Miss Jia’s conversation with Cheng Lu: Achieving “autopilot” in the energy field
2026-09-04 08:00Products & Apps🔥 42.2 heat score
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On September 4, 2026, Dr. Cheng Lu, CEO of Xinao Paneng Network, proposed the concept of a “self-driving” system with hierarchical levels from L1 to L5 in the energy sector. This initiative aims to utilize artificial intelligence to address challenges in global energy transformation. The core of this plan is to create a digital energy operation system that converts physical data into decision-making models, thereby enabling flexible energy supply for users and ecological optimization. The implementation strategy involves first building a foundational framework for specific scenarios, then moving towards integrated solutions, while emphasizing safety standards. Currently, the project faces difficulties such as insufficient accumulation of private data in the energy industry, complex labeling, and non-standardized heterogeneous data, which lengthens the cycle from on-site perception to solution implementation, accounting for approximately 70% of the project timeline.
Dr. Cheng Lu, CEO of Xinao Paneng Network, proposed a hierarchical concept of “autonomous driving in the energy sector” from L1 to L5, aiming to reimagine the future of energy through AI. Facing the “impossible trinity” challenges in the deep waters of global energy transformation, Dr. Cheng believes that the industry is at a critical point of change, and AI will lead to breaking through cognitive barriers. Xinao Paneng Network plans to develop a digital energy operation system, with the core logic being to convert physical perception data into decision-making models, enabling flexible energy supply and consumption for users as well as ecological optimization. This system adopts a classified breakthrough strategy, first building the underlying framework for specific scenarios before moving towards integration, while emphasizing safety as a key priority. Currently, due to difficulties such as insufficient private domain data in the energy industry, complex labeling, and non-standardized heterogeneous data, the cycle from on-site perception deployment to solution implementation is relatively long, accounting for approximately 70% of the project progress.