In order to solve the problem of analysis ductility and large consumption of cloud resources caused by massive non-intrusive load monitoring（NILM）data uploaded to the cloud

a non-intrusive load identification framework based on cloud-edge collaboration is proposed. Firstly

the Markov transition field（MTF）coding method is used to color code the power data

and the load identification with clear characteristics is constructed. Then

a lightweight deep learning model with the same structure is deployed in the cloud service layer and the edge service layer respectively to complete the training and load identification tasks. While reducing the pressure of cloud-edge resources

the cloud-edge coordination of load identification is realized through transfer learning. Finally

based on adaptive synthetic sampling（ADASYN）

the REDD dataset is extended to solve the model learning bias caused by dataset imbalance

and the identification performance of the framework proposed is validated based on the dataset. The results show that the framework can not only meet the requirements of high precision and real-time load identification

but also significantly reduce the pressure of cloud and edge storage and computing resources.