目的 满足瓦楞纸板行业日益个性化的定制需求，减小复杂多变的生产条件对瓦楞纸生产速度的影响，帮助企业合理安排生产，提高生产线管控水平。方法 首先对瓦楞纸板生产速度进行重采样，统一订单参数和各传感器参数采样间隔，采用ButterWorth滤波器进行高通滤波，并采用四分位距统计量方法筛选稳定的湿部生产速度区间，提取B瓦和BC瓦的数据，然后根据提取的数据使用BP神经网络和XGBoost预测湿部生产速度，并采用贝叶斯优化和网格搜索分别寻优2种模型的超参数，最后使用粒子群算法组合2种模型的预测结果。结果 实验结果表明，2个模型都具有一定的预测能力，其中XGBoost的预测效果更好，组合模型的预测效果最好。结论 基于BPNN-XGBoost组合模型的方法能有效预测瓦楞纸板湿部生产速度，可指导实际生产。

The work aims to meet the increasingly personalized customization needs of the corrugated board industry, reduce the impact of complex and variable production conditions on the production speed, help enterprises to arrange production reasonably, and improve the level of production line control. Firstly, the production speed of corrugated board was resampled to unify the sampling interval of order parameters and sensor parameters, and high pass filtering by ButterWorth filter. Quartile statistics were used to screen the stable wet end production speed interval and extract the data of types B and BC. Then, BP neural network and XGBoost were used to predict the wet end production speed based on the extracted data, and Bayesian optimization and grid search were used to optimize the hyperparameter of two models, respectively. Finally, PSO algorithm was used to combine the two models to predict the production speed. The experimental results showed that both models had certain prediction ability, among which XGBoost had better prediction performance and the combined model had the best prediction performance. The method based on BPNN-XGBoost combined model can effectively predict the wet end production speed of corrugated board and guide the actual production.