Abstract: With the development of the new generation of artificial intelligence technology,the manufacturing system has deve-loped from the humans-physical binary system to the humans-cyber-physical triad system,and the multivariate data fusion across domains and layers has become an inevitable trend.As a conceptual model capable of describing data semantically,ontologies are widely used for integration,sharing and reuse of multiple heterogeneous data.In traditional industries,research on using ontology fusion to drive data fusion typically focuses on information and physical systems.In order to solve the problem of humans-cyber-physical ontology fusion,an improved representation learning model TransHP is proposed in this paper.The classical translation model does not effectively use information other than the structure of the triad,TransHP makes improvements based on TransH,and elements in the ontology constitute the type triad and the instance triad.In the TransHP,first,for the type triad,the structure and properties of the triad are used for joint training.Next,the vector representations of the obtained type entities are used as the input of the training instance vectors,and the structure information of the instance is used for joint training,while the confidence level is added as the relational strength calculation to solve the problem of the disorderly distribution of entities in semantic space caused by the sparsity of the relational triad.In this paper,a human subject ontology is constructed as an example of a hot-rolling production process in an industrial field and tested as a small sample,and the experimental result shows that the TransHP model infers a richer and more accurate relationship between entities in the human subject ontology compared with the TransH model.The fusion of humans-cyber-physical ontologies has been realized through the TransHP model,which solves the problem of humans-cyber-physical information interaction and paves the way for collaborative decision-making.

Key words: Entity alignment, Humans-cyber-physical data, Knowledge inference, Ontology fusion, Representation learning