Effective modern transportation systems depend critically on accurate Signal Phase and Timing (SPaT) estimation. However, acquiring ground-truth SPaT information faces significant hurdles due to communication challenges with transportation departments and signal installers. As a result, Floating Car Data (FCD) has become the primary source for large-scale SPaT analyses. Current FCD approaches often simplify the problem by assuming fixed schedules and basic intersection designs for specific times and locations. These methods fail to account for periodic signal changes, diverse intersection structures, and the inherent limitations of real-world data, thus lacking a comprehensive framework that is universally applicable. Addressing this limitation, we propose an industrial-grade FCD analysis suite that manages the entire process, from initial data preprocessing to final SPaT estimation. Our approach estimates signal phases, identifies time-of-day (TOD) periods, and determines the durations of red and green lights. The framework's notable stability and robustness across diverse conditions, regardless of road geometry, is a key feature. Furthermore, we provide a cleaned, de-identified FCD dataset and supporting parameters to facilitate future research. Currently operational within our navigation platform, the system analyses over 15 million FCD records daily, supporting over two million traffic signals in mainland China, with more than 75\% of estimations demonstrating less than five seconds of error.