Data super-resolution reconstruction method, device and system based on reference transformer

By employing a reference transformer-based super-resolution reconstruction method, which utilizes feature extraction, attention processing, and reconstruction modules, the limitations of feature representation and noise issues in existing power data super-resolution methods are addressed, achieving efficient and accurate mapping from low-frequency data to high-frequency data.

CN117272027BActive Publication Date: 2026-04-28XIDIAN UNIV
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Patent Information

Application Number
CN202311301525.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-10-09
Publication Date
2026-04-28
Estimated Expiration
2043-10-09

AI Technical Summary

Technical Problem

Existing super-resolution methods for power data, based on convolutional neural networks, can only collect short-term information and have limited feature representation capabilities. On the other hand, methods based on generative adversarial networks are sensitive to hyperparameters, and the generated super-resolution data may contain noise. Furthermore, replacing high-frequency electricity meters is costly.

Method used

A super-resolution reconstruction method based on a reference transformer is adopted. Through a feature extraction module, an information supplementation module, and a super-resolution reconstruction module, the attention mechanism of the transformer is used for feature extraction, attention processing, and feature fusion. Combined with continuous reconstruction and periodic reconstruction, the mapping accuracy from low-frequency data to high-frequency data is improved.

Benefits of technology

It enhances the correspondence between low-frequency and high-frequency data, fully expresses the long-term characteristics of the data, reduces the loss of data details, and improves the accuracy and robustness of data reconstruction.

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Abstract

The application provides a reference transformer-based data super-resolution reconstruction method, device and system, and relates to the technical field of data processing.The reference transformer-based data super-resolution reconstruction method comprises the following steps: obtaining to-be-transformed low-frequency load data, performing logarithmic sum and normalization processing on the to-be-transformed low-frequency load data, and performing data division according to a conversion sequence length to obtain a preprocessed to-be-transformed low-frequency load sequence; inputting the preprocessed to-be-transformed low-frequency load sequence into a qualified data super-resolution reconstruction model to obtain reconstructed super-resolution high-frequency data; through the design of a separate feature extraction module and an information supplement module and the feature fusion mode of low-frequency load features, the corresponding relationship between low-frequency data and high-frequency data is strengthened, and feature expression is more comprehensive; in addition, continuous reconstruction and periodic reconstruction fully utilize the change characteristics of data, fully express long-term features of data, reduce the loss of data details, and improve the accuracy of data reconstruction.
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