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Research on fault-tolerant decision algorithm for data security automation.

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
PubMed
Summary
This summary is machine-generated.

This study introduces an automated operation and maintenance decision algorithm incorporating data source security analysis. The new method enhances decision rationality and algorithm convergence while avoiding local optima.

Keywords:
automationdata source securityfault tolerancemulti angle analysisniche mechanismoperation and maintenance decision

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Traditional operation and maintenance algorithms lack data source security analysis, leading to susceptibility to noise, inefficiency, and irrational decisions.
  • Existing methods often fail to address the critical aspect of data integrity in decision-making processes.

Purpose of the Study:

  • To design an automated operation and maintenance decision algorithm that integrates data source security analysis.
  • To improve the efficiency, rationality, and robustness of operation and maintenance decision-making.

Main Methods:

  • Developed a multi-angle learning algorithm to model noise data and assess data source security.
  • Constructed a particle swarm optimization (PSO) model with a niche mechanism to avoid local optima.
  • Incorporated fault-tolerant analysis for data source security to eliminate bad and malicious data.

Main Results:

  • The proposed algorithm significantly shortens operation and maintenance time.
  • Enhanced decision-making rationality and improved algorithm convergence rates.
  • Successfully avoided local optima and demonstrated improved performance through fault-tolerant analysis.

Conclusions:

  • The integration of data source security analysis into automated decision algorithms is crucial for efficient and rational operation and maintenance.
  • The developed algorithm offers a robust solution for optimizing decision-making in complex systems.
  • Fault tolerance in data security further bolsters the reliability and convergence of the optimization process.