【摘要】 为提升混凝土坝安全监测的预警准确性，提出一种基于多点监测数据的智能预警分析方法，旨在克服传统单测点预警方法易受非结构性因素影响的问题。首先，采用K-means聚类法按相似变形模式对监测点进行分区；然后，利用ConvLSTM模型提取各聚类中测点变形序列的时空特征并进行预测，通过分析残差序列并根据3-Sigma原则确定预警阈值，生成单点预警结果；最后，融合各聚类的预警结果，确保仅当聚类内所有监测点在同一时间点同时异常时才触发预警。结果表明：多测点联合预警方法通过综合多点信息，减少了单测点预警方法易受外界因素干扰而造成的误报和漏报问题，提高了预警系统的可靠性和稳定性。更多还原

【Abstract】 In order to enhance the accuracy of early warning in concrete dam safety monitoring, this study proposed an intelligent early warning analysis method based on multi-point monitoring data, aiming to overcome the susceptibility of traditional single-point methods to non-structural interference. Firstly, K-means clustering method was used to partition monitoring points with similar deformation patterns. Then, ConvLSTM model was employed to extract the spatial-temporal features of the deformation sequences from each cluster and make predictions. By analyzing the residual sequences and determining the early warning threshold based on the 3-Sigma principle, single-point early warning results were generated. Finally, the early warning results from all clusters were integrated to ensure that an early warning was triggered only when all monitoring points within a cluster exhibit anomalies simultaneously at the same time. Experimental results show that the proposed method reduces the false alarms and missed detections caused by external disturbances in single-point early warning methods by integrating multi-point information, thereby improving the reliability and stability of the early warning system.更多还原