基于ALBERT-BiGRU-CRF的气体聚乙烯管道知识图的构建
Zhilong Yu1, Juan Zhou2, Qiang Wang3
1College of Quality and Standardization, China Jiliang University, Hangzhou, 310018, China.
Scientific reports
|July 10, 2025
概括
本研究引入了一种使用ALBERT-BiGRU-CRF的新知识图法,用于气体聚乙烯管道的运行和维护. 它通过系统地建模故障模式和实体相关性来提高数据相关性和决策效率.
科学领域:
- 管道工程 管道工程 管道工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 气体聚乙烯管道的智能运行和维护 (O&M) 需要有效的数据管理,但当前的流程受到信息相关性和决策效率低下的影响.
- 与金属管道相比,现有的O&M知识系统对聚乙烯的发展较低,缺乏系统的故障模式建模,例如材料老化和第三方损坏.
- 关键管道实体 (管道段,接头,正极保护) 之间的多维相关性未被充分探索,阻碍了全面的运维.
研究的目的:
- 提出和验证一种针对气体聚乙烯管道运行和维护 (O&M) 量身定制的新型知识图构造方法.
- 通过系统地建模故障模式和实体相互依赖,解决现有的O&M系统的局限性.
- 通过先进的数据处理和知识表示,提高管道O&M中的信息相关性和决策效率.
主要方法:
- 基于ALBERT-BiGRU-CRF的知识图构建方法被开发用于气体聚乙烯管道运行和维护数据.
- 预处理并将结构化,非结构化和半结构化数据集成为一个具有18个参数类型的3D训练数据集.
- 采用了改进的ALBERT模型用于高质量的词向量,为语义理解输入双向封闭循环单元 (BiGRU),以及用于实体识别中的序列依赖性优化CRF层.
- 将知识提取到三元结构中,并将其存储在Neo4j图形数据库中进行可视化.
主要成果:
- 阿尔伯特-比格鲁-CRF模型在数据集识别方面表现出强的表现,达到分别95.35%,96.51%和95.93%的准确性,回忆和F1得分.
- 与其他基准模型相比,观察到精度,回忆和F1分数的显著改善.
- 构建的知识图有效地表示管道实体之间的多维相关性,增强对O&M的数据利用.
结论:
- 拟议的ALBERT-BiGRU-CRF知识图构建方法显著提高了气体聚乙烯管道O&M中的信息相关性和决策效率.
- 这种方法提供了一种系统的方式来建模故障模式和实体相关性,推进智能操作和管理能力.
- 与Neo4j的集成可以有效存储和可视化复杂的管道数据,支持更智能的管道管理.
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