The invention particularly relates to a multi-level self-
cognition system based on a
large model, and relates to the technical field of large models. A neural symbol world model module; a large
language model cognition core module; and a hierarchical decision planning
system module. According to the method,
deep integration of
perception,
cognition and
decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-
modal perception encoder adopts layered encoding and a cross-
modal attention mechanism, so that the
semantic alignment problem of multi-source
perception data is effectively solved, and the understanding ability of the
system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural
network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.