文章摘要
谢李祥,邢传玺,张东玉,吴耀文.浅海多节点环境参数测量数据融合方法研究[J].声学技术,2022,41(2):274~281
浅海多节点环境参数测量数据融合方法研究
Research on data fusion method of multi-node environmental parameter measurement in shallow sea
投稿时间:2020-11-11  修订日期:2020-12-17
DOI:10.16300/j.cnki.1000-3630.2022.02.019
中文关键词: NB-IoT节点  模糊综合评价  相关度函数  自适应加权算法
英文关键词: NB-IoT node  fuzzy comprehensive evaluation  correlation function  adaptive weighting algorithm
基金项目:国家自然基金项目(61761048)、云南省高校科技创新团队支持计划资助项目。
作者单位E-mail
谢李祥 云南民族大学电气信息工程学院, 云南昆明 650500  
邢传玺 云南民族大学电气信息工程学院, 云南昆明 650500 xingchuanxi@163.com 
张东玉 云南民族大学电气信息工程学院, 云南昆明 650500  
吴耀文 云南民族大学电气信息工程学院, 云南昆明 650500  
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中文摘要:
      为了提高浅海环境监测的及时性和避免人为主观评价水质的不合理性,引入窄带物联网(Narrow Band Internet ofThings,NB-IoT)多节点方式进行浅海环境参数回传,并在远程端使用数据融合技术对其进行融合处理,实现水质等级科学评价。采用两级并联融合方式进行环境参数融合,在融合前使用模糊理论中的相关性函数剔除外界及传感器本身噪声所造成的异常数据;然后使用自适应加权融合算法对其进行第一级融合,最后运用模糊综合评价方法将上一级的融合结果进行决策层融合,实现浅海环境的水质等级评价。经试验验证,使用上述方法能够更加及时、有效地获取水质环境参数且第一级融合相对误差小于6.5%,并能以概率形式直观展现出监测区域的水质等级,提高水质等级评价的准确性与可靠性。
英文摘要:
      In order to improve the timeliness of environmental monitoring in shallow sea and avoid the irrationality of subjective evaluation of water quality, the narrowband internet of things (NB-IoT) multi-node method is introduced to transmit back the environmental parameters in shallow sea, and the data fusion technique is used at the remote terminal to integrate them for scientific evaluation of water quality grade. In this paper, a two-stage parallel fusion method is adopted for environmental parameter fusion. Before fusion, the correlation function in fuzzy theory is used to eliminate the abnormal data caused by environmental noise and sensor itself; then the adaptive weighted fusion algorithm is used for the first-level fusion. Finally, the fuzzy comprehensive evaluation method is used to merge the first-level fusion results into the decision-making level to realize the water quality grade evaluation of shallow sea environment. It is verified by experiments that the above method can obtain water quality environmental parameters more timely and effectively, and the relative error of the first level fusion is less than 6.5%, which can directly show the water quality grade of the monitoring area in the form of probability, and improve the accuracy and reliability of water quality grade evaluation.
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