AI 中文总结
本文提出STREAM框架，通过六个阶段实现目标驱动且感知不确定性的工业能源数据采集，经两个工业案例验证，可明确数据可访问性与分析适用性的差异，支撑透明决策。
AI 中文摘要
工业能源管理需要将能源使用与设备状态、生产批次、物料流及工艺条件关联起来的数据集。然而，传统采集工作流通常侧重连通性与存储，却未验证可获取的信号是否满足既定能源-性能评估的要求。本文提出STREAM，这是一个目标驱动且感知不确定性的框架，包含目标规范、技术要求、资源映射、从源提取、归档元数据及迁移至数据库六个阶段。STREAM是核心工作流，其端到端输出为目标-数据可追溯性，且在全部六个阶段中评估测量、时间、上下文及处理不确定性。相较于原始概念性STREAM序列，本文新增了阶段级工件、最低证据门限、源适用性规则、元数据模板、不确定性准则及特定案例可追溯性矩阵。该框架通过两个工业批次流程案例验证：铸造厂的感应炉熔炼及奶酪粉干燥（使用SCADA与生产订单数据）。结果表明，数据可访问性不等同于分析适用性，且展示了STREAM如何支持关于即时数据使用、分析限制及优先基础设施改进的透明决策。
英文摘要
Industrial energy management requires datasets that connect energy use with equipment states, production batches, material flows, and process conditions. However, conventional acquisition workflows commonly emphasize connectivity and storage without verifying whether accessible signals satisfy the requirements of a defined energy-performance assessment. This paper presents STREAM, an objective-driven and uncertainty-aware framework comprising Specification of Objectives, Technical Requirements, Resource Mapping, Extraction from Sources, Archival Metadata, and Migration to Database. STREAM is the central workflow: objective-to-data traceability is its end-to-end output, while measurement, temporal, contextual, and processing uncertainty are assessed across all six stages. Compared with the original conceptual STREAM sequence, this paper adds stage-level artifacts, minimum-evidence gates, source-suitability rules, a metadata template, an uncertainty rubric, and case-specific traceability matrices. The framework is validated through two industrial batch-process cases: induction-furnace melting in a foundry and cheese-powder drying using SCADA and production-order data. The results demonstrate that data accessibility is not equivalent to analytical suitability and show how STREAM supports transparent decisions about immediate data use, analytical restrictions, and prioritized infrastructure improvements.
CommentsIt has been accepted by Energy Informatics.Academy Conference 2026 (EI.A 2026)
