摘要
针对黄河含沙量监测易受环境因素影响的特点,研究了基于云计算的分布式灰色数据融合技术,建立了黄河含沙量在线监测云平台。平台依靠分散在黄河流域的水温、测点深度等传感器建立分布式数据采集子云,然后用主成分分析法分析出含沙量监测的主成分因素,最后基于灰色GM(1,N)模型进行含沙量数据融合处理。为了比较灰色GM(1,N)模型含沙量监测的融合效果,在相同环境下进行了一元线性拟合、多元线性拟合的对比处理。结果表明,基于灰色GM(1,N)模型数据融合的精度最高,稳定性最强,能够拟合出较为准确的结果。
For monitoring the Yellow River sediment characteristics susceptible to environmental factors, it studied the distributed grey data fusion technology based on cloud computing and established the Yellow River sediment online testing cloud platform. Firstly, the platform would be distributed in the Yellow River basin water temperature, depth sensor measuring point to establish a distributed data acquisition sub-cloud and then analyzed the principal component factor of the sediment testing by using principal component analysis, and finally carried sediment data fusion based on gray GM (1, N) model. In order to compare the gray GM (1, N) fusion effect sediment detection methods, in the same environment also carried out a linear fit and multivariate linear fitting process. The results show that the highest accuracy and the strongest stability based on gray GM (1, N) model data fusion can fit more accurate results.
出处
《人民黄河》
CAS
北大核心
2015年第11期15-17,24,共4页
Yellow River
基金
水利部公益性行业科研专项(201301034)
水利部黄河泥沙重点实验室开放课题(2012005)
国家科技重大专项(2014ZX03005001)
河南省高校科技创新团队支持计划项目(13IRTSTHN023)
郑州市科技创新团队项目(131PCXTD595)
2014年河南省高等学校重点科研项目(14B170012)
2015年河南省高等学校重点科研项目(15A510003)
关键词
主成分分析法
GM(1
N)模型
云计算
数据融合
含沙量监测
黄河
principal component analysis method
GM(1,N) model
cloud computing
data fusion
sediment concentration monitoring
Yellow River
