基于图形变压器和自动编码器的多视角酒店运营过程异常预测方法
Yidan Ma1, Yue Wu2, Xinsheng Fang3
1School of Economics and Trade Management, Anhui Vocational College of Defense Technology, Luan, China.
Frontiers in artificial intelligence
|January 1, 2026
概括
本研究引入了一种用于预测复杂业务流程中运营异常的新方法. 多视角图形转换器和自动编码器 (MLGTAE) 通过分析行为和数据交互来提高异常检测的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据挖掘 数据挖掘
背景情况:
- 酒店运营涉及复杂的流程,容易发生不可避免的异常情况.
- 目前的深度学习方法很难完全代表活动关系和控制-数据流相互作用,以预测异常.
- 积极预测异常对于保持复杂系统的运行稳定至关重要.
研究的目的:
- 提出一个先进的业务流程异常预测方法.
- 在异常检测中增强行为关系和数据属性相互作用的表示.
- 提高在活动和数据属性层面预测异常的准确性.
主要方法:
- 使用Petri网和数据属性 (时间,资源) 构建多视角的轨迹图.
- 使用注意力机制,在过程行为和数据之间进行深层次的语义交互.
- 使用自动编码器进行基于重建的异常检测.
主要成果:
- 拟议的多视角图形转换器和自动编码器 (MLGTAE) 方法在真实世界的数据集上得到了验证.
- 与现有的最先进的异常预测方法相比,MLGTAE表现优越.
- 该方法在检测活动和数据属性级别的异常方面取得了更高的准确性.
结论:
- MLGTAE有效地解决了业务流程异常预测当前深度学习方法的局限性.
- 多视角图表表示和注意力机制增强了对复杂过程动态的理解.
- 这些发现突显了MLGTAE通过精确的异常检测来提高运行稳定性的潜力.
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