公开(公告)号：US20220092418A1

公开(公告)日：2022-03-24

申请号：US17457649

申请日：2021-12-03

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

Inventor： Hao LIU ,  Jindong HAN ,  Dejing DOU

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G01N33/00

Abstract: Provided are a training method for an air quality prediction model, a prediction method and apparatus, a device, a program, and a medium. The method includes the steps described below. A target monitoring range is divided into a plurality of regions; the air quality prediction model is pre-trained by adopting a pre-training sample and a pre-training objective function, where the pre-training sample includes measurement values; and the pre-trained air quality prediction model is trained by adopting a formal training sample and a formal training objective function, where the formal training sample includes the measurement values. The air quality prediction model is configured to predict air quality of the plurality of regions according to spatial information, historical information and environmental information.

公开(公告)号：US20230229913A1

公开(公告)日：2023-07-20

申请号：US18125327

申请日：2023-03-23

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

Inventor： Weijia ZHANG ,  Le ZHANG ,  Hao LIU ,  Jindong HAN ,  Chuan QIN ,  Hengshu ZHU ,  Hui XIONG

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: A method and apparatus for training an information adjustment model of a charging station, an electronic device, and a storage medium are provided. An implementation comprises: acquiring a battery charging request, and determining environment state information corresponding to each charging station in a charging station set; determining, through an initial policy network, target operational information of each charging station in the charging station set for the battery charging request, according to the environment state information; determining, through an initial value network, a cumulative reward expectation corresponding to the battery charging request according to the environment state information and the target operational information; training the initial policy network and the initial value network by using a deep deterministic policy gradient algorithm; and determining the trained policy network as an information adjustment model corresponding to each charging station.

公开(公告)号：US20220092433A1

公开(公告)日：2022-03-24

申请号：US17457903

申请日：2021-12-06

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

Inventor： Hao LIU ,  Jindong HAN ,  Hengshu ZHU ,  Dejing DOU

IPC: G06N3/08 ,  G06N3/04

Abstract: Provided are a training method and device for a heterogeneous generative adversarial network model, an equipment, a program and a storage medium. In the training method, measurement data of a heterogeneous station is acquired, the measurement data of the heterogeneous station is set as a training sample, and joint training is performed on the heterogeneous generative adversarial network model according to a total objective function. A generator is configured to predict environment data at a future occasion according to environment data of the heterogeneous station at a historical occasion so as to output predicted data. A discriminator is configured to be input the predicted data output by the generator and corresponding measurement data, and discriminate a similarity between the measurement data and the predicted data; a total objective function includes a first objective function of the generator and a second objective function of the discriminator.