臺灣博碩士論文加值系統

近20年來，全球多起重大食品安全事件引起關注，使食品安全成為重要議題，各國為提高食品安全並降低風險而採用不同的食品安全管理系統(Food Safety Management System, FSMS)。在臺灣，珍珠奶茶是一種重要的本土飲品，具有國際影響力。然而，如何追蹤追溯和確保珍珠奶茶的食品安全成為一個重要議題。有鑑於此，本研究規劃與設計一個智能化區塊鏈(Blockchain)珍珠奶茶食品安全服務平台(Intelligent Blockchain-based Bubble Tea Food Safety Service Platform, iBUFFER)，將危害分析重要管制點(Hazard Analysis and Critical Control Points, HACCP)食品安全管理系統進行模組化分割和整合，提升資料一致性與通透性，僅需每次將員工手動收集或自動感測的資料輸入即可提供各食品安全系統稽核(Audit)。此外，iBUFFER以區塊鏈技術建構起不可竄改之資料追蹤追溯平台，並提出一具深度學習食品安全推論模組(Deep Learning-Oriented Food Safety Risk Reasoning and Analysis Model, DORAM)，整合長短期記憶模型(Long Short-Term Memory)與決策樹(Decision Tree)，進行各原料與製程之風險管制，計算出模型之F1-Score效能為96.7%與92.1%。最後將風險因子、標準作業流程與內部稽核整合，實現追蹤追溯，並確保食品廠商資料之正確性，從而提高食品安全。

In the past 20 years, numerous notorious food safety incidents worldwide that have raised concerns and made food safety an important issue. To enhance food safety and reduce risks, different countries have adopted various Food Safety Management Systems (FSMS). In Taiwan, bubble tea is an important local beverage with international influence. Hence, this study designs an intelligent blockchain-based Bubble Tea Food Safety Service Platform (iBUFFER) to trace, track and ensure the food safety of bubble tea. The platform modularizes and integrates the Hazard Analysis and Critical Control Points (HACCP) food safety management system, enhancing data consistency and transparency. iBUFFER is composed of Diversion Agents, a Blockchain-based Risk Control and Management Service Platform and a Cloud-based Deep Learning Server. It only requires daily input of sensed data from employees to provide audits for various food safety systems. iBUFFER utilizes blockchain technology to construct an immutable data tracking and tracing platform. The proposed Deep Learning-Oriented Food Safety Risk Reasoning and Analysis Model (DORAM) utilizes Long Short-Term Memory (LSTM) and Decision Tree to provide capabilities for classifying and analyzing regulations, exception factors, noises, and important factors. The F1-Scores of LSTM and Decision Tree are 96.7% and 92.1%, respectively. Finally, it integrates risk factors, standard operating procedures, and internal audits to achieve traceability and ensure the accuracy of food manufacturer data. The proposed iBUFFER can serve as a valuable reference model to aid researchers and engineers in establishing and referencing future food safety service platforms.

誌謝 I
摘要 II
Abstract III
目次 IV
圖目錄 V
表目錄 VII
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 論文架構 4
第二章 文獻探討 5
第三章 系統架構設計 9
3.1 感知互動代理人 12
3.2 食品風險管制區塊鏈平台 15
3.3 深度學習雲端伺服器 21
第四章 系統分析與實作 32
4.1 系統分析 32
4.2 系統實作 44
第五章 結論與未來方向 51
參考文獻 53
附錄一 59

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