摘要： 随着区域智能化的发展，数据密集型与时延敏感型业务逐渐增多，边缘计算的引入虽然能缓解现有的区域专网压力，但面对设备能耗、时延等指标日益严格的综合性要求，仍需研究更高性能的边缘计算卸载策略。针对上述挑战，基于区域环境，综合考虑任务截止期限、队列积压状态与带宽资源约束，结合信道状况的突变感知提出一种节能的协同任务卸载模型。在构建云端-边缘-终端三端协同的模型时，对任务完成各阶段的时延、多用户卸载比例和带宽分配进行了联合优化，并基于李雅普诺夫优化方法将长时随机优化问题转化为在线决策框架。引入改进型粒子群算法，设计Lyapunov-PSO混合优化架构，增强非凸约束下的全局搜索能力，实现多目标协同优化。同时提出分层阈值映射编码方法以解决离散卸载决策与连续优化空间的映射冲突。实验结果表明，相比仅使用其他启发式算法和人工智能方法，所提算法可有效实现资源的整体优化配置，进一步节省了任务处理所产生的能耗。

Abstract: With the advancement of regional intelligentization, data-intensive and latency-sensitive services have proliferated. Although the introduction of edge computing alleviates pressure on existing regional dedicated networks, the increasingly stringent comprehensive requirements for metrics such as device energy consumption and latency necessitate research into higher-performance edge computing offloading strategies.To address these challenges, this paper proposes an energy-efficient collaborative task offloading model tailored for regional environments. The model holistically integrates task deadlines, queue backlog states, and bandwidth resource constraints while incorporating awareness of sudden channel condition variations.By establishing a cloud-edge-terminal tripartite collaborative framework, we jointly optimize multi-stage task completion latency, multi-user offloading ratios, and bandwidth allocation. Leveraging Lyapunov optimization, the long-term stochastic optimization problem is transformed into an online decision-making framework. An enhanced particle swarm optimization (PSO) algorithm is introduced to construct a Lyapunov-PSO hybrid optimization architecture, strengthening global exploration capability under non-convex constraints and achieving multi-objective collaborative optimization. Furthermore, a hierarchical threshold-mapping encoding method resolves mapping conflicts between discrete offloading decisions and continuous optimization spaces.Experimental results demonstrate that, compared to standalone heuristic algorithms and artificial intelligence methods, the proposed algorithm achieves holistic resource optimization configuration and further reduces energy consumption during task processing.