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对数据安全自动化故障耐受性决策算法的研究
Jianxin Li1, Ruchun Jia2, Ning Xiang3
1China School of Cyberspace Security, Changzhou College of Information Technology, Changzhou, Jiangsu, China.
Frontiers in big data
|November 5, 2025
概括
本研究介绍了一种自动化操作和维护决策算法,其中包含数据源安全分析. 新方法增强了决策的合理性和算法融合,同时避免了局部最佳.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 传统的操作和维护算法缺乏数据源安全分析,导致易受噪音,低效率和非理性决策的影响.
- 现有的方法往往无法解决决策过程中数据完整性的关键方面.
研究的目的:
- 设计一个集成数据源安全分析的自动化操作和维护决策算法.
- 提高运营和维护决策的效率,合理性和稳定性.
主要方法:
- 开发了一个多角度学习算法来建模噪音数据和评估数据源安全性.
- 构建了一个具有利基机制的粒子群优化 (PSO) 模型,以避免局部优化.
- 嵌入了对数据源安全的容错分析,以消除不良和恶意数据.
主要成果:
- 拟议的算法大大缩短了操作和维护时间.
- 提高决策的合理性和改进的算法趋同率.
- 成功避免了局部最佳状态,并通过故障耐受性分析证明了性能的提高.
结论:
- 将数据源安全分析集成到自动化决策算法中,对于高效合理的操作和维护至关重要.
- 开发的算法为优化复杂系统中的决策提供了强大的解决方案.
- 数据安全中的容错性进一步加强了优化过程的可靠性和趋同性.
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