Research on Digital Twin Data Modeling and Evaluation Method of Automated Container Terminal

doi:10.16182/j.issn1004731x.joss.22-0783

Abstract

Abstract:

To make full use of the massive operation data of automated container terminals and further realize the digital and intelligent transformation of terminals driven by digital twin, a method for digital twin data modeling and effect verification and evaluation of automated container terminals is proposed. The application framework and operation mechanism based on digital twin are studied. Based on the data processing logic of digital twin framework, a method of terminal operation process evolution and dynamic data modeling based on digital twin is proposed. To verify whether the data could meet the effective operation of the digital twin, a quantitative calculation method for the verification and evaluation of terminal digital twin data is given. The research provides method to realize the intelligent operation control and operation data management of automated container terminals based on digital twin.

Key words: digital twin, automated container terminal, operation process, data modeling, data validation evaluation

CLC Number:

TP391.9

Xu Guoxuan, Chang Daofang, Li Jiaqi, Ling Qiang. Research on Digital Twin Data Modeling and Evaluation Method of Automated Container Terminal[J]. Journal of System Simulation, 2023, 35(12): 2641-2654.

Figures/Tables 16

Fig. 1

Table 1

Fig. 2

Fig. 3

Fig. 4

Table 2

Table 3

Table 4

Fig. 5

Fig. 6

Fig. 7

Table 5

Quantitative calculation results of evaluation indexes and values of various parameters

指标	所含参数	参数取值	指标量化值
$I 1$	$n 1$	243	64.94
	$∑ i = 1 n 1 f p i n p i × δ C i$	197.27
	$δ C$	0.8
$I 2$	$h d 、 o d 、 s d 、 b i d$	1	90.00
	$w h d 、 w o d 、 w s d 、 w b i d$	0.25
	$λ C$	0.9
$I 3$	$n 3$	3 552	76.67
	$∑ i = 1 n 3 m p s i$	3 404
	$η C i$	0.8

Table 5

Table 6

Index comparison judgment matrix

	$I 1$	$I 2$	$I 3$
$I 1$	1	1/3	1/2
$I 2$	3	1	2
$I 3$	2	1/2	1

Table 6

Table 7

Index normalization judgment matrix

	$I 1$	$I 2$	$I 3$
$I 1$	1/6	2/11	1/7
$I 2$	1/2	6/11	4/7
$I 3$	1/3	3/11	2/7

Table 7

Table 8

Index weights and consistency judgment results

$W 1$	$W 2$	$W 3$	$λ m a x$	$C I$	$C R$
0.163 8	0.538 9	0.297 3	3.009 2	0.004 6	0.008 8

Table 8

Table 9

Quantitative calculation results of terminal digital twin data verification and evaluation

评价指标	量化值	综合权重	评价结果
$I 1$ 码头数字孪生多维数据融合程度	64.94	0.163 8	81.93
$I 2$ 码头数字孪生数据集成应用程度	90.00	0.538 9
$I 3$ 码头数字孪生数据交互实时性	76.67	0.297 3

Table 9

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树的自生长过程	码头作业过程
树干	作业过程
树杈	作业工序
树枝	作业要素数据源节点
树叶	作业要素数据值

评价级别	取值
多维度数据彼此孤立	0
少部分(1~2个)维度的数据可以实现融合	0.2
少部分维度的数据可以实现实时融合	0.4
大部分(3个以上)维度的数据可以实现融合	0.6
大部分维度的数据可以实现实时融合	0.8
多维度数据能够实现实时融合	1.0

评价级别	取值
孪生模型仅能分别对各部分数据独立访问应用	0
孪生模型可对2种类型的部分数据进行集成应用	0.3
孪生模型可对3种类型的部分数据进行集成应用	0.6
孪生模型可对4种类型的部分数据进行集成应用	0.9
孪生模型能完全对各部分数据进行关联集成应用	1.0

评价级别	取值
数据交互延迟>10 s	0
5 s<数据交互延迟≤10 s	0.2
1 s<数据交互延迟≤5 s	0.4
0.1<数据交互延迟≤1 s	0.6
1 ms<数据交互延迟在≤0.1 s	0.8
数据交互延迟≤1 ms	1.0

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