公开(公告)号：US20250053909A1

公开(公告)日：2025-02-13

申请号：US18930344

申请日：2024-10-29

Applicant: BOE Technology Group Co., Ltd.

Inventor： Jinxiao Wen ,  Xibo Zhou ,  Xiaogang Liu ,  Xingliang Wang ,  Dong Zhang ,  Hui Zeng ,  Zhengbao Dai ,  Peiyu Yu

IPC: G06Q10/0633 ,  G06Q10/067 ,  G06Q10/0875

Abstract: An apparatus for managing production of one or more products includes a business backend; an interface service; a persistent data storage; a processor; and a message middleware. The business backend is configured to extract and organize data; and dispatch business data to the interface service. The interface service is configured to perform data validation on the business data received from the business backend; and transmit the business data to the persistent data storage and the message middleware. The persistent data storage is configured to store the business data received from the interface service as a reference for the calculation and analysis; and transmit data to the processor. The processor is configured to calculate overstock quantity and determine overstock type. The message middleware is configured to receive the business data from the interface service, and receive analysis results from the processor; and transmit the analysis results to interface service.

公开(公告)号：US20240094693A1

公开(公告)日：2024-03-21

申请号：US18271966

申请日：2022-09-21

Applicant: BOE Technology Group Co., Ltd.

Inventor： Zhuoshi Yang ,  Xibo Zhou

IPC: G05B19/042

CPC classification number: G05B19/042 ,  G05B2219/2619

Abstract: The present disclosure provides a prediction method of wind power output, an electronic device, a storage medium and a system, and relates to the technical field of wind power. The method includes: periodically acquiring an initial meteorological data set corresponding to each received time node, wherein the initial meteorological data set includes initial meteorological sub-data of at least one dimension of at least one meteorological element; after acquiring the latest initial meteorological data set, identifying and smoothing the abnormal sub-data to obtain a smoothed meteorological data set; determining an average wind energy density in a target time period; taking the smooth meteorological data set and the average wind energy density in the target time period as the input features of the model, and obtaining a wind power output predictive value via the model.