Aiming at the problems of perennial low temperature and lack of oxygen, poor stability of energy supply, and low efficiency of multi-energy synergy in alpine and high-altitude areas, this study constructs a coupled heat-electricity-gas computational model, and proposes a third-order robust optimization and scheduling framework incorporating whale optimization algorithm. The physical constraints of heat, electricity and gas subsystems as well as the joint model are unified through the equivalent circuit theory; secondly, a layered optimization strategy is designed, with the first-order long-term planning layer optimizing the equipment capacity allocation, the second-order medium-term scheduling layer formulating the seasonal operation plan, and the third-order real-time layer responding to the uncertainty perturbation. The analysis results based on the example of the Party Service Center in Tibet show that: the model improves the PV consumption rate by 12.1%, reduces the system operation cost by 36.7%, and increases the effective energy supply ratio of renewable energy to 68%; the dynamic constraints of the pipeline network reduces the peak output of heat pumps by 10.3%, and improves the solar energy utilization rate by 39.6%; by setting up three kinds of uncertainty scenarios for the system to carry out a plausibility test, the calculated The standardized fluctuation absorption index A_nf ≥ 76%, the uncertainty tolerance is improved by 32.5% compared with the original model, and the load shortage rate is lower than 0.23%. This study solves the problem of matching and robust control of multiple energy flows in alpine and high-altitude areas, and provides theoretical support for the economic and efficient operation of local integrated energy systems.