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    INFERENCE COMPUTING APPARATUS, MODEL TRAINING APPARATUS, INFERENCE COMPUTING SYSTEM
    1.
    发明申请
    INFERENCE COMPUTING APPARATUS, MODEL TRAINING APPARATUS, INFERENCE COMPUTING SYSTEM 有权

    公开(公告)号：US20210209488A1

    公开(公告)日：2021-07-08

    申请号：US17044276

    申请日：2019-12-20

    Applicant: BOE TECHNOLOGY GROUP CO., LTD.

    Inventor： Zhaoyue LI ,  Dong CHAI ,  Yuanyuan LU ,  Hong WANG

    IPC: G06N5/04 ,  G06N3/08 ,  G06T7/00

    Abstract: An inference computing apparatus comprises at least one processor and a memory with program instructions stored therein, the program instructions can be executed by the at least one processor to cause the inference computing apparatus to perform the following operations: receiving a first inference model from a model training apparatus, the first inference model being obtained through a model training by the model training apparatus based on a first training sample library, the first training sample library comprising training samples from historical data generated in a manufacturing stage; performing an inference computing on data to be processed generated in the manufacturing stage based on the first inference model to obtain the inference result which is sent to a user-side device; and evaluating performance of the first inference model to determine whether the first inference model needs to be updated, and if yes, updating the first inference model.

    TASK PROCESSING METHOD BASED ON DEFECT DETECTION, DEVICE, APPARATUS AND STORAGE MEDIUM
    4.
    发明申请
    TASK PROCESSING METHOD BASED ON DEFECT DETECTION, DEVICE, APPARATUS AND STORAGE MEDIUM 有权

    公开(公告)号：US20230030296A1

    公开(公告)日：2023-02-02

    申请号：US17429013

    申请日：2020-10-30

    Applicant: Beijing Zhongxiangying Technology Co., Ltd. ,  BOE TECHNOLOGY GROUP CO., LTD.

    Inventor： Meijuan ZHANG ,  Yaoping WANG ,  Zhaoyue LI ,  Yuanyuan LU ,  Wangqiang HE ,  Dong CHAI ,  Hong WANG

    IPC: G05B23/02 ,  G05B13/02

    Abstract: The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus . The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

    IMAGE DATA CLASSIFICATION METHOD, DEVICE AND SYSTEM
    6.
    发明申请
    IMAGE DATA CLASSIFICATION METHOD, DEVICE AND SYSTEM 有权

    公开(公告)号：US20220092359A1

    公开(公告)日：2022-03-24

    申请号：US17477070

    申请日：2021-09-16

    Applicant: BOE TECHNOLOGY GROUP CO., LTD.

    Inventor： Libo ZHANG ,  Yuanyuan LU ,  Wangqiang HE ,  Dong CHAI ,  Hong WANG

    IPC: G06K9/62 ,  G06T7/00

    Abstract: The present disclosure relates to an image data classification method, device and system, and relates to the field of computer technology. The method includes: inputting test image data into a neural network model trained by using an original training sample set for classification, and determining an image type to which the test image data belongs and a membership probability of the image data belonging to the image type; establishing an easy-to-classify data set, according to test image data with a membership probability greater than a first threshold; adding test image data in the easy-to-classify data set that has a classification accuracy rate less than or equal to a second threshold and a correct classification result to the original training sample set to generate an augmented training sample set; and using the augmented training sample set to train the neural network model so as to determine an image class

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