This invention discloses a fault feedback and planning strategy iterative
repair method for high-
orbit satellite collaborative mission execution. This method determines whether a fault warning is triggered based on fused features and dynamic thresholds. A closed-loop framework of "fault diagnosis -
impact assessment - planning correction -
verification iteration" is constructed. The diagnosis stage employs a
hybrid diagnostic method combining
deep learning and rule-based reasoning. The
impact assessment stage establishes a
quantitative assessment model based on task priority and resource reserves to determine the degree of fault
impact. The planning correction stage introduces a
dynamic resource reallocation
algorithm to adjust the
satellite payload operating mode and inter-
satellite link topology based on the assessment results. The
verification iteration stage uses digital twin technology to construct a
simulation environment to verify the effectiveness of the correction strategy. Furthermore, an iterative optimization mechanism for the strategy
library based on case-based reasoning and
reinforcement learning is established. The strategy
library is iteratively optimized using
reinforcement learning algorithms to form an adaptive fault
response strategy library, thereby improving the efficiency of the
system's anti-interference capability evolution.