To effectively recognise the working condition in froth flotation, many existing methods separately focus on handcrafted features or deep features. Considering that deep features complement handcrafted features with more detailed information, a layered working condition perception method integrating handcrafted features with deep features is developed for zinc flotation. In the proposed method, a fuzzy algorithm layer (FAL) with handcrafted features and a convolutional perception layer (CPL) with deep features are constructed for working condition recognition. Given that similar handcrafted features exist in the boundary between adjacent working conditions, the handcrafted feature-based FAL is limited. Therefore, the layered evaluation agency (LEA) is established to determine whether the working condition needs to be reidentified. If needed, LEA obtains the information of two possible working conditions, and the CPL is applied to reidentify the working condition based on deep features extracted by VGG16 and the support vector machine (SVM) with the specific category designated by LEA. The effectiveness of the proposed framework was evaluated through experiments, and the results confirm the potentiality of the proposed method in working condition recognition.

为了有效识别泡沫浮选的工况，许多现有方法分别关注手工特征或深层特征。考虑到深层特征以更详细的信息补充手工特征，开发了一种将手工特征与深层特征相结合的分层工况感知方法用于锌浮选。在所提出的方法中，构建了具有手工特征的模糊算法层（FAL）和具有深度特征的卷积感知层（CPL）用于工况识别。鉴于相邻工作条件之间的边界存在类似的手工特征，基于手工特征的 FAL 是有限的。因此，建立分层评估机构（LEA）来确定是否需要重新识别工作条件。如果需要的话，LEA 获取两种可能的工况信息，并基于 VGG16 提取的深层特征和 LEA 指定的特定类别的支持向量机 (SVM) 应用 CPL 重新识别工况。通过实验评估了所提出框架的有效性，结果证实了所提出方法在工况识别方面的潜力。