Research on Real-Time Processing and Visualization Algorithms for Carbon Emission Data Based on the Internet of Things

Carbon emissions are a global research hotspot and a core issue in addressing climate change. As the country with the largest carbon emissions in the world today, China's industrial carbon emissions account for approximately 68% of the country's total carbon emissions. Accurate accounting of industrial carbon emissions is of great significance under the new system requirements of “dual control” of total carbon emissions and intensity. In the context of promoting low-carbon cities and sustainable growth in China, accurate assessment and dynamic monitoring of carbon emissions are key technological prerequisites. The collection, extraction, and understanding of data is one of the basic ways for humans to perceive and understand the world. Data visualization provides an important means for humans to understand the definition of data and the underlying laws of data. This article proposes a realtime processing and visualization algorithm for carbon emission data based on the Internet of Things (IoT). The research aims to collect and mine carbon emission data in realtime through IoT technology, and use visualization algorithms to present complex data in an intuitive manner, achieving realtime monitoring and effective management of carbon emissions, and providing strong support for environmental protection. The experimental results show that the algorithm proposed in this article exhibits good realtime and accuracy in processing carbon emission data, and the visualization results are intuitive and easy to understand.