In container terminal operations, truck lifting accidents pose a serious threat to the safety and efficiency of automated equipment. Traditional detection methods using visual cameras and single-line Light Detection and Ranging (LiDAR) are insufficient for capturing three-dimensional spatial features, leading to reduced detection accuracy. Moreover, the boundary features of key accident objects, such as containers, truck chassis, and wheels, are often blurred, resulting in frequent false and missed detections. To tackle these challenges, this paper proposes an accident detection method based on multi-line LiDAR and an improved PointNet++ model. This method uses multi-line LiDAR to collect point cloud data from operational lanes in real time and enhances the PointNet++ model by integrating a multi-layer perceptron (MLP) and a mixed attention mechanism (MAM), optimizing the model’s ability to extract local and global features. This results in high-precision semantic segmentation and accident detection of critical structural point clouds, such as containers, truck chassis, and wheels. Experiments confirm that the proposed method achieves superior performance compared to the current mainstream algorithms regarding point cloud segmentation accuracy and stability. In engineering tests across various real-world conditions, the model exhibits strong generalization capability.

With the continuous expansion of maritime trade scale, ports have put forward increasingly higher requirements for transshipment efficiency. Container twist lock disassembly is a key link in the loading and unloading process, and its automation level has a significant impact on the ship’s berthing time at the port. Aiming at the demand of automated disassembly for high-precision 3D vision, this paper proposes a multi-view point cloud local registration method for twist lock recognition. First, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is used to extract the keyhole region with the highest overlap in multi-view point clouds, reducing the interference from non-overlapping structures. Then, a two-stage strategy of “coarse registration + fine registration” is adopted: initial alignment is achieved through Random Sample Consensus (RANSAC), and the Iterative Closest Point (ICP) algorithm is improved by combining adaptive distance threshold and normal consistency constraint to complete fine registration. Experimental results show that the proposed method outperforms the global registration scheme in both accuracy and efficiency: the Root Mean Square Error (RMSE) is reduced to 2.15 mm, the Relative Mean Distance (RMD) is reduced to 1.81 mm, and the registration time is approximately 2.41 s. Compared with global registration, the efficiency is improved by 44.2%, which can meet the real-time requirements of continuous operation at automated terminals for the perception link and the time constraints for subsequent manipulator control. The research results preliminarily verify the application potential of this method in the scenario of automated twist lock disassembly.

As the core sensing method for port automation equipment, LiDAR is widely used in spatial distance measurement, three-dimensional positioning, and environmental perception. However, its ranging mechanism, parameter configuration, and role in complex port operations are relatively abstract, making traditional training difficult to achieve intuitive understanding and efficient application. To address this issue, this paper proposes a virtual LiDAR simulation training platform based on digital twin technology. The platform constructs a virtual environment highly consistent with real port operation processes, designs a virtual LiDAR module with adjustable horizontal/vertical field of view, resolution, channel count, and ranging distance, and achieves bidirectional synchronization of data and control signals between physical and twin spaces. Using a bulk cargo handling machine as the verification object, consistency comparison experiments were conducted on point cloud density, ranging accuracy, and geometric similarity. The results show that the virtual point clouds generated by the platform are highly consistent with real point clouds in key indicators, featuring low cost, high flexibility, and scalability, and can provide reliable support for operation training, algorithm testing, equipment deployment assessment, and operational safety analysis of port automation equipment.

Dual tower-crane crossing operations require timely identification of collision risks as the spatial relationship between the two jibs changes. Conventional systems based on crane-state sensors and static safety zones provide limited information about the surrounding three-dimensional workspace, while sparse point clouds, occlusion, and class imbalance complicate reliable jib recognition. This study develops a LiDAR-based method that converts point-cloud segmentation into geometric models for short-term collision-risk prediction. A geometric-temporal dynamic graph convolutional neural network (GT-DGCNN) combines geometric priors, class-balanced learning, and temporal consistency to identify the jibs and tower structures of both cranes. The segmented jibs are represented by capsule-based envelopes and evaluated using swept-volume prediction, dynamic safety distance, and time to collision. The complete method achieves a mean intersection over union of 87.3% and a jib recall of 91.7%, exceeding the DGCNN baseline by 6.4 and 7.4 percentage points, respectively. The missed-alarm rate decreases from 7.8% to 2.4%, with a processing rate of 17.2 frames per second. These results support the use of geometric-temporal point-cloud perception for jib recognition and collision warning in the evaluated dual-crane scenarios.

With the continuous improvement of port automation, remote control of container gantry cranes has become mainstream. However, real machine training poses significant safety risks, where novice drivers (Trainee) are prone to equipment collisions and violent container swings during operations. To address this issue, this paper designs and implements a digital twin system integrated with an operation risk assessment module, used for safety reproduction and risk quantification of gantry crane operations. The system adopts a time series prediction method based on attention mechanism, establishing a safety benchmark by modeling skilled drivers' (Trainer) operational data, and predicts and compares novice drivers' operational behaviors to quantify risk deviations. Experimental results demonstrate that the system can effectively distinguish operation risks across different driver experience levels, showing excellent application value in avoiding real machine training hazards, improving training efficiency, and achieving personalized risk assessment.