基于EEMD-WPT的温室环境数据优化处理研究
Research on the optimization processing of greenhouse environmental data based on EEMD-WPT
吴伟斌 1杨柳 2吴维浩 2吴贤楠 2沈梓颖 2张方任 2罗远强2
作者信息
- 1. 华南农业大学工程学院,广东广州 510642;南方农业机械与装备关键技术教育部重点研究室/国家柑橘产业体系机械化研究室/广东省山地果园机械创新工程技术研究中心,广东广州 510642
- 2. 华南农业大学工程学院,广东广州 510642
- 折叠
摘要
[目的]解决温室系统中的数据采集传感器容易受到多种环境因素的干扰,从而导致数据中存在噪声的问题.[方法]提出一种集合经验模态分解(Ensemble empirical mode decomposition,EEMD)与小波包自适应阈值(Wavelet packet adaptive threshold,WPT)算法联合的数据降噪处理方法,并采用卡尔曼滤波与自适应加权平均算法对降噪后的数据进行融合.[结果]将EEMD-WPT算法应用于含噪温、湿度数据的降噪处理,相较于降噪前的数据,信噪比提升了 73.08%.该算法相较于传统WPT算法具有更好的降噪效果,处理后的数据信噪比提升了 40.31%,均方根误差降低了 84.75%.[结论]该算法能解决数据跳动、冗余和丢失等问题,并为温室控制系统提供了有效的参数,具有较大的实际应用价值.
Abstract
[Objective]To address the problem that the data acquisition sensors in greenhouse system are easily disturbed by various environmental factors,leading to the presence of noise in the data.[Method]This study proposed a data noise reduction processing method combining ensemble empirical mode decomposition(EEMD)and wavelet packet adaptive threshold(WPT)algorithm,and the Kalman filter and adaptive weighted average algorithm were used to fuse the noise-reduced data.[Result]After applying the EEMD-WPT algorithm to the noise reduction processing of the noise-containing temperature and humidity data,the signal-to-noise ratio was improved by 73.08%compared with the data before noise reduction.The EEMD-WPT algorithm had better noise reduction effect compared with the traditional WPT algorithm,and the signal-to-noise ratio of the processed data was improved by 40.31%and the root mean square error reduced by 84.75%.[Conclusion]The algorithm can solve the problems of data skipping,redundancy and loss,and provides effective parameters for the greenhouse control system,making it highly practical and valuable for application.
关键词
EEMD/小波包/自适应阈值/降噪/温室/数据融合Key words
EEMD/Wavelet packet/Adaptive threshold/Noise reduction/Greenhouse/Data fusion引用本文复制引用
基金项目
广东省现代农业产业技术体系创新团队建设项目(2023KJ120)
国家自然科学基金青年科学基金(52005188)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据