The invention discloses a
power equipment detection method and device based on
big data analysis, and belongs to the field of
data processing.The
power equipment detection method based on
big data analysis comprises the following steps that a dynamic
metadatabase is constructed, and equipment models, sensor configuration and physical parameters are recorded; compared with the prior art, the method has the beneficial effects that the problem of protocol heterogeneity and sampling
frequency difference is solved and the data
utilization rate is improved through dynamic
metadatabase, proxy variable generation and sliding interpolation alignment aiming at a mixed scene of
electric power heterogeneous equipment and deep compatibility of multi-
source data; physical-data dual-drive modeling is carried out,
knowledge graph constraint and dual-channel
deep learning are fused, cross-device feature distribution alignment is realized under a transfer learning framework, and it is ensured that new and old device
anomaly detection is accurate; and dynamic closed-
loop optimization is carried out, and based on
health index hierarchical evaluation and
federated learning encryption gradient aggregation, threshold dynamic drift and model increment updating are realized, and fault early warning is advanced.