现有的单测点监测数学模型在反映大坝整体结构性态和诊断大坝异常现象等方面存在不足，有必要将多个测点的监测资料有机地联系起来进行建模。利用数据融合技术中的Bayes理论，以方差为特征参数，建立了多测点异常性态融合诊断模型，提出了多测点异常性态融合诊断准则，并给出了一个工程实例。研究表明：基于Bayes理论的多测点融合模型为大坝整体性态的定量描述和异常测点的分析诊断提供了一条有效的新途径。

Since the existing mathematical model of single-point monitoring is defective in reflecting the structural behavior of the whole dam and diagnosing dam's abnormal behavior, it's necessary to establish model by relating the data of multiple monitoring points. Based on Bayes Theory of data fusion and taking variance as characteristic parameter, we established a fusion model of diagnosing the abnormal behavior of dam using multi monitoring points, presented the criteria for the model, and provided a project case. As the research shows, the fusion model serves as a new and effective approach for the quantitative description of overall dam behavior and for  the diagnosis of abnormal monitoring points. 