Na Chen
ABSTRACT
In response to the challenges posed by redundancy, multiple sources, and heterogeneity in monitoring data within the power distribution Internet of Things (IoT), this study focuses on three key areas: multi-source data processing, parallel processing of extensive data, and handling heterogeneous data. The paper introduces a confidence function designed for multi-source data processing in power distribution networks, accomplishing data transformation, filtering, and correction through multidimensional data analysis. To cope with the large volume of data, a combination of the MapReduce algorithm and the Hermite orthogonal basis forward neural network model is proposed to enhance data processing efficiency, ensuring the accurate extraction of feature data. Experimental results demonstrate that this algorithm significantly improves the efficiency of aggregating vast amounts of secure data in power distribution networks. This enhancement facilitates rapid and effective data aggregation in scenarios involving massive data, ultimately reducing system backend data processing time.
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Index Terms
- Multi-Source Data Processing and Integration Technology for Low-Voltage Distribution Networks
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