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Control and cause analysis for high-risk operations combined knowledge graph and Apriori algorithm
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Hanjun Guo1, Huaying Cui2, Zhenqi Hu2, Rongxue Kang3, Jinlong Zhao**, 2
China Safety Science Journal | 2026, 36(4) : 75 - 84
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China Safety Science Journal | 2026, 36(4): 75-84
Safety Technology and Engineering
Control and cause analysis for high-risk operations combined knowledge graph and Apriori algorithm
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Hanjun Guo1, Huaying Cui2, Zhenqi Hu2, Rongxue Kang3, Jinlong Zhao**, 2
Affiliations
  • 1China Energy Investment Corporation Limited, Beijing 100011, China
  • 2School of Emergency Management & Safety Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
  • 3China Academy of Safety Science and Technology, Beijing 100012, China
Published: 2026-04-28 doi: 10.16265/j.cnki.issn1003-3033.2026.04.0702
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In order to improve the safety management level of high-risk operations, an intelligent management and control method was proposed based on the knowledge graph and the Apriori algorithm. First, some high-risk operation accidents were collected. Then, the accident causes were extracted and classified automatically by Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF) model, which constructed a knowledge graph of high-risk operation accidents, storing relevant accident data. Subsequently, an index system of high-risk operation causative factors was established, combined with the corresponding operation standard specification. Furthermore, the correlation among causative factors was determined by the Apriori algorithm. Finally, some countermeasures were proposed for targeted control. In this paper, the application of confined space operations was used to demonstrate this method. The results showed that the main causes of confined space accidents are not wearing safety protective equipment", blind rescue", not being equipped with gas detection and other equipment", missing safety warning signs", insufficient safety education", and inadequate safety management". Moreover, the correlation between insufficient safety education" and not wearing safety protective equipment" is greater (80.7% support, 60.41% confidence), and the relationship between insufficient safety education" and blind rescue" also has a strong association (80.7% support, 55.88% confidence).

knowledge graph  /  Apriori algorithm  /  high-risk operation  /  intelligent management  /  causative factor
Hanjun Guo, Huaying Cui, Zhenqi Hu, Rongxue Kang, Jinlong Zhao. Control and cause analysis for high-risk operations combined knowledge graph and Apriori algorithm[J]. China Safety Science Journal, 2026 , 36 (4) : 75 -84 . DOI: 10.16265/j.cnki.issn1003-3033.2026.04.0702
Year 2026 volume 36 Issue 4
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.04.0702
  • Receive Date:2025-11-14
  • Online Date:2026-07-08
  • Published:2026-04-28
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  • Received:2025-11-14
  • Revised:2026-02-04
Affiliations
    1China Energy Investment Corporation Limited, Beijing 100011, China
    2School of Emergency Management & Safety Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
    3China Academy of Safety Science and Technology, Beijing 100012, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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