为生产事件数据增强提供标准化模型与可复用模式
A Reference Model and Patterns for Production Event Data Enrichment
- 构建基于ISA-95与事件知识图谱的统一数据存储模型
- 提炼制造场景中常见信息提取任务并实现自动化
- 适合工业数据工程与流程分析领域的从业者
随着数字化转型推进，组织在跨系统流程执行中生成大量数据。整合异构数据源可为监控与流程分析提供关键洞察，但通常在数据预处理阶段手动完成，效率低且耗时。为此，本文提出一种参考模型与一系列模式，用于增强生产事件数据。参考模型结合ISA-95工业标准与事件知识图谱形式化方法，提供标准化的数据存储与提取方式；模式则基于制造场景的事件数据集实证观察，通过参考模型形式化表达常见信息提取任务及其自动化方法。通过典型用例验证了这些模式的相关性与实用性。
原文摘要 · Abstract (English)
With the advent of digital transformation, organisations are increasingly generating large volumes of data through the execution of various processes across disparate systems. By integrating data from these heterogeneous sources, it becomes possible to derive new insights essential for tasks such as monitoring and analysing process performance. Typically, this information is extracted during a data pre-processing or engineering phase. However, this step is often performed in an ad-hoc manner and is time-consuming and labour-intensive. To streamline this process, we introduce a reference model and a collection of patterns designed to enrich production event data. The reference model provides a standard way for storing and extracting production event data. The patterns describe common information extraction tasks and how such tasks can be automated effectively. The reference model is developed by combining the ISA-95 industry standard with the Event Knowledge Graph formalism. The patterns are developed based on empirical observations from event data sets originating in manufacturing processes and are formalised using the reference model. We evaluate the relevance and applicability of these patterns by demonstrating their application to use cases.
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