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<p>Aiming at the challenge of storing massive power grid data, this paper proposes an improved swing gate trend algorithm to effectively compress 5G data. The algorithm first performs least squares smoothing on the original data to reduce noise interference on the SDT algorithm, which enables the data compression process to more accurately determine the data trend. Further, the shortcomings of the original SDT algorithm are improved, including adaptive frequency conversion data processing, dynamic threshold adjustment, and anomaly recording strategy, to enhance the practicality and efficiency of the algorithm. Through simulation analysis and example data validation, the study shows that the data compression ratio can be stabilized at about 23.98 when the data compression time reaches 1.6 minutes, and the actual error is very close to the desired error. The time overhead of the improved SDT algorithm is only 0.225 seconds, indicating that the algorithm is efficient and reliable. Combined with different data compression storage strategies, the algorithm can further reduce the data compression time. This study provides an adequate data compression method for electric code violation identification, which offers a practical solution for processing and storing large-scale grid data.</p>
</abstract>

Aiming at the challenge of storing massive power grid data, this paper proposes an improved swing gate trend algorithm to effectively compress 5G data. The algorithm first performs least squares smoothing on the original data to reduce noise interference on the SDT algorithm, which enables the data compression process to more accurately determine the data trend. Further, the shortcomings of the original SDT algorithm are improved, including adaptive frequency conversion data processing, dynamic threshold adjustment, and anomaly recording strategy, to enhance the practicality and efficiency of the algorithm. Through simulation analysis and example data validation, the study shows that the data compression ratio can be stabilized at about 23.98 when the data compression time reaches 1.6 minutes, and the actual error is very close to the desired error. The time overhead of the improved SDT algorithm is only 0.225 seconds, indicating that the algorithm is efficient and reliable. Combined with different data compression storage strategies, the algorithm can further reduce the data compression time. This study provides an adequate data compression method for electric code violation identification, which offers a practical solution for processing and storing large-scale grid data.

Compression of electrical code violation recognition data using the improved swinging door trending algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Aiming at the challenge of storing massive power grid data, this paper proposes an improved swing gate trend algorithm to effectively compress 5G data. The...

Compression of electrical code violation recognition data using the improved swinging door trending algorithm

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 10 janv. 2024

Accepté: 15 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0478

Mots clés
Least squares, Swing gate trend algorithm, Adaptive frequency conversion, Dynamic threshold adjustment, Data compression

© 2024 Yingchun Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Compression of electrical code violation recognition data using the improved swinging door trending algorithm

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 10 janv. 2024

Accepté: 15 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0478

Mots clésLeast squares, Swing gate trend algorithm, Adaptive frequency conversion, Dynamic threshold adjustment, Data compression

© 2024 Yingchun Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Least squares, Swing gate trend algorithm, Adaptive frequency conversion, Dynamic threshold adjustment, Data compression