Residents have a wide variety of household electrical appliances with similar characteristics

which brings problems to non-intrusive identification such as uncertainty in the types of appliances and the need to improve the accuracy of identification.In response to this problem

a cloud-based collaborative load identification method based on multi - type feature interaction is proposed. Firstly

the end-side performs feature extraction and load identification based on high-frequency sampling

to improve the detection sensitivity of small offset events in the detection process based on the CUSUM event detection method

using the light-weight proximity recognition method to perform basic electrical appliance identification and upload the spatial-temporal characteristics of uncertain electrical appliances to the cloud. Secondly

the cloud side recognition ability is improved by constructing a 16-dimensional cloud-side historical feature database consisting of inherent features

spatio - temporal features and statistical features.An optimized identification technology based on the nearest neigh-bor principle for multi-dimensional spatio-temporal features is proposed. Finally

a closed - loop cloud upgrade mechanism is built.The cloud side sends back the different characteristics to the terminal to improve the terminal electrical feature library

comprehensively realizing the improvement of the ability to identify uncertain electrical appliances. Taking a user in Nanjing province as an example

the recognition rate of cloud collaboration has increased from 67% to 91% compared with that of the terminal. The recognition rate of unrecognized appliances has been realized

effectively verifying the effectiveness of the algorithm.