Accurately assessing the difficulty of material excavation is crucial for reducing excavator energy consumption, ensuring operational safety, and optimizing excavator efficiency. Addressing the challenges of uncertain and difficult-to-judge excavation conditions for underground materials, this paper proposes a Bayesian deep learning-based method that integrates excavation process data to identify excavation difficulty. Firstly, we constructed a deep learning model based on Bayesian theory and decomposed the uncertainty of the identification results into aleatory uncertainty and epistemic uncertainty. Next, through a mechanistic analysis of the interaction between materials and the excavator bucket during excavation, we identified the input features for the model. Finally, we validated the effectiveness of the method through experiments. The results show that the proposed method not only accurately identifies the excavation difficulty of the material but also quantifies and decomposes the uncertainty of the identification results, demonstrating both theoretical significance and practical application value.

中文翻译：

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基于贝叶斯深度学习的材料挖掘难度识别及不确定性分析


准确评估材料挖掘的难度对于降低挖掘机能耗、确保操作安全和优化挖掘机效率至关重要。针对地下材料开挖条件不确定和难以判断的挑战，本文提出了一种基于贝叶斯深度学习的方法，该方法整合了开挖过程数据来识别开挖难度。首先，我们基于贝叶斯理论构建了一个深度学习模型，并将识别结果的不确定性分解为随机不确定性和认识不确定性;接下来，通过对挖掘过程中材料与挖掘机铲斗之间相互作用的机理分析，我们确定了模型的输入特征。最后，我们通过实验验证了该方法的有效性。结果表明，所提方法不仅准确识别了材料的开挖难度，而且量化和分解了鉴定结果的不确定性，具有较强的理论意义和实际应用价值。