公开(公告)号：US20220179884A1

公开(公告)日：2022-06-09

申请号：US17683973

申请日：2022-03-01

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yanfang Zhang ,  Li Xue ,  Xudong Sun ,  Qinglong Chang ,  Lei Luo

IPC: G06F16/28 ,  G06N20/00

Abstract: A label determining method includes: obtaining a target feature vector of a first time series, where a time series is a set of a group of data arranged in a time sequence; obtaining a similarity between the target feature vector and a reference feature vector in a reference feature vector set, where the reference feature vector is a feature vector of a second time series with a determined label; and when a similarity between the target feature vector and a first reference feature vector is greater than a similarity threshold, determining that a label corresponding to the first reference feature vector is a label of the first time series, where the first reference feature vector is a reference feature vector in the reference feature vector set.

公开(公告)号：US20220166681A1

公开(公告)日：2022-05-26

申请号：US17669638

申请日：2022-02-11

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yanfang Zhang ,  Gang Li ,  Li Xue ,  Wei Lin

IPC: H04L41/14 ,  H04L43/08

Abstract: A traffic anomaly detection method includes obtaining a target time series including N elements; obtaining a target parameter of the target time series, where the target parameter includes at least one of a periodic factor or a jitter density, the periodic factor represents a wave-shaped change that is presented in the target time series and that is about a long-term trend, and the jitter density represents a deviation between an actual value and a target value of the target time series within a target time; determining, from a plurality of types based on the target parameter, a first type to which the target time series belongs, where each of the types corresponds to one parameter set, and the target parameter belongs to a parameter set corresponding to the first type; and detecting an anomaly of the target time series based on a first-type decision model corresponding to the first type.