Background
Meteorological services faced three challenges: vast amounts of data were hard to turn into clear video, scattered publishing channels made service quality uneven, and a cumbersome production process could not keep up with changing weather. An intelligent video service platform was needed to bring data together, unify channels and streamline production.
Key Requirements
- Turn meteorological data into clear video content faster
- Bring publishing channels together for consistent service quality
- Streamline production to respond quickly to changing weather
- Improve user interaction and keep improving the service
Solution
China's meteorological service worked with tsan.ai to build a meteorological video service platform on tsanCenter. Visual development and fast iteration shortened development, lowered costs and adapted flexibly to the special needs of weather services.
Core Capabilities
Smart Video Production
Data visualization tools that combine multiple elements
Multi-Channel Publishing
Publishes to TV, web and mobile at the same time
User Interaction Management
Collects comments and feedback to improve the service
Data Integration
Connects multiple data sources and standardizes the data
Use Cases
The platform covers the whole process of producing and publishing weather video and interacting with users, raising the level of meteorological service.
Video Production
Visualized weather data, multi-element editing and fast program generation
Multi-Platform Publishing
Scheduled broadcasts on TV, homepage features on the web and on-demand viewing on mobile
User Interaction
Collects comments, answers questions in real time and keeps improving the service
Impact
Built on tsanCenter, the platform brings meteorological data together efficiently and presents it visually. Low-code development lowers the technical threshold and building costs, and support for multi-channel publishing and user interaction lets it give the public clear, accurate weather services while supporting science outreach and disaster prevention — a successful example of digital transformation in meteorology.