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首页> 外文期刊>International Journal of Distributed Sensor Networks >Similarity analysis of dam behavior characterized by multi-monitoring points based on Cloud model
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Similarity analysis of dam behavior characterized by multi-monitoring points based on Cloud model

机译：基于云模型的多监测点特征的水坝行为的相似性分析

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摘要

The availability of massive amount of dam safety monitoring data can make it difficult to analyze and characterize dam behavior. This article describes the use of the Cloud model to transform quantitative monitoring data into qualitative information. Each monitoring point returning dam safety data is regarded as a cloud drop, and parameters such as the expectation, entropy, and hyper-entropy of the monitoring data are obtained through a backward cloud generator to represent the operational state of the dam. The monitoring points are then treated as vectors, and the cloud similarity is calculated using the cosine value of the angle between them. The cloud similarity coefficient is then determined to characterize the similarity of dam behavior. Experimental analysis shows that the process of identifying cloud parameters has a good effect on the discovery of abnormal monitoring values regarding dam safety and demonstrates the feasibility of characterizing the dam behavior. Clustering analysis is applied to the similarity coefficients to further achieve the hierarchical management of dam monitoring points.
机译：大量水坝安全监测数据的可用性可以使难以分析和表征水坝行为。本文介绍了云模型将定量监测数据转换为定性信息。每个监视点返回大坝安全数据被认为是云下降，并且通过向后云发生器获得监控数据的期望，熵和超熵的参数，以表示大坝的操作状态。然后将监测点视为载体，并且使用它们之间的角度的余弦值来计算云相似性。然后确定云相似度系数以表征水坝行为的相似性。实验分析表明，识别云参数的过程对发现有关大坝安全的异常监测值的良好影响，并展示了坝行为的表征的可行性。聚类分析应用于相似性系数，以进一步实现水坝监测点的分层管理。

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