山东电力技术2025，Vol.52Issue(5)：57-66,10.DOI:10.20097/j.cnki.issn1007-9904.2025.05.007
基于改进注意力机制的多类型数据分类分级方法
A Multi-type Data Classification and Grading Method Based on Improved Attention Mechanism
摘要
Abstract
Power data contains a large amount of sensitive information,and the leakage of such sensitive data may have a serious impact on society.In view of the characteristics of various types of power data,complex data features,and significant imbalance in data distribution,this paper innovatively proposes a multi-type data classification and grading method.Firstly,it maps the original power data to a high-dimensional space through a context-aware power multi-type data distribution feature learning network,so that the extracted high-dimensional vectors contain distribution information between various types of data,improving the model's ability to perceive the distribution of power data at different security levels.Then,combined with the sensitive information gain change rate of noise-added features,it optimizes the high-dimensional sensitive feature vectors containing different levels of data distribution information,and finally classifies the feature sensitive attention score vectors through a lightweight support vector machine classification model to achieve classification and grading of power data.Experimental results show that this method can effectively learn the distribution information of multi-class data,improve the accuracy of classification and grading,and strongly support asset inventory and refined security protection of power data.关键词
数据分类分级/改进注意力机制/深度学习Key words
data classification and grading/improve attention mechanisms/deep learning分类
计算机与自动化引用本文复制引用
张昊,翟雨佳,黄秀丽..基于改进注意力机制的多类型数据分类分级方法[J].山东电力技术,2025,52(5):57-66,10.基金项目
国家电网有限公司科技项目"电力业务敏感数据智能化分类分级技术研究"(5700-202258181A-1-1-ZN).Science and Technology Project of State Grid Corporation of China"Research on intelligent classification and grading technology for sensitive data of power services"(5700-202258181A-1-1-ZN). （5700-202258181A-1-1-ZN）
