Abstract:  
Abstract： 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.

Key words: Key words： cloud-edge collaboration, data transfer, task scheduling, precision decision making