将工业数据转化为可被大模型理解的结构化证据单元，实现跨系统智能推理。
Industrial Tokenization for LLM-Based Health Intelligence: A Federated Architecture for Industrial Evidence Integration
- 用工业证据令牌统一不同来源的分析结果，保留上下文与可信度信息。
- 构建联邦架构，各子系统自主运行并共享标准化令牌，提升系统可解释性。
- 适合需要整合多源工业数据的智能诊断与预测场景，如设备健康监测。
工业健康管理日益依赖多种异构信息源，包括状态监控系统、监督控制与数据采集系统、维护记录、检测结果及预测模型。尽管大语言模型为跨源推理提供了新机遇，但工业数据及其分析结果在结构、时间分辨率、物理意义和可靠性上差异显著。直接将这些异构信息集成到单一模型中会降低可解释性、可追溯性和对设备及数据变化的适应能力。本文提出工业标记化（Industrial Tokenization），一种将特定来源的分析输出转换为结构化、机器可读的工业证据单元——工业令牌的概念接口。不同于编码原始时序数据的数值令牌，工业令牌包含领域相关的证据及其来源、时间范围、运行工况、分析含义、质量或置信度信息以及溯源信息。基于此概念，提出一种联邦式工业架构，使异构分析子系统保持自治，同时向中央推理层暴露标准化的工业令牌。作为初步实现，本研究构建了基于振动诊断输出的端到端诊断令牌路径，涵盖规则事件聚合、结构化文本令牌生成及大模型解释。其他工业令牌，如基于SCADA的状态监控令牌、维护令牌和预测令牌，暂留作未来扩展。所提框架将工业标记化定位为领域专用工业智能与基于大模型或智能体推理之间的语义接口，而非另一种原始工业数据编码方法。
原文摘要 · Abstract (English)
Industrial health management increasingly relies on heterogeneous information sources, including condition monitoring systems, supervisory control and data acquisition systems, maintenance records, inspection results, and prognostic models. Although large language models provide new opportunities for cross-source reasoning, industrial data and analytical outputs differ substantially in structure, temporal resolution, physical meaning, and reliability. Directly integrating such heterogeneous information into a monolithic model may reduce interpretability, traceability, and adaptability to equipment and data changes. This paper introduces Industrial Tokenization, a conceptual interface for transforming source-specific analytical outputs into structured and machine-interpretable units of industrial evidence, termed Industrial Tokens. Unlike numerical tokens used to encode raw time-series data, Industrial Tokens represent domain-grounded evidence together with source, temporal scope, operating context, analytical meaning, quality or confidence information, and provenance. Based on this concept, a federated industrial architecture is proposed, where heterogeneous analytical subsystems retain autonomy while exposing standardized Industrial Tokens to a central reasoning layer. As an initial implementation, this study presents an end-to-end DiagnosisToken pathway based on vibration-diagnostic outputs, rule-based event aggregation, structured textual token generation, and LLM-based interpretation. Other Industrial Tokens, including SCADA-based condition-monitoring tokens, maintenance tokens, and prognostic tokens, are reserved as future extensions. The proposed framework positions Industrial Tokenization as a semantic interface between domain-specific industrial intelligence and LLM- or agent-based reasoning, rather than another method for encoding raw industrial data.
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