Heterogeneous Mapping Mechanism of Energy Internet Information Model Based on Big Data

In recent years, as energy technology innovation has entered a highly active period, the energy Internet, which is characterized by complementary energy flows and deep integration of physical information, is gradually becoming a key force to promote the transformation of the energy industry to clean, low-carbon and efficient. However, in the process of building and operating the energy Internet, a significant challenge lies in the heterogeneity of its internal information models, that is, the information models are diverse, complex in structure and different in standards, which greatly hinders the seamless sharing and efficient use of data. To solve the above problems, this paper deeply studies the heterogeneous mapping mechanism of energy Internet information model based on big data technology. This mechanism aims to achieve effective mapping and deep fusion between heterogeneous information models by integrating advanced methods of big data storage, processing, and analysis, combined with theoretical tools such as ontology and semantic similarity calculation. The experimental results show that this mechanism not only significantly improves the sharing efficiency and interaction quality of data within the energy Internet, but also effectively promotes the optimization of energy scheduling strategies and the improvement of energy utilization efficiency.