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論文名稱(外文):Development of Machine Learning based Method and Technology for Food Supply Chain Anomaly Detection
指導教授(外文):Yuh-Min ChenTsung-Yi Chen
外文關鍵詞:Food supply chainFood safetyFood safety monitoring and managementSafe food protectionData ScienceMachine LearningBlockchain
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食品供應鏈的複雜化導致食安事件頻繁發生,不僅造成社會不安,也直接或間接危害人們健康與生活,故食品安全的維護已成為世界各國重視之議題。隨著資料科學(Data Science)、機器學習(Machine Learning)與區塊鏈(Blockchain)的興起,系統智能化的理想已逐漸能夠實現,本研究參考資料科學之觀念與方法,以及機器學習與區塊鏈之技術,期為食品供應鏈之食品安全管理提供解方案,為人類帶來福祉。
本研究以資料科學之概念,設計一個「食安監控與管理模式」,針對「食安監控與管理模式」分析系統之需求,並參考區塊鏈之概念以及機器學習之原理,規劃「安全食品防護系統」之架構。依據該架構界定資料分析之需求、設計「食安稽查資料模型」,運用資料探勘(Data Mining)與機器學習技術,分析食品異常之影響因子與異常之模式,以開發與建置「異常偵測技術與機制」。
The complexity of the food supply chain leads to frequent food security incidents, which not only causes social unrest, but also directly or indirectly endangers people's health and life. Therefore, the maintenance of food safety has become an important factor for countries all over the world. With the rise of Data Science, Machine Learning and Blockchain, the ideal of system intelligence has gradually been realized. This study looks forward to solving food safety problems through the support of intelligent systems to thereby promote human welfare.

This study designed a food safety monitoring and management model based on the concept of data science. For this food safety monitoring and management mode, the functional requirements of its system are analyzed, and the functional architecture of the safe food protection system is planned and designed with reference to the concept of blockchain and the principles of machine learning. According to the functional framework, define the needs of data analysis, design the food safety inspection data model, use machine learning technology to analyze the impact factors and abnormal patterns of food anomalies to build anomaly detection mechanism.

In order to verify the validity and correctness, this study uses public data to detect. First, detect supply and sales anomalies. Since anomaly detection focuses on picking out as many anomalies as possible, the recall rate is used as an validation index. The final model's recall rate increases from 0.75027 to 0.86638. Then it detects the abnormality of the supplier's equipment, and also uses public data to detect the error rate of the model from 0.006077 to 0.004112. The above models are all evaluated with validation index, and the validation index can reflect the accuracy of the model, so it can verify the effectiveness of the anomaly detection methods and techniques in this study.
摘要 I
誌謝 VI
目錄 VII
表目錄 X
圖目錄 XI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
1.4 研究問題分析 3
1.5 研究項目與方法 3
1.6 研究流程 5
第二章 文獻探討 6
2.1 研究領域探討 6
2.1.1 供應鏈 6
2.2.2 食品安全 7
2.2 相關技術探討 8
2.2.1 區塊鏈 8
2.2.2 機器學習 11
2.2.3 深度學習 13
2.2.4 採樣優化 14
2.2.5 超參數優化 15
2.3 類似研究探討 16
第三章 基於區塊鏈之安全食品供應鏈模型與防護系統設計 18
3.1 安全食品供應鏈模型 18
3.2 食品安全防護系統架構 19
3.2.1 系統架構 19
3.2.2 食品安全監控與管理模式 21
3.3 食品安全防護技術架構 22
第四章 安全食品資料分析與資料模型設計 25
4.1 供應網路模型 25
4.2 供應鏈工廠模型 26
4.3 食安稽查資料模型 30
第五章 食品供應鏈異常偵測方法設計與實現技術開發 32
5.1 異常偵測方法 32
5.2 供銷異常偵測方法 33
5.2.1 資料前處理 33
5.2.2 模型訓練 34
5.2.3 模型評估與優化 36
5.3 設備異常偵測技術 40
5.3.1 資料前處理 41
5.3.2 模型訓練 44
5.3.3 模型評估與優化 46
5.4 異常程度估計算法 47
第六章 實作與驗證 49
6.1 實作環境介紹 49
6.2 實驗過程與結果 49
6.2.1 供銷異常偵測 49
6.2.2 設備異常偵測 60
6.2.3 異常程度估計算法 67
第七章 結論與未來展望 72
7.1 結論 72
7.2 未來展望 72
參考文獻 74
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