公开(公告)号：US20250086022A1

公开(公告)日：2025-03-13

申请号：US18569295

申请日：2023-10-11

Applicant: ZHEJIANG LAB

Inventor： Yong LI ,  Laiping ZHAO ,  Jie LI ,  Wen CHENG ,  Guang CHEN ,  Lingfang ZENG

IPC: G06F9/50 ,  G06F9/48

Abstract: A method for data processing is provided, and includes: obtaining each piece of to-be-processed data, determining whether a set amount of the to-be-processed data is capable to be processed under a current processing process by a data processing model, if not, obtaining data processing periods of the data processing model under multiple configuration combinations; for a data processing period of each of the multiple configuration combinations, determining an amount of data that is capable to be processed by the data processing model within the data processing period, as a target data amount; by taking the data processing model to be capable to process the set amount of the to-be-processed data as a target, according to the target data amount for a data processing period of each of the multiple configuration combinations, selecting a target configuration combination from the multiple configuration combinations.

公开(公告)号：US20240354577A1

公开(公告)日：2024-10-24

申请号：US18374669

申请日：2023-09-29

Applicant: ZHEJIANG LAB

Inventor： Yong LI ,  Laiping ZHAO ,  Zezheng MAO ,  Wen CHENG ,  Guang CHEN ,  Lingfang ZENG

IPC: G06N3/084

CPC classification number: G06N3/084

Abstract: A method, a system, a device, and a storage medium for operation resource placement of deep learning are provided. The method includes: acquiring training operations to be placed and corresponding priorities; based on an order of the priorities, selecting a network structure for operation placement according to required resource amount of the training operations in sequence; the network structure including a server, a top of rack, a container group set denoted as Podset and a trunk layer switch; based on the selected network structure, taking a transmission amount of network data in a training process as an optimization target to perform minimization optimization, and obtaining a corresponding operation placement scheme.

公开(公告)号：US20250103952A1

公开(公告)日：2025-03-27

申请号：US18521883

申请日：2023-11-28

Applicant: Hebei University of Technology ,  Zhejiang Lab ,  Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China ,  DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Bin CAO ,  Zeyu JIANG ,  Xin LIU ,  Wen CHENG ,  Yun LI ,  Rensheng SHEN ,  Yuchun CHANG

IPC: G06N20/00

Abstract: The present invention provides a federated large model adaptive learning system. Based on the combination of multiobjective optimization and incremental learning, multiple optimization indexes are constructed, and adaptive mini model incremental learning is designed. A gradient scaling method of mini models is proposed for data privacy protection under federated learning, to make full use of gradient information. A correlation between the generalization ability and sampling data is revealed to propose a generalization ability evaluation function. With respect to the real problems of performance degradation and fault faced by industrial equipment during operation, multiple optimization objectives are designed in combination with the generalization ability evaluation function, and the models are updated and repaired adaptively through multiobjective evolutionary learning, to improve the usability of large models in real industrial scenarios. Finally, the adaptive accurate update of the large models and mini models is realized to improve the generalization ability of the models.