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Research on Performance Monitoring and Data-driven Optimization Mechanisms for AI Systems Oriented Toward Decision Support
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Abstract
For intelligent decision-making tasks in complex scenarios, the stability and response efficiency of AI system performance have gradually become the key metrics for evaluating the quality of system deployment. In the context of the dynamic evolution of task requirements and increasing heterogeneity of input data, static assessment methods are unable to accurately reflect the system’s operational performance or issue effective warnings for potential performance degradation. Based on the “monitoring - feedback - optimization” operational closed-loop logic, this paper proposes a system mechanism framework that integrates real-time monitoring and data-driven optimization. This framework constructs a performance monitoring matrix covering multiple dimensions such as response speed, accuracy, and resource utilization, and is supported by a high-frequency data feedback path and a lightweight modeling platform, enabling online analysis and dynamic correction of model behavior. Additionally, a plug-in optimization module is introduced, leveraging reinforcement learning strategies to drive adaptive adjustment of system parameters. In complex tasks, this approach demonstrates stronger stability and task-matching capabilities, providing theoretical support and engineering direction for building decision-oriented AI systems with long-term adaptability.
Keywords
AI system; decision support; performance monitoring; data feedback; mechanism optimization
References
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How to cite this paper
Research on Performance Monitoring and Data-driven Optimization Mechanisms for AI Systems Oriented Toward Decision Support
How to cite this paper: Zhixian Zhang. (2026) Research on Performance Monitoring and Data-driven Optimization Mechanisms for AI Systems Oriented Toward Decision Support. Advances in Computer and Communication, 7(2), 67-71.
DOI: http://dx.doi.org/10.26855/acc.2026.06.001
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