## 1 Introduction  
The BF ironmaking production process is the focus of the steel industry, and the development of steel intelligent manufacturing is an important direction for the transformation and upgrading of the industry [1]. The application of modern technologies such as industrial Internet and artificial intelligence to the steel manufacturing industry is of great significance to enhancing the comprehensive competitiveness of the steel industry [2].  
The traditional method uses diverse sensors to detect the burden surface and obtain multi-source data. The burden surface detection instruments include the height and temperature sensors [3]. Height sensors use multiple radars or mechanical probes to measure different points on the burden surface and then reconstruct the non-sampling points according to the mechanism model method or the fitting method [4]. The temperature sensors, using cross-temperature measurement methods, obtain the temperature distribution map by interpolation and reconstruct the burden surface [5].  
The burden surface can reflect the production status and thus guide the upper adjustment of the BF. In actual production, the accuracy and adaptability of the burden surface model are the key factors to ensuring the normal operation of the smelting process [6]. The complex environment inside the BF makes it difficult to measure the data, so the 2D burden line is often used to describe the 3D burden surface. The 2D burden line data are from the height value of the sparse discrete data points, and the radial burden line is obtainedby interpolating or fitting the data [7]. [PERSON] described the shape of the burden surface in two straight lines [8]. [PERSON] et al. established the distribution model of the burden surface by using two straight lines and two curves [9]. [PERSON] et al. used two straight lines and one curve to describe the shape of the burden surface [10]. [PERSON] described the charge distribution using a single functional equation [11]. In the asymmetric state, a single fitted burden line cannot represent the entire burden surface, and the burden line's fitting model has a large error, which leads to losing a lot of information about the burden surface.  
The feature parameters of the burden surface can accurately reflect the real production status. [PERSON] et al. pointed out that the two key factors of the BF charge control are the width of the surface platform and the depth of the funnel [12]. Based on expert experience, [PERSON] et al. proposed a method for defining and extracting six features that can characterize the shape of the burden surface [13]. However, the above research also extracted and defined the features of the 2D burden line. How to define the 3D burden surface feature is the focus of this manuscript.  
Based on the phased array radar point cloud data, this manuscript proposes a method for defining the characteristics of the BF 3D burden surface. Based on expert experience, 18 sets of burden surface features were defined and extracted. To solve the problem that similar burden surfaces cannot be accurately classified from the data perspective, we use the BNVGG network to extract the 2D voxel features [14] and use PointCNN to extract the 3D point cloud features [15]. Through these three sets of features, the correct identification of the burden surface is realized, which lays the foundation for charge operation, a historical burden surface database, fault diagnosis, etc.  
This manuscript is divided into five sections. The second section introduces a 3D burden surface feature definition method based on expert experience. The third section is a 2D voxel feature extraction method based on BNVGG and a 3D point cloud feature extraction method based on PointCNN. The fourth section is about the simulation results. The fifth section has the conclusion, which summarizes the main content of the manuscript and future work.