The application belongs to the technical field of
electric power warehousing, and discloses a multi-
source material demand prediction and replenishment decision method for
electric power warehousing, wherein a cross-
system dynamic federal query
system is constructed, an OPC UA protocol and a RESTful API fusion gateway are adopted, seamless docking of multiple systems such as
electric power warehousing,
power grid monitoring and
weather warning is realized, data is standardized into a four-dimensional structure of materials, time, scene and attributes, and then implicit space-
time correlation is completed through a space-time enhanced
knowledge graph to form a standardized fusion
data set; a
hybrid prediction model combining migration learning is constructed,
small sample scenes are adapted through similar scene
data migration, and differentiated sub-models are designed for regular consumption materials and emergency materials, combined with
time series trend
decomposition and attention mechanism to strengthen the influence of key scenes; meanwhile, an adaptive data cleaning module and an automatic
feature extraction mechanism are adopted, abnormal data is removed through double
verification, missing values are intelligently completed, and core features are dynamically screened.