基于文本挖掘技术和信息熵-TOPSIS 法辨识重点工序
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Identification of Key Processes Based on Text Mining and Entropy-TOPSIS Method
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    摘要:

    为辨识影响火药安全生产的重点工序,利用文本挖掘技术提取相关文献中具有分析价值的信息,归纳出 设备状态、事故损失、事故影响、参数监控、物料特性、事故可能性以及防护设计等7 个指标,对某发射药连续化 制球工艺中重点工序进行评估。引入信息熵法优化了TOPSIS 法中指标权重的计算,综合求得各工序的重要度排序, 由此辨识出重点工序。实例分析结果表明:挤出、切粒和塑化工序是该制球工艺中最重要的工序,对发射药的生产 安全有重大影响;所得结果与实际相符,能为相关风险评估工作的有效进行提供思路。

    Abstract:

    In order to identify the key processes affecting the safety of propellant production, the text mining technology was used to extract the valuable information from the relevant literature, and 7 indicators were summarized, including equipment status, accident loss, accident impact, parameter monitoring, material characteristics, accident possibility and protection design, and the key processes of a propellant continuous pelletizing process were evaluated. The information entropy method is introduced to optimize the calculation of index weight in TOPSIS method, and the importance ranking of each process is comprehensively obtained, thus the key process is identified. The results show that the extrusion, pelletizing and plasticizing processes are the most important processes in the pelletizing process, which have a significant impact on the production safety of propellant. The results are consistent with the actual situation, which can provide ideas for the effective risk assessment.

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唐彦秋.基于文本挖掘技术和信息熵-TOPSIS 法辨识重点工序[J].,2022,41(3).

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  • 收稿日期:2021-11-27
  • 最后修改日期:2021-12-28
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  • 在线发布日期: 2022-04-11
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