Application research of real-time task scheduling framework based on hash algorithm in student work management

With the increasing complexity of college student work management, traditional task scheduling methods have made it difficult to meet the needs of real-time and efficiency. This study aims to explore the application of a real-time task scheduling framework based on the Hash algorithm in student work management to improve management efficiency and response speed. By analyzing the characteristics of student work management, a Hash algorithm framework suitable for real-time task scheduling is designed, which can dynamically allocate tasks and ensure the balance and efficiency of task processing. The experimental results show that after adopting this framework, the task scheduling time is shortened by 35% on average, and the task processing success rate is improved by 25%. In addition, the framework performs well in dealing with unexpected tasks, can quickly adjust resource allocation, and ensures the continuity and stability of student work management. This study not only enriches the application theory of real-time task scheduling in student work management but also provides effective technical support for practical management.

Authors

Institutions

Publication Details

Journal
Journal of Computational Methods in Sciences and Engineering
Published
2025-08-25
DOI
https://doi.org/10.1177/14727978251371190
Primary Topic
Cloud Computing and Remote Desktop Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application research of real-time task scheduling framework based on hash algorithm in student work management

Wenyue Guo, Yan Hong Fan, Yukun Fang, Lin Cheng
1 links
Journal of Computational Methods in Sciences and Engineering
Cloud Computing and Remote Desktop Technologies
article

Application research of real-time task scheduling framework based on hash algorithm in student work management

Wenyue Guo, Yan Hong Fan, Yukun Fang, Lin Cheng
article en

Abstract

With the increasing complexity of college student work management, traditional task scheduling methods have made it difficult to meet the needs of real-time and efficiency. This study aims to explore the application of a real-time task scheduling framework based on the Hash algorithm in student work management to improve management efficiency and response speed. By analyzing the characteristics of student work management, a Hash algorithm framework suitable for real-time task scheduling is designed, which can dynamically allocate tasks and ensure the balance and efficiency of task processing. The experimental results show that after adopting this framework, the task scheduling time is shortened by 35% on average, and the task processing success rate is improved by 25%. In addition, the framework performs well in dealing with unexpected tasks, can quickly adjust resource allocation, and ensures the continuity and stability of student work management. This study not only enriches the application theory of real-time task scheduling in student work management but also provides effective technical support for practical management.

Journal of Computational Methods in Sciences and Engineering
Jiangxi Science and Technology Normal University (CN), Jiangxi Applied Engineering Vocational Institute (CN)
Openalex Percentile: Top 77%
Cloud Computing and Remote Desktop Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Application research of real-time task scheduling framework based on hash algorithm in student work management — Wenyue Guo, Yan Hong Fan, et al. · Journal of Computational Methods in Sciences and Engineering (2025) | TGRS Research Map | TGRS