关于大坝监测数据质量评价因子及算法研究OA北大核心
Research on evaluation factors and algorithms of dam monitoring data quality
大坝监测数据是判断大坝运行安全的主要依据,为了鉴别数据优劣并选择出可信度较高的数据,文中构建一个大坝监测数据质量评价框架.针对测点之间的相关性、监测项目及仪器的特点,利用Kshape算法找出具有强相关性的测点,再通过相对偏移率、相对平滑率、周期波动程度和精度修正率等评价因子对大坝监测数据进行评价;其次,结合混合蝙蝠算法优化后的长短期记忆网络对大坝监测数据进行分类,构建了大坝监测数据质量评价算法流程.以新疆某大坝监测数据为研究对象进行试验,结果表明所提出的大坝监测数据质量评价算法的准确率为94.33%,可为评价大坝监测数据质量提供有效的解决方法.
Dam monitoring data is the main basis for judging the safety of dam operation.In order to identify the data quality and select the data with high reliability,a dam monitoring data quality evaluation framework is constructed.According to the correlation between measuring points and the features of monitoring items and instruments,Kshape algorithm is used to find out the measuring points with strong correlation,and then the dam monitoring data is evaluated by means of the evaluation factors such as relative offset rate,relative smoothness rate,periodic fluctuation degree and accuracy correction rate.In combination with the LSTM(long short-term memory network)optimized by hybrid bat algorithm,the dam monitoring data is classified,and the algorithm flow of dam monitoring data quality evaluation is constructed.The test is conducted by taking a dam monitoring data in Xinjiang as the research object.The results show that the accuracy of the proposed dam monitoring data quality evaluation algorithm is 94.33%,which can provide an effective solution for evaluating the quality of dam monitoring data.
冯宇扬;李登华;方博雅;丁勇
南京理工大学 理学院,江苏 南京 210094南京水利科学研究院,江苏 南京 210029||水利部水库大坝安全重点实验室,江苏 南京 210029华设检测科技有限公司,江苏 南京 211100
电子信息工程
大坝监测数据评价因子数据质量评价长短期记忆网络测点聚类相关性分析
dam monitoring dataevaluation factordata quality evaluationlong short-term memory networkmeasuring point clusteringcorrelation analysis
《现代电子技术》 2025 (002)
90-96 / 7
国家重点研发计划资助项目(2022YFC3005502);国家自然科学基金长江水科学研究联合基金项目(U2240221);国家自然科学基金资助项目(51979174)
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