Artificial Intelligence Information Processing Mechanism Under the Cloud-Edge-End Collaborative Architecture
云边端协同架构下人工智能信息处理机制

摘要

集中式云计算难以同时满足人工智能业务对实时响应、数据安全、复杂计算和规模扩展的要求。云边 端协同架构通过端侧感知与轻量推理、边缘侧聚合分析、云端训练和全局优化，形成分层信息处理链路。文 章分析数据筛选、任务卸载、模型切分、协同推理和分布式训练机制，结合公开研究数据讨论其性能基础及 现实问题，并提出任务分级、资源编排、模型治理和安全控制路径，为人工智能系统的协同部署提供参考。

Abstract

 Centralized cloud computing has difficulty simultaneously meeting the requirements of artificial intelligence services for real-time response, data security, complex computing, and scale expansion. The cloud-edge end collaborative architecture forms a hierarchical information processing chain through end-side perception and lightweight inference, edge-side aggregation and analysis, cloud-side training, and global optimization. This paper analyzes mechanisms such as data filtering, task offloading, model partitioning, collaborative inference, and distributed training, discusses their performance basis and practical problems in combination with publicly available research data, and proposes paths for task classification, resource orchestration, model governance, and security control, providing reference for the collaborative deployment of artificial intelligence systems.