|本期目录/Table of Contents|

[1]贺叔滢,郑文培,田野,等.基于遗传算法优化Apriori算法的LNG接收站隐患文本数据关联规则挖掘及预警研究[J].中国安全生产科学技术,2025,21(7):114-119.[doi:10.11731/j.issn.1673-193x.2025.07.015]
　HE Shuying,ZHENG Wenpei,TIAN Ye,et al.Research on association rule mining and early warning of hidden danger text data of LNG receiving terminals based on genetic algorithm-optimized Apriori algorithm[J].Journal of Safety Science and Technology,2025,21(7):114-119.[doi:10.11731/j.issn.1673-193x.2025.07.015]
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基于遗传算法优化Apriori算法的LNG接收站隐患文本数据关联规则挖掘及预警研究()

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
21
期数:
2025年7期
页码:
114-119
栏目:
职业安全卫生管理与技术
出版日期:
2025-07-30

文章信息/Info

Title:
Research on association rule mining and early warning of hidden danger text data of LNG receiving terminals based on genetic algorithm-optimized Apriori algorithm
文章编号:
1673-193X(2025)-07-0114-06
作者:
贺叔滢; 郑文培; 田野; 龚晓凤; 李宏霞; 张圣柱; 王旭
（1.中国石油大学（北京） 安全与海洋工程学院，北京 102249；
2.油气生产安全与应急技术应急管理部重点实验室，北京 102249；
3.中国安全生产科学研究院，北京 100012；
4.国家管网集团西部管道有限责任公司，新疆 乌鲁木齐 830010）
Author(s):
HE Shuying;  ZHENG Wenpei;  TIAN Ye;  GONG Xiaofeng;  LI Hongxia;  ZHANG Shengzhu;  WANG Xu
（1.College of Safety and Ocean Engineering,China University of Petroleum (Beijing),Beijing 102249,China;
2.Key Laboratory of Oil and Gas Production Safety and Emergency Technology,Ministry of Emergency Management,Beijing 102249,China;
3.China Academy of Safety Science and Technology,Beijing 100012,China;
4.PipeChina West Pipeline Co.,Ltd.,Urumqi Xinjiang 830010,China）
关键词:
LNG接收站隐患; 遗传算法; Apriori算法; 关联规则; 隐患预警
Keywords:
LNG receiving terminal hidden danger;  genetic algorithm;  Apriori algorithm;  association rules;  hidden danger earlywarning
分类号:
TP309.2；X937
DOI:
10.11731/j.issn.1673-193x.2025.07.015
文献标志码:
A
摘要:
为解决传统的Apriori算法在参数选择上存在困难的问题，提出1种基于遗传算法优化的Apriori算法。首先，利用Apriori算法挖掘隐患数据中的潜在关联规则；然后，引入遗传算法选择最优的最小支持度和最小置信度，以克服传统Apriori算法在参数选择上的不足；最后，结合优化后的参数重新挖掘关联规则，并给出预警建议。研究结果表明：遗传算法优化的Apriori算法平均支持度提升6.67%，平均置信度提升27.04%，平均提升度提升7.51%。同时，规则数量从65条减少到44条，有效过滤冗余信息，提高LNG接收站隐患识别的精确度和效率，并优化关联规则挖掘过程。研究结果可为LNG接收站的安全管理提供科学的预警参考和新的工具。
Abstract:
In order to address the challenges of parameter selection in traditional Apriori algorithms,this study proposed a genetic algorithm-optimized Apriori algorithm.Firstly,mining potential association rules from hidden danger data using the Apriori algorithm.Then,introducing a genetic algorithm to select optimal minimum support and minimum confidence thresholds,overcoming the limitations of manual parameter tuning in conventional Apriori.Finally,re-mining association rules with optimized parameters and generating early warning recommendations.The results demonstrate that genetic algorithm-optimized Apriori algorithm increased average support by 6.67%,improved average confidence by 27.04%,and enhanced average lift by 7.51%.Meanwhile,the number of rules is decreased from 65 to 44,which effectively filters redundant informationwhile enhancing the precision and efficiency of hidden danger identification at LNG receiving terminals and optimizing the association rule mining process.Thesefindings provide scientific early warning reference and novel analytical tools for the safety management at LNG receiving terminal.

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备注/Memo

备注/Memo:
收稿日期： 2025-03-21
作者简介： 贺叔滢，硕士研究生，主要研究方向为文本挖掘与安全管理。
更新日期/Last Update: 2025-07-28