Intelligent scheduling task adjustment method based on graph neural network and adaptive weights

Lei Liu; Xuexia Yu; Zhiyuan Hu; Xiaojie Li; Guowen Zhao; Jun Su

doi:10.1117/12.3055801

9 January 2025 Intelligent scheduling task adjustment method based on graph neural network and adaptive weights

Lei Liu, Xuexia Yu, Zhiyuan Hu, Xiaojie Li, Guowen Zhao, Jun Su

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 1348631 (2025) https://doi.org/10.1117/12.3055801
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

In order to make the scheduling of tasks to be executed by intelligent agents more flexible and reasonable, a method for adjusting intelligent scheduling tasks based on graph neural networks and adaptive weights is proposed. Based on the short-term memory ability of graph neural networks, search for temporary computing tasks received by intelligent agents in the database as the basic data for subsequent arrangement and adjustment. Extracting thematic content features of intelligent tasks based on recurrent neural networks; Identify priority task targets in the scheduled tasks through graph neural network models. Based on adaptive weights and combined with online learning listwise algorithm, intelligent task scheduling adjustment is achieved. The experimental results show that the conflict rate of task scheduling adjustment using this method remains stable within 5%, and the scheduling adjustment effect is more reasonable; The utilization rate of computing resources is higher after task scheduling adjustment, and task scheduling is more flexible; The longest response time for task scheduling adjustment is 29ms, which is more effective.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lei Liu, Xuexia Yu, Zhiyuan Hu, Xiaojie Li, Guowen Zhao, and Jun Su "Intelligent scheduling task adjustment method based on graph neural network and adaptive weights", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 1348631 (9 January 2025); https://doi.org/10.1117/12.3055801

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE