Abstract: Based on the technological framework for collaborative intelligent manufacturing in the steelmaking-continuous casting section, this paper explores various scales including the unit device scale, workshop process scale, and steelmaking-continuous casting process scale. By integrating and synthesizing data levels, process device levels, process interface levels, and planning and scheduling levels, a comprehensive system level has been established. In-depth research was conducted on vertical and horizontal collaborative intelligent manufacturing across these different levels and processes. Systematic modeling and development were carried out from process manufacturing at the process/device scale to the steelmaking-continuous casting section's manufacturing. Initially, process control models for converter, refining, and continuous casting processes were developed at the process/device level. Subsequently, a multi-process collaborative control model was constructed at the process linkage and production planning and scheduling level. Through the development of key process control models, production planning and scheduling models, and data interfaces between the Manufacturing Execution System (MES), dynamic knowledge graphs, and digital twins systems, the comprehensive integration and dynamic collaboration of MES, process control, process operation control, and production planning and scheduling systems were achieved. Additionally, the autonomous evolution of cognitive knowledge graphs and the visualization of virtual space twins were realized. Comprehensive modeling and development from the steelmaking-continuous casting section process/device level to planning and scheduling level, and finally to the system integration level, including cognitive knowledge graphs and digital twin systems. By leveraging mechanistic models, data models, and expert knowledge in a synergistic manner, along with bidirectional interactions between virtual and real models, and horizontal and vertical collaboration across multiple processes and levels, the study enabled collaborative operation control and dynamic decision-making for the steelmaking-continuous casting section. The results of this study offer significant reference value for the intelligent, green, and high-end development of the metallurgical industry and provide strong insights for intelligent manufacturing in process industries, contributing substantially to advancing new productive forces in the steel industry and addressing critical challenges.