机械与电子2026，Vol.44Issue(4)：114-118,126,6.
基于深度残差网络的轻量化电力敏感数据处理模型设计
Design of Lightweight Processing Model for Power-sensitive Data Based on Deep Residual Network
摘要
Abstract
Sensitive data in electrical power systems typically exhibits characteristics such as multi-scale fluctuations,channel heterogeneous,and strong temporal coupling,making it difficult for traditional models to balance recognition accuracy and structural lightweightness.To address this,a Lightweight Re-sidual Network(L-ResNet)is proposed,integrating Multi-scale Depthwise Convolution(MDC),Chan-nel Attention Gating(CAG),and Dynamic Residual Fusion(DRF)modules to construct a unified frame-work for processing sensitive data.This model enhances feature representation capability through multi-scale modeling and adaptive fusion.It demonstrates superior performance over mainstream models across tasks including load anomaly identification,user behavior classification,and sensitive interval extraction.The results show that L-ResNet significantly reduces the number of parameters and inference latency while maintaining high accuracy,providing a feasible solution for the efficient processing and edge deploy-ment of power-sensitive data.关键词
电力敏感数据/深度残差网络/负荷识别/多尺度卷积Key words
power-sensitive data/deep residual network/load identification/multi-scale convolution分类
信息技术与安全科学引用本文复制引用
何正军,石雪敏,杜涛,王雪梅,王佩霞..基于深度残差网络的轻量化电力敏感数据处理模型设计[J].机械与电子,2026,44(4):114-118,126,6.基金项目
国网甘肃省科学技术项目(B3270225Z359) （B3270225Z359）
