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  • 主管单位:
  • 上海市教育委员会
  • 主办单位:
  • 上海理工大学
  • 主  编:
  • 庄松林
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  • 国际标准刊号:
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  • 国内统一刊号:
  • 31-1739/T
  • 邮发代号:
  • 4-401
  • 单  价:
  • 15.00
  • 定  价:
  • 90.00
李晓春,张生.基于云模型的水闸泵站远程监测数据挖掘分析[J].上海理工大学学报,2020,42(3):298-304.
基于云模型的水闸泵站远程监测数据挖掘分析
Data mining analysis of remote monitoring of sluice pumping station based on cloud model
投稿时间:2019-03-21  
DOI:10.13255/j.cnki.jusst.20190321002
中文关键词:  安全预警  云模型  数据挖掘  二进制存储  Apriori算法
英文关键词:safety warning  cloud model  data mining  binary storage  Apriori algorithm
基金项目:国家重点研发计划项目(2018YFB1700902)
作者单位E-mail
李晓春 上海市青浦区河道水闸管理所上海 201799  
张生 上海理工大学 光电信息与计算机工程学院上海 200093 zhangsheng@usst.edu.cn 
摘要点击次数: 136
全文下载次数: 80
中文摘要:
      为提高水闸泵站监测的预测预警能力,考虑到影响水闸泵站安全的多因素性和监测数据量庞大的特点,提出了一种将云模型与改进Apriori算法相结合的水闸泵站监测数据关联规则挖掘方法。首先建立监测数据属性空间,利用逆向云模型实现属性空间的软划分,将监测数据离散化。为避免传统Apriori多次扫描数据库时间长的缺点,采用二进制存储数据并通过“与运算”获取频繁项集;考虑到数据的动态增加,采用增量更新方法对规则进行更新。最后以某市水闸泵站的监测数据分析为例,验证了所提方法的有效性。
英文摘要:
      In order to improve the forecasting and warning ability of sluice pumping station monitoring, considering the multi-factor affecting the safety of sluice pumping station and the huge amount of monitoring data, a method for mining association rules of sluice pumping station monitoring data was proposed, which combines the cloud model with the improved Apriori algorithm. Firstly, the attribute space of monitoring data was established, and the soft partition of attribute space was realized by using the reverse cloud model, and the monitoring data were discretized. In order to avoid the disadvantage of long time taken for multi-scanning database with the traditional Apriori algorithm, the binary was used to storage data and frequent itemsets were got through ‘and operation’. Considering the dynamic increase of data, an incremental update method was proposed to update rules. Finally, the monitoring data analysis of a sluice pumping station in a city was taken as an example to verify the effectiveness of the proposed method.
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