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    Traffic anomaly detection method, and model training method and apparatus
    1.
    发明授权
    Traffic anomaly detection method, and model training method and apparatus 有权

    公开(公告)号：US12237981B2

    公开(公告)日：2025-02-25

    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.

    Fault Detection Method, Apparatus, and System
    3.
    发明申请
    Fault Detection Method, Apparatus, and System 有权

    公开(公告)号：US20220368590A1

    公开(公告)日：2022-11-17

    申请号：US17871662

    申请日：2022-07-22

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Li Xue ,  Liang Zhang ,  Jian Cheng ,  Yanfang Zhang ,  Xiaoyun Si

    IPC: H04L41/0677 ,  H04L41/5009 ,  H04L69/16

    Abstract: In certain embodiments, a forwarding device receives at least one service flow. The forwarding device obtains service information of the at least one service flow, where the service information of the service flow includes identification information of a network object to which the service flow belongs and M key performance indicators KPIs of the service flow. M is an integer greater than o, and the network object includes one or more devices. The forwarding device sends detection information to a first device, where the detection information includes the service information of the at least one service flow or a feature set obtained based on the service information of the at least one service flow. The detection information is used to detect whether the network object is in a faulty state.

    Video quality assessment method and device
    4.
    发明授权
    Video quality assessment method and device 有权

    公开(公告)号：US11374681B2

    公开(公告)日：2022-06-28

    申请号：US16664194

    申请日：2019-10-25

    Applicant: HUAWEI TECHNOLOGIES CO., LTD.

    Inventor： Jie Xiong ,  Yanfang Zhang ,  Ligang Feng ,  Jian Cheng

    IPC: H04L1/00 ,  H04N19/154 ,  H04N19/89 ,  H04N17/00

    Abstract: A video quality assessment method and device are provided. The video quality assessment method includes: obtaining a to-be-assessed video, where the to-be-assessed video includes a forward error correction (FEC) redundancy data packet; when a quantity of lost data packets of a first source block in the to-be-assessed video is less than or equal to a quantity of FEC redundancy data packets of the first source block, generating a first summary packet for a non-lost data packet of the first source block, and generating a second summary packet for a lost data packet of the first source block; and calculating a mean opinion score of video (MOSV) of the to-be-assessed video based on the first summary packet and the second summary packet. The MOSV calculated according to the method is more consistent with real video experience of a user, so accuracy of video quality assessment can be improved.

    METHOD AND APPARATUS FOR IMPLEMENTING MODEL TRAINING, AND COMPUTER STORAGE MEDIUM
    6.
    发明申请
    METHOD AND APPARATUS FOR IMPLEMENTING MODEL TRAINING, AND COMPUTER STORAGE MEDIUM 有权

    公开(公告)号：US20220121994A1

    公开(公告)日：2022-04-21

    申请号：US17562724

    申请日：2021-12-27

    Applicant: HUAWEI TECHNOLOGIES CO., LTD.

    Inventor： Xudong Sun ,  Yanfang Zhang ,  Liang Zhang ,  Shucheng Liu ,  Yali Wang

    IPC: G06N20/00 ,  G06K9/62

    Abstract: A method and an apparatus for implementing model training, and a computer storage medium are disclosed, and belong to the field of machine learning. When a machine learning model deteriorates, an analysis device first obtains validity information of a first feature set, where the first feature set includes a plurality of features used for training to obtain the machine learning model, the validity information includes a validity score of each feature in the first feature set, and a validity score of a feature is negatively related to correlation of the feature with another feature in the first feature set. Then an invalid feature in the first feature set is determined based on the validity information. A second feature set that does not include the invalid feature is finally generated, where the second feature set is used to retrain the machine learning model.

    Fault Detection Method, Monitoring Device, and Network Device
    7.
    发明申请
    Fault Detection Method, Monitoring Device, and Network Device 审中-公开

    公开(公告)号：US20200099981A1

    公开(公告)日：2020-03-26

    申请号：US16695652

    申请日：2019-11-26

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Yanfang Zhang ,  Jian Cheng ,  Yan Bai ,  Liang Zhang

    IPC: H04N21/442 ,  H04N21/44 ,  H04N21/643 ,  H04L1/00 ,  H04L12/26 ,  H04L1/08

    Abstract: A fault detection method, a monitoring device, and a network device for accurately performing fault detection on a video service are provided. The method includes: obtaining a video quality parameter of a monitored video stream, where the video quality parameter is determined according to a packet loss recovery method of the monitored video stream, the video quality parameter includes an effective packet loss factor, and the effective packet loss factor is used to indicate effectiveness of network packet loss recovery performed by using the packet loss recovery method of the monitored video stream; and performing fault detection based on the video quality parameter of the monitored video stream.

    Federated Learning Method and Apparatus, Device, System, and Computer-Readable Storage Medium
    8.
    发明公开
    Federated Learning Method and Apparatus, Device, System, and Computer-Readable Storage Medium 审中-公开

    公开(公告)号：US20230306311A1

    公开(公告)日：2023-09-28

    申请号：US18325533

    申请日：2023-05-30

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Yanfang Zhang ,  Qinglong Chang ,  Jikun Ding ,  Liang Zhang ,  Xudong Sun

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: A federated learning method includes: each second device in a plurality of second devices first obtains data distribution information and sends the data distribution information to a first device. The first device receives the data distribution information from the plurality of second devices participating in federated learning. The first device selects a matched federated learning policy based on the data distribution information. The first device sends a parameter reporting policy corresponding to the federated learning to at least one second device in the plurality of second devices. A second device that receives the parameter reporting policy is configured to obtain second gain information based on the parameter reporting policy and a current training sample, and the second gain information is for obtaining a second model of the second device.

    Sample Data Annotation System and Method, and Related Device
    9.
    发明公开
    Sample Data Annotation System and Method, and Related Device 审中-公开

    公开(公告)号：US20230169096A1

    公开(公告)日：2023-06-01

    申请号：US18150505

    申请日：2023-01-05

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Qinglong Chang ,  Xinyu Hu ,  Yanfang Zhang ,  Xudong Sun ,  Liang Zhang

    IPC: G06F16/28 ,  G06F18/23213

    CPC classification number: G06F16/285 ,  G06F18/23213

    Abstract: A sample data annotation system includes an edge node and a central node. The edge node obtains a key feature of sample data, determines, based on the key feature, whether the sample data is unknown sample data, when the sample data is unknown sample data, performs annotation processing on the sample data to obtain a first annotation result, and sends the first annotation result to the central node. The central node receives the first annotation result, and determines whether the first annotation result indicates successful annotation; and when the first annotation result indicates that the unknown sample data is successfully annotated, performs consistency processing on the first annotation result to obtain a second annotation result, or when the annotation result indicates that the unknown sample data fails to be annotated, performs annotation processing on the unknown sample data to obtain a third annotation result.

    Method, Apparatus, and Computing Device for Constructing Prediction Model, and Storage Medium
    10.
    发明公开
    Method, Apparatus, and Computing Device for Constructing Prediction Model, and Storage Medium 审中-公开

    公开(公告)号：US20230146912A1

    公开(公告)日：2023-05-11

    申请号：US18148305

    申请日：2022-12-29

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Yanfang Zhang ,  Xudong Sun ,  Qinglong Chang ,  Liang Zhang

    IPC: G06N20/00 ,  G06F18/22

    CPC classification number: G06N20/00 ,  G06F18/22

    Abstract: A method, an apparatus, and a computing device for constructing a prediction model, and a storage medium are disclosed, and relate to the field of artificial intelligence technologies. The method includes: obtaining, based on a target dataset of a target prediction scenario and/or scenario information of the target prediction scenario, model search space corresponding to the target prediction scenario; performing model training based on the target dataset and models and hyperparameters that are included in the model search space, to obtain trained prediction models; and obtaining, based on evaluation results of the trained prediction models, a prediction model corresponding to the target prediction scenario. Efficiency of constructing the prediction model can be improved.

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