Core Viewpoint - The Beidou Navigation team at Hefei University of Technology has successfully developed a Beidou satellite-based water vapor inversion device for rainfall prediction, integrating artificial intelligence to address challenges in monitoring "cloud rain" and predicting "ground rain" [1][2] Group 1: Technology Innovations - The device incorporates three key technological innovations: 1. A method for detecting and suppressing interference in Beidou signals based on mixed time-frequency distribution, which addresses the issue of signal interference in complex electromagnetic environments [1] 2. An improved synchronous perturbation random approximation method for solving atmospheric parameters, achieving a 65% increase in the accuracy of precipitable water vapor (PWV) inversion [1] 3. A dual-stage attention mixed learning model for multivariable time series prediction, achieving a precision forecast rate of 87.6% and a minimum false alarm rate of 8.5% for short-term severe convective rainfall warnings [1] Group 2: Applications and Future Plans - The rainfall prediction technology effectively resolves issues in water conservancy disaster prevention, renewable energy power forecasting, and low-altitude economic air traffic scheduling [2] - The team plans to continue advancing technology iterations and accelerate market deployment, aiming for comprehensive coverage across domestic provinces and ultimately creating an integrated monitoring system that enhances the application of "Beidou + AI" technology for national economic development [2]

Core Insights - The integration of Beidou navigation and AI technology has successfully addressed the challenge of monitoring "cloud rain" through the development of a GNSS occultation observation device for accurate rainfall prediction [1][2] Group 1: Technology Innovations - The technology features three core innovations: 1. A mixed time-frequency distribution method for GNSS signal interference detection, improving interference signal frequency estimation accuracy by 25.6% [2] 2. An improved synchronous perturbation random approximation method for atmospheric parameter solving, enhancing the precision of precipitable water vapor (PWV) inversion by 65.0% [2] 3. A dual-stage attention mixed learning model for multivariable time series forecasting, achieving an industry-leading 87.6% true positive rate and a minimum false alarm rate of 8.5% for short-term severe convective rainfall warnings [2] Group 2: Applications and Impact - The technology has been deployed in over 50 units in critical areas such as the Yangtze River flood control section and the Huai River flood discharge area, providing precise decision-making support for flood dispatch and personnel relocation [2] - In the renewable energy sector, the technology offers high-precision meteorological data for photovoltaic and wind power plants, enhancing operational maintenance and power forecasting efficiency [2] - The research team has established multiple intellectual property rights and aims to improve the true positive rate to over 95% by 2026 [2]