针对高原环境不同海拔地区工程机械柴油发动机NOx排放与实际运行工况之间的复杂非线性关系，提出了一种基于维度学习的狩猎(DLH)搜索策略改进GWO-神经网络的NOx排放预测模型。利用便携式排放测试系统(PEMS)对高原地区叉车进行不同海拔下的实际运行工况排放试验，并将试验数据作为数据集，通过随机森林算法完成预测模型的输入特征选择。结果表明：I-GWO-BP模型相对于BP和GWO-BP模型在评价指标RMSE和R²上表现更优，RMSE和R²分别为4.623 3 mg/s和0.925 1，该模型对高原地区不同海拔下工程机械NOx排放的预测精度更高。

For addressing the complex nonlinear relationship between NOx emissions from diesel engines of construction machinery in different altitudinal regions of plateau environments and actual operational conditions, a diminishing learning based hunting(DLH) search strategy to improve the grey wolf optimizer(GWO)-BP was proposed to optimize a BP neural network model for predicting NOx emissions. A portable emission measurement system(PEMS) was used to conduct emission tests on forklifts in plateau areas under various altitudinal operational conditions, and the experiment data were served as the dataset. Feature selection for the prediction model input was completed using the random forest algorithm. The results showed that the I-GWO-BP model outperformed both the BP and GWO-BP models in terms of evaluation metrics RMSE and R², with RMSE and R² values of 4.623 3 mg/s and 0.925 1 respectively. The model exhibited good prediction accuracy for NOx emissions from construction machinery at different altitudes in plateau areas.