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

Air quality plays an important role in mutton sheep breeding environment, in order to reduce the stress response of CO2 to the growth of large-scale mutton sheep and ensure the healthy growth of mutton sheep in the appropriate environment, the key is to accurately control the CO2 in the mutton sheep breeding environment. A CO2 prediction model of mutton sheep breeding environment was proposed based on light gradient boosting machine (LightGBM), sparrow search algorithm (SSA) and extreme learning machine (ELM). Firstly, LightGBM was used to screen out the important characteristics of carbon dioxide concentration and reduce the input dimension of the prediction model. Then, ELM neural network algorithm with single hidden layer with strong nonlinear processing ability was used to build the CO2 prediction model. Finally, through the sparrow intelligent optimization algorithm, the super parameters needed in ELM model were optimized to obtain the best prediction model. The prediction model was applied to a large-scale mutton sheep breeding base in Manas County, Changji Hui Autonomous Prefecture, Xinjiang Uygur Autonomous Region, and good prediction results were obtained. The experimental results showed that the prediction model had good prediction effect, and the root mean square error (RMSE) of ELM was higher than that of SVR, BPNN, LSTM, GRU and LightGBM. The RMSE, mean absolute error (MAE) and R2 were 0.0213mg/L, 0.0136mg/L and 0.9886, respectively. The results showed that the combined model can not only achieve accurate control of carbon dioxide in sheep house, but also meet the needs of fine decision-making for mutton sheep breeding. It also can help farmers make decisions and reduce farming risks.