Research on the Construction of Hybrid Data Graph Model for Smart Grid Dispatching Based on Multidimensional Data Mining

The database is more and more unable to meet the grid's demand for fast access and analysis of big data, and the large amount of data has brought greater difficulty in data analysis and processing, and also faces problems such as large grid scheduling, difficult load prediction, and fault detection. In order to ensure the smooth operation of grid scheduling, a research on the construction of a hybrid data graph model for smart grid scheduling based on multidimensional data mining is proposed. This study extracts useful features from raw data collected from various data sources, selects the graph convolutional neural network hybrid data graph model, constructs the data graph model based on the physical topological relationship of the grid and continuously optimizes and updates the model. After the simulation test, this model is able to handle a large amount of data, through the study of smart grid scheduling hybrid data graph model construction can effectively solve the problems such as the difficulty of analyzing and processing a large amount of data.