## 1 Introduction
[14] established the trajectory motion model of the electric shovel by kinetic, combined with the pseudospectral method to convert the kinetic model into the algebraic format. They obtained the optimal excavation trajectory by solving the nonlinear programming. [PERSON] et al. [15] modeled the whole excavation process and obtained the excavation trajectory by a rule-based planning method, which significantly reduced fuel consumption. [PERSON] et al. [16] used a dynamic planning method to generate the shoveling trajectory. They conducted tests in a gravel pile, and the results showed a 15% improvement in fuel economy with this method. [PERSON] et al. [17] established a mathematical model of the shoveling process to optimize the shoveling process to reduce fuel consumption, which can be reduced by 30% through simulation analysis. However, in these studies, the optimization of shoveling trajectory is mainly carried out by theoretical analysis and simulation analysis. It ignores the high randomness and complexity of the shoveling operation process, and there is a specific error between the dynamics or mathematical model and the actual shoveling operation [18; 19; 20]. As a result, it is difficult to achieve the desired effect in the actual shoveling operation. Research has always focused on modeling the fuel consumption of shoveling operations. At present, the common fuel consumption modeling method is the theoretical calculation method [21; 22; 23]. This method is mainly based on theoretical analysis of energy consumption during the shoveling operation of wheel loaders and then calculates the overall fuel consumption. This method usually cannot consider the large randomness in the process of wheel loader shoveling operation, and there is a large error between it and the actual shoveling operation. In recent years, machine learning methods have shown excellent properties in solving real engineering problems. There have been several successful applications of using machine learning to achieve modeling of operational fuel consumption of engineering vehicles. [PERSON] et al. [24] analyzed the fuel consumption of mining dump trucks at each stage of operation with the objective of fuel consumption of a single operating cycle and constructed a fuel consumption model using machine learning methods. [PERSON] et al. [25] established an Artificial Neural Network fuel consumption model to achieve fuel consumption prediction of mining trucks in each cycle with a model error of 10%. [PERSON] et al. [26] used machine learning methods such as multiple linear regression, Random Forest, Artificial Neural Network, Support Vector Machine, and Kernel nearest neighbor to predict the fuel consumption of transport trucks and compared the accuracy of each model. [PERSON] et al. [27] used a binary logistic regression model to analyze 21 fuel consumption influencing factors of heavy trucks and obtained 8 influencing factors that have a significant effect on fuel consumption. Meanwhile, the accuracy of the fuel consumption model was compared using Decision Tree, Artificial Neural Network, and Random Forest. [PERSON] et al. [28] constructed an excavator data collection system and proposed an improved PSO-BP model for excavator energy consumption prediction, which achieved good prediction accuracy. However, there are fewer applications for modeling the fuel consumption of wheel loaders using machine learning. [PERSON] et al. [29] constructed a BP neural network model with wheel loader driver, pile type, and bucket capacity as input features, although the accuracy of the model was high. However, in this study, there are problems of fewer data samples and unreasonable selection of fuel consumption influencing factors, which are difficult to be extended to other application scenarios. Since the shoveling process itself is a high randomness process. There are many factors that can have a great impact on the fuel consumption of shoveling operations.