The invention relates to the technical field of storage garbage recycling algorithms, in particular to a garbage recycling
system based on a
genetic algorithm, which comprises an environment sensing module, a dynamic excitation
pool module, a
gene coding module, a genetic operation module, a strategy evaluation module, a cold and hot
data verification bridge module, a
wear leveling compensator and an iterative deployment module. The environment sensing module is used for scene
feature extraction and load mode classification. Through dynamic parameter optimization and a multi-scene
adaptation mechanism, an optimal strategy parameter combination is automatically searched by utilizing a
genetic algorithm, and controllable disturbance is introduced by dynamically adjusting
sample weight and polynomial variation in combination with an
entropy weight method, so that multi-target balance of
write amplification, block efficiency and wear balance is realized; meanwhile, through a cold and hot
data verification bridge and a
wear leveling compensator module, the data state transition trend is predicted, parameter deviation is actively corrected, high robustness and long-term stability can still be kept in a complex load scene, and finally a closed-loop self-adaptive
garbage collection strategy generation
system is formed.