Real-time optimization and resource scheduling system for carbon emission disclosure in thermal power industry based on adaptive computing

This paper proposes a real-time optimization and resource scheduling system for carbon emission disclosure in thermal power industry based on adaptive computing, to improve the transparency of carbon emission management and resource use efficiency. The system integrates data acquisition, real-time optimization and scheduling execution functions within a layered architecture, and utilizes adaptive algorithms for dynamic adjustment of carbon emission disclosure strategies and resource allocation plans. The real-time optimization model for carbon emission disclosure reduces disclosure deviation and delay time. Experimental results show that its average disclosure error is 0.45 tons and the delay time is 0.32 seconds, significantly improving accuracy and timeliness compared with traditional methods. The resource scheduling algorithm realizes the optimization of energy allocation through a reinforcement learning mechanism, with a resource efficiency of up to 0.90 under different load conditions, while significantly reducing carbon emissions. This system not only verifies the application potential of adaptive computing in complex application scenarios in the thermal power industry, but also provides technical support for low-carbon production and sustainable development, with important practical significance.