用知识图谱打通11个制造系统数据孤岛，实现跨系统故障溯源。
Semantic Graph Unification for Industrial Digital Threads: Bridging 11 Heterogeneous Manufacturing Systems Through Ontology-Driven Knowledge Graphs
- 构建基于本体的RDF图谱，通过五阶段ETL和实体消歧统一11类异构系统数据。
- 实验证明69%的关键信号需跨系统关联才能发现，断开24个工具后召回率降至0.31。
- 支持大模型接入，已在航空、医药等5个行业验证通用性，适合工业4.0落地场景。
现代制造企业运行着多种异构系统（如ERP、MES、PLM、SCADA、QMS、SCM），各自拥有独立的数据模型与API，形成数据孤岛，阻碍整体分析、根因追溯与工业4.0可追溯性。点对点集成呈O(n²)扩展，依赖关系脆弱。本文提出一个开放框架，实现工业数字主线的语义图统一。通过基于本体的RDF知识图谱，经五阶段ETL流程与自动化实体消歧（涵盖97条owl:sameAs关联），整合11个模拟来源、九个领域的数据。本体包含78个RDFS类、108个对象属性、243个数据属性，融合ISA-95、OPC UA、eClass、资产信息壳、RAMI 4.0等标准。自动发现引擎采用九类策略（跨站关联、报警覆盖、ECN影响、CUSUM/EWMA漂移检测）挖掘跨系统洞察。实证结果显示：阻断24个跨系统工具后，召回率从1.00降至0.31（F1从1.00降至0.48），表明69%可探测信号依赖跨系统图连接。留一法消融测试确认九种策略中六种贡献独特信号。与作者构建的65信号清单（16正例，49负例）比对，获得F1=1.00（95% Clopper-Pearson置信区间[0.79, 1.00]），虽为验证非独立验证。图谱通过287个Model Context Protocol工具向大模型代理开放，作为原生SPARQL语义层。五个行业模板（航空、快消品、制药、医疗器械、涡轮叶片）证明其在不同制造垂直领域中的模式稳定性。
原文摘要 · Abstract (English)
Modern manufacturing enterprises operate heterogeneous systems -- ERP, MES, PLM, SCADA, QMS, SCM -- each with its own data model and API. The resulting silos prevent holistic analysis, delay root-cause investigation, and obstruct Industry 4.0 traceability. Point-to-point integration scales as O(n^2) and accumulates brittle dependencies. This paper presents an open framework for semantic graph unification of industrial digital threads. An ontology-driven RDF knowledge graph unifies data from 11 simulated sources across nine domains through a five-stage ETL pipeline with automated entity resolution spanning 97 owl:sameAs identity links. The ontology encompasses 78 RDFS classes, 108 object properties, and 243 data properties, drawing on ISA-95, OPC UA, eClass, the Asset Administration Shell, RAMI 4.0, and additional standards. An automated discovery engine applies nine strategy categories -- cross-station correlation, alarm coverage, ECN impact, CUSUM/EWMA drift detection -- to surface insights spanning system boundaries. The primary empirical result: blocking 24 cross-system tools reduces recall from 1.00 to 0.31 (F1 from 1.00 to 0.48), showing that 69% of discoverable signals require cross-system graph joins. Leave-one-out ablation confirms six of nine strategies contribute unique signals. Verification against a 65-signal manifest (16 positive, 49 null) yields F1 = 1.00 (95% Clopper-Pearson CI [0.79, 1.00]); as the manifest was author-constructed, this constitutes verification not independent validation. The graph is exposed to LLM agents via 287 Model Context Protocol tools as a SPARQL-native semantic layer. Five industry templates (aerospace, CPG, pharma, medical devices, turbine blades) demonstrate schema stability across manufacturing verticals.
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