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Article(id=1322164729309679975, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322164546295410919, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1730649600000, receivedDateStr=2024-11-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793158236099, onlineDateStr=2026-10-28, pubDate=1764000000000, pubDateStr=2025-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793158236099, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793158236099, creator=13701087609, updateTime=1793158236099, updator=13701087609, issue=Issue{id=1322164546295410919, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='11', pageStart='1', pageEnd='284', issueExtLink='null', onlineDate='null', pubDate='1764000000000', pubDateStr='2025-11-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793158192465, creator='13701087609', updateTime=1793158905697, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322167537857573862, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322164546295410919, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322167537861768167, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322164546295410919, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=242, endPage=249, ext={EN=ArticleExt(id=1322164729540366696, articleId=1322164729309679975, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Multi-Hop Intelligent Retrieval-Based OPC UA Information Parsing Method for Mining Machinery Equipment, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To enhance the data parsing capability of mining machinery equipment based on the OPC UA network communication protocol, an OPC UA information parsing method based on multi-hop intelligent retrieval for mining machinery equipment was proposed. Firstly, a multi-hop intelligent retrieval model tailored for mining machinery equipment was designed in this method, which integrated GCN and RoBERTa networks to achieve multi-hop reasoning of equipment entities, thereby inferring unknown entity relationships and attributes. On this basis, an OPC UA-based system information model architecture was constructed, divided into four sub-models, including basic functional components, production equipment information models, operational progress information models, and safety monitoring information models. The sensor components of the machinery equipment were also instantiated and parsed. Finally, experimental analysis of the multi-hop intelligent retrieval model was conducted, and the OPC UA-based intelligent system information model was tested and validated. The results show that, compared to the relatively optimal MIMKGISI multi-hop intelligent retrieval method, the Hit@1 performance metric is improved by 1.7 percentage points in short-term contexts, the Hit@5 performance metric is improved by 1.2 percentage points, and the Hit@10 performance metric remains largely unchanged in long-term contexts.

, authors=Shengqing YAO1, Yicun SHI1, Kai LI1, Yibo HAN2, authorsList=Shengqing YAO, Yicun SHI, Kai LI, Yibo HAN, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322164737249497493, articleId=1322164729309679975, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于矿山机械设备多跳智能检索的OPC UA信息解析方法, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

为提升矿山机械设备基于OPC UA网络通信协议的数据解析能力，提出了一种基于矿山机械设备多跳智能检索的OPC UA信息解析方法。该方法首先设计了一种针对矿山机械设备的多跳智能检索模型，通过融合GCN和RoBERTa网络实现设备实体的多跳推理，从而推断未知的实体关系与属性。在此基础上，构建了基于OPC UA的系统信息模型架构，将其划分为基础功能组件、生产设备信息模型、作业进度信息模型及安全监测信息模型等四个子模型，并对机械设备的传感器组件进行实例化解析。最后，通过对多跳智能检索模型进行试验分析，并对基于OPC UA的智能系统信息模型进行测试验证。结果表明，相较于较优的MIMKGISI多跳智能检索方法，在短期语境下Hit@1性能指标提高了1.7个百分点，Hit@5性能指标提高了1.2个百分点，在长期语境下Hit@10性能指标基本持平。

, authors=姚胜卿1, 史义存1, 李凯1, 韩一搏2, authorsList=姚胜卿, 史义存, 李凯, 韩一搏, authorCompany=null, correspAuthors=null, authorNote=

姚胜卿（1984—），男，陕西神木人，工程师，主要从事机电、煤矿智能化方面的研究工作。E-mail：

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姚胜卿（1984—），男，陕西神木人，工程师，主要从事机电、煤矿智能化方面的研究工作。E-mail：

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姚胜卿（1984—），男，陕西神木人，工程师，主要从事机电、煤矿智能化方面的研究工作。E-mail：

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label=Fig.10, caption=Comparison of contrast test model Hit@10, figureFileSmall=pxKpC11Mkl+1Yb5KzKdB0w==, figureFileBig=8I3tTIT+shTNjFKGoQTlnA==, tableContent=null), ArticleFig(id=1322164743624839630, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164729309679975, language=CN, label=图10, caption=对比试验模型Hit@10值对比, figureFileSmall=pxKpC11Mkl+1Yb5KzKdB0w==, figureFileBig=8I3tTIT+shTNjFKGoQTlnA==, tableContent=null), ArticleFig(id=1322164743691948495, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164729309679975, language=EN, label=Table 1, caption=

Results of ablation tests

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试验方式Hit@1值/％Hit@5值/％Hit@10值/％
本文方法88.698.499.1
消融试验①84.997.398.9
消融试验②79.195.897.9
消融试验③46.668.471.2
), ArticleFig(id=1322164743780028880, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164729309679975, language=CN, label=表1, caption=

消融试验结果

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试验方式Hit@1值/％Hit@5值/％Hit@10值/％
本文方法88.698.499.1
消融试验①84.997.398.9
消融试验②79.195.897.9
消融试验③46.668.471.2
), ArticleFig(id=1322164743855526353, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164729309679975, language=EN, label=Table 2, caption=

Results of contrast test

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模型Hit@1值/％Hit@5值/％Hit@10值/％
本文模型88.698.499.1
MIMKGISI[18]86.997.299.6
IR-MT[14]82.796.598.3
TransferNet[19]81.993.195.0
PullNet[20]79.586.789.4
), ArticleFig(id=1322164743943606738, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322164729309679975, language=CN, label=表2, caption=

对比试验结果

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模型Hit@1值/％Hit@5值/％Hit@10值/％
本文模型88.698.499.1
MIMKGISI[18]86.997.299.6
IR-MT[14]82.796.598.3
TransferNet[19]81.993.195.0
PullNet[20]79.586.789.4
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基于矿山机械设备多跳智能检索的OPC UA信息解析方法
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姚胜卿 1 , 史义存 1 , 李凯 1 , 韩一搏 2
矿业研究与开发 | 矿山机电与矿业智能化 2025,45(11): 242-249
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矿业研究与开发 |矿山机电与矿业智能化 2025 , 45 (11) : 242 -249
基于矿山机械设备多跳智能检索的OPC UA信息解析方法
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姚胜卿（1984—），男，陕西神木人，工程师，主要从事机电、煤矿智能化方面的研究工作。E-mail：

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姚胜卿（1984—），男，陕西神木人，工程师，主要从事机电、煤矿智能化方面的研究工作。E-mail：

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姚胜卿1 , 史义存1, 李凯1, 韩一搏2
作者信息
  • 1.陕西陕北矿业韩家湾煤炭有限公司，陕西 榆林市　719315
  • 2.西安科技大学 计算机科学与技术学院，陕西 西安　710054
作者简介:

姚胜卿（1984—），男，陕西神木人，工程师，主要从事机电、煤矿智能化方面的研究工作。E-mail：

Multi-Hop Intelligent Retrieval-Based OPC UA Information Parsing Method for Mining Machinery Equipment
Shengqing YAO1 , Yicun SHI1, Kai LI1, Yibo HAN2
Affiliations
  • 1.Shaanxi Shanbei Mining Hanjiawan Coal Co., Ltd., Yulin, Shaanxi 719315, China
  • 2.College of Computer Science & Technology, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
出版时间: 2025-11-25
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摘要
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为提升矿山机械设备基于OPC UA网络通信协议的数据解析能力，提出了一种基于矿山机械设备多跳智能检索的OPC UA信息解析方法。该方法首先设计了一种针对矿山机械设备的多跳智能检索模型，通过融合GCN和RoBERTa网络实现设备实体的多跳推理，从而推断未知的实体关系与属性。在此基础上，构建了基于OPC UA的系统信息模型架构，将其划分为基础功能组件、生产设备信息模型、作业进度信息模型及安全监测信息模型等四个子模型，并对机械设备的传感器组件进行实例化解析。最后，通过对多跳智能检索模型进行试验分析，并对基于OPC UA的智能系统信息模型进行测试验证。结果表明，相较于较优的MIMKGISI多跳智能检索方法，在短期语境下Hit@1性能指标提高了1.7个百分点，Hit@5性能指标提高了1.2个百分点，在长期语境下Hit@10性能指标基本持平。

OPC UA  /  矿山机械设备  /  多跳智能检索  /  GCN  /  RoBERTa

To enhance the data parsing capability of mining machinery equipment based on the OPC UA network communication protocol, an OPC UA information parsing method based on multi-hop intelligent retrieval for mining machinery equipment was proposed. Firstly, a multi-hop intelligent retrieval model tailored for mining machinery equipment was designed in this method, which integrated GCN and RoBERTa networks to achieve multi-hop reasoning of equipment entities, thereby inferring unknown entity relationships and attributes. On this basis, an OPC UA-based system information model architecture was constructed, divided into four sub-models, including basic functional components, production equipment information models, operational progress information models, and safety monitoring information models. The sensor components of the machinery equipment were also instantiated and parsed. Finally, experimental analysis of the multi-hop intelligent retrieval model was conducted, and the OPC UA-based intelligent system information model was tested and validated. The results show that, compared to the relatively optimal MIMKGISI multi-hop intelligent retrieval method, the Hit@1 performance metric is improved by 1.7 percentage points in short-term contexts, the Hit@5 performance metric is improved by 1.2 percentage points, and the Hit@10 performance metric remains largely unchanged in long-term contexts.

OPC UA  /  Mining machinery and equipment  /  Multi-hop intelligent retrieval  /  GCN  /  RoBERTa
姚胜卿, 史义存, 李凯, 韩一搏. 基于矿山机械设备多跳智能检索的OPC UA信息解析方法. 矿业研究与开发, 2025 , 45 (11) : 242 -249 .
Shengqing YAO, Yicun SHI, Kai LI, Yibo HAN. Multi-Hop Intelligent Retrieval-Based OPC UA Information Parsing Method for Mining Machinery Equipment[J]. Mining Research and Development, 2025 , 45 (11) : 242 -249 .
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0　引言
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金属矿业作为我国重要的基础产业，对国民经济和国防建设具有重要战略意义。金属矿业开采作业的机械化水平直接关系到矿山建设的安全性、生产效率和可持续发展能力[1]。在金属矿开采过程中，矿山机械设备类型繁多、系统关系复杂，加之缺乏统一的通信协议与数据标准，导致设备间的数据传输和信息集成面临较大困难，易形成“信息孤岛”[2]。这一问题不仅影响设备的日常管理与维护，也降低了多设备协同作业的效率。针对上述问题，曾杰[3]探讨了金属矿综合机械化设备技术的改进措施，以提升金属矿开采作业的效率和安全性。柴泽山[4]提出应借助多种现代监测与检验技术，实现矿上机械设备的最优管理。张明杰[5]则是概述了金属矿无轨出矿设备的使用与维护管理方法，强调了监测和记录设备维护保养的重要性。胡健等[6]构建了基于CNN和BiLSTM的电流趋势融合预测模型，为选矿设备实现预测性维护提供了新途径。然而，目前矿山机械设备所采用的通信协议仍不完全一致，导致设备数据解析难度大，制约了设备的日常管理与维护水平的进一步提升。
OPC UA（OLE for Process Control Unified Architecture）[7]作为一种新兴的工业通信协议，在推动矿业自动化和物联网设备信息交互方面发挥着关键作用，其信息模型作为OPC UA协议的关键组成部分，定义了在不同设备、系统和应用程序之间进行数据交换时所用的数据结构与规范。目前，已有学者基于OPC UA信息模型进行了初步应用探索。例如，徐善永等[8]针对系统繁杂、数据多源异构的特点，提出了一种基于OPC UA的数字孪生矿井系统信息模型构建方法。荣雪等[9]设计了安全生产监控系统信息模型的整体架构。杜毅博等[10]则提出一种智能化综采工作面信息模型。然而，目前的OPC UA信息模型主要依赖人工构建，数据信息分散于不同文档中，易导致信息碎片化。为解决该问题，本文提出一种基于矿山机械设备多跳智能检索的OPC UA信息解析方法，通过矿山机械设备知识图谱的多跳推理，深入挖掘各类矿山机械设备实体、属性及关系之间的语义关联，从而提升OPU UA信息的解析能力，构建更为完整的智能化系统信息模型。本文主要贡献如下。
（1）构建融合关系路径的多跳知识检索模型。该模型通过在图卷积网络（GCN）中引入深度优先探索（DFS）模块，用以提取实体关系路径信息。在此基础上，利用Transformer模型对路径特征与知识图谱节点、关系的特征进行融合，并依据语义相似性度量匹配最优检索结果。
（2）构建基于OPC UA的系统信息模型架构。依据OPC UA信息解析的通用建模规则，分析矿山机械设备与信息模型的映射关系，进而设计适用于智能系统信息模型的整体架构。
（3）测试验证模型可行性。依托矿山机械设备的OPC UA实例化信息解析任务，验证本文所建方法的有效性与实用性。
1　矿山机械设备的多跳智能检索模型构建
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为提高OPC UA信息模型架构设计的完整性，本文提出一种面向矿山机械设备的多跳智能检索模型。该模型利用图卷积网络（Graph Convolutional Network，GCN）对知识图谱中的节点和关系进行嵌入表示，以构建有效的知识图谱表达。然后，通过深度优先搜索（Depth First Search，DFS）和路径注意力机制提取关系路径，以捕获多跳推理过程中的关键信息及实体之间的长期依赖关系。在此过程中，引入路径注意力机制，使模型有针对性地关注路径中的重要信息，增强对复杂关系的理解能力。进一步地，通过Transformer模型对关系路径向量与知识图谱中的节点、关系向量进行特征整合，从而在关系路径、节点和关系之间建立全局语义关联。该机制使模型能够更全面地理解知识图谱中直接相邻及间接关联的语义关系。在此基础上，利用RoBERTa模型[11]对检索语句进行编码，并通过相似性度量筛选最优检索结果。最终，基于知识检索模型的输出，构建智能系统信息模型架构，并完成实例化数据解析。总体思路如图1所示。
1.1　矿山机械设备知识图谱构建
本文基于“知识图谱”概念[12]，将矿山机械设备核心概念分为设备整机、设备部件、传感器、通信协议、设备维护、设备维修、工种、相关资料等八大类，并采用三元组本体模型O＝〈C，R，A〉构建本体模型。其中，“O”表示设备本体模型；“C”表示设备的概念实体，例如设备整机、设备部件、设备维护等；“R”表示概念实体之间的关系，例如组成，使用，维护等；“A”表示概念实体具有的属性，如型号，生产厂家，生产编号等。
具体而言，本文首先构建设备本体模型，明确设备概念实体及其相互关系。随后对收集的设备数据通过词嵌入、位置嵌入、句子嵌入和任务嵌入的结构化表示方法，将文本转换为向量形式，以捕捉实体间的语义关系、位置信息及语句信息；同时，利用Transformer-Encoder模型捕捉实体间的依赖关系及上下文信息。针对少量样本训练数据易导致模型欠拟合的问题，本文引入字库进行样本扩充，并将设备数据集与字库分别进行编码。由于两类向量具有不同的特征分布，本文对其进行标准化处理，将不同特征映射到同一尺度，并采用GRU模型实现向量定长化，再通过横向拼接的方式进行特征融合。在此基础上，将融合的特征向量输入Lattice-LSTM模型，进行字符解码，完成设备实体的自动提取。最终，借助Neo4j图数据库进行知识存储，并实现矿山机械设备知识图谱的构建。图2展示了通过上述流程所实现的部分矿山机械设备知识图谱。
1.2　基于矿山机械设备知识图谱的多跳智能检索
（1）矿山机械设备知识图谱表示学习。
矿山机械设备知识图谱的表示学习旨在将矿山机械设备知识图谱中的节点与关系映射至连续的低维向量空间，通过嵌入表示使模型更好地捕捉节点之间的长依赖关系与复杂语义关联，从而提升智能检索的准确性。为此，本文采用GCN模型作为表示学习的基础模型。
本文首先将矿山机械设备知识图谱中的三元组（头实体，关系，尾实体）进行向量化表示为（h，r，t），通过Xh＝Embedding（h），Xt＝Embedding（t），Xr＝Embedding（r）的操作，将初始输入表示为节点特征矩阵。在此基础上，对头实体和尾实体分别进行图卷积运算，得到中间层的节点表示矩阵，见式（1）。之后，基于节点表示矩阵，采用ReLU激活函数得到三元组中实体及关系的特征表示。
式中：表示邻接矩阵；表示度矩阵；表示权重矩阵。
（2）矿山机械设备知识图谱关系路径推理。
关系路径在知识图谱中表示从一个主题实体到某个候选实体之间的有序关系序列。路径的跳数即为该关系路径的长度：若路径长度为1，则为简单检索问题；若路径长度大于1，则为多跳检索问题[13]。多跳检索要沿着多个关系连接进行推理，通过融合不同关系路径上的信息得出最终结果。本文从矿山机械设备知识图谱中抽取出所有以设备主题实体为起点、候选结果实体为终点的关系路径。
本文采用改进的GCN模型进行关系路径抽取，利用DFS及路径注意力机制提取有效的关系路径。在此过程中，引入DFS路径编码机制，使其能够沿深度优先搜索路径，捕获更长的关系路径。
当节点i在l层表示为，通过式（2）进行DFS路径编码：
式中：DFS_Neighbours（i）表示通过DFS获得的邻居节点集合；α和βj是路径注意力权重。
在此基础上，引入路径注意力机制，用于学习矿山机械设备知识图谱中每个邻居节点在路径中的权重。对每个节点i，可以按式（3）计算路径注意力权重：
式中：a表示可学习的注意力权重向量；LeakyReLU（•）表示修正线性单元激活函数；Softmax表示归一化指数函数。
然后，基于上述路径注意力权重，计算矿山机械设备知识图谱中每个邻居节点的加权表示。
（3）检索语句表示学习。
检索语句表示学习旨在将检索语句嵌入至低维向量空间，并将其映射至与知识图谱相对应的向量空间[14]。本文采用RoBERTa模型捕捉多跳检索语句中蕴含的丰富语义信息。给定的矿山机械设备检索语句序列Q＝{w1，w2，…，wn}为每个实体对应的词语wi加入位置信息，最终得到的检索语句向量表示xi和xpos分别见式（4）和式（5）：
式中：xpos表示位置信息；dmodel表示模型维度；pos表示位置编码。
然后，利用全连接网络将检索语句向量Q映射到矿山机械设备知识图谱所在的向量空间，可以得到新的检索语句表示。
（4）矿山机械设备知识图谱路径嵌入。
本文采用Transformer模型[14]实现对关系向量与知识图谱节点关系特征的融合。通过Transformer模型的多头自注意力机制，能够同时关注输入序列中不同位置的信息，从而捕捉到矿山机械设备知识图谱中关系路径、节点和关系之间的语义关联。
具体来说，利用Transformer模型融合关系路径特征，其中的自注意力机制对不同位置之间的语义关联进行建模，首先将上述抽取的关系路径矩阵和矿山机械设备知识图谱节点矩阵Zt进行横向拼接，得到拼接矩阵M。
然后，采用Transformer模型对拼接矩阵M进行编码（见式（6）），得到融合后的表示矩阵M′。
式中：MultiHeadAttention（•）表示多头注意力函数；FeedForward（•）表示全连接函数；LayerNorm（•）表示归一化函数。
在此基础上，利用全连接层学习的不同位置和通道之间的非线性关系，进行归一化操作，并将归一化后的结果输入到前馈神经网络进行非线性变换。最终，基于融合关系路径的特征向量与检索语句向量，利用式（7）的余弦相似度进行语义相似度度量，获得最优的检索结果。
式中：Q为检索语句向量；V为融合关系路径的特征向量；Sim（Q，V）表示余弦相似度度量函数。
2　基于OPC UA的矿山机械设备信息解析
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在基于上述矿山机械设备多跳智能检索模型检索获取设备实体关联信息的基础上，进一步设计基于OPC UA的系统信息模型架构，以实现矿山机械设备的OPC UA信息解析。
针对智能化系统规模大、子系统繁多、控制复杂等特点，信息建模需遵循完整性、一致性及可扩展性等原则[11]。依据该原则，本文设计了系统信息模型架构，如图3所示。
该架构将系统信息模型划分为基础功能组件、生产设备信息模型、作业进度信息模型及安全监测信息模型等4个子系统模型，各个模型均包含静态属性集、动态属性集及设备集。其中，静态属性集为系统中不变或长周期不变的静态数据；动态属性集记录系统运行过程中持续变化的动态数据；设备集则涵盖该子系统所包含的设备整机及其组成部件。
（1）基础组件。系统基础组件反映了系统的基础信息及总体运行情况，其静态属性集主要描述系统基础信息、结构化信息及关联结构等。其中，系统基础信息包括系统缩写、系统名称与系统布局等；结构化信息包括系统结构和数据结构等；关联信息包括数据关联和系统功能关联等。动态属性集主要记录设备的实时使用状态、在线数量和运行变化情况等。
（2）功能组件集。功能组件集定义了系统所含的功能组件，主要包括生产设备组件、作业进度组件及安全监测组件等。
（3）生产设备信息模型主要描述实际生产设备的运行状态。其中，静态属性集包括设备型号、生产厂家、设备规格、额定参数等。动态属性集包括设备运行状态、工作模式、故障代码等。设备集包括进行生产活动的各类设备。
（4）作业进度信息模型。该模型主要描述系统的作业进度和生产过程的相关信息，包括作业计划、作业状态、生产指标等信息。静态属性集包括生产计划编号、作业区域等。动态属性集包括作业进度、作业产量等。
（5）安全监测信息模型。安全监测信息模型描述系统中的安全监测设备和安全管理的相关信息，其静态属性集包括监测点、监测频率等。动态属性集包括风速、实时温度等。设备集主要包括气体传感器、温度传感器等。
基于上述的系统信息模型总体架构，以部分矿山机械设备为例，利用OPC UA官方基金会提供的XML文件模板[15-16]，实现了基于矿山机械设备的实例化信息解析，具体过程如图4所示。
在图4中，本文通过上述矿山机械设备多跳智能检索模型，能够分别将检索的实体名填充至XML文件模板〈UAObjectType〉下的〈DisplayName〉标签中，并将实体属性分别添加至〈UAVariable〉下的〈DisplayName〉标签中，将实体节点ID添加至〈UAObjectType〉中的NodeId属性，将实体属性的数据类型添加至〈UAVariable〉中的DateType属性，最终实现矿山机械设备的OPC UA实例化信息解析。
例如，查询“铲运车上的速度传感器”时，如图4所示，通过多跳检索关键词，能够获得关键词“铲运车”和“速度传感器”的属性名称及其关联关系。在此过程中，首先检索矿山机械设备知识图谱中的“铲运车”节点，通过多跳检索的方式，得到关系路径上的“速度传感器”节点。然后，遍历“速度传感器”所包含的属性名称，可以将具体的“速度传感器”节点名称填充至〈UAObjectType〉下的〈DisplayName〉标签中，并将速度传感器的“型号”节点属性填充至〈UAVariable〉下的〈DisplayName〉标签中，将“安装位置”“报警阈值”等数据填充至〈UAVariable〉标签中的〈DisplayName〉标签中。在此基础上，基于上述的矿山机械设备知识图谱关系路径推理，利用设备知识图谱，能够获得“铲运车”上包含的“噪声传感器”和“温度传感器”节点数据，并将其自动填充至〈UAObjectType〉下的〈DisplayName〉标签中。
3　试验分析
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3.1　数据集与评价指标
为了验证矿山机械设备多跳智能检索模型的有效性，考虑到机械设备训练样本规模不足的问题，通过在WebQuestionSP[17]公开检索数据集上进行模型预训练，然后通过迁移学习的方式，实现矿山机械设备知识的学习与推理检索。具体来说，在WebQuestionSP数据集上的实体数量共188 633个，关系数量共572个，三元组数量共5 780 246个，节点出度均值为1.3。此外，以矿山机械设备为研究对象，在矿山机械设备的数据集上，实体数量共8 744个，关系数量共7个，三元组数量共14 347个，节点出度均值为2.1。
在python3.8的开发环境下，利用Intel（R）Xeon（R）Silver 4210 CPU@2.20GHz处理器和NVIDIA GeForce PTX 2080Ti（11GB）GPU处理器进行模型训练与测试。GCN模型的学习率设置为5×10－3，RoBERTa模型的学习率设置为2×10－5，Batch Size、Dropout参数均设置为32和0.2，模型优化器均采用Adam函数，训练次数均为150次。
采用Hit@N作为评价指标，Hit@N衡量了模型在给定的N个候选结果中是否包含了正确结果，从而反映模型的推理能力。Hit@N的值越大代表模型的性能越高。例如，当N＝1时，Hit@1表示1个候选结果为正确结果。
3.2　模型对比试验
设计了三种消融试验方式：①采用GCN模型对用户检索语句及知识图谱同时编码，完成多跳智能检索任务；②取消融合关系路径特征，完成多跳智能检索任务；③仅使用矿山机械设备知识图谱训练模型，完成多跳智能检索任务。
消融试验结果如图5至图7所示，图中横坐标为迭代次数，纵坐标为模型的Hit@1、Hit@5、Hit@10值。由图5可知，本文所建模型的Hit@1值分别比消融试验①至③模型高3.7个百分点、9.5个百分点、42个百分点。由图6可知，本文所建模型的Hit@5值分别比消融试验①至③模型高1.1个百分点、2.6个百分点、30个百分点。由图7可知，本文所建模型的Hit@10值分别比消融试验①至③模型高0.2个百分点、1.2个百分点、27.9个百分点。
本文所建模型能够取得较高性能指标，主要归因于以下两方面：首先，采用了GCN模型对检索语句及知识图谱同时编码，增强了模型对复杂语义的理解能力；其次，关系路径为模型提供了可解释的推理线索，从而提升了检索结果的完整性与准确性。消融试验结果（见表1）进一步验证了上述模块对模型性能的贡献。
为进一步验证本文所建模型的准确性，将本文所建模型与近年不同的多跳智能检索模型进行对比，对比结果如图8至图10所示。
由图8可知，本文所提模型在97轮迭代后趋向收敛，最终模型的Hit@1值相较于MIMKGISI、IR-MT、TransferNet及PullNet分别提高1.7个百分点、5.9个百分点、6.7个百分点、9.1个百分点。由图9可知，本文所建模型的Hit@5值相对于其他模型分别提升1.2个百分点、1.9个百分点、5.3个百分点、11.7个百分点。由图10可知，本文所建模型的Hit@10值相较MIMKGISI差异很小。
通过对比试验可知，本文所建模型在不同的性能指标方面，相较于近年不同的多跳智能检索模型均有提升。具体对比结果见表2。
4　结论
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为了提升矿山机械设备基于OPC UA协议的数据解析能力，本文提出一种基于矿山机械设备多跳智能检索的OPC UA信息解析方法。该方法通过设计一种矿山机械设备多跳智能检索模型，实现了矿山机械设备信息的智能推理和检索。
（1）本文构建的基于矿山机械设备知识图谱的多跳智能检索模型在Hit@1和Hit@5两项性能指标上，相较于较优的MIMKGISI多跳智能检索模型分别提升1.7个百分点、1.2个百分点。尽管Hit@10指标略低0.5个百分点，但模型在多项指标上表现优异，表明其能够有效提升智能检索模型的准确度。
（2）通过构建基于OPC UA的系统信息模型，实现了对铲运车设备的实例化信息解析，为矿山机械设备数据的关联融合和智能化维护提供了技术支撑。
（3）未来工作中，将进一步尝试通过深度学习模型学习矿山机械设备知识图谱与OPC UA信息模型之间的映射规则，实现动态的OPC UA信息模板生成与智能解析，进一步推动金属矿山机械设备管理与维护的智能化进程。
基金
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  • 国家自然科学基金项目(62102309)
  • 陕西省教育厅科研计划专项项目(8146119003)
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2025年第45卷第11期
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  • 接收时间：2024-11-04
  • 首发时间：2026-10-28
  • 出版时间：2025-11-25
补充材料
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  • 收稿日期：2024-11-04
基金
国家自然科学基金项目(62102309)
陕西省教育厅科研计划专项项目(8146119003)
作者信息
    1.陕西陕北矿业韩家湾煤炭有限公司，陕西 榆林市　719315
    2.西安科技大学 计算机科学与技术学院，陕西 西安　710054
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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