The invention relates to power distribution network fault
processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and
processing capabilities of a power
system. The equipment comprises a pre-training module, a
neural network architecture module, a
data acquisition layer, an edge calculation layer, a core
processing layer, a context learning module, a reasoning processing module, an output and
interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power
system is combined; according to the method, deep understanding and analysis of a complex
power grid state are realized, through multi-
source data fusion, edge calculation and central cooperative processing, the equipment can monitor the
power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-
supervised learning and
federated learning technologies; using
reinforcement learning to optimize a fault
response strategy; the
interpretability of the model is realized, and the decision transparency is improved.