水电工程施工安全隐患类别辅助校正方法  

Auxiliary correction methods for categories of potential safety hazards in hydropower project construction

作　　者：卢冰 陈述[1,2] 曹坤煜 陈云[1,2] 聂本武[2,3] LU Bing;CHEN Shu;CAO Kunyu;CHEN Yun;NIE Benwu(Hubei Key Laboratory of Construction and Management in Hydropower Engineering,China Three Gorges University,Yichang 443002,China;College of Hydraulic and Environmental Engineering,China Three Gorges University,Yichang 443002,China;National Energy Investment Corporation,Chengdu 610000,China)

机构地区：[1]三峡大学水电工程施工与管理湖北省重点实验室,湖北宜昌443002 [2]三峡大学水利与环境学院,湖北宜昌443002 [3]国家能源投资集团有限责任公司,成都610000

出　　处：《水力发电学报》2025年第4期42-49,共8页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金(52479127;52209163);湖北省自然科学基金计划青年A类项目(2025AFA074)。

摘　　要：为加强水电工程施工安全隐患排查治理,施工人员可利用随手拍工具及时上报安全隐患,但是隐患类别判定准确性存在主观依赖,人工校正耗时耗力。为避免水电工程施工安全隐患管理混淆,提出水电工程施工安全随手拍的隐患类别辅助校正NRBO-CNN-BiLSTM方法。首先,对安全隐患数据进行分词、预处理,将其转化为词向量并进行归一化处理。其次,引入注意力机制增强特征表示能力,构建以卷积神经网络和双向长短期记忆网络为主干的安全隐患分类模型。最后,设计牛顿-拉夫逊优化算法,训练最佳模型参数。通过案例结果表明:18类隐患类别分类结果 P值69.19%,主要原因在部分隐患类别出现频率较低;选取6类数量均衡的隐患类别进行实验,NRBO-CNN-BiLSTM模型P值上达到了94.62%,R值为94.58%,F1值为94.59%,各项数据均优于其他分类模型,反映该模型可为隐患类别校正提供辅助。To enhance the investigation and management of potential hazards in hydropower construction,workers can use mobile reporting to announce safety hazards promptly.However,hazard classification and its accuracy are often subjective,and manual correction is time-consuming and labor-intensive.To mitigate confusion in hazard management during construction,this paper describes a NRBO-CNN-BiLSTM method for auxiliary correction of the mobile phone-reported hazard categories.First,safety hazard data are tokenized,preprocessed,and converted into word vectors,followed by normalization.Then,we apply an attention mechanism to enhance the feature representation capability,and construct a safety hazard classification model using convolutional neural networks and bidirectional long-short-term memory networks.Finally,we work out a Newton-Raphson optimization algorithm to train the model for optimal parameters selection.Case studies demonstrate the probability is 69.2%for the classification of 18 types of hazards.The main reason lies in a relatively low frequency of certain hidden danger categories.In the tests of 6 hazard categories with balanced datasets,our new model achieves a classification probability of 94.6%,a recall value of 94.6%,and an F1 score of 94.6%.The accuracies of these indexes are superior to those of alternative classification models,indicating this correction model is effective and better.

关 键 词：水电工程 随手拍 施工安全 安全隐患 文本分类 

分 类 号：TV512[水利工程—水利水电工程]