本研究聚焦在國內航太工業製造場域，場域內所有製造設備都在大量的產生數據，而這些數據又會經過擷取而儲存在某個位置等待應用端進行資料轉換與分析或者機器學習建模，我們可以把這些數據流歸納為數據產生、數據擷取、數據儲存、數據轉換、數據服務等過程，為有效率使用這些數據，我們針對上述過程發展了應用雲端軟體技術的時序數據系統(Time Series Data System, TSDS)。經過實務中的觀察與歸納，時序數據系統配置包括多個數據產生物件、多個數據擷取物件及多個數據存儲物件，形成複雜的時序數據服務網路。本研究透過實體關係模型來描述時序數據服務網路的複雜性，提升了數據流的結構和預測性，也為後續的應用提供理論基礎及質量保證。在系統運行階段，我們發現使用傳統的單體式架構與人力維運難以達到理想的系統可用性(Availability)水準，尤其是面對大規模的時序數據服務網路環境下，本研究透過分散式軟體架構及雲原生實踐來有效克服。透過對單體式和分散式時序數據系統的比較分析，詳細探討了可用性的量化方法和改善策略。針對時序數據系統在場域的可用性問題，本研究定義了場域的0-1 Integer Programming Problem(NP-Hard)並透過Empirical Solution盡可能地最大化目標函數?以提升可用性。研究結果顯示，分散式軟體架構具有更高的可擴展性及故障容忍性，能有效應對國內航太工業製造場域中時序數據服務網路的高度複雜性。分散式軟體架構實現超過99.95%的可用性等級，明顯優於單體式軟體架構的不到90%。這證明了本研究發展的分散式時序數據系統，在可用性等級上滿足了場域實務需求。

This research focuses on the aerospace manufacturing industry, where data is continuously generated from various equipment. To efficiently utilize this data, we developed a Time Series Data System (TSDS) leveraging cloud software technology. Our system includes multiple data generation, ingestion, and storage components, forming a complex service network. Using an entity-relationship model, we enhanced the structured and predictable data flow, providing a theoretical foundation and quality assurance for future applications. Traditional monolithic architectures and manual maintenance are insufficient to achieve high availability in large-scale environments. Therefore, we implemented a modern distributed software architecture. Through comparative analysis, we explored quantification methods and improvement strategies for system availability. Our results indicate that the distributed architecture significantly improves fault tolerance and can effectively handle the complexity of time series data service networks in aerospace manufacturing. The distributed system achieved over 99.95% availability, surpassing the less than 90% of the monolithic system, demonstrating its practical applicability in meeting field requirements.

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