Power equipment data processing method and device, electronic equipment and storage medium

By using multi-order moving average filtering algorithms, weighted averaging, classified identification and compression perception technologies to process power equipment data on edge computing devices, the challenge of edge equipment processing large-scale power equipment data is solved, and efficient data processing and transmission is achieved.

CN119884862APending Publication Date: 2025-04-25GUANGZHOU KETENG INFORMATION TECH
0 Cites 0 Cited by

Patent Information

Application Number
CN202411905292.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-23
Publication Date
2025-04-25

AI Technical Summary

Technical Problem

In smart grids, edge computing devices face the challenge of processing large-scale power equipment data, especially when the data volume is huge and the acquisition frequency is high, the computing power and storage space of edge devices are limited, making it difficult to process complex data.

Method used

The multi-order moving average filtering algorithm is used to smoothly process the power equipment data, group and weighted average, classify and identify and apply different aggregation strategies, and finally compress and transmit the data through compression perception technology.

Benefits of technology

Through these steps, instantaneous noise in the data is removed, data accuracy and stability are improved, data redundancy is reduced, data processing speed is improved, data transmission volume and delay are reduced.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The embodiment of the invention discloses a power equipment data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring power equipment data, and smoothing the power equipment data by adopting a multi-order moving average filtering algorithm; grouping the smoothed power equipment data according to time periods, allocating a corresponding weight to each group and performing weighted averaging to obtain each group of weighted aggregated power equipment data; classifying and identifying each group of weighted and aggregated power equipment data, and applying different aggregation strategies based on the identified data types to obtain aggregated power equipment data of different data types; and compressing the aggregated power equipment data of different data types in a compressed sensing mode, and transmitting the compressed power equipment data to a central server according to different data types. By adopting the scheme, the data redundancy is reduced, the data processing speed is improved, and the data transmission quantity and delay are reduced.
Need to check novelty before this filing date? Find Prior Art