油田环保安全领域标准智能问答关键技术研究

摘要：

油田环保安全领域标准对于规范和引导油田行业安全生产、绿色发展和效率提升具有重要意义。油田环保安全领域标准知识复杂程度较高，难以形成对标准数字知识的双向理解路径，为有效解决上述问题，本论文进行油田环保安全领域标准智能问答关键技术研究。首先，进行FAQ引擎设计，包括研究基于语义相似度的问题快速匹配技术、基于深度学习的相似度重排技术，对用户行为进行评分；其次，进行KGQA引擎设计，包括研究语义库设计模型和基于Graph的搜索匹配模型；最后，设计多引擎加权打分机制，能够实现油田环保安全领域标准智能问答。

Abstract:

The standards in the field of oilfield environmental protection and safety are of great significance for regulating and guiding the safety production, green development, and efficiency improvement of the industry. The standard knowledge in this field is relatively complex, which is difficult to be understood. To effectively solve the above problems, this paper conducts research on key technologies for intelligent Q&A of standards in the field of oilfield environmental protection and safety. Firstly, it designs the FAQ engine, including the research on the fast matching techniques based on semantic similarity and similarity rearrangement techniques based on deep learning, to rate user behavior; Secondly, it designs the KGQA engine, including the research on semantic library design models and Graph based search matching models; Finally, it designs a multi engine weighted scoring mechanism that can achieve intelligent Q&A in the field of oilfield environmental protection and safety standards.

作者简介：鲁小辉，硕士，高级工程师，研究方向为石油安全管理。王凯月，本科，工程师，研究方向为石油标准化、信息化研究。