在建築資訊模型的世界中，數位孿生
(Digital Twin)是非常重要的核心概念，
已連續三年被資訊科技研究機構 Gartner評為全球十大科技趨勢之一，簡單來說，
數位孿生代表 透過虛擬世界中的數位物件 ，顯示現實世界中的物體可能的反應、
狀況與效能等。 在建築資訊模型的產業領域中， 無線基地台的布建是重大且具風
險的策略，若無線基地台擺設不好，輕則導致室內空間收訊不佳，重則須重新 施
工，造成施工成本大幅增加，因此許多業者期望透由數位孿生的 概念與機器學習
演算法的結合，在建築物尚未施工時期，利用無線訊號分布模型掌握場域中的訊
號分布作為無線基地台的布建策略參考依據。
在這篇論文中， 我們提出收集實際建築物場域中的無線訊號分布，導入至機
器學習演算法中進行模擬預測空間中的訊號分布模型，以三維空間的型式進行視
覺化呈現與評估該呈現方法是否能無痛導入至建築資訊模型中。為了評估此實驗
假設是否成立，我們 在實際建築物場域中 實地 擺設無線基地台 進行大量的無線訊
號強度值收集，並撰寫 KNN與 DNN機器學習演算法建立無線訊號模型，依此
模型數據比對實際空間的真實無線訊號強度值，作為評估本實驗目的的可行性。

In the of building information modeling, digital twin is a very important core concept. It has been rated as one of the top ten global technology trends by Gartner, an information technology research organization for three consecutive years. In simple terms, digital twin represents Digital objects in the world show the possible reactions, conditions and performance of objects in the real world. In the industrial field, the deployment of wireless base stations is a significant and risky strategy. If the wireless base station is not well-equipped, it will result in poor reception of indoor space, and heavy construction will require re-construction, resulting in significant construction costs. Increase, so many companies expect to combine the concept of digital twins with machine learning algorithms. Before the building is under construction, use the wireless signal distribution model to grasp the signal distribution in the field as a reference for the deployment strategy of wireless base stations.
In this paper, we propose to collect the wireless signal distribution in the actual building field, import it into a machine learning algorithm to simulate and predict the signal distribution model in the space, and visualize and evaluate the representation in a three-dimensional space. Whether the method can be painlessly imported into the building information model. In order to evaluate whether this experimental hypothesis is true, we set up wireless base stations in the field of actual buildings to collect a large number of wireless signal strength values, and wrote KNN and DNN machine learning algorithms to establish wireless signal models, based on this model data comparison The actual wireless signal strength value in the actual space is used to evaluate the feasibility of this experiment.

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