The application relates to the technical field of data enhancement and
system engineering, in particular to a user behavior data enhancement method based on an RFLP-driven large
language model. The method comprises the following steps: determining a core driving problem and an expected application
scenario of to-be-generated user behavior data, determining a task target, a
data structure requirement, a data characteristic requirement, a constraint condition and a non-
functional requirement of a large
language model data generation
system based on the RFLP driving, and coding the requirement specifications into a plurality of functional modules, forming a data generation function set according to the modules, constructing a logic scheme based on "strategy-component-combination" for the
system, and converting the logic scheme into an
executable scheme, finally controlling the large
language model to execute the
executable scheme to generate the to-be-generated user behavior data. Therefore, the problem that a prompt word is difficult to guarantee the quality and consistency of generated data is solved, and scientific methodological support is provided for constructing a high-quality, controllable and expandable synthetic user data generation system.