Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems, yet introduce significant challenges in model deployment and resource management. In this survey, we comprehensive examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, enabling technologies, and emerging applications. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and latency requirements. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examines practical deployments through diverse applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements and practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments, fostering innovation in next-generation intelligent systems.

翻译：边缘-云协同计算已成为应对现代智能应用计算需求的关键范式，通过整合云资源与边缘设备实现高效低延迟处理。人工智能领域的近期进展，特别是深度学习与大语言模型，显著增强了这类分布式系统的能力，但同时也带来了模型部署与资源管理的重大挑战。本综述系统审视了边缘-云环境中分布式智能与模型优化的交叉领域，围绕基础架构、使能技术和新兴应用提供了结构化指导。我们进一步系统分析了模型优化方法，包括压缩、自适应和神经架构搜索，以及基于人工智能的资源管理策略，以平衡性能、能效与延迟需求。我们还深入探讨了边缘-云协同计算系统中的隐私保护与安全增强关键问题，并通过自动驾驶、医疗健康和工业自动化等多样化应用考察其实践部署。为建立这些复杂系统的评估标准，本文全面研究了性能分析与基准测试技术。此外，本综述指出了包括大语言模型部署、6G集成、神经形态计算和量子计算在内的关键研究方向，为应对异构性管理、实时处理与可扩展性等持续存在的挑战提供了路线图。通过衔接理论进展与工程实践，本综述为研究人员和实践者提供了利用人工智能优化分布式计算环境的全景视角，从而推动下一代智能系统的创新。