臺灣博碩士論文加值系統

隨著工業4.0時代的到來，工業製造領域正面臨著資訊數位化的推動。在這個轉型過程中，自動化設備、物聯網、大數據和雲端運算等技術的整合成為了重要的趨勢。然而，台灣的工業場域中，有90%是中小企業，這些企業仍然依賴人工進行機器設備事件紀錄和分析，這樣的作業方式既繁瑣又耗時，同時也限制了製程的可見度。此外，資訊數位化所需的資金成本也讓中小企業望而卻步。因此，本研究目的在利用視覺影像辨識技術，設計一個基於人機介面的監控系統，以提升工業場域的效率並加強製程的可見度，從而協助中小企業踏出資訊數位化的第一步。這樣的監控系統能夠自動辨識和記錄機器設備事件，提供了即時的數據分析和製程監控。透過這樣的資訊數位化，中小企業能夠提高生產效率，降低成本，並加強對製程的控制和管理能力。
採用了YOLO（You Only Look Once）物件偵測技術實現了一個視覺辨識系統，該系統能夠即時監測傳統射出成形機人機介面的運作情況。系統的前端感測媒介包括攝影機、7688晶片和樹莓派，透過物聯網技術將機械設備的資訊數位化。隨後，透過無線傳輸方式將基於AI視覺影像辨識的數據和結果傳送至設計的網路平台介面。透過數位化的資訊，工作人員的角色由勞動操作者升級為流程管理者。
本研究所提出的監控系統使得使用者能夠透過網路平台介面即時監控和操作機械設備，並結合物聯網和AI視覺影像辨識技術，使工作人員能夠不受地理位置的限制，即時獲取工廠的運營狀況，並指揮調度人員和機台設備的操作。這樣的監控系統提供了工業製造業一種高效的解決方案，同時提升製程控制的可見度，有助於提高工廠的生產效率和管理效能，並推動台灣工業場域引入先進的數位化技術。

With the arrival of the Industry 4.0 era, the industrial manufacturing sector is undergoing a digital transformation driven by the integration of automation equipment, the Internet of Things (IoT), big data, and cloud computing. However, in Taiwan's industrial field, where 90% of companies are small and medium-sized enterprises (SMEs), these businesses still rely on manual recording and analysis of machine equipment events, a tedious and time-consuming process that limits process visibility. Additionally, the financial costs associated with digital transformation deter SMEs from adopting such technologies. Therefore, the purpose of this study is to utilize visual image recognition technology to design a human-machine interface-based monitoring system that enhances efficiency and process visibility in industrial settings, thus assisting SMEs in taking the first step towards digital transformation. This monitoring system automates the identification and recording of machine equipment events, providing real-time data analysis and process monitoring. Through this digital transformation, SMEs can improve production efficiency, reduce costs, and enhance their control and management capabilities over processes.

A visual recognition system based on the You Only Look Once (YOLO) object detection technology has been implemented to monitor the operation of traditional injection molding machine human-machine interfaces in real-time. The system's front-end sensing media include cameras, 7688 chips, and Raspberry Pi, which digitize the information of mechanical equipment through IoT technology. Subsequently, the AI-based visual image recognition data and results are transmitted to the designed web platform interface via wireless transmission. Through digitized information, the role of workers is upgraded from labor operators to process managers.

The monitoring system proposed in this study enables users to monitor and operate machinery equipment in real-time through the web platform interface. By combining IoT and AI visual image recognition technology, workers can access real-time operational status of the factory and direct the scheduling and operation of personnel and machine equipment regardless of geographical location. This monitoring system provides an efficient solution for the industrial manufacturing sector, improving process control visibility, enhancing factory production efficiency and management effectiveness, and promoting the adoption of advanced digital technologies in Taiwan's industrial field.

摘要 i
ABSTRACT ii
致謝 iv
目錄 v
圖目錄 vii
表目錄 viii
壹、 緒論 1
1.1研究背景 1
1.2研究動機和目的 4
1.3研究架構 5
貳、 文獻探討 7
2.1工業4.0 7
2.2物聯網 10
2.3視覺影像辨識技術 13
2.4工業場域中的智慧監控管理人機互動 19
2.5工業製造中的應用案例 21
參、 研究方法 23
3.1研究模型 23
3.2物聯網架構 24
3.3雲端多層服務 27
3.4實驗步驟 29
肆、 實驗設計 31
4.1實驗工具 31
4.2實體層 32
4.3應用層 33
4.4系統實作流程與運作方式 34
4.5商務層 38
伍、 實驗結果討論 40
5.1系統評估 40
5.2結果討論 43
陸、 結論 44
6.1研究結論 44
6.2研究限制 46
6.3未來展望 47
參考文獻 48

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