Recently, the Ministry of Industry and Information Technology (MIIT) officially released its 2025 list of typical cases for artificial intelligence (AI) applications, with the aim of leveraging the exemplary and guiding role of these cases to accelerate the large-scale deployment of outstanding outcomes. Three self-developed achievements of China Shenhua’s DigitalTech Co., Ltd. were successfully selected, a recognition that affirms the Company’s authoritative standing in the AI innovation space and highlights its innovative practices, technical expertise, and proven implementation results in AI-empowered software R&D, thereby advancing the construction of Digital and Intelligent CHN Energy.
Among the selected cases, the Agent-Based Foundational Software R&D Platform for Extra-Large Energy Groups was named a typical case in the technical infrastructure track, while AI-Empowered Violation Identification and Control for High-Risk Operations and Application Practice of an AI-Driven Salary Management Platform for Extra-Large Energy Groups were recognized as typical cases in the industry empowerment track, collectively demonstrating the Company’s comprehensive digital and intelligent innovation capabilities.
The Application Practice of an AI-Driven Salary Management Platform for Extra-Large Energy Groups directly tackles industry-wide challenges, including the vast employee base of extra-large energy groups, complex multi-tiered salary structures, cumbersome accounting workflows, and insufficient data support for HR decision-making. By deeply integrating core AI capabilities such as rule self-validation, salary data analytics, and intelligent risk alerting, the platform enables intelligent, end-to-end online control over the entire salary process, substantially improving the efficiency of group-level salary calculation, verification, and disbursement. Moreover, leveraging AI-powered big data analysis, it furnishes a scientific basis for optimizing salary incentives, thereby establishing a new paradigm for digital and intelligent salary management in the energy sector.
The Agent-Based Foundational Software R&D Platform for Extra-Large Energy Groups innovatively delivers standard and systematic AI-driven R&D support, establishes a normalized R&D system, and implements a novel human-machine collaborative R&D model. This effectively addresses longstanding issues such as low development efficiency, lack of unified standards, and difficulty in reusing past experience, while enhancing quality, efficiency, and compliance across the R&D lifecycle. The platform has been validated in multiple energy digitalization projects, yielding significant improvements in overall R&D productivity and significantly elevating the enterprise’s digital construction quality and effectiveness.
The project AI-Empowered Violation Identification and Control for High-Risk Operations focuses on the core safety control challenges in high-risk operations within the thermal power industry. Built upon the overall architecture of the “Qingyuan” large model, it integrates computer vision (CV) recognition and multimodal reasoning capabilities, coupled with an AI application management platform deployed at the power plant side, to enable deep data fusion and intelligent analysis. For high-risk scenarios, its core algorithmic suite can precisely identify and provide real-time warnings for rule violations across a variety of hazardous operational contexts. In addition, by analyzing accumulated violation big data, the system offers robust data support for refining safety training regimens, enhancing safety management frameworks, and tightening on-site control standards. The solution has already been piloted successfully in Hequ County and Hanchuan City, achieving 24/7 intelligent surveillance without blind spots over high-risk zones, thereby fortifying safety defenses and promoting standard, routine, and intelligent on-site safety management.
It is reported that the MIIT’s case collection initiative focuses on the overarching theme of AI empowering new industrialization, spanning five core tracks of technical infrastructure, industry empowerment, product applications, and others, with a particular emphasis on AI application practices that are highly innovative, deliver demonstrable results, and possess strong potential for industry-wide demonstration and dissemination.
