| Title | Whole-chain supervision method of industrial product quality and safety based on knowledge graph |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Junwei, Liu, Jiaqi, Zhu, Yujie, He, Fan, Feng, Su, Li, Jing |
| Conference Name | 2021 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) |
| Date Published | dec |
| Keywords | BiLSTM-CRF, Conferences, Data models, industrial products, knowledge graph, Metrics, named entities recognition, Neo4j, Product design, Production, pubcrawl, quality and safety supervision, quality assessment, Regulation, relation extraction, Safety, supply chain risk assessment |
| Abstract | With the rapid improvement of China's industrial production level, there are an increasing number of industrial enterprises and kinds of products. The quality and safety supervision of industrial products is an important step to ensure people's livelihood safety. The current supervision includes a number of processes, such as risk monitoring, public opinion analysis, supervision, spot check and postprocessing. The lack of effective information integration and sharing between the above processes cannot support the implementation of whole-chain regulation well. This paper proposes a whole-chain supervision method of industrial product quality and safety based on a knowledge graph, which integrates massive and complex data of the whole chain and visually displays the relationships between entities in the regulatory process. This method can effectively solve the problem of information islands and track and locate the quality problems of large-scale industrial products. |
| DOI | 10.1109/IAAI54625.2021.9699906 |
| Citation Key | zhang_whole-chain_2021 |
Whole-chain supervision method of industrial product quality and safety based on knowledge graph