航运工业应用数据驱动网络中的分散系统智能:区块链技术的数字模型

L. Perera, Karen V. Czachorowski
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引用次数: 2

摘要

本研究考虑了适用于航运工业应用的数据驱动网络,以创建分散的系统智能。这种系统智能可以促进提高各自在本地(即船舶操作)和全球(即物流操作)规模上的运作效率,以航运为主要优势。本研究的第一部分总结了这些数据驱动网络的主要特征。在本研究的第二部分中,讨论并比较了数字模型和区块链技术的两种应用,以说明它们的异同。数字模型表示基于船舶性能和导航数据集的矢量数学结构,并被归类为低级信息模型。工业物联网的数据集也应该通过这样的低层次模型来提高质量。这些数据驱动的网络可用于量化船舶性能和航行条件,其结果也可用于提高船舶能源效率,并在局部范围内减少发动机排放。区块链代表了公共领域的去中心化、分布式和数字分类账系统,可以处理和记录由许多用户执行的交易。由于这些网络正在处理来自工业过程的高质量数据集,因此它被归类为高级信息模型。这种数据驱动的网络可以在全球范围内制定航运中的各种物流操作,并优化其操作条件。这些数据驱动网络的成果可用于提高航运业的运营效率并降低各自的成本。
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Decentralized System Intelligence in Data Driven Networks for Shipping Industrial Applications: Digital Models to Blockchain Technologies
Data driven networks applicable for shipping industrial applications to create decentralized system intelligence are considered in this study. Such system intelligence can facilitate to improve the respective operational efficiency in local (i.e. vessel operations) and global (i.e. logistics operations) scales in shipping as the main advantage. The main features of these data driven networks are summarized in the first part of this study. Two applications of digital models and blockchain technologies are discussed and compared with their features to illustrate their similarities and differences in the second part of this study. A digital model represents a vector based mathematical structure derived from ship performance and navigation data sets and has categorized as a low-level information model. It is also believed that the respective data sets from industrial IoT (internet of things) should go through such low-level models to improve their quality. These data driven networks can be used to quantify ship performance and navigation conditions, where the outcome can also be used to improve vessel energy efficiency and reduce engine emissions in a local scale. A blockchain represents a decentralized, distributed and digital ledger system in a public domain and can handle and record transactions executed by many users. That has categorized as a high-level information model due the high quality data sets from industrial processes that these networks are handling. Such data driven networks can be used to formulate various logistics operations in shipping and optimize their operational conditions in a global scale. The outcomes of these data driven networks can be used to improve operational efficiency and reduce the respective costs in the shipping industry.
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