The invention discloses an AI-driven block chain
hybrid consensus dynamic hierarchy optimization method and
system, and aims to solve the problems that an existing block chain
consensus mechanism is insufficient in adaptability, low in intelligent contract execution efficiency, difficult in security and privacy balance and the like. A dynamic hierarchical adaptive
hybrid consensus architecture is constructed, a hierarchical structure is adjusted in real time according to a
service load, and super nodes are dynamically elected to process high-frequency transactions; predicting a load by combining a deep
reinforcement learning model, and automatically selecting an optimal consensus combination; high-
frequency data memory storage and low-
frequency data distributed storage are realized through intelligent contract routing, and graphic processor acceleration nodes are allocated based on contract complexity; establishing a node reputation
scoring system, verifying a reputation level in combination with zero-knowledge proof, and automatically switching to DAG asynchronous consensus and starting node cleaning when an
attack in a specific
numerical range is detected; the deep fusion of the
hybrid consensus and the dynamic hierarchy is realized, so that the execution efficiency is improved. The high-frequency
transaction processing efficiency is improved, and the complex contract efficiency is improved.