摘要
随着电力物联网多源非结构化数据传输量的增加,导致云边协同任务调度和资源分配具有极大的延迟性。对此,本文利用知识图谱在数据存储和知识抽取方面的优势,提出一种多模块联合知识提取方法,该方法包括2个独立的子模块,一个用于提取头部实体,另一个用于抽取尾部实体及其对应关系。首先通过枚举传输数据中的标记序列来生成候选实体和关系,然后使用2个子模块来预测实体和关系,最后对预测的实体和关系进行联合解码,得到关系三元组,并以传输数据包含的知识为依托,实现传输调度图谱的可视化展示。实验结果显示该模型的F1值达79%,精确度相较于其他传统方法提高6%,知识抽取效果较好,能够高效对非结构化传输数据进行高度解析,实现云边协同任务调度和资源分配的精准决策。
With the increase in transmission volume of massive multi-source unstructured data in power IoT,it leads to the cloud-edge collaborative task scheduling and resource allocation with great latency.In this regard,this paper proposes a multi unit knowledge joint extraction method by virtue of the advantages of knowledge graph in data storage and knowledge extraction,which consists of two independent sub-modules,one for extracting head entities and the other for extracting tail entities and their corresponding relationships.Firstly,candidate entities and relations are generated by enumerating the tagged sequences in the transmission data.Then,the two sub-modules are used to predict the entities and relations.Finally,the predicted entities and relations are jointly decoded to obtain the relation triples,and the knowledge contained in the transmission data is used as a basis for the visual display of the transmission scheduling mapping.The experimental results show that the F1 value of the model reaches 79%,the accuracy is 6%improved compared to other traditional methods,the knowledge extraction effect is better,and it can efficiently make highly parse to the unstructured transmission data to realize the accurate decision-making of the cloud edge collaborative task scheduling and resource allocation.
作者
尚博祥
郭晓艳
郑剑
孙先范
SHANG Boxiang;GUO Xiaoyan;ZHENG Jian;SUN Xianfan(State Grid Tianjin Electric Power Company,Tianjin 300010,China;Information and Communication Company,State Grid Tianjin Electric Power Company,Tianjin 300010,China)
出处
《计算机与现代化》
2025年第9期104-108,118,共6页
Computer and Modernization
基金
国网天津市电力公司科技项目(信通-研发 2023-03)。
关键词
云边协同
数据传输
任务调度
精准决策
cloud-edge collaboration
data transfer
task scheduling
precision decision making
