Research on Risk Identification Technology for Power System Supply Chain Based on Knowledge Graph

The stability of the power supply chain is crucial for maintaining the long-term development and cost-effectiveness of the power industry. Although existing equipment can maintain power supply in the short term, interruptions in the supply chain of components, such as geopolitical tensions, policy changes, limited supplier numbers, or poor management, may have an impact on the long-term stable operation and cost control of the power system. This article proposes a risk identification technique based on the Neo4j graph database, aiming to discover potential risk points in the supply chain in a timely manner through the analysis ability of the graph database. By constructing a supply chain knowledge graph(PSSCKG), this study aims to enhance the risk warning capability of the supply chain, optimize spare parts management and cost control, and thus support the sustainable development of the power system.

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Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/icmlc63072.2024.10935164
Citations
2
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
0.42
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article

Research on Risk Identification Technology for Power System Supply Chain Based on Knowledge Graph

HAI-LIN CHEN, Lin Liu, Shujun Li, Jing Lü
2 citations
Power Systems and Technologies
0.42
article

Research on Risk Identification Technology for Power System Supply Chain Based on Knowledge Graph

HAI-LIN CHEN, Lin Liu, Shujun Li, Jing Lü
article en
2 citations

Abstract

The stability of the power supply chain is crucial for maintaining the long-term development and cost-effectiveness of the power industry. Although existing equipment can maintain power supply in the short term, interruptions in the supply chain of components, such as geopolitical tensions, policy changes, limited supplier numbers, or poor management, may have an impact on the long-term stable operation and cost control of the power system. This article proposes a risk identification technique based on the Neo4j graph database, aiming to discover potential risk points in the supply chain in a timely manner through the analysis ability of the graph database. By constructing a supply chain knowledge graph(PSSCKG), this study aims to enhance the risk warning capability of the supply chain, optimize spare parts management and cost control, and thus support the sustainable development of the power system.

Guangdong University of Technology (CN), Energy Research Institute (CN)
Openalex Percentile: Top 35%
Power Systems and Technologies
0.42
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Research on Risk Identification Technology for Power System Supply Chain Based on Knowledge Graph — HAI-LIN CHEN, Lin Liu, et al. · (2024) | TGRS Research Map | TGRS