公开(公告)号：US20240355152A1

公开(公告)日：2024-10-24

申请号：US18492767

申请日：2023-10-23

Applicant: ZHEJIANG LAB

Inventor： Hongyang CHEN ,  Chenxi LIU ,  Zhu XIAO

IPC: G07C5/02

CPC classification number: G07C5/02

Abstract: The present application discloses a method and a system for analyzing and predicting a vehicle stay behavior based on multi-task learning, and the method includes the following steps: acquiring vehicle GPS and OBD data including a vehicle ID, a travel start time, a start longitude, a start latitude, an end time, an end longitude, and an end latitude after desensitization; preprocessing vehicle GPS and OBD data to obtain vehicle stay behavior data including stay location and stay duration; extract a spatial-temporal characteristic of the preprocessed vehicle stay behavior data by a deep recurrent neural network; inputting the spatial-temporal characteristic into a multi-task learning and predicting network, and obtaining the correlation between a stay location prediction task and the stay duration prediction task based on the historical stay behavior of the vehicle through the multi-task learning and predicting network to predict the stay location and stay duration.

公开(公告)号：US20240119314A1

公开(公告)日：2024-04-11

申请号：US18454036

申请日：2023-08-22

Applicant: ZHEJIANG LAB

Inventor： Jingsong LV ,  Hongyang CHEN ,  Hao WANG ,  Xianzhong FENG

IPC: G06N5/022 ,  G16B20/00 ,  G16B40/20 ,  G16B45/00

CPC classification number: G06N5/022 ,  G16B20/00 ,  G16B40/20 ,  G16B45/00

Abstract: A method and a device for predicting gene coding breeding based on graph clustering. According to the present disclosure, a gene map is constructed based on inter-gene correlation strength; the gene map is subjected to clustering solution to obtain a number of co-regulated genomes and a genome cluster number information of each gene; gene allelic information and genome cluster number information are fused to obtain the gene cluster code of the sample; based on gene cluster code information and biological phenotype information to be predicted, a deep convolutional neural network is constructed to optimize the prediction performance of genetic breeding.

公开(公告)号：US20250005109A1

公开(公告)日：2025-01-02

申请号：US18689934

申请日：2023-06-30

Applicant: ZHEJIANG LAB

Inventor： Hongyang CHEN ,  Rong CAO

IPC: G06F18/25 ,  G06N3/042 ,  G06N3/045 ,  G06N3/0464

Abstract: Predicting parking space vacancy rate methods and apparatuses, storage media and devices, acquiring parking space vacancy rates of each of parking lots in the area to be predicted at a plurality of moments before the moment to be predicted as historical vacancy rates of each of the parking lots; obtaining a first feature by inputting the historical vacancy rates of each of the parking lots into the feature extraction network, wherein the first feature is used to characterize a relationship between the historical vacancy rates of each of the parking lots and time; obtaining a fusion feature by inputting the spatial relationship diagram and the first feature into the graph fusion network; and obtaining a parking space vacancy rate of each of the parking lots in the area to be predicted at the moment to be predicted by inputting the fusion feature into the result prediction network.

公开(公告)号：US20240331425A1

公开(公告)日：2024-10-03

申请号：US18460680

申请日：2023-09-04

Applicant: ZHEJIANG LAB

Inventor： Yao QI ,  Hongyang CHEN ,  Jingsong LV ,  Wentao YANG

IPC: G06V30/19 ,  G06N5/02

CPC classification number: G06V30/19187 ,  G06N5/02 ,  G06V30/19173

Abstract: A method, a device, computer equipment and a storage medium for identify an illegal commodity. The method comprises: firstly, constructing a multi-modal knowledge graph according to a multi-modal knowledge graph data set, and extracting visual features of all visual modality entities and text features of all text modality entities in the knowledge graph; then obtaining a commodity image and a commodity text according to a database; then, generating commodity visual feature according to the commodity image; then generating the commodity text feature according to the commodity text; secondly, according to the visual features and text features, as well as the commodity visual feature and the commodity text feature, linking the commodity image and the commodity text to the knowledge graph by using an entity linking method; finally, obtaining the correlation between the commodity image and the commodity text according to the linked knowledge graph to determine the illegality of the commodity.

公开(公告)号：US20240054339A1

公开(公告)日：2024-02-15

申请号：US17990617

申请日：2022-11-18

Applicant: ZHEJIANG LAB

Inventor： Hongyang CHEN ,  Bingyang HU ,  Qingguo QI ,  Zhao LI

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Disclosed are a method and system for predicting spatio-temporal perception information based on a graph neural network. The method includes the following steps: step S1: constructing a perception data monitoring network, and acquiring original perception data through data acquisition nodes in the perception data monitoring network; step S2: pre-processing the original perception data and converting the same into spatio-temporal graph perception data; step S3: constructing a graph neural network model, and training parameters of the graph neural network model by using the spatio-temporal graph perception data; and step S4: inputting given spatio-temporal graph perception data to the trained graph neural network model and outputting a predicted value, and sending early warning information when the predicted value exceeds a preset threshold.