In order to overcome the notable limitations of current methods for monitoring grain storage states, particularly in the early warning of potential risks and the analysis of the spatial distribution of grain temperatures within the granary, this study proposes a multi-model fusion approach based on a deep learning framework for grain storage state monitoring and risk alert. This approach combines two advanced three-dimensional deep learning models, a grain storage state classification model based on 3D DenseNet and a temperature field prediction model based on 3DCNN-LSTM. First, the grain storage state classification model based on 3D DenseNet efficiently extracts features from three-dimensional grain temperature data to achieve the accurate classification of storage states. Second, the temperature prediction model based on 3DCNN-LSTM incorporates historical grain temperature and absolute water potential data to precisely predict the dynamic changes in the granary’s temperature field. Finally, the grain temperature prediction results are input into the 3D DenseNet to provide early warnings for potential condensation and mildew risks within the grain pile. Comparative experiments with multiple baseline models show that the 3D DenseNet model achieves an accuracy of 97.38% in the grain storage state classification task, significantly outperforming other models. The 3DCNN-LSTM model shows high prediction accuracy in temperature forecasting, with MAE of 0.24 °C and RMSE of 0.28 °C. Furthermore, in potential risk alert experiments, the model effectively captures the temperature trend in the grain storage environment and provides early warnings, particularly for mildew and condensation risks, demonstrating the potential of this method for grain storage safety monitoring and risk alerting. This study provides a smart grain storage solution which contributes to ensuring food safety and enhancing the efficiency of grain storage management.

In the Formula Student Racing Car, the frame is a fundamental component that supports the body. The frame’s anti-deformation ability will impact the four-wheel positioning parameters of the car, which subsequently affect the car’s stability. The quality of the frame directly determines the power and efficiency of the racing car, making the frame crucial to the overall race performance. Therefore, researching lightweight frame design is particularly important to enhance frame performance and reduce its total weight. In this paper, based on the variable density method, the global topology optimization of the frame is carried out to obtain the distribution of the frame material, realize the efficient utilization of the material, and improve the torsional stiffness of the frame. Compared with the previous local topology optimization, the global topology optimization involves a wider range, and the results are more accurate. Based on the adjoint variable method, the sensitivity analysis of the frame is carried out to obtain the influence level of each design variable. The size of the frame is optimized according to the variables with high influence levels. After optimization, the total mass of the frame is reduced by 12.8%; the performance in terms of maximum displacement and maximum stress is improved; and the lightweight design of the frame is realized as a whole.

Road detection technology is an important part of the automatic driving environment perception system. With the development of technology, the situations that automatic driving needs to consider will become broader and more complex. This paper contributes a lightweight convolutional neural network model, incorporating novel convolution and parallel pooling modules, an improved network activation function, and comprehensive training and verification with multiple datasets. The proposed model achieves high accuracy in detecting drivable areas in complex autonomous driving situations while significantly improving real-time performance. In addition, we collect data in the field and create small datasets as reference datasets for testing algorithms. This paper designs relevant experimental scenarios based on the datasets and experimental platforms and conducts simulations and real-world vehicle experiments to verify the effectiveness and stability of the algorithm models and technical solutions. The method achieves an MIoU of 90.19 and a single batch time of 340 ms with a batch size of 8, which substantially reduces the runtime relative to a typical deep network structure like ResNet50.

In the Formula Student Racing Car, the anti-deformation ability of the frame will affect the four-wheel positioning parameters of the racing car, and then affect the handling and stability performance of the racing car. The quality of the frame directly determines the power and economy of the racing car, so the performance of the frame is very important to the performance of the race. In order to improve the performance of the frame and reduce the total weight of the frame, the research on the lightweight design of the frame is particularly important. In this paper, based on the variable density method, the global topology optimization of the frame is carried out to obtain the distribution of the frame material, realize the efficient use of the material, and improve the torsional stiffness of the frame. Based on the adjoint variable method, the sensitivity analysis of the frame is carried out. According to the sensitivity analysis results, the size of the frame is optimized. The total mass of the frame is reduced by 12.8 %, and the performance of the maximum displacement and maximum stress is improved. The lightweight design of the frame is realized as a whole.