Abstract
Abstract Traditional indexing methods fail to meet the power industry’s needs for rapid content retrieval and collaborative management of heterogeneous data. This study proposes a learning-based hashing function using autoencoders, which compresses raw data into binary hash codes by learning data features and reconstruction. Extensive experiments demonstrate its advantages in reducing collision rates and computation time. Compared to various models, this method shows clear benefits in search and index creation times, better addressing the indexing requirements of power data and supporting intelligent processing in power systems.
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