Discussion on the development of maglev transportation technology driven by AI from the perspective of systems engineering thinking

The integration path and practical effectiveness of artificial intelligence (AI) with maglev transportation technology were explored from the perspective of systems engineering thinking to tackle the complex control and management challenges arising from the coupling of multiple subsystems in maglev transportation and promote its upgrading towards intelligence and efficiency. Firstly, at the theoretical level, the core characteristics of different technical routes of maglev transportation were analyzed, and the technical logic of AI adapting to the core contradiction of "dynamic uncertainty and high precision control" in maglev transportation was clarified. Meanwhile, the internal mechanism of AI realizing subsystem collaborative optimization under the guidance of systems engineering thinking was explained, and the systems engineering value of AI in constructing an intelligent closed-loop system of "perception, decision, control, optimization" was defined. Secondly, in five core subsystems including suspension guidance, traction transmission, the power supply system, signal communication, and intelligent operation and maintenance scheduling, the technical breakthroughs and scene adaptability of AI algorithms such as the BP neural network, LSTM, and reinforcement learning were elaborated. Additionally, the improvement effect of AI technology on suspension accuracy, traction efficiency, power supply stability, and signal reliability was quantitatively analyzed, and the full-dimensional empowering effect of AI on maglev systems was verified. Thirdly, an AI empowerment path of "top-level data specifications, software and hardware iterative verification, compound talent cultivation, and cross-domain collaborative innovation" was proposed, and the technical development context of medium-low speed, high-speed and ultra-high speed vacuum pipeline maglev was sorted out, with the engineering practice results under different scenarios summarized. Finally, existing challenges were analyzed, such as data silos, insufficient reliability in extreme operating conditions, disconnections between industry, academia, research and application, shallow integration of AI with emerging technologies, and lack of cross-industry collaborative mechanisms. The future development direction was proposed from five aspects of full-domain data fusion, robust model R&D, deepening of collaborative innovation mechanisms, integration of emerging technologies, and intelligent collaboration for cross-industry multimodal transportation. The results indicate that the deep integration of AI and maglev transportation technology under the guidance of systems engineering thinking is the core support for solving the multi-subsystem coupling difficulties of maglev systems and realizing the leap from high speed to intelligence, and its subsystem collaborative optimization mechanism provides a theoretical paradigm and practical guidance for the intelligent development of maglev transit.