263 such as multiple linear regression and support vector regression, this paper selects the deep learning model as the core modeling tool for energy efficiency, and simplifies the model selection process into a background explanation of method comparison. Considering the continuity and dependence of fuel consumption data in the time dimension, this paper finally chooses the Long Short-Term Memory (LSTM) network to construct the energy efficiency model. By introducing a gating mechanism, LSTM effectively alleviates the gradient disappearance problem of traditional recurrent neural networks in long-sequence modeling, and can capture both short-term fluctuation characteristics and long-term change trends, which is suitable for the joint modeling needs of multi-source time-series data in the mine transportation process. While ensuring strong non-linear fitting ability, the model also has good stability and scalability, providing a reliable model basis for subsequent energy efficiency analysis. 4.2. Model Input and Sample Organization In terms of model structure design, this study employs the LSTM network as the main structure of the energy efficiency model. The model input is a time-series feature vector after multi-source fusion, covering multi-dimensional information such as mining trucks operation status, load and road condition information, and environment and operation conditions, which are uniformly used as the time-series input of the model. The model input is a time-series feature vector after multi-source fusion, which includes 26 core dimensions in total: Mining trucks number, mining trucks type, engine speed, oil temperature, transmission gear, reverse state, parking state, braking state, design slope, loading point, unloading point, operation state, mining trucks load, speed, acceleration, angular acceleration, operation duration, movement distance, lifting height, actual slope, vibration intensity, peak factor, kurtosis parameter, weather level, temperature, humidity and average precipitation. The data is collected every second as a set of original data. The original data is merged by feature clustering according to the mining trucks number on the continuous time axis to form one input sequence. 4.3. Network Structure Design The model is composed of multiple layers of LSTM units stacked to enhance the ability to express complex time-series features. Each LSTM layer performs feature abstraction on the input sequence through non-linear mapping and extracts high-level time-series features layer by layer. The model adopts a lightweight architecture of "input layer - 2 LSTM layers - Dropout layer - fully connected layer - output layer" to ensure fitting accuracy while reducing computational complexity. The specific structure is as follows: LSTM layers: The first layer is set with 64 neurons, which is responsible for extracting basic timeseries features such as speed, slope and speed. The second layer is set with 32 neurons to further explore multi-factor coupling features such as strong vibration, large slope and sudden acceleration. Both layers use the tanh activation function, taking into account the feature expression ability and computational efficiency. Dropout layer: The dropout ratio is set to 0.2, which randomly shields the connections of some neurons to avoid the model's over-reliance on local samples and improve the generalization ability. Fully connected layer: It is set with 16 neurons and uses the ReLU activation function to integrate the high-dimensional features output by the LSTM layer. Output layer: The linear activation function is used to output the average instantaneous fuel consumption (L/h) corresponding to the per-second time window as the core index of energy efficiency. 4.4. Model Training Strategy In the process of model training, an adaptive optimization algorithm based on gradient descent is used to update the network parameters, and the mean square error is used as the loss function to guide the model to continuously approach the real fuel consumption level. By setting the learning rate and training
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