Numerical-Optimization-Based Implementation Strategies for the Mechanical Engineering AI Talent Granary Model in the Intelligent Digital Age

DOI: https://doi.org/10.62517/jhet.202515602

Author(s)

Chang Liu1, Fang Liu1, Yawen Fan2, Jingfeng Shen1,*

Affiliation(s)

1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China 2School of Engineering and Computing, The Sino-British College, University of Shanghai for Science and Technology, Shanghai, China *Corresponding Author

Abstract

Amidst the digital intelligence age, the rapid evolution of intelligent technologies in mechanical engineering necessitates a critical demand for “AI-Mechanical” composite talent. To supersede the static and lagging nature of traditional cultivation models, this paper introduces an iterative optimization model as its core theoretical metaphor. This innovative approach informs the construction of the “AI Talent Granary Model for Mechanical Engineering” framework and its implementation strategy, which develops through three progressively advancing strategic phases: establishing a dynamic iterative model for talent development centered on the gradient descent method; constructing hybrid optimization strategies to effectively address elastic and rigid constraints in educational practice; and finally, utilizing a multi-objective optimization framework to achieve the “Dual-Helix” integration of technical capabilities and ideological-political literacy.. The series of strategies proposed in this study not only establishes the AI Talent Granary Model but also delivers a comprehensive, adaptive, and actionable solution. This approach systematically facilitates the intelligent upgrading of traditional engineering disciplines and the cultivation of outstanding engineers through a clear, visual implementation pathway.

Keywords

Numerical Optimization; AI Talent Granary Model; Implementation Strategy; Mechanical Engineering; Artificial Intelligence; Iterative Cultivation

References

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