## 5 Discussion  
A 3D burden surface feature definition system was proposed in this manuscript. We divided the burden surface into the central coke area, funnel area, and platform area. Based on expert experience, the feature parameters for each region were defined and extracted. The feature parameters contain the shape information of the burden surface. Based on this underlying mathematical shape information, specious surface shapes that are difficult to recognize in neural networks can be excluded. The depth features (voxel features and point cloud features) were extracted based on improved BNVGG and PointCNN. Deep learning of the burden surface can improve the accuracy of recognition. The 3D burden surface feature definition system is perfected through different angles, which lays the foundation for the subsequent matching of surface types, fault diagnosis, and the establishment of the surface history database. Experiments have verified the accuracy of burden surface recognition under each feature. The following conclusions were obtained through the analysis of the experimental results of Section 4:  
1. The 18 feature parameters based on expert experience can accurately classify some burden surfaces with obvious differences, but they are difficult to classify on similar shapes. The accuracy of the test set was about 65.37%. It defined the shape of the burden surface from the perspective of the data and had a certain reference value. 2. We extracted the voxel features of the burden surfaces by the optimized BNVGG network. The accuracy of the burden surface recognition was increased (95.67%), and the time of network training was reduced (238 s). The comparative experiment verified the effectiveness of the network structure, which had good identification and classification effects on the burden surface data. 3. The two neural network models of PointNet (96.5%) and PointCNN (94.4%) can obtain better results when processing point cloud data. PointNet implements the extraction and classification of the global features, while PointCNN realizes the extraction of the global and local features of the burden surface data. However, due to the particularity of the burden surface data, the local feature part of PointCNN has no effect. In the extraction of global features, PointNet's performance is better, and it is more suitable for the feature extraction and classification of the burden surface. According to the experimental results, in subsequent research, some objectives are the following:  
* Increase the scale and category of the data before training. By increasing data complexity, the advantages and disadvantages of deep neural networks can be reflected. * Due to the difficulty of obtaining the original data, the generative confrontation network can be used to learn the original data features and generate more authentic simulation data. * The neural network is trained through various feature parameters, determining by trial and error the optimal topology of the neural network, the number of hidden layer \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Feature** \\ **Parameters** \\ \end{tabular} & **BNVGG** & **PointNet** & **PointCNN** \\ \hline
**Train Accuracy** & 73.0 & 99.2 & 99.7 & 99.5 \\ \hline
**Test Accuracy** & 65.4 & 95.7 & 96.5 & 94.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy of recognition under each features. nodes, and the neural network layer transfer function. This method provides a new idea for network training [24; 25]. * For the three types of features extracted in this manuscript, the feature fusion operation is performed through the method of attention mechanism. The fusion features could be used to test the effect of burden surface recognition. * Through the effective fusion of the features of burden surfaces and the parameters of furnaces, the matching and fault diagnosis of new burden surface data could be achieved.