Abstract
The State Grid Enterprise Public Data Model (SG-CIM 4.0) is a semantically unified data model that can be provided for smart grid business applications. The model is based on a standard table to find similar entities in the physical model for consistency checking. The standard table entities and physical model entities contain both continuous attributes and discrete attributes. How to accurately calculate the similarity of these different attributes and fuse them into a unique similarity, which can be used to efficiently and accurately mine the entity pairs with the highest similarity, are problems to be solved. In order to solve the above problems and make this similarity calculation and fusion method scalable, this paper calculates the syntactic similarity of continuous attributes, semantic similarity, and discrete attribute similarity to the content of different attributes in the entity and introduces a NAS (Neural Architecture Search) based on these similarities Similarity Fusion Neural Network automatically designed a method to achieve the fusion of similarity, which will be called SFNAS (Similarity Fusion NAS). The neural network fusion similarity calculated using SFNAS is better than the traditional linear weighted average similarity in terms of entity pair matching hit rate. This paper can provide useful references for subsequent research on SG-CIM models.
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This work financially supported by Science and Technology Program of State Grid Corporation of China under Grant No.: 5211DS21000T.
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Liao, X. et al. (2022). An Automatic Design Method of Similarity Fusion Neural Network Based on SG-CIM Model. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_32
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