公开(公告)号：US20250149886A1

公开(公告)日：2025-05-08

申请号：US18431933

申请日：2024-02-03

Applicant: Capital Normal University

Inventor： Keni Qiu ,  Chuting Xu ,  Kunyu Zhou ,  Dehui Qiu

IPC: H02J3/00

Abstract: Provided is a setting method for resilient checkpointing based on machine learning, which predicts a future power level in advance through a lightweight power level predictor and dynamically adjusts checkpoint intervals to accommodate a future energy input. During the operation of a power harvesting system, a resilient checkpointing mechanism assigns an appropriate checkpoint interval to a future power cycle to match a current harvesting power based on a state of a future harvesting power. Therefore, the power harvesting system with the resilient checkpointing mechanism can realize low checkpointing overhead and rollback punishment. The method involves a lightweight power level predictor based on a fully connected neural network and a resilient checkpoint setting mechanism based on power level prediction, which determines a checkpoint interval of a current cycle based on a predicted power level of a future power cycle.