The photovoltaic irradiance calculation model utilizes long-sequence satellite images provided by geostationary satellites combined with AI to calculate solar radiation intensity and the irradiance on photovoltaic module surfaces, providing data support for the design and operation of photovoltaic power generation systems.

Utilizes various sensors (such as slope radar, GNSS displacement monitoring devices, etc.) to conduct real-time monitoring of mountains and slopes, collecting data on displacement, cracks, tilt, and more. Through data analysis, it assesses slope stability and provides early warning information.

Based on digital twin systems, weather forecasts, and drainage facility data, an urban waterlogging model is constructed to assess waterlogging risks in cities, providing real-time warnings and decision support.

The tropical cyclone (typhoon) monitoring and early warning system integrates multi-source data such as meteorological satellites, radar, and ground observation stations to monitor the typhoon’s location, intensity, and movement path in real time. It uses artificial intelligence and big data technologies for high-precision forecasting, providing typhoon warning signals and defence guidance.

The wind power forecasting system utilizes historical power data, meteorological data, and terrain data. Through machine learning and deep learning algorithms, it predicts the wind farm’s power generation for specific future time periods, providing accurate power forecast results.

The marine digital twin platform builds digital models of the marine environment, integrating multi-source data to achieve real-time monitoring, simulation, and forecasting of the marine environment and vessel operations, supporting intelligent decision-making and optimization.

Greenhouse gas emission monitoring and assessment involves comprehensive observation, numerical simulation, and statistical analysis to systematically monitor the emission intensity and environmental concentration of greenhouse gases such as carbon dioxide (CO₂) and methane (CH₄). It provides data support for carbon peaking and carbon neutrality goals. The main monitoring targets include emission sources from energy activities and industrial processes, as well as carbon emission dynamics in the atmosphere, ocean, and terrestrial ecosystems.

The carbon sink and carbon credit precise quantification model is a tool that uses multi-source data and advanced algorithms to accurately calculate the carbon sequestration benefits of ecosystems and carbon credit projects. The model aims to quantify the additionality, permanence, and social benefits of carbon removal, providing scientific support for carbon trading, ecological compensation, and ensuring fairness and transparency in carbon market mechanisms.