Roll forming is a plastic processing technology that gradually bends metal strips and sheets laterally using sequentially arranged forming dies. Due to its low cost， high efficiency， and flexibility， it has become one of the important technologies for lightweighting， energy saving， emission reduction， and safety improvement in various fields such as new energy， aerospace， and rail transportation in China. However， the complexity and discreteness of the process， uneven material thickness properties， and low level of equipment informatization and flexibility automation have made it similar to a “black box”， making it difficult to predict product quality， highly dependent on manual experience for production debugging， limited in formable cross-sections， and unstable in yield. Therefore， this paper proposes a data-driven intelligent roll forming equipment （system） and introduces in detail its technical architecture and characteristics. By building a data architecture based on artificial intelligence， this system collects， screens， integrates， stores， and analyzes discrete data in traditional roll forming. Meanwhile， it integrates digital twins， artificial intelligence， contour detection technology， and multi-agent collaborative control to construct a self-correcting production mode that can replace manual experience. For the new energy vehicle industry， this paper provides a case study of using this system to solve springback control of structural components of roll-formed battery packs. Finally， suggestions and prospects for the development of this system are given.