distributed transmission sequence fitting of information system operation data are conducted by binary semantic information representation method. The principal component feature detection and matching of information system operation data are carried out. High-order spectral feature analysis and extraction of information system operation data is realized based on big data analysis, and the prediction algorithm is improved. The proposed method has high accuracy, good convergence and high real-time performance, which can improve the scheduling ability of information system operation data.

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Data availability

The datasets used and/or analyzed during the current study are available.

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Funding

This paper is a project supported by the information center of Yunnan Power Grid Co., Ltd. the project name is business monitoring center (business monitoring platform phase I) construction project, and the Number is 059300hk42170046.

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  1. Ministry of Science and Technology, Yunnan Power Grid Co., Ltd., Kunming, 650000, China

    Beining Sun

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  1. Beining Sun
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Correspondence to Beining Sun.

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Sun, B. Operation data prediction algorithm of information system based on discrete second-order difference. Wireless Netw 28, 2765–2774 (2022). https://doi.org/10.1007/s11276-021-02746-4

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