电商运营数据异常识别技术研究

Research on Anomaly Detection Technology for E-Commerce Operational Data

摘要: 在电商经济精细化运营阶段，运营数据是平台优化决策的核心资产，但刷单炒信、库存超卖、系统故障等数据异常频发，每年导致商家因异常销量损失数百亿元。这类异常不仅是技术问题，更会扭曲市场信号、推高交易成本、损害消费福利，给电商生态带来系统性经济风险。传统异常识别方法有明显局限：或依赖固定统计假设，难适配电商数据特性，漏检率高；或存在黑箱问题，商家申诉率高，难支撑风险防控。本研究结合电商经济规律，构建“动态波动分析 + 滑动窗口 + 多指标联动”的识别技术，适配周期波动、贴合场景需求、精准定位异常。经1000天模拟数据验证，该技术准确率达0.82，召回率达0.88，F1值为0.75，正常样本误判率18.00%，异常样本漏检率12.00%，经济损失降低率达41.55%，能识别促销期异常。其可降漏检率减损失、缩排查耗时降成本、净化数据优化资源配置，助力电商良性循环。未来结合真实数据与理论优化，可进一步提升风险防控能力。

Abstract: In the stage of refined operation of the e-commerce economy, operational data serves as the core asset for platforms to optimize decision-making. However, frequent data anomalies such as fake transactions (brush sales), overstock shortages, and system failures cause merchants to suffer losses of tens of billions of yuan annually due to abnormal sales volumes. Such anomalies are not merely technical issues; they further distort market signals, drive up transaction costs, impair consumer welfare, and pose systemic economic risks to the e-commerce ecosystem. Traditional anomaly identification methods have obvious limitations: some rely on fixed statistical assumptions, making it difficult to adapt to the characteristics of e-commerce data and resulting in high missed detection rates; others suffer from the “black box” problem, leading to high merchant appeal rates and failing to provide effective support for risk prevention and control. Combining the economic laws of e-commerce, this study constructs an identification technology integrating “dynamic fluctuation analysis + sliding window + multi-indicator linkage”. This technology adapts to periodic fluctuations, aligns with scenario-specific needs, and accurately locates anomalies. Verified by 1,000 days of simulated data, the technology achieves an accuracy of 0.82, a recall rate of 0.88, an F1-score of 0.75, a false positive rate of 18.00% for normal samples, a missed detection rate of 12.00% for abnormal samples, and an economic loss reduction rate of 41.55%, enabling the identification of anomalies during promotion periods. Practically, it can reduce missed detection rates and losses, shorten troubleshooting time to cut costs, and purify data to optimize resource allocation, thereby facilitating the healthy circulation of e-commerce. In the future, by integrating real-world data and theoretical optimization, its risk prevention and control capabilities can be further enhanced.