基于 GRU 模型的低成本高性能异常轨迹检测与云计算中的深度时空序列分析

Guohao Tang, Huaying Zhao, Baohua Yu
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引用次数: 0

摘要

轨迹异常可作为潜在问题的早期指标,并经常为事件发生提供有价值的见解。检测异常轨迹的现有方法主要侧重于比较轨迹的空间特征。然而,这些方法无法捕捉轨迹数据中的时间维度模式和演变,因此无法充分识别目标群体的行为惯性。少数包含时空特征的检测方法也未能充分分析时空序列演变信息;因此,忽略时空相关性的检测方法过于片面。递归神经网络(RNN),尤其是设计重置和更新门控单元的门递归单元(GRU),具有处理非线性序列的能力,能有效提取和分析时空特征。然而,基本 GRU 网络模型的表达能力有限,可能无法充分捕捉复杂的序列模式和语义信息。针对上述问题,本文提出了一种基于改进 GRU 模型的云计算异常轨迹检测方法。为提高相关模型的异常检测能力和训练效率,严格控制无关特征的输入,改善模型拟合效果,设计了一种结合随机森林算法和全连接层网络的改进模型。该方法通过重置和更新门控单元解构时空语义,同时利用全连接层网络的特征整合和非线性映射能力,有效捕捉特征演化信息和目标行为惯性。基于 GeoLife GPS 轨迹数据集的实验结果表明,所提方法的泛化能力提高了 1%,训练成本降低了 31.68%。这一成功为异常轨迹检测任务提供了实用的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-cost and high-performance abnormal trajectory detection based on the GRU model with deep spatiotemporal sequence analysis in cloud computing
Trajectory anomalies serve as early indicators of potential issues and frequently provide valuable insights into event occurrence. Existing methods for detecting abnormal trajectories primarily focus on comparing the spatial characteristics of the trajectories. However, they fail to capture the temporal dimension’s pattern and evolution within the trajectory data, thereby inadequately identifying the behavioral inertia of the target group. A few detection methods that incorporate spatiotemporal features have also failed to adequately analyze the spatiotemporal sequence evolution information; consequently, detection methods that ignore temporal and spatial correlations are too one-sided. Recurrent neural networks (RNNs), especially gate recurrent unit (GRU) that design reset and update gate control units, process nonlinear sequence processing capabilities, enabling effective extraction and analysis of both temporal and spatial characteristics. However, the basic GRU network model has limited expressive power and may not be able to adequately capture complex sequence patterns and semantic information. To address the above issues, an abnormal trajectory detection method based on the improved GRU model is proposed in cloud computing in this paper. To enhance the anomaly detection ability and training efficiency of relevant models, strictly control the input of irrelevant features and improve the model fitting effect, an improved model combining the random forest algorithm and fully connected layer network is designed. The method deconstructs spatiotemporal semantics through reset and update gated units, while effectively capturing feature evolution information and target behavioral inertia by leveraging the integration of features and nonlinear mapping capabilities of the fully connected layer network. The experimental results based on the GeoLife GPS trajectory dataset indicate that the proposed approach improves both generalization ability by 1% and reduces training cost by 31.68%. This success do provides a practical solution for the task of anomaly trajectory detection.
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