计算机工程2025，Vol.51Issue(2)：86-93,8.DOI:10.19678/j.issn.1000-3428.0068621
基于领域自适应与注意力机制的电梯安全风险预测
Elevator Safety Risk Prediction Based on Domain Adaptation and Attention Mechanism
摘要
Abstract
As special equipment,the operational safety risk prediction of elevators is crucial.Currently,most research on elevators is based on their component data,and the prediction method has problems such as low prediction accuracy and poor generalization ability in the case of changing application scenarios.Therefore,a method for elevator safety risk prediction based on domain adaptation and attention mechanisms is proposed.This method is based on an adversarial domain adaptive network and uses an attention mechanism to optimize the feature extraction ability of the network.The method includes three parts:feature extractor,label classifier,and domain classifier.The input data are the elevator safety risk factor,containing both source domain and target domain data.The feature extractor optimized by the attention mechanism adaptively extracts and retains the common key features between the source and target domains.The key features are simultaneously input to the label classifier and the domain classifier.Transfer learning from the source domain to the target domain is realized through domain adaptation,and the elevator operation status is output through the label classifier.The experimental results show that the prediction accuracy of the proposed method can reach 86.9％when it is transferred to the target domain application scenario,which is 2.6 percentage points higher than that before optimization,and it is 9.5,8.3,3.7,and 1.2 percentage points higher than that of LSTM-AE,CNN-LSTM,TrAdaBoost.R2,and Deep Subdomain Adaption Network(DSAN),respectively.Therefore,it can effectively predict elevator safety risks.关键词
电梯/安全风险预测/注意力机制/对抗领域自适应网络/迁移学习Key words
elevator/safety risk prediction/attention mechanism/adversarial domain adaptive network/transfer learning分类
计算机与自动化引用本文复制引用
张欢,王晨,单景东,仇润鹤..基于领域自适应与注意力机制的电梯安全风险预测[J].计算机工程,2025,51(2):86-93,8.基金项目
上海市自然科学基金(20ZR1400700). （20ZR1400700）
