Windy weather, characterized by high uncertainty and extreme events, poses a significant threat to the safety of urban residents. Existing methods rely on numerical models to obtain wind field across large areas, but these models have high computational costs, slow processing speeds, and fail to meet real-time assessment requirements. This paper focuses on real-time assessment of urban pedestrian distress in windy environments. A risk assessment model for pedestrian distress is developed through mechanical analysis, combined with publicly available human body size standards for Chinese adults. Furthermore, by integrating the distress threshold model with a physics-inspired, data-driven two-phase wind field reconstruction method, we propose a hybrid physics-data driven framework for assessing pedestrian distress risk in windy environments. A case study conducted in a Beijing community demonstrates the feasibility of the proposed method. The results show that the method provides visualized risk assessment outcomes. In a scenario involving a Beaufort scale of 8, more than 42.98% of the community area is considered highly uncomfortable and dangerous for the elderly, making outdoor activities not recommended. Overall, the framework developed in this study offers an intuitive visualization of risk assessment results to assist urban stakeholders in emergency decision-making and enhance smart community safety.