Data fusion method for power grid digital transformation based on deep neural decision forest
In the process of digital transformation of power grid,the data needed to be processed are various,large in scale,in-consistent in data source distribution and format,resulting in poor data quality and low efficiency of data analysis and processing.Therefore,a data fusion method for power grid digital transformation based on deep neural decision forest is proposed.The digital transformation of the power grid is carried out through the power grid management platform(accounting domain).The sub database design is carried out according to the functional differences of the database,and the read-write separation design is carried out to bet-ter support the centralized deployment of platform applications and reduce the transaction processing pressure of the main instance.Combining the deep neural decision forest to fuse the power grid data after the digital transformation,the convolutional neural network is used to extract the characteristics of the power grid data after the digital transformation,input them into the decision forest composed of multiple decision trees,and complete the data fusion through data classification.The experimental results show that the method in this paper can effectively complete the data fusion of power grid digital transformation,and the fused data is easier to view and facili-tate subsequent application analysis;The final data fusion accuracy rate reached 98.7％,the data loss function value was only 0.7,and the data fusion coverage was high,which could improve the grid data application effect in the process of digital transformation of the grid.
power grid management platformdigital transformationdata fusiondeep neural decision-making forestsepara-tion of reading and writinghorizontal sub database and sub table
NETL
NSTL
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