以流程導向設計區塊鏈支援的電動車智能化生產流程

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姓名

莊仁皓(Jen-Hao Chuang) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

以流程導向設計區塊鏈支援的電動車智能化生產流程
(Process Oriented Design of Blockchain Supported Intelligent Production Process of Electric Vehicles)

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摘要(中)

近十年來，隨著工業4.0的蓬勃發展以及大數據時代的來臨，為各行各業皆帶來了新形態的工作型態。工業4.0是由德國在2011年所提出的概念，藉由創新技術(工業物聯網、自動化機器人、雲端運算等等)及虛實整合系統(Cyber-Physical System, CPS)將實體世界與數位世界結合而成。大數據分析又稱巨量資料分析，藉由大量數據進行分析，幫助人工智慧進行運算達到製造生產線自動化，賦予製造生產線更多的智慧與彈性。
目前工業4.0以及大數據分析技術正逐步應用至智慧製造中，使傳統的製造生產線發展成無人的智慧生產線。其中使用了大量的物聯網技術，對實時數據進行監控，同時藉由大數據分析技術精進製造與生產。然而面對如此新型態的數位模式，一旦衍生出資訊安全問題，將會對智慧生產線造成非常巨大的影響，導致智慧生產線停擺，最後造成企業巨大的損失。本研究期望在製造生產線上導入區塊鏈技術，結合工業4.0下的智慧生產線，藉由區塊鏈技術本身具有特性，例如：去中心化、加密、匿名性以及不可篡改性等等，確保智慧生產線的數據是安全的，使智慧生產線能夠永續運作。
本研究將應用整合資訊架構(Architecture of Integrated Information System, ARIS)進行流程建模分析。利用業務流程模型和標記法(Business Process Modeling Notation, BPMN)對工業4.0下的電動車製造之智慧生產流程進行建模，同時結合區塊鏈技術建構數據流模型，使得新的智慧生產線具備高度自動化及高度安全性。當建構出新型態的企業流程後，藉由統一塑模語言(Unified Modeling Language, UML)將新流程轉化為軟體系統功能進行塑模工作，使得新型態的流程能夠更容易被完整實現。

摘要(英)

In the past ten years, with the vigorous development of Industry 4.0 and the advent of the era of big data, new forms of work have been brought to all walks of life. Industry 4.0 is a concept proposed by Germany in 2011 that combines the physical world with the digital world through innovative technologies (Industrial Internet of Things, automated robots, cloud computing, etc.) and virtual-real integration system (Cyber-Physical System, CPS). Big data analysis, also known as Massive Data Analysis that analyzes a large amount of data to help artificial intelligence perform operations to automate the production line. Big data analysis give the production line more wisdom and flexibility.
At present, Industry 4.0 and big data analysis technology are being gradually applied to smart manufacturing, making the traditional manufacturing production line develop into an unmanned smart production line. Among them, a large number of Internet of Things technologies are used to monitor real-time data, and at the same time, manufacturing and production are strengthened through big data analysis technology. However, in the face of such a new type of digital model, once the information security problem is derived, it will have a huge impact on the smart production line, causing the smart production line to stop. Finally, it causes huge losses to the company. This paper expects to apply blockchain technology and combined with the smart production line under Industry 4.0 based on the characteristics of blockchain technology, such as decentralized, encryption, anonymity and immutability, etc. These characteristics ensure that the data of smart production line is Safe. Then the smart production line can operate continuously.
This paper will apply the Architecture of Integrated Information System (ARIS) for process modeling analysis.Using Business Process Modeling Notation (BPMN) to model the smart production process of electric vehicle manufacturing under Industry 4.0, and combines blockchain technology to construct data flow. These make the new smart production line highly automated and High security. When a new type of enterprise process is constructed, the new process is transformed into software system functions by the Unified Modeling Language (UML) for modeling. Finally, the new type of process can be more easily to be implementation.

關鍵字(中)

★ 工業 4.0
★ 智慧製造
★ 區塊鏈技術
★ 電動車
★ ARIS
★ BPMN
★ UML

關鍵字(英)

★ Industry 4.0
★ smart manufacturing
★ blockchain technology
★ Electric vehicle
★ ARIS
★ BPMN
★ UML

論文目次

摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 viii
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究章節 3
第二章文獻回顧 5
2-1 電動汽車產業探討 5
2-1-1 電動汽車產業概況 5
2-1-2 電動汽車未來發展 6
2-2 智能工廠現況討論 7
2-2-1 智能工廠發展 7
2-2-2 智能工廠技術探討 8
2-2-3 智能工廠架構 10
2-3 區塊鏈技術 12
2-3-1 區塊鏈核心技術 12
2-3-2 區塊鏈架構 15
第三章研究方法 17
3-1 整合資訊架構 17
3-1-1 ARIS架構 17
3-1-2 ARIS三階段 23
3-1-3 BPMN業務流程模型與標記法 24
3-2 統一塑模語言 29
3-2-1 結構圖形（Structure Diagram） 30
3-2-2 行為圖形（Behavior Diagram） 32
第四章個案應用 40
4-1 個案公司描述 40
4-2 應用整合資訊架構 42
4-2-1 基於ARIS之流程分析 42
4-2-2 基於ARIS與BPMN之流程設計 45
4-3 應用UML 56
4-3-1 使用案例圖 57
4-3-2 活動圖 58
4-3-3 類別圖 60
4-3-4 循序圖 61
4-3-5 元件圖 62
4-3-6 部署圖 64
第五章研究結論與建議 65
5-1 研究結論 65
5-2 後續建議 65
參考文獻 67

參考文獻

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指導教授

高信培(Hsing-Pei Kao)

審核日期

2023-6-19

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