Aiming at the challenges of insufficient scheduling flexibility and rising operational costs in multi-park electric-thermal systems under large-scale renewable energy integration，this paper proposes an optimal scheduling model for tiered dual-time-scale distributed electric-thermal system based on edge computing. Firstly，a three-tier collaborative architecture comprising a physical equipment layer，edge computing layer，and cloud layer is constructed. Edge computing facilitates rapid data processing and distributed decision-making among parks. Secondly，the improved analytical target cascading method is employed with a dual-time-scale strategy： the lower layer optimizes electrical energy interactions at a 5 min granularity，while the upper layer coordinates thermal energy interactions at a 1 h granularity. The augmented Lagrangian method is integrated to decouple and iteratively solve multi-time-scale optimization problems. Finally，a benefit redistribution mechanism based on energy contribution degrees is designed，utilizing an asymmetric mapping function to quantify each park’s contributions to electric-thermal exchanges and renewable energy consumption，ensuring equitable profit distribution. Case studies demonstrate that the proposed model reduces comprehensive operational costs by 34.46% compared to conventional methods，significantly improves renewable energy consumption rates，and achieves convergence within eight iterations. The findings confirm that the integration of edge computing and dual-time-scale strategies effectively addresses spatiotemporal disparities in energy flows，providing theoretical and practical insights for coordinated optimization in multi-energy-coupled systems.