ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

Accurate prediction of heliostat temperature was the key to achieve efficient greenhouse regulation, which was of great importance to crop growth and development, but it was difficult to achieve continuous and accurate prediction due to the characteristics of time series, nonlinearity and multi coupling of temperature. At the same time, the current production regulation of greenhouse mostly depended on the relevant experience of producers. This method had caused the lag of feedback control and affected the growth of crops.A temperature prediction model of solar greenhouse based on 1D CNN-GRU was proposed. The internal and external environmental factors were obtained through the monitoring platform inside and outside the greenhouse, and the strong correlation features and structural features were obtained by Spearman correlation coefficient and the two-dimensional matrix input network with time step, which was used for temperature prediction. The determination coefficient of the model after 1~4h prediction on the test set was 0.970~0.994, the root mean square error was 0.612~1.358℃, the average error was 0.428~0.854℃,and the maximum absolute error after the absolute value was 0.856~1.959℃. The model was verified under different KT and the results showed that the model had the best prediction effect when KT≥0.5(sunny), and the model also achieved ideal prediction accuracy under other KT, indicating that the model was universal and provided an important basis for accurate and efficient temperature control of solar greenhouse.