Recently

artificial intelligence technology in power system has some application achievements in various electric business fields

but most of them are at the application level

lacking system level solutions. The problems existing in the application of artificial intelligence in power industry are discussed

and the solutions are given. Aiming at the situation that the sample collection is faced with data dispersion and collection difficulties

on the one hand

a unified platform is built for sample collection to make it fast and simple

on the other hand

the idea of data backflow is introduced to collect the data detected on the reasoning side to the sample collection platform

which realizes the automation of sample screening and collection process. Since data annotation is a laborintensive work

an active interactive annotation technology is proposed to realize the intelligent annotation of sample data. For the problem of small sample size of model training

the idea of transfer learning is introduced

and the pretraining model is adopted

which not only does not affect the effect of the model

but also reduces the training time of the model. For the model migration to edge devices

the poor portability of the model is caused by the edge device architecture and model framework. The model conversion of different target architectures is realized based on open neural network exchange(ONNX) to solve the problem of hardware compatibility and improve the reusability of the model.