The State Grid Corporation of China proposed the idea of building a ubiquitous power Internet of Things, which intends to endow the power system with adjustable perceptivity through the Internet of Things technology. Aiming at the power system under the Internet of Things, this article propounds a framework for the construction and intelligent assessment of the knowledge graph (KG). First of all, the introduction of the power Internet of Things system architecture and data life cycle will be introduced from the aspects of organisational structure, management system, team building, technical support and data security protection. Then, the key information is mined from the complicated physical text data of the power grid by using NLP technology. At the same time, a hybrid model is propounded for named entity recognition, which effectively uses context information to meliorate the accuracy of extraction. The experimental results evince that the accuracy rate of the Internet-based grid physical data KG construction and intelligent analysis framework proposed in article reaches 96.53%. It is a new guidance for the research and evolution of future power grid objects.

Problems exist in power grid data management that have unclear relationships, weak security and low accuracy. By analysing the knowledge graph construction characteristics of smart grid data information and knowledge extraction, the grid data management platform is reshaped architecturally, and the knowledge graph construction technology is embedded in the grid data management framework. For the aforementioned problems, the knowledge graph construction and Internet of Things optimisation framework of power grid data knowledge extraction are proposed in this article. Firstly, the semantic search (KGSS) algorithm based on the knowledge graph is used. The KGSS algorithm extracts knowledge from structured, semi-structured and unstructured grid data through the massively parallel processing acquisition model and Hadoop database, and constructs knowledge entities, attributes and inter-entity relationships. Then, it optimises and predicts through the knowledge graph construction and Internet of Things optimisation framework extracted from power grid data knowledge. Finally, the experimental results show that the accuracy rate of the KGSS algorithm is 92%. The experimental results also show that it provides new ideas and research directions for power grid data under big data in the future.

With the development of smart grids, power grids have accumulated massive amounts of data in various links such as power generation, transmission, substation, distribution, power consumption, and dispatch. More and more big data applications are beginning to be applied in various professional fields of the power grid. Promote the application and value discovery of smart grid big data through data fusion inside and outside the grid. Grid data has become an important asset for enterprise development, but power grid enterprises lack effective technical means to solve the whole life cycle monitoring and relationship of power grid data assets. Aiming at the relationship between power grid data assets, this paper proposes a set of grid data asset relationship and intelligent classification framework that integrates knowledge graph and Internet of Things. First, the grid knowledge graph extraction relationship is carried out by ProjE algorithm. Then, the relationship between power grid data assets and intelligent classification framework that integrates knowledge graph and Internet is proposed. Finally, the corresponding classification application is proposed by using intelligent classification algorithm. Experimental results show that the intelligent classification accuracy rate can reach 93.12% under the relationship between the knowledge graph and the Internet data assets, which has a new idea for the future development of the relationship between power grid data assets.