构建全链条科创体系 本报记者 马宇薇 当前，新能源行业的竞争已不再局限于规模扩张的单一维度，而是转向以科技创新破解"效率瓶 颈"与"技术壁垒"的深层较量。 作为中国新能源行业的领军企业、国家能源集团新能源板块核心上市平台，龙源电力集团股份有限公司 （以下简称"龙源电力"）在"十四五"期间始终秉持"创新是企业的责任"这一核心理念，用全链条创新体 系筑牢根基、以"擎源"大模型重塑行业生态、借助多元实践拓宽应用边界，不仅交出了新质生产力培育 的亮眼答卷，更成为能源行业智能化转型的"领航者"。 近日，中国上市公司协会联合《证券日报》等媒体启动"我在'十四五'这五年 上市公司在行动"主题活 动，探寻龙源电力如何以创新之笔绘就绿色未来。 筑基： 让技术突破落地为产业实效，从来不是实验室里的"孤芳自赏"，而是从研发到应用的"全链条贯通"。龙 源电力深谙此道，在"十四五"期间依托"1+1+4+N"创新体系，搭建起平台、人才、成果转化环环相扣的 创新生态，让每一项技术突破都能精准对接产业需求，真正转化为推动发展的"生产力引擎"。 建设国家能源局授牌的研发平台——国家能源风电运营研发（实验）中心，发展壮大新能源生产运营、 新型电 ...

治理末梢的"痛"正是推进基层减负的"要"。经过2个月的研发建设，值长智能填报助手在神华九江 电厂上线运行，在保持业务连续性和现有系统功能不变前提下，智能贯通数据获取、校验、调整、填报 全流程，将值长从大量的、繁复的数据填报工作中解放出来。 "现如今，值长可以通过数据获取工具，登录后仅用1分钟便可一站式完成所有业务数据获取，数据 处理从约40分钟下降到约12分钟，实现15分钟内完成填报，值长数据填报总时长降低超80%，大幅提升 了基层工作效率和质量。" 电厂值长通过智能填报助手一键获取数据，仅需1分钟即可完成；员工借助智能报销助手，平均用 时不到1分钟就能填完所有差旅报销信息；有了智能收单机器人，财务的所有报销单据均可实现"一扫即 传"，轻松完成智能审核……2025年8月28日，在国家能源集团数智化产品应用推广交流会上，一批自主 研发的数智化产品集中亮相："值长智能填报助手""差旅智能报销助手""报账智能收单机器人"以及"擎 源"发电大模型中的"火电班组安全管理智能体""风电机组状态感知与检修派单智能体""电力现货交易数 字员工"，这些让数据多跑路，员工少跑腿、少填表的数智化产品正为国家能源集团基层治理注入强劲 ...

Core Viewpoint - The launch of the "Qingyuan" power generation model marks a significant advancement in the digital transformation of China's energy sector, integrating multi-dimensional data for enhanced operational efficiency and safety [1][2]. Group 1: Model Development and Features - The "Qingyuan" model, developed by the State Power Investment Corporation, incorporates operational monitoring, equipment status, and meteorological data, boasting a scale of over one hundred billion parameters [1]. - The model is designed to address the challenges of the power generation industry, which include the need for specialized AI models tailored to specific business requirements and the difficulty in obtaining high-quality industry data [2][3]. - Over six months, the company collected and processed more than 700TB of industry data, resulting in a high-quality dataset of 450GB, which was annotated by 380 industry experts [3]. Group 2: Application Areas - The "Qingyuan" model has been successfully applied in four key business areas: safety and environmental protection, electricity trading, production scheduling, and equipment maintenance, covering 13 scenarios and deploying 41 intelligent agents [4][5]. - In the safety and environmental protection sector, the model enhances lifecycle supervision of equipment and improves safety management efficiency [4]. - In electricity trading, the model predicts weather changes and market conditions, improving price prediction accuracy by 6.2% compared to traditional methods, leading to a 0.3% reduction in production costs and a 2% increase in profitability for a 600MW generator [5]. Group 3: Data Security and Future Directions - The company is addressing data security challenges by implementing strict transmission protocols and establishing a trusted data space for effective data management [7]. - Future efforts will focus on overcoming real-time response challenges in industrial control scenarios, with plans to utilize model distillation techniques to create lightweight models for local deployment [8]. - The company aims to promote the "Qingyuan" model through pilot validation, large-scale promotion, and ecosystem building, collaborating with universities and research institutions to enhance key technologies [8].

Group 1 - The "Xiaomiao" industrial model, developed by the Smart Building Materials Research Institute funded by China National Building Material Group, has been publicly launched, focusing on the cement sector as a testing ground [1] - The model integrates three core technologies: the fusion of time-series data with industrial mechanisms, multi-modal scenario collaboration, and decision-making fault tolerance, achieving over 1% reduction in cement batching costs [1] - After over two years of application, the model has established a mature engineering delivery capability, successfully implemented in nearly 100 cement enterprises, with data governance cycles reduced to as short as 14 days and model deployment within 7 days [1] Group 2 - China National Building Material Group's chairman believes AI will act as a "super accelerator" for new material research, significantly shortening development cycles and reducing trial-and-error costs [2] - The group is currently promoting AI's integration into strategic emerging industries for new materials, having built 231 scenario models covering the entire chain from core manufacturing to R&D and supply chain management [2] - In 2024, the State-owned Assets Supervision and Administration Commission will launch the "AI+" initiative for central enterprises, with several enterprises releasing industrial models, including China National Petroleum and State Grid [2]

Core Viewpoint - The launch of the "Qingyuan" power generation model, the first trillion-level model in China's power generation industry, aims to support safe, efficient, green, and intelligent operations in the energy sector [1] Group 1: Model Development and Features - The "Qingyuan" model integrates multi-dimensional data such as operational monitoring, equipment status, and meteorological conditions, boasting a parameter scale of trillions [1] - The model is a benchmark achievement for promoting the intelligent transformation of the energy industry and is expected to lead the sector towards digitalization [2] - Over 700TB of industry data was collected, resulting in a high-quality dataset of 450GB, covering various data types, which was annotated by 380 industry experts [3] Group 2: Application Areas - "Qingyuan" has been successfully applied in four major business areas: safety and environmental protection, electricity trading, production scheduling, and equipment maintenance, covering 13 scenarios and deploying 41 intelligent agents [4] - In the safety and environmental protection sector, "Qingyuan" enhances safety management efficiency and supports compliance in hazardous waste management [4] - In electricity trading, "Qingyuan" improves price prediction accuracy by 6.2% compared to traditional methods, leading to a 0.3% reduction in production costs and a 2% increase in profitability for a 600MW generator [5] Group 3: Challenges and Solutions - The power generation industry faces challenges in intelligent upgrades due to its strong specialization and the need for high-quality industry data [2] - There is a cognitive gap between power generation professionals and AI experts, necessitating close collaboration to align technology capabilities with industry needs [2] - To ensure data security, the company implements strict transmission and data management protocols, including one-way data flow and classified data control [6] Group 4: Future Plans - The company plans to advance the "Qingyuan" model through three phases: pilot verification, large-scale promotion, and ecosystem co-construction [7] - The goal is to create an open ecosystem for the power generation industry by unifying technical standards and integrating real-time data with expert knowledge [7]

Core Insights - The "Qingyuan" power generation model, the world's first trillion-level model in the power industry, has been officially released by the State Energy Group, integrating various data such as operational monitoring, equipment status, and meteorological conditions [1] Group 1: Model Features and Applications - The release of the "Qingyuan" model is a benchmark achievement in implementing the national digital economy strategy and promoting the intelligent transformation of the energy industry [1] - The model shifts safety management from traditional human and physical defenses to an AI-enabled proactive protection system [1] - Operational maintenance transitions from "post-failure repairs and regular maintenance" to "predictive maintenance and condition-based repairs" [1] - Decision-making in trading evolves from relying on experience and localized information to intelligent auxiliary decision-making based on massive data integration and multi-model optimization [1] - Scheduling operations upgrade from manual judgment and single-point optimization to globally coordinated intelligent scheduling that incorporates multi-dimensional information such as meteorological conditions, market supply and demand, and equipment status [1] Group 2: Impact on Specific Areas - In the field of electricity trading, "Qingyuan" acts as a "smart trading advisor," accurately predicting weather changes, warning of water risks, and analyzing market conditions to support spot trading decisions [2] - For a 600-megawatt power generation unit, production costs can decrease by 0.3%, enhancing profitability by 2% [2] - In equipment maintenance, "Qingyuan" can keenly sense the status of units, intelligently formulate maintenance strategies, and shift the maintenance model from traditional "reactive fault handling" to "preventive condition-based maintenance" [2]

Core Insights - The State Energy Group has officially launched the "Qingyuan" power generation model, a billion-level model aimed at creating an intelligent decision-making system covering safety, environmental protection, electricity trading, production regulation, and equipment maintenance [1][2] Group 1: Innovations and Features - The "Qingyuan" model achieves three major innovative breakthroughs: 1. It integrates multi-source heterogeneous data such as operational monitoring, equipment status, and meteorological conditions to create a full-stack product matrix of "model-intelligent agent-application," enabling efficient dynamic collaboration across business units [1] 2. It provides a comprehensive AI solution specifically designed for the power system, covering "source-network-load-storage" scenarios, achieving vertical integration of power production through intelligent technology [1] 3. It utilizes a fully domestic technology stack, combining reinforcement learning and multi-modal fusion technology to establish an adaptive training and decision-making framework, creating a closed-loop verification system covering the entire lifecycle of power generation [1] Group 2: Application and Impact - The "Qingyuan" model has been successfully applied in four major business areas: safety and environmental protection, electricity trading, production regulation, and equipment maintenance, covering 13 scenarios and deploying 41 intelligent agents, effectively addressing pain points such as high safety risks, difficult trading decisions, complex multi-energy coordination, and passive equipment operation and maintenance [1] - In the area of equipment maintenance, the model has been applied in 179 pilot power stations, where it has detected 2,633 defects over six months by monitoring real-time data and sensing minute changes, overcoming challenges related to early defect detection and quantification [2] - The State Energy Group plans to advance the "Qingyuan" model through three phases: pilot verification, large-scale promotion, and ecosystem co-construction, gradually opening API interfaces to industry chain partners to build an open ecosystem for the power generation industry [2]

Core Viewpoint - The launch of China's first trillion-level power generation industry model, "Qingyuan," marks a significant advancement in the energy sector, aiming to lead the industry towards intelligence and digitalization [1][2]. Group 1: Model Overview - "Qingyuan" is developed by the State Power Investment Corporation and encompasses 15 business domains and 75 key application scenarios, integrating various aspects of power generation including construction, operation, maintenance, and fuel management [1]. - The model aims to leverage AI and big data to reshape the energy industry, utilizing the vast data assets of the State Power Investment Corporation, which is the largest power generation company globally [1]. Group 2: Innovations and Features - "Qingyuan" introduces three major innovations, including a multi-energy collaborative dynamic optimization engine that integrates operational monitoring, equipment status, and environmental data [2]. - The model features a full-stack product approach, allowing efficient collaboration among different business intelligent agents, and provides intelligent optimization solutions for the entire power generation process [2]. - It establishes a fully autonomous and controllable intelligent decision-making system based on domestic technology, utilizing reinforcement learning and multi-modal fusion methods [2]. Group 3: Application and Impact - Currently, "Qingyuan" has been successfully applied in various business areas such as safety and environmental protection, power trading, and equipment maintenance, covering 13 scenarios and deploying 41 intelligent agents [2]. - The model addresses long-standing challenges in the power generation industry, including high safety risks, complex multi-energy coordination, and passive equipment maintenance, thereby supporting safe, efficient, green, and intelligent power generation [2].

Core Insights - The National Energy Group launched the world's first trillion-level power generation large model, "Qingyuan," aimed at addressing key pain points in the power generation industry such as high safety risks and complex decision-making [1][3] Group 1: Model Features and Applications - "Qingyuan" is designed specifically for the power system, achieving breakthroughs in heterogeneous data fusion, cross-business intelligent collaboration, and autonomous intelligent decision-making [1][3] - The model's initial applications cover four business domains: safety and environmental protection, power trading, production scheduling, and equipment maintenance, encompassing 13 application scenarios and 41 intelligent agents [1][4] - The model is built on a high-quality industry dataset of 450G, resulting in 610 million sets of SFT Q&A pairs, outperforming base models by 19.6 percentage points in the power generation field [4] Group 2: Industry Challenges and Solutions - The power generation industry faces challenges in the intelligent transformation process, including technical barriers and a lack of understanding of large model applications among production personnel [3][4] - The National Energy Group employs a "dual-domain responsibility" model to bridge the gap between technology and business personnel, fostering collaboration to explore AI empowerment in the power generation sector [3][4] Group 3: Efficiency Improvements - In the safety and environmental protection domain, "Qingyuan" significantly enhances the efficiency of technical supervision evaluations, reducing the evaluation time from one week to one day for a power plant [4] - In the power trading domain, "Qingyuan" improves electricity price prediction accuracy by optimizing model combinations based on different environments [5] - The model also optimizes scheduling and predictive indicators in production scheduling and provides precise diagnostics and maintenance strategies in equipment maintenance [5] Group 4: Data Security and Decision Timeliness - The implementation of the large model faces challenges related to data security and decision-making timeliness, with measures in place to ensure data safety through strict transmission protocols and classified data management [6] - Future plans include "model distillation" to refine large model capabilities into smaller models for localized deployment, addressing specific scenarios requiring rapid response [6][7] - The National Energy Group aims to promote the "Qingyuan" model through pilot verification, large-scale promotion, and ecosystem co-construction [6][7]

Core Insights - The China National Energy Group has launched the world's first trillion-level power generation model, "Qingyuan," which integrates 15 business domains and 75 key application scenarios in the power generation industry [1] - "Qingyuan" aims to address long-standing issues in the power generation sector, such as high safety risks, complex multi-energy coordination, and passive equipment maintenance [1] - The model has already been successfully applied in 13 scenarios and deployed 41 intelligent agents, showcasing its effectiveness in real-time decision-making [1] Group 1 - "Qingyuan" covers a comprehensive intelligent decision-making system that includes safety and environmental protection, electricity trading, production scheduling, and equipment maintenance [1] - The model demonstrated its predictive capabilities by forecasting a heavy rainfall event seven days in advance, allowing for timely flood management decisions [1] - Central enterprises are increasingly adopting artificial intelligence, with trillion-level models emerging across various industries [1] Group 2 - In December of last year, the State Grid released the "Guangming" multi-modal industry model, which serves the entire power industry chain [2] - In May, China Petroleum introduced the "Kunlun" model with 300 billion parameters, applicable across the oil and gas industry chain [2] - As of late March this year, central enterprises have deployed artificial intelligence in over 500 scenarios across key industries such as industrial manufacturing and energy power [2]