水电工程施工安全隐患语义匹配模型  

Semantic matching model of potential safety hazards in hydroelectric project construction

作　　者：陈述[1,2] 王典学 杨应柳 曹坤煜 聂本武 CHEN Shu;WANG Dianxue;YANG Yingliu;CAO Kunyu;NIE Benwu(Hubei Key Laboratory of Construction and Management in Hydropower Engineering,China Three Gorges University,Yichang Hubei 443002,China;College of Hydraulic&Environmental Engineering,China Three Gorges University,Yichang Hubei 443002,China;School of Management Science&Real Estate,Chongqing University,Chongqing 400044,China;China Energy Investment Co.,Ltd.,Chengdu Sichuan 610095,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]重庆大学管理科学与房地产学院,重庆400044 [4]国家能源投资集团有限责任公司,四川成都610095

出　　处：《中国安全科学学报》2024年第12期40-47,共8页China Safety Science Journal

基　　金：国家自然科学基金资助(52479127,52079073,52209163)。

摘　　要：为辅助制定水电工程施工安全隐患治理措施,收集水电工程施工巡检积累的隐患文本,借助Python工具对半结构化的隐患文本进行实体与关系抽取,构建安全隐患知识图谱,并导入到neo4j图数据库中进行存储;搭建水电工程施工隐患语义匹配的基于双向编码器表征的句子嵌入(Sentence-BERT)模型,学习目标隐患与历史隐患的深层语义特征,推荐与目标隐患最相似的历史安全隐患;利用Cypher查询语句,检索该历史安全隐患对应的治理措施。结果表明:Sentence-BERT模型对于施工隐患与历史相似隐患的识别准确率为96.48%,明显优于双向编码器表征(BERT)模型、基于词向量的深度语义匹配模型(Word2vec-DSSM)和基于BERT的DSSM模型(BERT-DSSM)。在随机抽取的150条目标隐患数据中测试历史相似隐患推荐精确度达到92%,并通过隐患知识图谱展示隐患治理措施的检索效果,验证了该方法的适用性和有效性。In order to assist in the development of safety hazard management measures for hydropower project construction,the safety hazard texts accumulated during the construction inspection of hydropower projects were collected.Entities and relationships from the semi-structured safety hazard texts were extracted using Python.A knowledge graph of safety hazards was constructed and imported into the neo4j graph database for storage.A Sentence-Bidirectional Encoder Representations from Transformer(BERT)model based on bidirectional coding was built for the semantic matching of construction hazards in hydropower projects.The deep semantic features of target hazards and historical hazards were learned,and the historical safety hazards most similar to target hazards were recommended.Using the Cypher query statement,the governance measures corresponding to the historical security risk were searched.The results show that the Sentence-BERT model has an accuracy of 96.48%in identifying architecturally and historically similar safety hazards,which is significantly better than BERT,Word2vec-Deep Semantic Similarity Model(Word2vec-DSSM),and BERT-DSSM models.Among 150 randomly selected target safety hazard data,the accuracy rate of testing historical similar safety hazard suggestions reaches 92%,and the retrieval effect of hazard management measures is demonstrated through the hazard knowledge graph,which verifies the applicability and effectiveness of the method.

关 键 词：水电工程施工 安全隐患 治理措施 智能推荐 知识图谱 语义匹配 

分 类 号：X948[环境科学与工程—安全科学]