The phenomenon of excessive short-circuit current seriously hinders the formulation of power system operation modes. The current decision to limit flow is based on the worst-case scenario predicted recently, ensuring safety at the cost of economy. However, with the large-scale integration of new energy, its uncertainty may lead to the worst-case scenario being a very low probability scenario that is far from normal. Based on this, making flow limiting decisions will seriously affect economic efficiency. If a high probability scenario is processed in advance and a very low probability scenario is urgently processed within the day without any omissions, a more economical flow limiting strategy can be developed. To this end, this paper proposes an optimization framework for two-stage day-ahead and intra-day flow limiting measures based on scenario probability. The day-ahead and intra-day processing scenarios are divided based on the principle of minimizing the expected comprehensive cost from day-ahead to intra-day, and develop day-ahead flow limiting measures; A real-time prediction method for short-circuit current based on data-physical joint drive is proposed for emergency current limiting during the day, which can predict and perform emergency current limiting in real time to discover and handle scenarios with minimal probability of exceeding the limit. The simulation results show that the proposed two-stage optimization framework has good feasibility, security, and better economy.