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This paper introduces SafeDivertor, a novel framework for reconstructing time-resolved radial heat-flux profiles from macroscopic plasma-state signals during magnetic-confinement fusion discharges. By leveraging a multi-source dataset, DivMPS2HF, and employing techniques such as physical prior-aware initialization and spectral-aware reconstruction optimization, SafeDivertor significantly enhances the accuracy of heat-flux analysis compared to traditional infrared methods. Experimental results indicate that SafeDivertor sets a new benchmark in performance across multiple evaluation metrics, underscoring its potential for real-time applications in fusion energy research.
Achieving unprecedented accuracy in divertor heat-flux reconstruction, SafeDivertor outperforms traditional methods by directly utilizing plasma-state signals during discharge.
Divertor heat-flux analysis is essential for understanding plasma-wall interactions and protecting plasma-facing components in magnetic-confinement fusion devices, while conventional infrared-based inversion is usually performed after discharge and requires heat-conduction modeling with device-specific material properties, divertor geometry, and boundary conditions. Rather than accelerating this conventional infrared-based inversion paradigm, we introduce a new online-oriented signal-based reconstruction paradigm that directly reconstructs time-resolved radial heat-flux profiles from multi-source macroscopic plasma-state signals available during discharge. To enable systematic study of this task, we construct \textbf{DivMPS2HF}, a multi-source discharge dataset that provides the data foundation and benchmark for signal-based divertor heat-flux reconstruction. We further propose \textbf{SafeDivertor}, a task-driven framework designed to address the key challenges of signal-based heat-flux reconstruction. It employs physical prior-aware initialization to provide radial-distribution guidance for target channels, input perturbation to reduce over-reliance on specific heterogeneous signals, spectral-aware reconstruction optimization to exploit time-frequency priors and preserve transient dynamics, and progressive training to stabilize the optimization of these complementary objectives. Experiments on DivMPS2HF demonstrate that SafeDivertor achieves the best overall performance among the evaluated time-series baselines across all five metrics, establishing a new performance benchmark for signal-based divertor heat-flux reconstruction. The source code will be released on https://github.com/Event-AHU/OpenFusion
