使用基于马尔科夫过程的区块链对智能医疗保健系统的队列系统的建模
Shadab Siddiqui1, Shahin Fatima1, Aleem Ali2
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, 500075, India.
Scientific reports
|May 18, 2025
概括
这项研究应用了队列理论和马尔科夫链来优化区块链系统,特别是用于智能医疗数据处理. 这项研究引入了一个新的Markovian批量服务框架,用于增强系统性能分析.
科学领域:
- 计算机科学 计算机科学
- 应用数学 应用数学 应用数学
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 排队理论为系统效率和优化提供了必要的指标.
- 区块链技术在各种应用中提供了重要的好处,如安全性,可靠性和匿名性.
- 智能医疗体系结构越来越依赖区块链来管理大量患者数据.
研究的目的:
- 为了利用队列理论和马尔科夫链来分析区块链系统的性能.
- 评估使用区块链技术的智能医疗体系结构的效率.
- 为可靠的模拟开发"马科维亚批量服务"排队框架.
主要方法:
- 利用队列理论和数学分析来衡量有效性.
- 制定优化模型,对系统效率进行重大数据测量.
- 应用马尔科夫链来建立区块链系统的队列理论.
- 设计了一个"马科维亚批量服务"排队框架,以建模输入和处理参数.
主要成果:
- 建立了使用马尔科夫链的区块链系统的队列理论.
- 评估了智能医疗体系结构的性能.
- 开发了一个"Markovian-batch-service"框架,用于可靠的队列网络模拟.
- 证明了排队模型对于智能医疗保健中高效的数据处理是不可或缺的.
结论:
- 排队理论和马尔科夫链对于优化区块链系统至关重要,特别是在智能医疗等数据密集型领域.
- 拟议的"Markovian-batch-service"框架为基于区块链的系统的性能分析和模拟提供了一个强大的方法.
- 整合队列模型可以提高利用区块链技术的智能医疗体系结构的效率和可靠性.
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