AI 中文总结
针对工业现场总线设备手动配置的低效易错问题，提出SysName流水线，结合RAG、本地LLM与两级弃权门控，在多协议设备配置任务中实现高效高准确率的端到端自动化，经基准测试与案例验证效果优异。
AI 中文摘要
工业设备调试需要工程师从异构的PDF手册中手动提取数百种协议特定参数，并将其转录到监控控制系统中，这是一个耗时且易出错的工作流程。本文提出了SysName，这是一个面向生产的流水线，可端到端自动化Modbus RTU、OPC-UA、Profibus DP和CANopen的设备配置。它构建了一个混合密集-稀疏检索索引，该索引由源自ECLASS、AAS和SOSA/SSN的本体图增强，使用BGE-M3编码器和交叉编码器重排器来检索相关手册段落。一个本地大语言模型（LLM，温度T=0.1）通过协议特定提示和四步修复流水线生成与本体对齐的JSON-LD配置。一个两级弃权（不执行）门控，结合重排器分数阈值和IRI解析率，在SHACL验证前阻止不安全的LLM调用并过滤低覆盖率配置。在包含28个现场级查询的黄金测试集上，混合检索器达到0.96的HitRate@10，重排器将MRR@10从0.56提升至0.63，并实现弃权的完美分数分离。生成器达到现场级F1=0.87，12次运行中有9次精确匹配。在H100 GPU上的端到端运行中，每个设备耗时2.6-6.6秒，在五设备基准测试中实现零不安全写入和零隐性故障；所有不成功运行均被弃权或部署验证标记。组件级评估将单一系统性故障定位到OPC-UA生成，该故障在端到端指标中不可见。案例研究使用未修改的供应商文档（254页手册、8页寄存器列表、496个分块）调试了一个物理仿真的Universal Robots UR5e机器人，三次运行均达到现场级F1=1.0，并具备回读和联合一致性验证。消融研究和与五个工业LLM系统的比较完成了分析。
英文摘要
Industrial device commissioning requires engineers to manually extract hundreds of protocol-specific parameters from heterogeneous PDF manuals and transcribe them into supervisory control systems, a time-intensive, error-prone workflow. This paper presents SysName, a production-oriented pipeline that automates device configuration end-to-end for Modbus RTU, OPC-UA, Profibus DP, and CANopen. It builds a hybrid dense-sparse retrieval index augmented by an ontology graph derived from ECLASS, AAS, and SOSA/SSN, using a BGE-M3 encoder with a cross-encoder reranker to surface relevant manual passages. A local LLM (T=0.1) generates ontology-aligned JSON-LD configurations via protocol-specific prompts and a four-step repair pipeline. A two-stage abstention gate, combining a reranker-score threshold and an IRI resolution ratio, blocks unsafe LLM invocations and filters low-coverage configurations before SHACL validation. On a gold set of 28 field-level queries, the hybrid retriever reaches 0.96 HitRate@10, and the reranker raises MRR@10 from 0.56 to 0.63 with perfect score separation for abstention. The generator attains field-level F1=0.87 with exact match on 9 of 12 runs. End-to-end runs on an H100 GPU complete in 2.6-6.6s per device with zero unsafe writes and zero silent failures on a five-device benchmark; every unsuccessful run is flagged by abstention or deployment verification. Component-wise evaluation localises the single systematic failure to OPC-UA generation, invisible to end-to-end metrics alone. A case study commissions a physics-simulated Universal Robots UR5e robot from unmodified vendor documentation (254-page manual, 8-page register list, 496 chunks), reaching field-level F1=1.0 over three runs with read-back and joint-consistency verification. An ablation study and comparison with five industrial-LLM systems complete the analysis.
