本研究提出一種「基於向量的增量儲存架構」（Vector-Based Delta Storage Architecture），將文件演變過程抽象化為有序的結構化差異向量，並結合 Amazon DynamoDB 的單一資料表設計，實現高效的儲存模型 。透過導入確定性的正向重建與反向回滾演算法，並配合自適應基準重置策略，確保查詢延遲不會隨歷史深度線性增長 。實驗結果顯示，在百萬級紀錄規模下，本架構較傳統系統降低 60% 的查詢延遲（平均 0.29 秒），並節省約 80% 的儲存空間 。本架構一方面解決了 NoSQL 版本控制的效能權衡問題，並透過 PC 製造業工程變更管理與 HL7 FHIR 醫療報告案例，驗證了其在滿足 FDA 21 CFR Part 11 合規性要求上的實用性 。;In the era of big data， domains such as healthcare and industrial manufacturing increasingly demand precise lifecycle management of data， particularly for legally binding and traceable historical records. However， existing database solutions face significant bottlenecks when handling high-frequency， fine-grained updates in semi-structured data. Traditional Relational Database Management Systems (RDBMS) suffer from excessive join overhead during historical queries due to high normalization， while mainstream NoSQL document databases typically rely on full-snapshot strategies， leading to exponential growth in storage costs as versions accumulate. To address these challenges， this dissertation proposes a Vector-Based Delta Storage Architecture. We abstract the document evolution process into a sequence of ordered， structured delta vectors and integrate them with a Single-Table Design in Amazon DynamoDB to achieve a schema-agnostic and efficient storage model. The architecture introduces deterministic forward reconstruction and reverse rollback algorithms， coupled with an adaptive baseline reset policy， ensuring that query latency does not grow linearly with historical depth. Experimental results on a synthetic dataset of one million records demonstrate that， compared to traditional relational systems， the proposed architecture reduces query latency for historical version reconstruction by 60% (average 0.29 seconds) and decreases storage space requirements by approximately 80%. Furthermore， the system exhibits superior scalability and stability under high-concurrency write workloads. This research not only resolves the trade-off between storage efficiency and performance in NoSQL version control but also validates the architecture′s practical value through case studies in PC manufacturing Engineering Change Orders (ECO) and HL7 FHIR medical reports. It effectively supports strict compliance requirements， such as FDA 21 CFR Part 11， and robust audit trails.
