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    吴伟斌, 杨柳, 吴维浩, 等. 基于EEMD-WPT的温室环境数据优化处理研究[J]. 华南农业大学学报, 2024, 45(3): 397-407. DOI: 10.7671/j.issn.1001-411X.202305029
    引用本文: 吴伟斌, 杨柳, 吴维浩, 等. 基于EEMD-WPT的温室环境数据优化处理研究[J]. 华南农业大学学报, 2024, 45(3): 397-407. DOI: 10.7671/j.issn.1001-411X.202305029
    shu
    WU Weibin, YANG Liu, WU Weihao, et al. Research on the optimization processing of greenhouse environmental data based on EEMD-WPT[J]. Journal of South China Agricultural University, 2024, 45(3): 397-407. DOI: 10.7671/j.issn.1001-411X.202305029
    Citation: WU Weibin, YANG Liu, WU Weihao, et al. Research on the optimization processing of greenhouse environmental data based on EEMD-WPT[J]. Journal of South China Agricultural University, 2024, 45(3): 397-407. DOI: 10.7671/j.issn.1001-411X.202305029
    shu

    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

      摘要
    • 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.

       

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