語意事件驅動之大型複雜場域數位孿生資料架構研究

專案經理 ：黃千溥

是否同意遠距 ：是

大型複雜場域（如校園、高科技廠房與大型公共設施）通常包含多種設備系統與感測資料來源，場域中的設備狀態、環境資訊與事件彼此高度關聯。當場域運作出現異常或效率問題時，往往需要整合多種資料來源進行分析與決策。然而現有數位孿生系統多著重於幾何模型或單一資料來源，缺乏能整合資料、語意描述與分析能力的資料架構。

本實習將探討語意事件驅動的數位孿生資料架構，研究如何建立設備事件與空間關係的語意資料模型，並整合不同資料來源以支援場域分析與可視化應用。學生將參與資料架構分析、語意資料建模與數位孿生平台整合驗證等研究工作，理解如何以語意資料架構支援大型複雜場域的分析與決策。

Large-scale complex environments, such as campuses, high-tech facilities, and large public infrastructures, typically involve multiple equipment systems and sensing data sources. Equipment states, environmental conditions, and operational events within these environments are often highly interconnected. When anomalies or efficiency issues occur, effective analysis and decision-making require integrating information from multiple data sources. However, many existing digital twin systems primarily focus on geometric models or individual data streams and lack a comprehensive data architecture capable of integrating data, semantic descriptions, and analytical capabilities.

This internship focuses on exploring semantic event-driven data architectures for digital twins. The research investigates how semantic data models can be developed to represent equipment events and spatial relationships, and how heterogeneous data sources can be integrated to support facility analysis and visualization applications. Students will participate in research activities, including data architecture analysis, semantic data modeling, and integration validation on digital twin platforms, gaining insight into how semantic data architectures can support analysis and decision-making in large-scale complex environments.

實習項目 / Internship Tasks

A. 數位孿生資料架構與資料治理機制分析

Digital Twin Data Architecture and Data Governance Mechanism Analysis

分析大型複雜場域中不同系統的資料來源與資料結構，整理資料整合方式並探討跨系統資料治理架構。

Analyze data sources and data structures from different systems in large-scale complex environments, summarize data integration approaches, and explore cross-system data governance architectures.

B. 設備事件與空間拓撲之語意資料模型建構

Semantic Data Modeling for Equipment Events and Spatial Topology

建立設備事件、環境資料與空間關係的語意資料模型，設計事件分類與資料結構。

Develop semantic data models representing equipment events, environmental data, and spatial relationships, including event classification and semantic data structure design.

C. 語意事件資料模型於數位孿生平台之整合驗證

Integration and Validation of Semantic Event Data Models on Digital Twin Platforms

將語意資料模型整合至數位孿生平台，建立場域數位孿生場景並進行資料可視化與應用驗證。

Integrate the developed semantic data models into a digital twin platform, construct digital twin environments of complex facilities, and conduct visualization and application validation.