With the accelerated evolution of new industrialization and traditional industries, the development of new quality productive forces has become a major strategic mission on the new journey in the new era, and has also become a necessary path for mining enterprises to seek intelligent, efficient, green and low-carbon high-quality development. From the new quality productive forces perspective, based on domestic and foreign smart mines and China’s mining industry and enterprise development situation, this paper elaborates the problem and challenges, development connotation and development path of the mining enterprises when pursuing digital intelligence and green transformation. It hopes to provide a model reference for the mining enterprises to achieve high-quality transformation and development and explore high-quality development path with Chinese characteristics for the mining industry.

Wind power is a kind of important clean energy. The deep integration of artificial intelligence and wind power generation to enhance the intelligence of energy scheduling and utilization is in line with the current development trend. By searching and analyzing patent applications of artificial intelligence in the field of wind power generation, the main application trends and hotspots in this field are sorted out. Meanwhile, this paper analyzes the current development situation and trends of energy digital transformation from a patent perspective, in order to provide relevant information for optimizing the patent layout.

This paper explores the application scenarios and models of digital technology in the energy transition and transformation, with a focus on the specific applications of 5G, artificial intelligence, big data, cloud computing, and the Internet of Things in the fields of electricity, new energy, as well as oil and gas. Through case studies, this paper demonstrates the role of digital technology in various stages of energy production, management, transmission, and consumption. Additionally, it proposes strategies such as increasing infrastructure investment, improving policy support, and strengthening talent development to promote the digital transformation and sustainable development of the energy industry.

China’s coal industry has entered a stage of high-quality development, with the intelligent construction emerging as the core theme. Among these, the application and promotion of autonomous driving technology in open-pit coal mines have achieved remarkable effects. To systematically investigate the application effects and future trends of autonomous driving technology in open-pit coal mines, this study analyzes the topic from three aspects: application background, system architecture and core technologies, as well as application cases and achievements. The findings reveal that driven by policy requirements, industry guidance, and enterprise initiatives, the autonomous driving technology has formed an integrated technical architecture and been implemented in various open-pit coal mines with certain effects. Finally, the study summarizes the current characteristics and development trends of autonomous driving in open-pit coal mines, providing a reference for the subsequent promotion and application of autonomous driving in both open-pit and underground mines.

To clarify the path and effectiveness of China’s digital transformation of energy in the industrial sector when pursuing the dual carbon goals, this paper compiles energy digital transformation cases of industrial enterprises, industrial parks, industrial cities, and carbon monetization based on industry analysis, policy analysis, survey questionnaires, and enterprise visits. Then, it analyzes and summarizes the experience of green energy transformation in the industrial sector. Finally, it gives policy suggestions from multiple levels such as policy, technology, and management.

With the first year of the development of computing power in 2022, computing power has become the most dynamic and innovative new productivity, and all walks of life are actively exploring the implementation path for the development of the computing power empowerment industry. As a pillar and basic industry of the national economy, the chemical industry has become one of the important power points of current computing power with its large economic total, long industrial chain, many product types and wide coverage. Based on the current computing development environment, this paper focuses on the chemical industry, explores the implementation path of computing empowering the digital development of the chemical industry, and provides a reference for the future digital transformation of the chemical industry.

This paper systematically analyzes the application status and challenges of data elements in the energy industry, outlines the efforts of promoting energy digital transformation at home and abroad, and proposes strategies such as building a high-quality data resource ecosystem, standardizing the data market, and improving the data security system to promote the deep digitalization and sustainable development of the energy industry.

Based on the requirements of offshore oil and gas production, an Internet of Things (IoT) system design scheme for intelligent control of offshore oil and gas production processes is proposed, covering various technical links such as data collection, data transmission, data processing, and business applications of offshore oil and gas production facilities and onshore processing terminals. It has real-time monitoring, data transmission, and production warning functions, which can promote the digital transformation of offshore oil and gas production methods and improve the intelligent management level of oil and gas production.

As a data -intensive industry, the security and efficient circulation of data elements is particularly important. The development of artificial intelligence technology has played a role in the performance of data factor value, especially the emergence of large models has accelerated the digital transformation process of the entire industry. This article aims to explore the current status, problems and development recommendations of data elements in the energy industry under the integration of big data, artificial intelligence and other technologies, in order to provide a reference for the digital transformation of the energy industry.

The digital transformation of enterprise is a complex systematic project, and its core path is to build digital core competence. According to Bloom’s taxonomy theory, the process of building digital core competence can be divided into three stages: learning and understanding, applying and practicing, and pioneering and creating. Most of the energy central state-owned enterprises have crossed the learning and understanding stage and entered the applying and practicing stage, but there is still a gap from the pioneering and creating stage. In order to achieve breakthroughs in digital transformation, energy enterprises should take innovative talents as a breakthrough point, promote positive interaction between top-down high-end leadership and bottom-up mass innovation, and deepen the construction of innovation consortiums and co-innovation mechanisms with science and technology-based enterprises.

Under the background of the reshaping of the global energy pattern and the transformation and upgrading of the domestic energy and chemical industry, digital transformation has become an inevitable path for the energy enterprises to enhance their competitiveness. This paper deeply discusses the innovative technologies such as intelligent migration of heterogeneous databases, database autonomy optimization and flexible expansion architecture, then effectively solves the problem of database migration and improves data processing efficiency and system stability. With its multi-mode data processing and cloud database technology, the domestic database has built a strong data management and service capability for the petroleum and petrochemical industry, which has effectively promoted the overall improvement of industry data sharing, business collaboration and operational efficiency. The successful practice of integrating the upstream development and production system of an energy enterprise into the cloud upgrade project fully verifies the good performance of the domestic database in practical application, and shows its great potential and value in the digital transformation of the energy industry.

With the advancement of digital transformation of energy enterprises, the value of ET data is becoming increasingly significant. ET data governance ensures high quality and availability of data, which is of great significance for digital transformation of energy enterprises. Based on this, a complete set of ET data governance technological system and key supporting technologies are developed, main ET data governance scenarios are described, and finally two application cases are introduced.

In response to the digital transformation of the energy industry, China Mobile Shanghai Industrial Research Institute has launched a comprehensive energy management platform that integrates the OnePower multimodal big model, providing products and solutions that integrate cutting-edge technologies such as 5G, artificial intelligence, cloud computing, the Internet of Things, and big models for the energy industry. This article first introduces the significance and challenges of energy digital transformation, followed by a detailed introduction to the architecture and functions of the OnePower comprehensive energy management platform. Then, it introduces the power station inspection products empowered by multimodal large models, and finally introduces the landing application case of the OnePower multimodal large model comprehensive energy management platform in a certain power enterprise.

Industrial parks are facing great pressure in energy conservation and emission reduction, making it imperative to integrate digital technologies into energy management. First, this paper analyzes the typical problems with traditional energy management approaches in industrial parks. Then, it proposes an energy management method based on evolutionary deep learning. This method is applied to three key scenarios: energy consumption forecasting, intelligent lighting, and equipment early warning in industrial parks. Finally, it summarizes the safeguard measures for the practical application of evolutionary deep learning, providing a mature approach to expanding the application scenarios of energy management.