缝纫设备的智能运维与管理,关键在于解决非结构化文本的信息挖掘及语言模型构建问题.这对于加快设备缺陷和故障诊断速度、提高诊断准确性及实现设备检修的智能辅助决策,具有重要意义.该研究提出了通过基于 BERT 的条件随机场(bidirectional encoder representations from transformers-conditional random field,BERT-CRF)的实体抽取模型抽取关键实体信息,如设备名称、属性等,再通过基于双向门控循环单元注意力机制(bidirectional gated recurrent unit-attention,BiGRU-Attention)的关系抽取模型有效捕捉实体之间的语义关联,为缝纫设备知识图谱的构建提供支持.针对缝纫设备文本分析场景,模型在缝纫设备文本实体识别、信息抽取和故障诊断等任务场景进行了专门的训练和优化.与现有的深度学习算法相比,该研究所提方法在验证集和测试集上实现了 20％到30％的性能提升,体现了其在召回率和精确度上的显著优势.缝纫设备知识的非结构化文本信息挖掘,可为平缝设备数据集成、设备故障运维、平缝工艺路线设计等方面的知识图谱构建提供参考.
Exploring Techniques for Building Language Models Targeted at Sewing Equipment Operation and Maintenance Management
The intelligent operation and maintenance management of sewing equipment needs to solve the problem of information mining and language model construction of unstructured text,which is of great significance to improve the speed and accuracy of the diagnosis of equipment defects and faults,and realize the intelligent decision-making of equipment maintenance.In this paper,firstly,we propose a method based on bidirectional encoder representations from transformers-conditional random fields(BERT-CRF)to extract key entity information,such as device names and attributes.Then,through the relationship extraction model based on bidirectional gated recurrent unit-attention(BiGRU-Attention),the semantic association between entities is captured effectively to provide support for the construction of the sewing equipment knowledge graph(KG).According to the text analysis scenario of sewing equipment,the model is specially trained and optimized in the task scenarios of text entity recognition,information extraction and fault diagnosis of sewing equipment.Compared with existing deep learning algorithms,the proposed method achieves a 20％to 30％performance improvement on the validation and test sets,demonstrating significant advantages in the recall rate and the accuracy.To facilitate the mining of unstructured text information on sewing equipment,this study provides a reference for constructing a KG that integrates data on flat sewing equipment,including aspects of equipment fault operation,maintenance and flat sewing process route design.
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