The 2 640 MW large scale centralized grid-connected photovoltaic power station in Karamay，Xinjiang，is taken as an example in this paper，and an intelligent design method for photovoltaic power stations is proposed，based on the Particle Swarm Optimization （PSO） algorithm. This method uses photovoltaic module type selection，installation angle，array spacing and other design parameters as optimization variables，with annual power generation maximization as the objective function and investment costs as constraints，to construct a multi-objective optimization model. Power generation simulation is performed using the PVsyst software combined with MATLAB programming to implement PSO algorithm optimization. Simulation results show that compared with traditional design methods，the intelligent optimization method proposed in this paper can increase the annual power generation of the 2 640 MWphotovoltaic power stations by 3.2% to 41.26×10⁸ kW·h，and shorten the investment payback period by 0.5 years. After completion，the project can reduce CO₂ emissions by approximately 7.176×10⁷ t per year，with significant economic and social benefits. The proposed method provides new ideas for intelligent design of large scale photovoltaic power stations.

The metaverse represents the next generation of internet and is also an immersive virtual world connected to the real world on which it is having a profound effect. It is capable，for example，of real time rendering of digital twin scenarios and high-precision simulations that follow the laws of physics. Carbon Capture，Utilization，and Storage （CCUS） has come to be regarded as a critical core technology for China to achieve its double carbon goal. Compared with conventional Carbon Capture and Storage （CCS） technology，it offers tangible economic benefits and practical operability. The metaverse can energize every aspect of CCUS. However，current CCUS engineering technology is not standardized，which means high industrialization costs. Our analysis of the current development status of CCUS technology at home and abroad reveals that machine learning based on supervised and unsupervised algorithms can play a huge role in CO₂ capture using adsorption techniques；in particular on the selection of adsorbent materials. In addition，process simulation and optimization using quantum computing offers a lower cost option for CO₂ capture than the technologies currently in use. This paper proposes an intelligent，integrated，on and offline management system—using metaverse technology clusters （particularly machine learning）—as a vision for the future to solve the industrialization issues facing CCUS and help to achieve its large scale application.

Digitalization is a new trend in the development of human society today，and digital economy has become a key differentiator in the new landscape of international competition. The volume of oil and gas trading between China and the countries along the "Belt and Road" routes is huge，but the management model for cross-border oil and gas supply chain is rather outdated，handicapped by high cost，low efficiency，long cycle and high risks. To solve these problems，this paper proposes a blockchain-based oil and gas supply chain system designed for massive heterogeneous supply chain data. The prototype system based on Springboot framework and Thymeleaf engine template was implemented，which verified the effectiveness of the system. The experiment shows that the storage overhead of massive heterogeneous supply chain data in this system is about 16.7% of the conventional blockchain storage overhead. This paper provides reference for relieving the storage pressure of blockchain nodes and building a large-scale supply chain system that meets the needs of cross-border oil and gas trade.