【目的】 为应对新型电力系统中智能配电网复杂故障场景下抢修任务繁重、资源约束强、调度效率低等问题，提升故障应急响应速度并降低社会经济损失，提出一种考虑强资源约束的配电网快速抢修二阶段优化调度策略。【方法】 一阶段以快速恢复非故障区供电为目标，考虑停电区域配电网网络重构、移动式应急发电车等资源约束，构建以最小化停电时间为目标的配电网快速恢复抢修模型。二阶段针对故障区域内多个抢修任务优化调度难题，设计改进的串行调度生成机制，以供电可靠性等级高低为主要依据形成了串行调度生成机制。进一步，采用改进的飞蛾扑火优化（moth-flame optimization， MFO）算法对二阶段模型进行最优解搜索，保证精度的同时提升了求解速度。【结果】 基于44节点系统的仿真表明：第一阶段节点3、12等关键区域加权失电负荷大，需要优先分配应急发电车辆；第二阶段改进MFO算法收敛速度提高18%，总修复时间缩短14%，经济损失减少0.7%，且算法稳定性优于传统方法。【结论】 两阶段策略通过分阶段协调资源与任务，显著提升了抢修效率；改进MFO算法在强约束多目标优化中表现高效，为智能配电网故障调度提供了新思路，所提优化调度策略具备工程推广价值。

[Objective] This study proposes a two-phase optimal scheduling strategy for the rapid repair of distribution grids that considers strong resource constraints to cope with the problems of heavy repair tasks， strong resource constraints， and low dispatch efficiency under complex fault scenarios of smart distribution grids in new power systems； to improve the emergency response speed to faults； and to reduce socioeconomic losses. [Methods] The first phase aims to rapidly restore power supply to non-failed areas， considering constraints such as the reconstruction of the distribution network in the power outage area and the availability of mobile emergency power generation vehicles. A distribution network rapid restoration repair model is constructed with the objective of minimizing the power outage duration. The second stage addresses the challenge of optimizing the scheduling of multiple repair tasks within a faulted area. An improved serial scheduling generation mechanism is designed， based primarily on the reliability level of the power supply. Furthermore， an improved moth-flame optimization （MFO） algorithm is employed to search for the optimal solution for the second-stage model， ensuring accuracy while improving the solution speed. [Results] Simulations based on a 44-node system indicated that nodes 3 and 12 in the first stage caused a loss of power to the primary loads with a significant weighted loss of the load. Priority allocation of emergency power generation vehicles was required. In the second stage， the improved MFO algorithm achieved an 18% increase in the convergence speed， a 14% reduction in the total repair time， 0.7% reduction in economic losses， and superior algorithm stability compared with traditional methods. [Conclusions] The two-stage strategy significantly improved repair efficiency by coordinating resources and tasks in stages. The improved MFO algorithm performed efficiently in strong-constraint multi-objective optimization， providing new ideas for intelligent distribution network fault dispatching. The proposed optimized dispatching strategy has engineering application value.