Steel logistics platforms often need to split steel products into multiple waybills for transportation when handling customer orders. Less-Than-Truckload （LTL） cargo， which fails to meet the minimum load requirements of a truck， needs to be consolidated with goods from other customer orders to optimize transportation efficiency. Although previous studies had proposed some solutions for consolidation decision-making， none considered the issues of detour distance and prioritizing high-priority cargo simultaneously in consolidated shipments. Therefore， a multi-objective optimization framework for steel cargo consolidation under multiple constraints was proposed. The globally optimal cargo consolidation decisions were achieved by the framework through designing a hierarchical decision network and a representation enhancement module. Specifically， a hierarchical decision network based on Proximal Policy Optimization （PPO） was used to determine the priorities of the optimization objectives first， and then the LTL cargo was consolidated and selected on the basis of these priorities. Meanwhile， a representation enhancement module based on Graph ATtention network （GAT） was employed to represent cargo and LTL cargo information dynamically， which was then input into the decision network to maximize long-term multi-objective gains. Experimental results on a large-scale real-world cargo dataset show that compared to other online methods， the proposed method achieves a 17.3% increase in the proportion of high-priority cargo weight and a 7.8% reduction in average detour distance， with reducing the total shipping weight by 6.75% compared to the LTL cargo consolidation method that only maximizes cargo capacity. This enhances the efficiency of consolidated transportation effectively.

Train assisted driving depends on the real-time detection of train operating environment. There are abundant small targets in the images of train operating environment. Compared with large and medium targets， small targets with the proportion of less than 1% of original image have problems of high missed detection and poor detection accuracy due to low resolution. Therefore， a target detection algorithm based on improved YOLOv3 in train operating environment was proposed， namely YOLOv3-TOEI （YOLOv3-Train Operating Environment Image）. Firstly， k-means clustering algorithm was used to optimize the anchor to speed up the convergence of the network. Then， dilated convolution was embedded in DarkNet-53 to expand the receptive field， and Dense convolutional Network （DenseNet） was introduced to obtain richer low-level details of the image. Finally， the unidirectional feature fusion structure of original YOLOv3 was improved to bidirectional and adaptive feature fusion structure， which realized the effective combination of deep and shallow features and improved the detection effect of the network on multi-scale targets （especially small targets）. Experimental results show that compared with original YOLOv3 algorithm， YOLOv3-TOEI algorithm has the mean Average Precision （mAP）@0.5 reached 84.5%， which increased by 12.2%， and the Frames Per Second （FPS） of 83， verifying that this algorithm has better detection ability of small targets in images of train operating environment.