摘要
当前,智能制造面临的许多问题都具有不确定性和复杂性,单纯地利用专家经验和机理模型难以有效解决.鉴于此,面向跨层跨域的复杂制造系统网络化协同控制机制,提出一种基于本体的人机物三元数据融合方法,研究复杂制造环境下的人机物三元数据融合建模.在抽取三元组时,区别于传统的流水线式抽取方式,提出一种基于实体-关系联合抽取的模型ErBERT.该模型首先经过预训练模型BERT进行词序列化,经过最大池化、全连接和Softmax等操作后,完成实体识别和关系分类任务,得到抽取完毕的人机物三元组.将抽取好的三元组按照规则映射至OWL文件,最终存储在图数据库中,实现本体模型的构建.经实验验证,经过ErBERT抽取出的三元组有较好的准确率,能够达到通过本体融合人机物三元数据的目标,并为实现制造企业人机物三元协同决策与优化提供技术支撑.
At present,many problems of intelligent manufacturing are uncertain and complex,which cannot be solved effectively by expert experience and mechanism models.Therefore,the human-cyber-physical data fusion modeling in complex manufacturing environments is studied,and the ontology is proposed as the approach of the human-cyber-physical data fusion.In the extraction of triplets,a model named ErBERT based on entity-relation joint extraction is proposed,which is different from the traditional pipeline extraction.After word serialization by the pre-training model BERT,the model completes entity recognition and relationship classification by max pooling,fully connection and Softmax,and obtains the extracted human-cyber-physical triplets.The extracted triplets are mapped to OWL files according to rules,and finally stored in the graph database to realize ontology construction.The experimental result shows that the triplets extracted by the ErBERT have good accuracy and achieve the goal of fusion of human-cyber-physical data through ontology,which provides theoretical method support for realizing the ternary collaborative decision-making and optimization of human-cyber-physical data.
作者
董津
王坚
王兆平
DONG Jin;WANG Jian;WANG Zhao-ping(College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China)
出处
《控制与决策》
EI
CSCD
北大核心
2022年第5期1251-1257,共7页
Control and Decision
基金
科技创新2030新一代人工智能重大课题项目(2018AAA0101800)。
关键词
人机物
制造领域
数据融合
本体
三元组抽取
human-cyber-physical data
manufacturing
data fusion
ontology
triplets extraction
