基于深度残差网络的轻量化电力敏感数据处理模型设计OA
Design of Lightweight Processing Model for Power-sensitive Data Based on Deep Residual Network
电力系统中的敏感数据具有多尺度波动、通道异构和时序耦合等特性,传统模型难以兼顾识别精度与结构轻量性.为此,提出一种轻量化深度残差网络L-ResNet,融合多尺度深度卷积、通道注意力门控与动态残差融合模块,构建统一的敏感数据处理框架.该模型通过多尺度建模与自适应融合提升特征表达能力,并在负荷异常识别、用户行为分类和敏感区间提取等任务中均表现出优于主流模型的性能.结果表明,L-ResNet在保持高精度的同时显著降低了参数量与推理延迟,为电力敏感数据的高效处理与边缘部署提供了可行方案.
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.
何正军;石雪敏;杜涛;王雪梅;王佩霞
国网甘肃省电力公司天水供电公司,甘肃 天水 741000国网甘肃省电力公司天水供电公司,甘肃 天水 741000国网甘肃省电力公司天水供电公司,甘肃 天水 741000国网甘肃省电力公司天水供电公司,甘肃 天水 741000国网甘肃省电力公司天水供电公司,甘肃 天水 741000
信息技术与安全科学
电力敏感数据深度残差网络负荷识别多尺度卷积
power-sensitive datadeep residual networkload identificationmulti-scale convolution
《机械与电子》 2026 (4)
114-118,126,6
国网甘肃省科学技术项目(B3270225Z359)
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