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With the rapid advancement of Industry 4.0 and intelligent manufacturing, the complexity of tasks and data processing in CNC systems continues to grow, rendering traditional centralized resource allocation methods inadequate in meeting real-time and efficiency demands, thereby creating system performance bottlenecks. To address this challenge, this paper proposes an adaptive task allocation method for CNC systems based on edge intelligence. By monitoring system states in real time, this approach leverages a greedy algorithm to adjust task priorities and optimize resource allocation. The proposed method significantly enhances task processing efficiency, enabling dynamic adjustments upon detecting anomalies, thus ensuring system stability and safety. Experiments conducted in real industrial scenarios validate the effectiveness of this method, demonstrating a notable improvement in resource utilization and response speed of CNC systems.

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An Edge-Intelligent Task Allocation Strategy for CNC Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Zheng Zhou,
  • Dong Yu,
  • Nan Wu,
  • Tianyu Wang,
  • Yusong Qiao,
  • Weiqiang Chen

摘要

With the rapid advancement of Industry 4.0 and intelligent manufacturing, the complexity of tasks and data processing in CNC systems continues to grow, rendering traditional centralized resource allocation methods inadequate in meeting real-time and efficiency demands, thereby creating system performance bottlenecks. To address this challenge, this paper proposes an adaptive task allocation method for CNC systems based on edge intelligence. By monitoring system states in real time, this approach leverages a greedy algorithm to adjust task priorities and optimize resource allocation. The proposed method significantly enhances task processing efficiency, enabling dynamic adjustments upon detecting anomalies, thus ensuring system stability and safety. Experiments conducted in real industrial scenarios validate the effectiveness of this method, demonstrating a notable improvement in resource utilization and response speed of CNC systems.