[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-f9206efa-6fcf-4a2f-9020-cf1450946d73":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"f9206efa-6fcf-4a2f-9020-cf1450946d73","把世界模型搬进工厂：基点起源「全要素大模型」如何压缩定制化交付周期","原零一万物联创、华为云 AI CTO 戴宗宏创立的基点起源，近日披露自研的「全要素大模型」。这套工业 AI 操作系统把定制化项目从「几百人驻场、数月交付」压缩到「一人控制、两周交付」，核心是构建能反映真实生产过程的数字孪生工业世界模型。\n\n技术上，全要素大模型围绕「学习—寻优—交付」三步运行：学习阶段用原始业务数据训练出可持续更新的数字孪生模型，把注意力集中在对良品率、产能等关键指标影响更大的数据上；寻优阶段借助强化学习在数字孪生体中持续推演，找更优工艺组合；交付阶段一线工人通过极简 App 输入现场参数即可获得当下最优操作方案。\n\n这种把世界模型范式落地到工业场景值得关注：传统咨询把专家经验建模成工作流，全要素大模型把工作流嵌入可自我推演的数字孪生体，用模型挖掘生产链每个潜在优化点，提升整条产线良率与产能，而非替代工人。冶金、化工、精密制造等行业的落地表明，工业 LLM 的核心价值不在「聊天」，而在「替企业沉淀出可计算的业务知识」。\n\n工业大模型这一年正在快速去泡沫：能跑通数字孪生 + RL + 现场交付闭环的产品，比任何参数量数据都更能决定谁能真正留在 B 端定制化市场。","https:\u002F\u002F36kr.com\u002Fp\u002F3869445453305090","5e4fd3d1-9cb4-44a6-bae5-9ffb449c05c1",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"e676a5cf-1f24-472f-a765-86fa21a1bc3c","ai-model",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"id":18,"name":19,"slug":19,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-06-28T12:27:33.080882Z","2026-06-28T12:27:39.761275Z","2026-06-28T12:27:39.761290Z",true,"agent",3]