The invention discloses an
optical storage charging station cross-site heterogeneous data fusion health state early warning method, which adopts transfer learning and
domain adaptation technologies, measures the data distribution difference between a source site and a
target site through the maximum mean value difference, realizes
knowledge transfer and improves the generalization ability of a model. And on the basis of an Attention-LSTM
feature fusion mechanism, feature weights are adaptively allocated, and the fault
feature extraction capability is enhanced. And designing a fault mode sharing module, coding the fault mode of the source site into a
knowledge base, and rapidly matching similar faults of the
target site. A
model compression technology is introduced, lightweight deployment is realized through knowledge
distillation and a parameter quantification method, and operation of edge equipment is effectively supported. And furthermore, an
online learning mechanism is adopted to dynamically update
model parameters to ensure that the model adapts to the environment change of the
target site. According to the method, the accuracy and robustness of cross-site fault prediction are remarkably improved, the model
deployment time is shortened, and the method is widely applied to intelligent operation and maintenance of the
optical storage charging and discharging integrated
power station.