臺灣博碩士論文加值系統

隨著網購的流行，超商取貨等更便利的物流方式，使倉儲貨物流動的數量快速增長，更需要優良的倉儲管理方式以利貨物的運送。現今的趨勢式是導入物聯網(Internet of Things，IoT)、情境感知等技術，使得整個倉儲在管理的運作上更有效益。物聯網為將一般物品連上網路，可以透過網路來操作或定位，應用於倉儲中，可以將所有貨品連上網路，透過網路來監控位置，提高揀貨的效率，亦可以避免因為人為疏失導致貨品錯亂，卻找不到貨品的窘境；情境感知可以針對周遭的情境反應，當應用於倉儲內部時，可以自動化的操作電燈、風扇或防災等，除了倉儲物流的基本流程之外，亦能提升倉儲內人員管控、倉儲防災等效益。
本研究為提出適用於倉儲貨物定位及情境感知的機制，並開發應用系統。系統中使用近幾年出現的低功耗藍牙(Bluetooth Low Energy，BLE)的藍牙4.0技術來做情境定位，並透過藍牙4.0 Beacon及樹莓派3來形成物聯網，運用反向情境定位的方式找尋貨品，且藍牙附加於多數的智慧型手持行動裝置上，內部人員可以透過智慧型手持行動裝置使用順向情境定位的方式來找尋周邊貨品。本系統除了情境定位外亦加入多種感測器蒐集情境資訊，並且可依需求配置不同種類的感測器。由於藍牙使用訊號強度來做定位，過去提出的定位方法有飄移及精確度不夠的問題，我們提出了「均值偏大取向濾波器」以及「可適性均值定位法」定位演算法來提升定位的精確度，並藉由實驗來觀察其實際成果，實驗結果顯示本論文中所提出之定位演算法，有助於提升倉儲貨物定位的效益。

The popularity of online shopping and logistics through convenience stores makes the shipping number of warehouse goods grow fast. A better management for warehouse goods is required to facilitate the goods delivery. Internet of Things (IoT) and context-aware technologies are introduced into warehouse management to improve the warehouse operating performance. When goods are attached with sensors, the goods can be monitored on line and the performance of picking goods can be improved especially in the case goods are not arranged properly. Context-awareness indicates the system can respond according to the environment. When applied in warehouse management, context-aware technology can automate the control of equipment or facilities such lights, fans, and air conditioning as well as preventing possible damages. Not only goods but also personnel can be monitored by the system so that the performance of warehouse can be improved.
In this study we proposed the positing and context-aware mechanism that can facilitate the positioning objects as long as they are attached with Bluetooth 4.0 devices. A system constructed by integration of Bluetooth 4.0 Beacon and Raspberry Pi 3 is implemented. We can either use a mobile phone to identify an object or monitor an object through a computer. We also add sensors of light and temperature on Raspberry Pi so that the environment change can be detected. In order to improve the precision of an object location we proposed two positioning methods: mean value filtering and adaptive positioning method. The method is validated by experiments. The experimental results show the proposed methods are able to improve the positioning precision of goods.

摘要 i
Abstract ii
致謝 iii
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 論文架構 3
第二章 相關文獻與技術探討 4
2.1 情境感知 4
2.2 定位技術 5
2.2.1 GPS 5
2.2.2 RFID 6
2.2.3 NFC 6
2.2.4 ZigBee 7
2.2.5 Wi-Fi 7
2.2.6 Bluetooth 8
2.2.7 室內定位技術比較 10
2.3 物聯網元件 11
2.3.1 Beacon 11
2.3.2 Raspberry Pi 3 12
2.4 RSSI相關演算法 13
2.4.1 濾波器 13
2.4.1.1 中值濾波器 13
2.4.1.2 均值濾波器 14
2.4.2 二五進制數記法 14
2.4.3 RSSI轉換距離演算法 15
2.5 倉儲貨品管理 15
第三章 研究方法 17
3.1 反向情境定位 17
3.2 樹莓派3之倉儲情境感知 18
3.3 濾波演算法 20
3.3.1 均值偏大取向濾波器 20
3.3.2 均值偏中取向濾波器 23
3.3.3 均值偏小取向濾波器 25
3.4 情境定位演算法 27
第四章 系統架構與範例實作 34
4.1 系統設計簡介與分析 34
4.2 系統架構設計 35
4.3 系統開發環境及測試環境 36
4.4 系統流程與開發介面 38
第五章 系統實驗與分析 46
第六章 結論與未來展望 56
6.1 結論 56
6.2 未來展望 56
參考文獻 58

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