Service Function Chains Scheduling of Information Networks Used for Emergency Treatment

ZHANG Qing-hua; ZHANG Xian-chao; WANG Yin-hao; LU Jun

doi:10.12263/DZXB.20230293

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Service Function Chains Scheduling of Information Networks Used for Emergency Treatment

PAPERS | 更新时间：2025-12-08

- Service Function Chains Scheduling of Information Networks Used for Emergency Treatment
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3128-3136(2023)
- 作者机构：
  
  1.北京交通大学电子信息工程学院,北京 100044
  2.嘉兴学院浙江省医学电子与数字健康重点实验室,浙江嘉兴 314001
  3.生命健康智能感知浙江省工程研究中心,浙江嘉兴 314001
  4.北京邮电大学信息与通信工程学院,北京 100876
  5.中国电子科学研究院,北京 100041
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61941104);National Key Research and Development Program of China(2022YFC33014)
- DOI：10.12263/DZXB.20230293
  CLC： TP393
- Received：04 April 2023，
  
  Revised：2023-07-12，
  
  Published：25 November 2023
- 稿件说明：
移动端阅览
张庆华,张先超,王寅昊等.面向医疗急救的信息网络服务功能链调度方法[J].电子学报,2023,51(11):3128-3136.

ZHANG Qing-hua,ZHANG Xian-chao,WANG Yin-hao,et al.Service Function Chains Scheduling of Information Networks Used for Emergency Treatment[J].ACTA ELECTRONICA SINICA,2023,51(11):3128-3136.
张庆华,张先超,王寅昊等.面向医疗急救的信息网络服务功能链调度方法[J].电子学报,2023,51(11):3128-3136. DOI： 10.12263/DZXB.20230293.

ZHANG Qing-hua,ZHANG Xian-chao,WANG Yin-hao,et al.Service Function Chains Scheduling of Information Networks Used for Emergency Treatment[J].ACTA ELECTRONICA SINICA,2023,51(11):3128-3136. DOI： 10.12263/DZXB.20230293.

摘要

针对医疗急救分类分级服务需求，本文研究健康医疗信息网络服务功能链调度问题.首先，将急救业务按照紧急程度和服务区域分为四类，设置优先级与权重，设计健康医疗信息网络架构、服务模式以及网络工作过程.其次，以最小化总加权完成时间作为调度目标，建立服务功能链调度模型，针对不同问题规模，分别设计匹配博弈算法和

-learning强化学习算法，求解功能链调度方案.最后，开展仿真实验.实验结果表明，本文的研究可以有效实现对医疗急救分级服务，能够推进健康医疗信息网络的建设，促进智慧医疗发展.

Abstract

To address the diverse emergency treatment needs

we investigate the scheduling problem of service function chains in healthcare networks. Firstly

emergency treatment services are categorized into four groups based on service area and urgency level

with each group assigned a corresponding priority and weight. We then propose a network framework

service model

and running process for healthcare networks. Secondly

we establish a service function chain scheduling model with the objective of minimizing the weighted total completion time. To address this problem at different scales

we develop a matching game algorithm and a

reinforcement learning algorithm

both of which can yield an optimal scheduling scheme. Lastly

we conduct simulation experiments to assess our approach’s effectiveness

which confirms that our research can enable classified and graded emergency treatment services

and facilitate the development of intelligent medical and healthcare networks.

关键词

Keywords

references

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