The invention belongs to the technical field of power distribution network leakage current detection, and particularly relates to a CNN and FiLM-based leakage current type intelligent identification method. Comprising the following steps: S1, collecting real-time operation data of a typical power supply area, obtaining leakage current waveforms and related environment characteristic parameters under different working conditions, and constructing an original leakage
current sample data set; s2, a CNN-based leakage current classification model is constructed, a FiLM condition modulation module is introduced, a multi-
task learning framework is used at the
tail of the network, a main task is leakage current type identification, and an auxiliary task is grounding
system discrimination; s3, using a weighted
cross entropy loss function to alleviate a class sample imbalance problem; an OneCycleLR
dynamic learning rate scheduling strategy is introduced; s4, evaluating the performance of the model on the
test set, and using the accuracy and the
confusion matrix as evaluation indexes; according to the method, high-precision identification of multiple types of faults such as single-phase grounding, arc type electric leakage and
direct current system electric leakage is realized, different grounding systems can be adapted, and the accuracy and robustness of
system diagnosis are improved.