公开(公告)号：US20250132057A1

公开(公告)日：2025-04-24

申请号：US18600800

申请日：2024-03-11

Applicant: ZHEJIANG LAB

Inventor： Zenghui XU ,  Ji ZHANG ,  Yu ZHANG ,  Ting YU ,  Jin ZHAO ,  Linlin HOU ,  Zhan ZHANG

IPC: G16H50/80

Abstract: An infectious disease infection prediction method, an apparatus, and a storage medium based on macro-micrograph fusion are provided. The method includes: acquiring macrographs of a plurality of first regions and micrographs of second regions within a set period; inputting the macroscopic graphs and the microscopic graphs into two graph convolutional neural networks to obtain two hidden layer vectors respectively, and fusing the two hidden layer vectors to obtain fusion hidden layer information of the first regions; performing a time sequence calculation of the fusion hidden layer information to obtain time sequence hidden layer information of the first regions; inputting the time series hidden layer information into two prediction networks to obtain two prediction results, respectively, and performing fusion calculation of the two prediction results to obtain a final prediction result of infectious diseases in the first regions.

公开(公告)号：US20240273118A1

公开(公告)日：2024-08-15

申请号：US18472202

申请日：2023-09-21

Applicant: ZHEJIANG LAB

Inventor： Ting JIANG ,  Yu ZHANG ,  Ting YU ,  Ji ZHANG ,  Linlin HOU ,  Jin ZHAO

IPC: G06F16/28

CPC classification number: G06F16/285

Abstract: A data classification method and apparatus, a device and a storage medium. A structural feature of the respective node in graph data may be determined according to a neighbor node of the respective node in the graph data through a deviation between the decoded feature obtained by decoding the embedded coding feature of the respective node in the graph data and the initial feature of the respective node, and then the embedded coding feature corresponding to the respective node is adjusted according to the decoded feature of the respective node and the structural feature of the respective node in the graph data to obtain the adjusted feature corresponding to the respective node, so that accuracy of an obtained feature of the respective node is improved, and thus accuracy of data classification may be improved.

公开(公告)号：US20240311300A1

公开(公告)日：2024-09-19

申请号：US18470346

申请日：2023-09-19

Applicant: ZHEJIANG LAB ,  Huazhong University of Science and Technology

Inventor： Zhan ZHANG ,  Yu ZHANG ,  Jin ZHAO ,  Haifei WU

IPC: G06F12/0804

CPC classification number: G06F12/0804 ,  G06F2212/1032

Abstract: The present disclosure provides a data storage system, including data cache module, data processing module, and a persistent memory. The data cache module includes an on-chip mapping data cache and an on-chip counter cache, where the mapping data cache is configured to cache mapping data, and when the free space of the mapping data cache is less than a preset threshold, the least recently used mapping data cache line will be evicted from the cache and written back to the persistent memory. The data processing module encrypts/decrypts persistent memory data by using their counters, and accesses the persistent memory blocks indicated by their corresponding mapping data. The persistent memory comprises the first and second storage regions for the latest checkpoint data and modified working data in the current checkpoint interval respectively.

公开(公告)号：US20240282465A1

公开(公告)日：2024-08-22

申请号：US18212727

申请日：2023-06-22

Applicant: ZHEJIANG LAB

Inventor： Zenghui XU ,  YU ZHANG ,  Zhan ZHANG ,  Ting YU ,  Jin ZHAO ,  Linlin HOU

IPC: G16H50/80

CPC classification number: G16H50/80

Abstract: A method of predicting infectious disease infections and a system thereof, a device, and a storage medium are provided. An increment module controls an input module to obtain new incremental data in the current cycle in response to a preset instruction. A graph engine iteratively trains a first graph model based on new incremental data until the first graph model meets a convergence condition, so as to obtain a second graph model and perform dynamic updating of the graph model. Each region acts as a node in the graph model, each node feature is obtained based on the regional disease information, edges are defined by nodes connected to each other according to a geographic location relationship among regions, and each edge is assigned an edge weight according to regional population information. The updated graph model predicts infections based on data to be predicted that is selected by the interaction module.

公开(公告)号：US20240303277A1

公开(公告)日：2024-09-12

申请号：US18396493

申请日：2023-12-26

Applicant: ZHEJIANG LAB ,  Huazhong University of Science and Technology

Inventor： Yu ZHANG ,  Hao QI ,  Kang LUO ,  Jin ZHAO ,  Zhan ZHANG

IPC: G06F16/901

CPC classification number: G06F16/9024

Abstract: Systems, methods, devices and storage media for graph data processing are provided. In one aspect, a graph data processing system includes a memory and a plurality of processing units, and each processing unit is provided with a decision module. Each processing unit is configured to determine set operations required for extracting one or more subgraphs matching a specified graph pattern from target graph data according to a preset graph pattern matching algorithm. Then, for each set operation, the decision module is configured to determine a cost value corresponding to a performance of the processing unit occupied to execute the set operation in accordance with different execution policies, and further select a target execution policy with a smallest cost value to execute the set operation.

公开(公告)号：US20250125003A1

公开(公告)日：2025-04-17

申请号：US18608945

申请日：2024-03-19

Applicant: ZHEJIANG LAB

Inventor： Zenghui XU ,  Jin TANG ,  Yu ZHANG ,  Gaoxiang CHEN ,  Ting YU ,  Jin ZHAO ,  Ji ZHANG

IPC: G16B15/00

Abstract: A graph calculation method of RNA similarity analysis, an apparatus, a device, and a medium are provided. The method includes: converting sequence data of a looked-up RNA into a looked-up RNA structure graph; obtain a first similarity between the looked-up RNA structure graph and a target RNA structure graph; obtaining a second similarity based on the number of base constituent structures in the looked-up RNA structure graph and the number of base constituent structures in the target RNA structure graph; reconstructing the looked-up RNA structure graph based on the base constituent structures in the looked-up RNA structure graph to generate a looked-up RNA higher-order graph; and analyzing similarity between the looked-up RNA higher-order graph and a target RNA higher-order graph to obtain a third similarity; and obtaining a final similarity between the looked-up RNA and the target RNA based on the first similarity, the second similarity, and the third similarity.