目前市售的風扇無論是傳統風扇或者BLDC風扇皆為定速定角度擺頭，傳統風扇為透過齒輪結構，而BLDC風扇雖為使用直流馬達作為擺頭馬達，但也無角度功能。至於風量部分傳統風扇只有一般大、中、小三種段速，且傳統風扇為使用交流感應馬達作為風扇轉動之馬達，因此掛上葉片或有負載時轉速便會有所變動；而BLDC風扇雖然歸功於直流無刷馬達的特性可以達到更多段轉速的設定，但現今在風扇智慧功能方面相當不普遍。
為了將智慧功能結合家用風扇，本論文應用影像辨識系統於家用電風扇之智能控制系統之設計研究，是在現今市售風扇外觀及基礎功能下，將擺頭變換為可控制角度之馬達，而風扇馬達沿用現今主流BLDC直流風扇所使用的直流無刷馬達，結合人體影像辨識技術以及紅外線溫度感測，達到多人員風量自動控制系統，預期透過新增這些智慧化系統，讓一般的家用風扇在智能模式功能方面提升更大幅度的便利。
本研究系統是以樹莓派為主要控制核心，結合周邊硬體：微控制器、紅外線溫度感測器、影像辨識鏡頭、馬達驅動器以及硬體電路等，並透過自行訓練符合本智能風扇之影像辨識系統以及各項硬體間的軟體控制，來達到本研究新型智能風扇的完整系統。
根據功能測試結果，於有效範圍內，人體影像辨識與紅外線溫度感測在作為人員辨識下的相互搭配有相當高的準確度，並且在準確的辨識人員存在依據後，再根據人體影像辨識所偵測之人員數量達到風扇自動多段調速之功能。而透過對人體影像辨識進行準確度的評估，可得知本系統自行建立之人體影像辨識模型有高達92.5%之準確度，且可大致辨識家中人員所處的姿勢與多人員彼此的位置狀況。最後透過參考文獻有效風量的風量節能效益新的評估公式，分別計算市售風扇的風量效益與本研究新型智能風扇的風量評估效益，並且與市售風扇的有效風量效益作為比較；在智慧功能狀況下，有效風量效益最高可達89.5%，且在實測不同情況下之有效風量的節能效益皆有明顯提升。由此證實，本文所提出之新型智能風扇的智慧功能，具有高度實用性且符合智慧化控制，達到有效運用風量之最終目的。

Currently, all commercially available fans, whether they are traditional fans or BLDC fans, have a fixed speed and angle swinging head. As for airflow, traditional fans only have three speeds: large, medium, and small. Moreover, traditional fans use AC induction motor as the motor for fan rotation, so the rotation speed will change when the blades are hung or when there is a load.
In order to integrate intelligent functions into home fans, the thesis applied the image recognition system to design the of intelligent control system for home electric fans. It is expected that by adding these intelligent systems, the general household fans will be more convenient in terms of intelligent mode functions.
This research is based on the Raspberry Pi as the main control core According to the functional test results, the interplay between human image recognition and infrared temperature sensing is highly accurate in the effective range of human identification, and the fan can automatically adjust the speed according to the number of people detected by human image recognition after the accurate identification of the presence of people. The accuracy evaluation of the human image recognition shows that the human image recognition model built by the system has 92.5% accuracy, and can roughly identify the posture of the people in the home and the position of multiple people with each other. Finally, by referring to the new evaluation formula of the effective airflow efficiency, the airflow efficiency of the commercially available fan and the airflow efficiency of the new intelligent fan in this study were calculated separately, and compared with the effective airflow efficiency of the commercially available fan, the effective airflow efficiency was up to 89.5% under the smart function condition, and the effective airflow efficiency was significantly improved under different conditions. This proved that the intelligent function of the new intelligent fan proposed in this thesis is highly practical and meets the ultimate goal of intelligent control and effective airflow utilization.

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