人工智能驱动下的大坝安全监测数据异常识别算法研究

Key Points

• The aim is to explore core algorithms for identifying anomalies in dam safety monitoring data driven by artificial intelligence.
• Analyzed three categories of anomaly detection methods: statistical learning, machine learning, and deep learning.
• Outlined algorithm design principles tailored for the characteristics of dam monitoring data, including spatiotemporal modeling and data fusion.
• Discussed challenges in implementing AI-driven anomaly detection, emphasizing data quality and model adaptability.
• Identified a hybrid intelligent model combining physical mechanisms and data-driven approaches enhances anomaly detection accuracy.
• Proposed improvements in robustness and reliability of monitoring systems are necessary for effective implementations.
• Addressed ethical compliance and real-time processing challenges in engineering applications.