Physics-consistent constraint probabilistic modeling and prospective risk assessment method for intelligent decision-making in flight test points

Addressing the real-time monitoring challenges posed by high dynamics, strong nonlinearity, and observational uncertainty in flight test envelope boundary missions, traditional threshold methods and pure data-driven models struggle to balance identification accuracy, physical consistency, and predictive warning capability. To this end, this paper proposes a CPSSMF aimed at providing interpretable and calibratable unified probabilistic evidence for safety-critical decision-making. Based on sequential Bayesian inference, the framework achieves closed-loop monitoring through three coupled modules: (1) PSA module, which injects physical constraints into posterior distribution shaping through endogenous potential functions, combined with output-side safety masking and multi-source anomaly detection to suppress non-physical state jumps; (2) HPRF module, which performs multi-step rolling prediction based on corrected posteriors, outputting trend risks within a fixed time horizon to achieve quantitative trade-off between lead time and false alarm rate; (3) EGUAQ module, which dynamically evaluates data value using posterior entropy to support active verification. Evaluation results based on simulated flight stall test data demonstrate that CPSSMF maintains high phase identification accuracy (Acc 0.969, F1 0.785) while achieving an average warning lead time of approximately 3.34 s under the same false alarm constraints, significantly outperforming baseline methods. Furthermore, under sensor contradiction injection and out-of-distribution disturbance scenarios, the method exhibits excellent robustness and stability. This study establishes an interpretable analysis chain integrating situational awareness, risk quantification, and anomaly diagnosis, effectively enhancing the engineering applicability of flight test safety monitoring.