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    Filter debugging method, device, electronic apparatus and readable storage medium
    2.
    发明授权
    Filter debugging method, device, electronic apparatus and readable storage medium 有权

    公开(公告)号：US11469975B2

    公开(公告)日：2022-10-11

    申请号：US17207571

    申请日：2021-03-19

    Applicant: Beijing Baidu Netcom Science and Technology Co., Ltd.

    Inventor： Ming Xu ,  Xin Xie ,  Ying Liu ,  Yuezhen Qi ,  Ruifeng Li ,  Lu Bai

    IPC: H04L43/028 ,  H04L41/16 ,  H04W84/02

    Abstract: A filter debugging method, a device, an electronic apparatus and a readable storage medium are provided. The filter debugging method includes: step S1: inputting a current hole parameter and a current index value of a filter into a policy network which is pre-trained; step S2: determining, by the policy network, a target hole to be polished of the filter, according to the current hole parameter and the current index value of the filter; step S3: controlling a mechanical arm to polish the target hole of the filter; and step S4: determining whether the filter is qualified according to an index value of the polished filter; in a case that the filter is qualified, ending a process including the steps S1 to S4; in a case that the filter is unqualified, performing the steps S1 to S4 circularly until the filter is qualified.

    METHOD, APPARATUS AND ELECTRONIC DEVICE FOR CONSTRUCTING REINFORCEMENT LEARNING MODEL AND MEDIUM
    3.
    发明申请
    METHOD, APPARATUS AND ELECTRONIC DEVICE FOR CONSTRUCTING REINFORCEMENT LEARNING MODEL AND MEDIUM 有权

    公开(公告)号：US20210216686A1

    公开(公告)日：2021-07-15

    申请号：US17215932

    申请日：2021-03-29

    Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

    Inventor： Ying LIU ,  Xin Xie ,  Ming Xu ,  Yuezhen Qi ,  Ruifeng Li ,  Lu Bai

    IPC: G06F30/27

    Abstract: Embodiments of the present disclosure disclose a method, apparatus and electronic device for constructing a reinforcement learning model, and a computer readable storage medium, relate to the field of big data and deep learning technology. An implementation of the method can include: establishing a first simulation model between a calciner coal feed amount and a calciner temperature; establishing a second simulation model among a kiln head coal feed amount, a kiln current, a secondary air temperature, and a smoke chamber temperature; establishing a prediction model among: an under-grate pressure; the calciner temperature output by the first simulation model; the kiln current, the secondary air temperature, and the smoke chamber temperature content output by the second simulation model; and a free calcium; and constructing a reinforcement learning model according to a preset reinforcement learning model architecture, using the first simulation model, the second simulation model, and the prediction model.

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