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Proceedings of the 4th International Conference on Education, Knowledge and Information Management, ICEKIM 2023, May 26–28, 2023, Nanjing, China

Research Article

Research on the Method of Integrating Knowledge Graph and Deep Neural Network for Automotive Technology Foresight

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  • @INPROCEEDINGS{10.4108/eai.26-5-2023.2337248,
        author={Lian  Liu and jun  Wang and dan  Wu and xingde  Huang and DeXiang  Jia and Aiqiang  Pan},
        title={Research on the Method of Integrating Knowledge Graph and Deep Neural Network for Automotive Technology Foresight},
        proceedings={Proceedings of the 4th International Conference on Education, Knowledge and Information Management, ICEKIM 2023, May 26--28, 2023, Nanjing, China},
        publisher={EAI},
        proceedings_a={ICEKIM},
        year={2023},
        month={9},
        keywords={technological foresight; knowledge graph; electric vehicles deep neural networks},
        doi={10.4108/eai.26-5-2023.2337248}
    }
    
  • Lian Liu
    jun Wang
    dan Wu
    xingde Huang
    DeXiang Jia
    Aiqiang Pan
    Year: 2023
    Research on the Method of Integrating Knowledge Graph and Deep Neural Network for Automotive Technology Foresight
    ICEKIM
    EAI
    DOI: 10.4108/eai.26-5-2023.2337248
Lian Liu1,*, jun Wang2, dan Wu3, xingde Huang3, DeXiang Jia2, Aiqiang Pan2
  • 1: State Grid Shanghai Electric Power Research Institute
  • 2: State Grid Shanghai electric power company
  • 3: State Grid Energy Research Institute Co., Ltd.
*Contact email: 872616590@qq.com

Abstract

Realizing precise foresight of emerging technologies in the field of electric vehicles helps the company to advance its layout in this field, seize the technological commanding heights, and empower high-level technological self-reliance and self-reliance. This study is based on knowledge graph technology and constructs a technical knowledge graph based on the relationships and attributes between scientific and technological papers in the field. It revolves around the three main characteristics of novelty, social impact, and fundamental innovation in the field of electric vehicle technology, and constructs a complete and quantifiable indicator system for new electric vehicle technologies. The knowledge graph is used to extract feature values of each indicator, relying on deep neural network algorithms, Train the industry's emerging technology foresight model to achieve precise foresight for the development of new electric vehicle technologies. This study can provide valuable reference for technology foresight in the field of electric vehicles and support industrial development decisions.

Keywords
technological foresight; knowledge graph; electric vehicles deep neural networks
Published
2023-09-13
Publisher
EAI
http://dx.doi.org/10.4108/eai.26-5-2023.2337248
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