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研究生： 張嘉瑩
Jia-Ying Jhang
論文名稱： 國中生飲水量分析
Analysis of the water intake of junior high school students
指導教授： 鄭瑞光
Ray-Guang Chen
口試委員: 張鈞法
Chun-Fa Chang
周碩彥
Shuo-Yan Chou
黃琴雅
Chin-ya Huang
王瑞堂
Jui-Tang Wang
鄭瑞光
Ray-Guang Cheng
學位類別： 碩士
Master
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2022
畢業學年度： 110
語文別： 中文
論文頁數： 51
中文關鍵詞： 物聯網 、飲水量 、數據分析 、國中生
外文關鍵詞： water intake, junior high school students
相關次數： 點閱：179  下載：5 
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    • 在本文中，我們分析了臺灣臺北市新興國中學生的飲水量。為了收集數據，我們將飲水機替換成飲水機公司ACUO所提供之能夠提供飲水機數據的特定型號，並將該飲水機的主機板替換為特規主機板，並將數據採集器裝設在這些飲水機上。為此，我們針對收集到的43天的數據進行分析，其中分析了每台飲水機在課間時段的平均供水時長和用戶構成（7年級、8 年級、9年級、教師以及其它）。接著我們分析這些飲水數據並評估不同因素（班級、性別-年級、日均溫以及各個教室到最近的飲水機的距離）所帶來的影響。並且我們還與學校展開了造成超高飲水量原因的調查。

    In this work, we analyze the water intake of students in Xin Xing Junior High in Taipei, Taiwan. To collect the data, we substituted the motherboard of the water dispenser with the specific one that water dispenser company, ACUO provided and deployed the data collector on the specific water dispensers. To this extent, we leveraged 43 days data to analyze the average supplying water time in break time and the composition of users (7th graders, 8th graders, 9th graders, faculty and other) of each water dispenser. Then we analyze the water intake data and evaluated the influence of different factors (class, gender-grade, daily mean temperature, and the distance from the closest water dispenser to each classroom). And we also launched an investigation into the causes of the ultra-high water intake with the school.

    1 Introduction 1 1.1 Background 1 1.2 Water dispenser 5 1.3 Motherboard of the water dispenser 6 1.4 Chapters summary 6 2 System Architecture 8 2.1 System architecture of the data collector 8 3 Proposed Method 10 3.1 End device 10 3.1.1 Raspberry Pi 10 3.1.2 Converter adapter module (USB to RS485) 11 3.1.3 Relay module 12 3.1.4 RFID card reader 13 3.1.5 USB numeric keypad 14 3.1.6 I2C LCD display module 15 3.1.7 The integration of the data collector 16 3.1.8 Identity verification and water supply control 20 3.1.9 Obtaining the data of water intake 21 3.2 Cloud 22 3.2.1 MongoDB 22 3.2.2 API 23 3.2.3 LINE Bot server 23 3.3 Application 24 3.3.1 LINE Bot 24 3.3.2 Report generator 27 3.4 Data format 28 3.5 Assumptions 30 4 Experimental Result 31 4.1 Experimental result I 31 4.2 Experimental result II 35 4.3 Experimental result III 38 4.4 Experimental result IV 42 4.5 Experimental result V 46 5 Conclusion 49 References 50

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