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    This study introduces parallel control for complex distributed parameter systems, integrating social factors and big data. This innovative approach uses computational experiments for effective system management in a rapidly evolving society.

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    Area of Science:

    • Control Theory
    • Systems Engineering
    • Computational Science

    Background:

    • Traditional control methods struggle with complex distributed parameter systems.
    • Accurate modeling is challenging due to the increasing influence of social factors.
    • Limitations of existing control approaches necessitate novel methodologies.

    Purpose of the Study:

    • To address the control challenges in complex distributed parameter systems.
    • To integrate social factors and big data into system modeling and control.
    • To introduce and explore the concept of parallel control for these systems.

    Main Methods:

    • Modeling complex distributed parameter systems within artificial societies or systems.
    • Utilizing computational experiments for analysis and evaluation.
    • Implementing parallel control through the interaction of virtual and actual components.
    • Employing data-driven control and computational control techniques.

    Main Results:

    • Parallel control offers a viable solution for managing complex distributed parameter systems.
    • The integration of virtual and actual interactions facilitates task accomplishment.
    • Computational experiments provide a robust framework for system analysis.
    • The proposed method adapts to societal changes and technological advancements.

    Conclusions:

    • Parallel control is an effective strategy for modern distributed parameter systems.
    • This approach enhances system management by leveraging big data and computational power.
    • The methodology is well-suited for systems influenced by dynamic social factors and rapid technological development.