Under the background of low-carbon economic development, carbon emission trading, as an important institutional arrangement, can effectively coordinate the role of emission reduction constraints, price signals and resource allocation. However, there are still some problems in the current market operation, such as wide and large amount of emission data, wide range of price transmission and long chain, and lagging regulation. In this paper, how to realize the dynamic regulation of carbon emission trading in carbon emission market is studied. With the help of computer science, an intelligent computing support system based on data layer, modeling layer, decision layer and interaction layer is constructed. Based on the system, the carbon emission monitoring and prediction model, the carbon quota market price change prediction model, and the dynamic adjustment optimization model of carbon emission trading using deep reinforcement learning method were established. Finally, the closed-loop system of “monitoring-prediction-decision-feedback” is obtained. Finally, the system is simulated and the effectiveness of different control strategies is compared. The results show that the proposed DRL strategy outperforms the benchmark strategy and MILP strategy in terms of net income, volatility control, and response efficiency. In the typical 30-day simulation window, the cumulative net income reaches 7.52 million yuan, the carbon price volatility rate drops to 8.7%, the compliance deviation rate drops to 3.2%, and the average adjustment response duration is shortened to 6 minutes. The study suggests that embedding intelligent computing in the adjustment process of the carbon emission rights market helps improve the accuracy, forward-looking nature, and coordination of market operation.
Povzetek: Študija obravnava uporabo tehnologije navidezne resničnosti pri športnem pouku. Združuje računalniški vid, 3D rekonstrukcijo skeleta, semantično prepoznavanje gibov in virtualno interakcijo. Rezultati kažejo boljšo vizualizacijo gibov, hitrejšo povratno informacijo in večjo interaktivnost. Kljub temu ostajajo izzivi pri večuporabniških scenarijih, udobju opreme, stabilnosti sistema in stroških uvajanja v šolski učni praksi.
