The monitoring of data anomaly identification is an important basis for dam safety online monitoring and evaluation. In this research, a cluster of anomaly identification models for dam safety monitoring data was constructed, and a three-stage online anomaly identification method was proposed to discriminate outliers. The proposed method combined anomaly detection for measured values based on a single-point time series simulation, measurement error reduction based on remote retesting and spatio-temporal analysis, and environmental response mutation recognition. It brought about efficient and accurate detection for data mutation and online classified identification for its inducement. Additionally, problems such as missing outliers, misjudging normal values induced by the environmental response, and difficulty in online identification for measurement errors were effectively solved. The research productions were applied to the online monitoring system for the safety risk of reservoirs and dams in the Dadu River Basin. The results showed that the proposed method could effectively improve the accuracy of anomaly identification and reduce the misjudgment and omission rate to less than 2%. It could also successfully recognize and subtract nonstructural anomalies such as accidental errors, instrument faults, and environmental responses online, which provided reliable data for online dam safety monitoring.

监测数据异常识别是大坝安全在线监测评估的重要依据。本研究构建了大坝安全监测数据异常识别模型集群，提出了一种三阶段在线异常识别方法来识别异常值。该方法结合了基于单点时间序列模拟的测量值异常检测、基于远程重测和时空分析的测量误差减少以及环境响应突变识别。它带来了高效准确的数据突变检测和在线分类识别的诱因。此外，由于环境响应引起的缺失异常值、误判正常值等问题，有效解决了测量误差在线识别难的问题。研究成果应用于大渡河流域水库大坝安全风险在线监测系统。结果表明,所提方法能够有效提高异常识别的准确率,将误判和漏检率降低到2%以内。它还可以在线成功识别和减去意外错误、仪器故障和环境响应等非结构异常，为在线大坝安全监测提供可靠数据。结果表明,所提方法能够有效提高异常识别的准确率,将误判和漏检率降低到2%以内。它还可以在线成功识别和减去意外错误、仪器故障和环境响应等非结构异常，为在线大坝安全监测提供可靠数据。结果表明,所提方法能够有效提高异常识别的准确率,将误判和漏检率降低到2%以内。它还可以在线成功识别和减去意外错误、仪器故障和环境响应等非结构异常，为在线大坝安全监测提供可靠数据。