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.
下载: