Abstract
Digital twin (DT), machine learning, and industrial Internet of things (IIoT) provide great potential for the transformation of the container terminal from automation to intelligence. The production control in the loading and unloading process of automated container terminals (ACTs) involves complex situations, which puts forward high requirements for efficiency and safety. To realize the real-time optimization and security of the ACT, a framework integrating DT with the AdaBoost algorithm is proposed in this study. The framework is mainly composed of physical space, a data service platform, and virtual space, in which the twin space and service system constitute virtual space. In the proposed framework, a multidimensional and multiscale DT model in twin space is first built through a 3D MAX and U3D technology. Second, we introduce a random forest and XGBoost to compare with AdaBoost to select the best algorithm to train and optimize the DT mechanism model. Third, the experimental results show that the AdaBoost algorithm is better than others by comparing the performance indexes of model accuracy, root mean square error, interpretable variance, and fitting error. In addition, we implement empirical experiments by different scales to further evaluate the proposed framework. The experimental results show that the mode of the DT-based terminal operation has higher loading and unloading efficiency than that of the conventional terminal operation, increasing by 23.34% and 31.46% in small-scale and large-scale problems, respectively. Moreover, the visualization service provided by the DT system can monitor the status of automation equipment in real time to ensure the safety of operation.
Highlights
Digital twin (DT), machine learning, and industrial Internet of things (IIoT) provide great potential for the transformation of the container terminal from automation to intelligence. e production control in the loading and unloading process of automated container terminals (ACTs) involves complex situations, which puts forward high requirements for efficiency and safety
Another key problem is the loading and unloading efficiency, which restricts ACT’s economic development. e processing speed of handling equipment, horizontal transportation time, and waiting time between equipment are the key factors affecting the efficiency of ACT operation. us, it is of great significance to further improve the safety and operation efficiency of ACTs with advanced technology and production control methods to monitor the operation in real time
Researchers mostly focus on the analysis of historical data and seldom consider the real-time and virtual data for the operation of the ACT. e common feature of the above research is based on physical space. e lack of integration between physical space and virtual space and the data generated by each system are independent of each other, which provides a low value for the optimization of ACT production control [7]
Summary
DT reflects the precise mapping between physical space and virtual space [13]. the research on theory and practical application of DT is still at its infancy. E DT framework of production optimization in petroleum industry based on machine learning is proposed. E DT model is trained by machine learning and big data technology to realize the optimization of production control. Chen et al [40] proposed a method based on the computer vision technology to extract vehicle trajectory efficiently and accurately from video and enrich more trajectory data sets under traffic conditions for traffic flow research. Homayouni et al [8] proposed a mixed integer programming model for the integrated scheduling of handling equipment in ACTs and used simulated annealing algorithm to find the optimal solution. E acquisition and processing of time series data in different periods is a difficult problem to support machine learning to complete model training. En, machine learning is used to enhance DT application framework to achieve the production control optimization of ACT. The framework is applied to an actual automated terminal to verify its availability
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