Abstract: The present invention discloses a method for updating artificial intelligence model data for smart vending machines, the method including: acquiring an actual purchase video including an untrained new product and determined by a product recognition algorithm or manual review, the actual purchase video being annotated with target product SKU information; intercepting new product sub-images from the actual purchase video; storing the product partial sub-images in a database of new product sub-images to be processed; initiating verification of all the product partial sub-images in the database of new product sub-images to be processed when the number of the product partial sub-images in the database of new product sub-images to be processed exceeds a given threshold; obtaining a new product replacement image by screening the product partial sub-images in the database of new product sub-images to be processed, and verifying the recognition accuracy of the screened new product replacement image; after the verifica

Abstract: A method of a power user classification based on distributed K-means, a storage medium and a classification device are provided. The method includes: obtaining, by N load aggregators, power consumption data of power users managed by respective load aggregators; performing, by each load aggregator, a normalization operation on time series load data of the power users managed by the load aggregator; forming a N×N dimensional adjacency matrix A; performing K-means clustering on normalized time series load data, to obtain the respective centroids and user groups characterized by the respective centroids; sharing, by the respective load aggregators, the centroids and the number of users under the respective centroids based on the adjacency matrix A, and obtaining consistent centroids by multiple load aggregators; after an overall iteration ends, obtaining, by the respective load aggregators, the consistent centroids consistent with the K-means centroid based on global data, to realize user classification.

Abstract: A method of a power user classification based on distributed K-means, a storage medium and a classification device are provided. The method includes: obtaining, by N load aggregators, power consumption data of power users managed by respective load aggregators; performing, by each load aggregator, a normalization operation on time series load data of the power users managed by the load aggregator; forming a N×N dimensional adjacency matrix A; performing K-means clustering on normalized time series load data, to obtain the respective centroids and user groups characterized by the respective centroids; sharing, by the respective load aggregators, the centroids and the number of users under the respective centroids based on the adjacency matrix A, and obtaining consistent centroids by multiple load aggregators; after an overall iteration ends, obtaining, by the respective load aggregators, the consistent centroids consistent with the K-means centroid based on global data, to realize user classification.