用分层网格在边缘端高效检测地理大数据异常，节省99.7%计算量。
Lightweight Multi-scale Hierarchical Anomaly Detection and Localization for Geospatial Big Data Applications at the Edge
- 基于H3网格与多尺度下钻逻辑，边端轻量化处理
- 相比传统方法减少99.7%评估开销，提升实时性
- 可过滤噪声干扰，精准识别持续性空间异常
随着环境、应急、气象和农业等关键应用对地理空间数据流的实时异常检测需求不断增长，数据存储、处理与传输面临挑战。传统方法将海量数据集中处理，但在数据量与速度持续上升的背景下已不可行。本文提出一种面向边缘计算的轻量级地理空间数据流异常检测与定位方法。该方法利用H3离散全球网格系统及多尺度下钻逻辑，显著降低计算开销，相比传统平扫方法减少99.7%的评估次数。通过在低分辨率层过滤噪声引起的闪烁异常，能够高效识别具有空间持续性的异常信号。实验表明，该框架可有效将大规模地理空间数据提炼为可操作洞察。
原文摘要 · Abstract (English)
As an increasing number of critical applications, including environmental, emergency, meteorological, and agricultural, rely on real-time anomaly detection in geospatial data streams, challenges related to the storage, processing, and communication of this data arise. Traditionally, large volumes of data have been sent to centralized processing locations for insight extraction. Given the big data context of these applications, this approach becomes increasingly infeasible as data volume and velocity continue to increase. This paper proposes a lightweight edge-oriented approach for anomaly detection and localization for geospatial data streams. By leveraging the H3 discrete global grid system and a multi-scale drill-down logic, the proposed approach significantly reduces computational overhead, achieving a 99.7\% reduction in evaluations compared to traditional flat-scan methods. Furthermore, by filtering out noise-induced flickering anomalies at lower resolutions, spatially-persistent anomalous signals can be efficiently identified. The results demonstrate that the proposed framework effectively distills massive geospatial data into actionable insights.
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