基于FP-growth的大坝安全监测数据挖掘方法

doi:10.3880/j.issn.1006-7647.2019.05.012

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基于FP-growth的大坝安全监测数据挖掘方法

DOI:

10.3880/j.issn.1006-7647.2019.05.012

作者:

毛宁宁
毛宁宁
河海大学水文水资源与水利工程科学国家重点实验室, 江苏南京 210098; 河海大学水利水电学院, 江苏南京 210098
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苏怀智
苏怀智
河海大学水文水资源与水利工程科学国家重点实验室, 江苏南京 210098; 河海大学水利水电学院, 江苏南京 210098
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高建新
高建新
河海大学水文水资源与水利工程科学国家重点实验室, 江苏南京 210098; 河海大学水利水电学院, 江苏南京 210098
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中图分类号:

TV689.1

基金项目:

国家重点研发计划(2018YFC0407101,2016YFC0401601)；广西重点研发计划(桂科AB17195074)

Data mining method for dam safety monitoring based on FP-growth algorithm

Author:

MAO Ningning
MAO Ningning
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
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SU Huaizhi
SU Huaizhi
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
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GAO Jianxin
GAO Jianxin
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
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摘要:

为改善大坝安全监测数据库的数据挖掘方法运行速度慢、占用内存大的问题，提出改进FP-growth算法，将已预处理的监测数据剪枝后，生成Priority树再进行频繁项挖掘。以此方法挖掘大坝变形量与水温等环境量的相关关系，不仅挖掘速度快、精度高、结果简洁，还能够对比单个因子或分析多个因子耦合与目标变量的关系。实例表明改进后的FP-growth算法思想为大坝安全监测数据挖掘提供了一条良好的思路。

关键词:大坝安全监测;大坝变形分析;数据挖掘;关联法则;FP-growth算法

Abstract:

In order to improve the current data mining method of the dam safety monitoring database which runs slowly and takes up a lot of computational space, a modified FP-growth algorithm was proposed. The pre-processed monitoring data was pruned, and then frequent item mining was performed after the Priority tree was generated. In the application of exploring the correlation between dam deformation and water temperature and other environmental quantities, the proposed method not only has high mining speed, high precision, and simple results, but also can compare a single factor or analyze the relationship between multiple factor coupling with target variables. The example shows that the improved FP-growth algorithm provides a good idea for dam safety monitoring data mining.

Key words:dam safety monitoring; dam deformation analysis; data mining; association rule; FP-growth algorithm

参考文献

[1] 顾冲时,张晶梅.大坝服役非概率可靠性分析方法[J].水利水电科技进展,2018,38(5):1-9.(GU Chongshi, ZHANG Jingmei. Non-probabilistic reliability analysis methods of dam service performance[J]. Advances in Science and Technology of Water Resources,2018,38(5):1-9.(in Chinese))

[2] SU H Z, WU Z R, WEN Z P. Identification model for dam behavior based on wavelet network[J]. Computer-Aided Civil and Infrastructure Engineering,2007,22(6):438-448.

[3] CHEN B, WU Z R, LIANG J C, et al.Time-varying identification model for crack monitoring data from concrete dams based on support vector regression and the Bayesian framework[J]. Mathematical Problems in Engineering,2017(5):1-11.

[4] 苏振华,周宜红,赵春菊,等.基于数据挖掘技术的溪洛渡大坝施工期温度监测数据分析[J].水电能源科学,2016,34(3):70-73.(SU Zhenhua, ZHOU Yihong, ZHAO Chunju, et al. Analysis of temperature monitoring data of Xiluodu Dam during construction based on data mining[J]. Water Resources and Power,2016,34(3):70-73.(in Chinese))

[5] 张海燕.数据挖掘技术在大坝安全监测系中的研究与应用[D].兰州:兰州理工大学,2013.

[6] 王伟,沈振中,钟启明.基于混合蛙跳算法的混凝土坝加权变形预报模型[J].水利水电科技进展,2013,33(2):37-41.(WANG Wei, SHEN Zhenzhong, ZHONG Qiming. Weighted deformation forecast for concrete dams based on shuffled frog leaping algorithm[J]. Advances in Science and Technology of Water Resources,2013,33(2):37-41.(in Chinese))

[7] 阮志毅.多尺度FP-Growth算法及其在规律路径挖掘中的应用[J].自动化学报,2019,45(2):1-17.(RUAN Zhiyi.Multiscale FP-Growth algorithm and its application on regular route mining[J].Acta Automatica Sinica,2019,45(2):1-17.(in Chinese))

[8] 顾军华,武君艳,许馨匀.基于Spark的并行FP-Growth算法优化及实现[J].计算机应用,2018,38(11):3069-3074.(GU Junhua, WU Junyan, XU Xinyun.Optimization and implementation of parallel FP-Growth algorithm based on Spark[J].Journal of Computer Applications,2018,38(11):3069-3074.(in Chinese))

[9] 刘冲,陈晓辉,宋小小.关联规则中FP树算法的研究与改进[J].网络安全技术与应用,2012(10):53-55.(LIU Chong, CHEN Xiaohui, SONG Xiaoxiao. Research and improvement on FP-tree algorithm of association rule[J]. Network Security Technology & Application,2012(10):53-55.(in Chinese))

[10] SU H Z, WEN Z P, CHEN Z X, et al. Dam safety prediction model considering chaotic characteristics in prototype monitoring data series[J]. Structural Health Monitoring,2016,15(6):639-649.

[11] HE Jinping, JIANG Zhenxiang, ZHAO Cheng, PENG Zhengquan, SHI Yuqun. Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions[J]. Water Science and Engineering, 2018, 11(1): 61-67.

[12] 王振武.数据挖掘算法原理与实现[M].北京:清华大学出版社,2017.

[13] SU H Z, LI X, YANG B B, et al. Wavelet support vector machine-based prediction model of dam deformation[J]. Mechanical Systems and Signal Processing, 2018,110(15): 412-427.

[14] 李家田,苏怀智,赵海超,等.基于蒙特卡罗模拟的混凝土坝渗流性态区间综合评价[J]. 水利水电科技进展,2018,38(3):32-35.(LI Jiatian, SU Huaizhi, ZHAO Haichao, et al. Comprehensive interval assessment of seepage behavior for concrete dams based on Monte Carlo simulation[J]. Advances in Science and Technology of Water Resources,2018,38(3):32-35.(in Chinese))

[15] SU H Z, WEN Z P, WU Z R. Study on an intelligent inference engine in early-warning system of dam health[J]. Water Resources Management,2011,25(6):1545-1563.

引用本文

毛宁宁,苏怀智,高建新.基于FP-growth的大坝安全监测数据挖掘方法[J].水利水电科技进展,2019,39(5):78-82.(MAO Ningning, SU Huaizhi, GAO Jianxin. Data mining method for dam safety monitoring based on FP-growth algorithm[J]. Advances in Science and Technology of Water Resources,2019,39(5):78-82.(in Chinese))

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