Regional integrated energy side data adaptive deduplication backup algorithm

Regional integrated energy side data usually has a large number of generation and flow, involving multiple devices and sensors, so it is difficult to predict its clustering and redundant data, which affects the data de-duplication and backup effect. Therefore, this paper designs a new regional integrated energy side data adaptive deduplication algorithm. The characteristic quantity of fuzzy association rules of regional integrated energy data is established and the clustering is completed. Based on this, the redundant data of energy data is predicted, and the weight removal of energy data is realized by eliminating the redundant data. The backup of regional integrated energy data is realized through data pattern matching and backup coding threshold calculation. The experimental results show that the proposed method can effectively remove weight from regional integrated energy data, and the data loss rate is low in the process of data backup, so it has ideal application performance.

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Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1117/12.3067320
Citations
1
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
1.49
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Regional integrated energy side data adaptive deduplication backup algorithm

Xiaojing Li, Qin Wang, Chao Wei, Pengyang Li et al.
1 citations
Distributed and Parallel Computing Systems
1.49
article

Regional integrated energy side data adaptive deduplication backup algorithm

Xiaojing Li, Qin Wang, Chao Wei, Pengyang Li, Xiaoxi Ru
article en
1 citations

Abstract

Regional integrated energy side data usually has a large number of generation and flow, involving multiple devices and sensors, so it is difficult to predict its clustering and redundant data, which affects the data de-duplication and backup effect. Therefore, this paper designs a new regional integrated energy side data adaptive deduplication algorithm. The characteristic quantity of fuzzy association rules of regional integrated energy data is established and the clustering is completed. Based on this, the redundant data of energy data is predicted, and the weight removal of energy data is realized by eliminating the redundant data. The backup of regional integrated energy data is realized through data pattern matching and backup coding threshold calculation. The experimental results show that the proposed method can effectively remove weight from regional integrated energy data, and the data loss rate is low in the process of data backup, so it has ideal application performance.

Shanghai Electric (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 17%
Distributed and Parallel Computing Systems
1.49
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