Analysis Model of Railway Container Floor Maintenance Quality Based on Neural Network

In recent years, the number and cost of railway container repairs in China's railway cargo transportation have continued to rise. Therefore, investigating the relationship between the types of goods transported and the level of container damage, enhancing the control of container maintenance quality, extending the service life of containers, and reducing maintenance costs are of great significance for the China Railway Container Corporation in managing railway container assets. This study aims to explore the relationship between container floor repairs and maintenance levels. Machine learning datasets for container floor repair and maintenance levels are obtained through data preprocessing, and the relationships are analyzed using BP and SCG neural network models. Subsequently, a deep neural network model is optimized and reconstructed within the TensorFlow deep learning framework to address the shortcomings of the two previous shallow neural networks, resulting in the final deep neural network model. This model provides a reference for decision-making in container transportation and maintenance.