基于EEMD-WPT的温室环境数据优化处理研究OA北大核心CSTPCD

[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.