This paper presents a hybrid optimization strategy for determining the setting values of burden surface through measured data and domain knowledge integration manner. The proposed hybrid optimization strategy, including broad learning-based soft sensing models for production indicators, novel twin information fusion based pre-setting model, knowledge-mining based feedback compensation model, data-based production status evaluation and knowledge-based adjustment model, can adjust the setting values of burden surface in response to the changes in production status and safe operation can be reached as well. Finally, comprehensive experiments are conducted to verify the effectiveness and feasibility of the proposed method.

本文提出了一种混合优化策略，通过实测数据和领域知识整合的方式确定载重面的设定值。提出的混合优化策略，包括广泛的基于学习的生产指标软传感模型，新颖的基于双信息融合的预设模型，基于知识挖掘的反馈补偿模型，基于数据的生产状态评估和基于知识的调整模型，都可以实现。根据生产状况的变化来调整装载面的设定值，也可以达到安全操作。最后，进行了综合实验，验证了该方法的有效性和可行性。