A distribution network can be divided into small-scale microgrids to respond effectively to system disturbances.This f lexible structural change not only facilitates fast fault recovery，but also ensures the supply of critical loads.Therefore，a regional microgrid division and adjustable interval self-healing optimization approach for high uncertainty conditions is proposed.First，the existing large-scale distribution network is split into a collection of well-supplied microgrids with sufficient resilience to facilitate the system ’s self-healing function during faults.Under this framework，the operation of microgrids is decomposed into interconnection and island modes by identifying the combination of heterogeneous renewable generation resources and the configuration of remote control switches.Scheduling optimization for the two modes is carried out separately.After the faults of the system occur，social benefits are maximized through regional segmentation，load reconstruction，power rescheduling，and necessary load reduction operations.Secondly，reliability and resilience needs are considered and a self-healing performance index applicable to interconnection and island modes is proposed to quantify the recovery ability and resilience level of regional microgrids.Furthermore，the self-healing control problem is constructed as a mixed-integer linear programming model，and an adjustable interval optimization strategy is introduced to make the scheduling plan robust under renewable energy prediction errors.The model is solved using a column and constraint generation algorithm to ensure feasibility and calculation efficiency under uncertain conditions.Finally，the effectiveness of the proposed methodology is fully validated through a case study.