最近，互联网兴起了一个新"梗"——"一觉醒来，我们成'工业克苏鲁'了？" 根据美国商务部经济分析局数据，2024年，在执行了数年轰轰烈烈的"制造业回流"计划后，美国工业增加值创下3.74亿美元的历史新高，其中制造业增加值 为2.91亿美元，同样创下新高。 而根据德国联邦统计局与日本内阁府及日本银行数据，2024年，德国与日本——仅次于美国——全年工业增加值分别为0.98亿美元、0.93亿美元；制造业增 加值0.83亿美元、0.82亿美元，均不到美国的三成。 那么中国呢？ 根据我国工业和信息化部数据，2024年，中国全部工业增加值完成40.5万亿人民币（约5.69亿美元），总规模连续15年保持全球第一，占全球超30%，是 美、德、日三国总和还多。 1995-2024年中、美、欧、德、日、印、俄、韩各国工业增加值变化；图片来源：经济数据智能分析平台 尤其是能源、材料、冶金、机械制造、化工这些重工业领域，举例而言，2024年，我国规模以上企业水泥产量18.25亿吨，占全球总产量近50%，连续39年 位居世界第一。 而作为全球领先的非金属材料制造商中国建材集团，不仅全面推进"水泥+"战略，更围绕"无机非金属材料、有机 ...

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]

Group 1 - The core viewpoint of the articles is the successful development and application of the "Xiaomiao" industrial model by China National Building Material Group, which enhances decision-making and operational efficiency in the cement industry through AI technology [1][2] - The "Xiaomiao" model integrates various data and AI architectures, enabling real-time control of production processes and end-to-end optimization of business decisions, resulting in over 1% reduction in cement mixing costs [1] - The model has been successfully implemented in nearly 100 cement enterprises, demonstrating a standardized and replicable implementation plan that compresses data governance cycles to as short as 14 days and model deployment to 7 days [2] Group 2 - The application of AI technology is expected to drive a deep transformation in the building materials industry towards smarter, greener, and higher-end operations, significantly improving overall operational efficiency across key processes such as R&D, manufacturing, supply chain management, and sales services [2] - The average investment return period for the "Xiaomiao" model is approximately 1 year, indicating a favorable economic impact for enterprises adopting this technology [2]

Core Viewpoint - The integration of artificial intelligence (AI) technology is driving the transformation of the building materials industry towards a more intelligent, greener, and high-end direction [1][4]. Group 1: AI Integration and Digital Transformation - Through digital transformation, building material companies can achieve digitalization in R&D design, integrated production operations, and agile customer service, enhancing decision-making efficiency and overall competitiveness [1][3]. - The "Xiao Miao" industrial model developed by the Smart Building Materials Research Institute has shown significant application results in the cement industry, enabling real-time closed-loop control of production and end-to-end optimization of business decisions [2][3]. Group 2: Cost Reduction and Efficiency Improvement - The "Xiao Miao" model has reduced the cost of cement batching by over 1%, demonstrating its effectiveness in lowering costs and increasing efficiency [3]. - The model has established a replicable and lightweight standardized implementation plan, with data governance cycles reduced to under 14 days and model deployment times under 7 days, achieving an average return on investment period of about one year [3]. Group 3: Industry Transformation and Future Applications - AI-driven changes in the building materials industry manifest in three areas: profound changes in human-machine interaction at the factory level, regional central control and collaborative operations at the regional company level, and optimization of operational models and decision-making at the corporate group level [4]. - The industrial model currently includes over 200 scenario models, covering the entire supply chain from procurement to production and sales in the cement industry [4]. Future applications will focus on intelligent product design, digital twin factories, and dynamic pricing among others, aiming for comprehensive coverage of business operations [4].