Principles and Applications of Data Simulation Systems

In the context of the current big data era, while high-quality big data models continue to evolve and update, the source of data training has long remained in its original state. Many enterprises commonly adopt real-world data as the basis for training, with the main acquisition methods relying on manual or machine annotation. This singular approach possesses significant limitations. Firstly, the data production cycle is long, as the generation of training data requires time accumulation. Secondly, data privacy is difficult to overcome, as training data often contains sensitive information, whose usage is strictly restricted. Lastly, the data screening process consumes significant human and material resources. To address these challenges, this paper delves into the potential feasibility of generating data through code, aiming to provide a novel data acquisition strategy that overcomes the limitations of traditional methods and enhances the efficiency and effectiveness of big data model training.