Optimizing the Power Station Project Management Using Knowledge Graphs

Authors

  • Yang Cao State Grid East Inner Mongolia Electric Power Supply Co., Ltd, Hohhot Inner Mongolia 010010, China
  • Linhua Su State Grid East Inner Mongolia Electric Power Supply Co., Ltd, Hohhot Inner Mongolia 010010, China
  • Guoyi Jiang State Grid East Inner Mongolia Electric Power Supply Co., Ltd, Hohhot Inner Mongolia 010010, China
  • Haibo Liu State Grid East Inner Mongolia Electric Power Supply Co., Ltd, Hohhot Inner Mongolia 010010, China
  • Yuanmiao Gui Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui 230031, China
  • Pengcheng Liu Institute of Physical Science and Information Technology, Anhui University, Anhui 230039, China Institute of Technology Innovation, Hefei Institute of Physical Science, Chinese Academy of Sciences, Anhui 230088, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.3622

Keywords:

Neo4j, hydropower station project, knowledge graph.

Abstract

In “The Belt and Road”, there are more and more investment projects
of hydropower stations abroad in China. This paper proposes to use the
semantic interconnection ability of knowledge map to manage the big data
of hydropower using knowledge graphs. It solves the problem that a lot of
project information is in the state of dispersion. To further understand the
development space and potential of hydropower construction investment in
different countries.

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Author Biographies

Yang Cao, State Grid East Inner Mongolia Electric Power Supply Co., Ltd, Hohhot Inner Mongolia 010010, China

Yang Cao received his B.Sc. degree in Electrical Engineering and Automa-
tion from Northeast Electric Power University, China. He is mainly engaged
in the field of power system research.

Linhua Su, State Grid East Inner Mongolia Electric Power Supply Co., Ltd, Hohhot Inner Mongolia 010010, China

Linhua Su received his B.Sc. degree in Electronic Radio Technology from
Dalian University of Technology, China. He is mainly engaged in the field of
power system research

Guoyi Jiang, State Grid East Inner Mongolia Electric Power Supply Co., Ltd, Hohhot Inner Mongolia 010010, China

Guoyi Jiang received his M.Sc. degree in Thermal Power and Automation of
Power Plant from Northeast Electric Power University, China. He is mainly
engaged in the field of power system research.

Haibo Liu, State Grid East Inner Mongolia Electric Power Supply Co., Ltd, Hohhot Inner Mongolia 010010, China

Haibo Liu received his M.Sc. degree in Electrical Engineering and Automa-
tion from North China Electric Power University, China. He is mainly
engaged in the field of power system research.

Yuanmiao Gui, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui 230031, China

Yuanmiao Gui received his B.Sc. degree in Mathematics and Applied Math-
ematics from Shandong University of Science and Technology, China; M.Sc.
and Ph.D. degrees in Control science and Engineering from the University
of Science and Technology of China. He is currently an assistant researcher
at the Hefei Institute of Intelligent Machinery, Hefei Institutes of Physical
Science, Chinese Academy of Sciences, mainly engaged in the theoretical
research of deep learning, knowledge graphs, big data, and applications in
electric power, water conservancy, biology and other industries.

Pengcheng Liu, Institute of Physical Science and Information Technology, Anhui University, Anhui 230039, China Institute of Technology Innovation, Hefei Institute of Physical Science, Chinese Academy of Sciences, Anhui 230088, China

Pengcheng Liu holds a B.Sc. degree in automation from Anhui University.
He is currently a graduate student in material science and information tech-
nology at Anhui University. His research interests include knowledge map
construction and the application of big data in disaster and power.

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Published

2021-06-24

How to Cite

Cao, Y., Su, L., Jiang, G. ., Liu, H. ., Gui, Y. ., & Liu, P. . (2021). Optimizing the Power Station Project Management Using Knowledge Graphs. Distributed Generation &Amp; Alternative Energy Journal, 36(2), 113–124. https://doi.org/10.13052/dgaej2156-3306.3622

Issue

2021: Vol 36 Iss 2

Section

Articles