CHAM Digital Center Deploys AI Large Models to Strengthen the Foundation of Future Digital Intelligence

Amidst the wave of intelligent transformation in the energy industry, CHAM Digital Center is advancing the deployment of various AI large models, including the full-fledged DeepSeek 671B. By reconstructing its technological foundation, CHAM aims to enhance its digitalization strategy in the renewable energy sector.

AI-Driven R&D: Accelerating Innovation

AI large models will play a pivotal role in research and development. Their code-generation capabilities are expected to reduce error rates by 60% while significantly boosting development efficiency. The intelligent coding assistant system will generate high-reliability code frameworks in real-time, aligning with the optimization needs of renewable energy equipment control algorithms. This enhancement is projected to improve core module development efficiency by 200%. Furthermore, AI's real-time decision-making capabilities will be embedded in the Manufacturing Execution System (MES). By analyzing data streams from production line equipment, the system will dynamically adjust process parameters, enabling real-time optimization of production workflows. In key areas such as Battery Management Systems (BMS), AI models will leverage historical data to develop predictive debugging capabilities, effectively mitigating hidden risks associated with traditional development approaches. A fully interpretable AI framework will also be established to enhance decision traceability in critical applications like BMS fault diagnostics. This framework will achieve an AI decision reliability rate of 99.6%, far exceeding industry standards. In manufacturing, CHAM is building proprietary industrial AI models, such as a "Manufacturing Health Index." By collecting data on environmental conditions, equipment performance, product quality, and production processes, the system will enable intelligent anomaly detection and proactive solutions, preventing unexpected downtime and improving equipment utilization rates by over 10%. Additionally, CHAM is building an AI-powered vertical model for process optimization. Through predictive process alerts and real-time analysis, the system will integrate intelligent analytics, reasoning, decision-making, and execution to enhance manufacturing efficiency and accelerate process improvements. On the green energy front, CHAM's AI-driven control system will dynamically adjust settings for compressors, cooling systems, and other energy-consuming equipment based on real-time production demands. This smart energy optimization is expected to reduce annual energy costs by over 20%.
AI will also provide end-to-end support in business strategy, from data insights and scenario simulations to risk forecasting, making business decisions more scientific and accurate. Predictive models, built on multi-source heterogeneous data, will enable early warnings of supply chain disruptions up to 14 days in advance. Meanwhile, the renewable energy industry's dynamic monitoring system will continuously capture global technical literature, policy regulations, and market intelligence. By leveraging DeepSeek's causal inference capabilities, CHAM will generate forward-looking analytical reports to guide strategic decision-making.

AI-Powered Workflow Optimization: Enhancing Knowledge Management Efficiency

In the workplace, the comprehensive integration of AI large models is set to revolutionize CHAM Group's office operations, significantly boosting efficiency. Automated meeting minutes, powered by speech-to-text transcription and key-point extraction, will drastically reduce communication costs. The introduction of intelligent assistants will enable predictive rule assessments and risk warnings, further streamlining workflow processes. AI-powered KPI assessments will ensure the rigorous execution of key performance targets. To maximize employee adoption of these AI tools, CHAM Digital Center is offering comprehensive professional training programs, equipping employees with the necessary skills to fully leverage AI's capabilities and enhance overall productivity. CHAM has also laid out a clear vision for knowledge management. The company plans to build an exclusive enterprise knowledge base, using Retrieval-Augmented Generation (RAG) technology to create a multi-domain vertical knowledge hub. This will enable millisecond-level precision retrieval of patent literature and experimental data. By deploying a private knowledge engine, CHAM will transform over two decades of accumulated process know-how and equipment maintenance logs into structured knowledge assets, allowing technical personnel to instantly access targeted solutions. For instance, in solid-state battery research, the system will automatically link electrolyte material properties with thermal stability parameters, providing R&D teams with cross-disciplinary innovation solutions that accelerate the validation process. In customer service, CHAM will harness AI large models and its knowledge base to deliver accurate, 24/7 automated responses, significantly enhancing customer satisfaction.

CHAM's deployment of AI large models and other cutting-edge technologies underscores its commitment to digital transformation and innovation. The company is building an interdisciplinary research platform to overcome technical bottlenecks, establishing intelligent production lines to enhance precision and flexibility, and consolidating knowledge assets to reinforce its technological leadership. Through advanced natural language processing, data analysis, and inference capabilities, CHAM will greatly improve customer experiences while delivering multi-dimensional value: increasing service efficiency and response speed, enabling personalized solutions, enhancing data security and privacy protection, fostering innovative services, and reducing costs—all of which indirectly benefit customers. These efforts align with CHAM's vision of a "data-driven factory" while laying a solid foundation for future intelligent development.