AI 中文总结
针对O-RAN赋能工业系统的闭环应用，提出过程感知协同适配引擎框架，通过整合多维度信息选择协调工作点，经工厂巡检案例验证其适配资源分配的有效性及高效配置的识别效率。
AI 中文摘要
无线网络正越来越多地支持闭环工业应用，这类应用中感知数据必须经过传输、处理并转换为动作，否则物理过程会使结果失效。单独测量的吞吐量、延迟和推理精度无法确定这类应用是否完成了有用任务。我们提出过程感知协同适配引擎（Process-Aware Co-adaptation Engine）框架，它结合应用结果、过程状态、无线电遥测、边缘计算状态和感知配置，以在整个闭环中选择协调的工作点。我们在工厂巡检案例研究中评估该方法，该研究整合了基于物理的数字孪生、可编程5G O-RAN网络和基于边缘的视觉推理。实验表明，优选的资源分配随生产速度变化，且单独适配各系统组件可能效率低下；我们还发现，仅需相对少量的全系统评估即可识别高效配置。
英文摘要
Wireless networks increasingly support closed-loop industrial applications in which sensed data must be transmitted, processed, and converted into an action before physical process makes the result obsolete. Throughput, latency, and inference accuracy measured separately cannot determine whether such an application completed a useful task. We propose a Process-Aware Co-adaptation Engine framework that combines application outcomes, process state, radio telemetry, edge-compute state, and sensing configuration to select coordinated operating points across the complete loop. We evaluate the proposed approach in a factory-inspection case study that integrates a physics-based digital twin, a programmable 5G O-RAN network, and edge-based visual inference. The experiments show that the preferred resource allocation changes with production speed and that adapting individual system components independently can be inefficient. We further show that efficient configurations can be identified with relatively few full-system evaluations.
