[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"chain-detail":3,"chain-related-ch60_T01":478,"chain-scenarios-ch60_T01":486,"chain-expert-resolve-ch60_T01":1654,"chain-related":1671},{"id":4,"name":5,"category":6,"groupCategory":7,"intro":8,"scale":9,"maturity":10,"meta":11,"checkState":249,"version":250,"createdBy":9,"createdAt":251,"updatedAt":252,"nodes":253},"ch60_T01","钢铁产业链","传统产业","TRADITIONAL","黑色金属冶炼及压延加工业，向特种钢、绿色低碳转型",null,"转型期",{"codes":12,"counts":14,"source":19,"experts":20,"isNew60":61,"shaanxi":62,"clusters":63,"maturity":10,"source60":71,"chainCode":13,"bottlenecks":72,"subCategory":215,"lifecycleStage":216,"key_technologies":217,"national_policies":225},[13],"T01",{"clusters":15,"segments":16,"companies":17,"bottlenecks":18},6,9,14,7,"全产业智慧图谱数据库（公开版）",[21,27,32,37,42,46,51,56],{"org":22,"raw":23,"name":24,"note":25,"title":26},"中国钢研科技集团","干勇（中国钢研科技集团），中国工程院院士，钢铁冶金与氢冶金战略研究","干勇","钢铁冶金与氢冶金战略研究","中国工程院院士",{"org":28,"raw":29,"name":30,"note":31,"title":26},"钢铁研究总院","殷瑞钰（钢铁研究总院），中国工程院院士，冶金流程工程学与绿色制造","殷瑞钰","冶金流程工程学与绿色制造",{"org":33,"raw":34,"name":35,"note":36,"title":26},"东北大学","王国栋（东北大学），中国工程院院士，轧制技术与连轧自动化","王国栋","轧制技术与连轧自动化",{"org":38,"raw":39,"name":40,"note":41,"title":26},"北京科技大学","毛新平（北京科技大学），中国工程院院士，硅钢与薄板坯连铸连轧技术","毛新平","硅钢与薄板坯连铸连轧技术",{"org":38,"raw":43,"name":44,"note":45,"title":26},"朱荣（北京科技大学），中国工程院院士，电弧炉短流程与氢冶金低碳冶炼","朱荣","电弧炉短流程与氢冶金低碳冶炼",{"org":47,"raw":48,"name":49,"note":50,"title":26},"中钢集团洛阳耐火材料研究院","李红霞（中钢集团洛阳耐火材料研究院），中国工程院院士，耐火材料领域专家","李红霞","耐火材料领域专家",{"org":52,"raw":53,"name":54,"note":55,"title":26},"西北有色金属研究院","张平祥（西北有色金属研究院），中国工程院院士，超导与稀有金属材料","张平祥","超导与稀有金属材料",{"org":52,"raw":57,"name":58,"note":59,"title":60},"赵永庆（西北有色金属研究院），副总工程师、教授级高工，钛合金专家","赵永庆","钛合金专家","副总工程师、教授级高工",true,"钢铁深加工",[64,68],{"city":65,"name":66,"level":67},"韩城","韩城经济技术开发区","省级",{"city":69,"name":70,"level":9},"中国钛谷","宝鸡高新技术产业开发区","全产业60链图谱",[73,98,128,146,162,183,199],{"name":74,"risk":75,"type":9,"status":76,"experts":77,"policies":79,"progress":86,"companies":87,"policy_support":97},"高端取向硅钢","风险：高","国产替代",[78],{"org":38,"raw":39,"name":40,"note":41,"title":26},[80],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},"《钢铁行业稳增长工作方案（2025—2026年）》（工业和信息化部等部门，2025-08）：提出钢铁行业稳增长目标，推进产能治理、结构优化与绿色低碳转型","2025-08","钢铁行业稳增长工作方案（2025—2026年）","工业和信息化部等部门","提出钢铁行业稳增长目标，推进产能治理、结构优化与绿色低碳转型","宝武、首钢高牌号取向硅钢",[88,91,94],{"name":89,"note":90},"宝钢股份","取向硅钢全球领先",{"name":92,"note":93},"首钢股份","首钢智新电磁材料",{"name":95,"note":96},"望变电气","民营取向硅钢","钢铁稳增长",{"name":99,"risk":100,"type":101,"status":76,"experts":102,"policies":105,"progress":118,"companies":119,"policy_support":127},"氢冶金低碳冶炼技术","风险：中","工艺技术",[103,104],{"org":22,"raw":23,"name":24,"note":25,"title":26},{"org":38,"raw":43,"name":44,"note":45,"title":26},[106,112],{"raw":107,"date":108,"name":109,"doc_no":9,"issuer":110,"summary":111},"《全国碳排放权交易市场覆盖钢铁、水泥、铝冶炼行业工作方案》（生态环境部，2025-03）：将钢铁行业纳入全国碳排放权交易市场，以市场化机制推动行业减碳","2025-03","全国碳排放权交易市场覆盖钢铁、水泥、铝冶炼行业工作方案","生态环境部","将钢铁行业纳入全国碳排放权交易市场，以市场化机制推动行业减碳",{"raw":113,"date":114,"name":115,"doc_no":9,"issuer":116,"summary":117},"《中共中央关于制定国民经济和社会发展第十五个五年规划的建议》（中共中央，2025-10）：部署建设现代化产业体系，推动钢铁等传统产业转型升级、绿色化发展","2025-10","中共中央关于制定国民经济和社会发展第十五个五年规划的建议","中共中央","部署建设现代化产业体系，推动钢铁等传统产业转型升级、绿色化发展","宝武八一气基氢冶金、河钢示范",[120,122,125],{"name":89,"note":121},"湛江氢基竖炉＋电炉近零碳产线",{"name":123,"note":124},"河钢集团","张宣科技氢冶金示范工程",{"name":22,"note":126},"氢冶金技术研发","钢铁低碳",{"name":129,"risk":75,"type":9,"status":130,"experts":131,"policies":132,"progress":134,"companies":135,"policy_support":145},"航空航天高温合金","已突破",[],[133],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},"抚顺特钢、宝武特冶产业化",[136,138,141,143],{"name":137,"note":9},"抚顺特钢",{"name":139,"note":140},"钢研高纳","高温合金龙头",{"name":142,"note":9},"图南股份",{"name":144,"note":9},"西部超导","特钢专项",{"name":147,"risk":75,"type":9,"status":148,"experts":149,"policies":150,"progress":152,"companies":153,"policy_support":9},"高端轴承钢（高铁\u002F风电\u002F机床用）","部分突破",[],[151],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},"中国钢研新一代长寿命轴承钢首次通过165万公里耐久台架考核验证，未来有望在时速最高等级高铁CR450实现装车应用（2025年国务院国资委披露）",[154,157,160],{"name":155,"note":156},"中信泰富特钢","兴澄特钢轴承钢",{"name":158,"note":159},"济源钢铁","优特钢\u002F轴承钢",{"name":161,"note":9},"宝武特冶",{"name":163,"risk":100,"type":101,"status":148,"experts":164,"policies":166,"progress":174,"companies":175,"policy_support":9},"电炉短流程与废钢高效利用",[165],{"org":38,"raw":43,"name":44,"note":45,"title":26},[167,173],{"raw":168,"date":169,"name":170,"doc_no":9,"issuer":171,"summary":172},"《关于2025年加力扩围实施大规模设备更新和消费品以旧换新政策的通知》（国家发展改革委、财政部，2025-01）：加力推进设备更新与消费品以旧换新，带动钢铁需求及废钢回收循环利用","2025-01","关于2025年加力扩围实施大规模设备更新和消费品以旧换新政策的通知","国家发展改革委、财政部","加力推进设备更新与消费品以旧换新，带动钢铁需求及废钢回收循环利用",{"raw":107,"date":108,"name":109,"doc_no":9,"issuer":110,"summary":111},"河北建立全国首个废钢铁与短流程电炉钢产业联盟（河北省政府2025年公开披露）",[176,178,181],{"name":89,"note":177},"湛江电炉短流程",{"name":179,"note":180},"沙钢集团","电炉炼钢",{"name":123,"note":182},"石钢电炉短流程",{"name":184,"risk":100,"type":9,"status":148,"experts":185,"policies":187,"progress":189,"companies":190,"policy_support":9},"超高强汽车板与热成形钢",[186],{"org":33,"raw":34,"name":35,"note":36,"title":26},[188],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},"河北布局2.2GPa级高耐蚀耐高温镀层热成形汽车钢开发等6个省级科技专项；河钢全球首条氢冶金绿色汽车板连铸生产线投产（2025年河北省政府披露）",[191,193,195,198],{"name":89,"note":192},"吉帕钢等超高强钢",{"name":194,"note":9},"鞍钢股份",{"name":196,"note":197},"华菱钢铁","VAMA汽车板",{"name":92,"note":9},{"name":200,"risk":75,"type":9,"status":201,"experts":202,"policies":203,"progress":205,"companies":206,"policy_support":9},"铁矿石资源保障（高品位矿对外依存）","依赖进口",[],[204],{"raw":113,"date":114,"name":115,"doc_no":9,"issuer":116,"summary":117},"中国钢铁工业协会推进铁资源保障基石计划，推动国内铁矿开发、废钢资源利用与海外权益矿建设，降低铁矿石对外依存度",[207,209,211,213],{"name":208,"note":9},"中国矿产资源集团",{"name":210,"note":9},"宝武资源",{"name":212,"note":9},"鞍钢矿业",{"name":214,"note":9},"河钢资源","冶金（钢铁）","TRANSFORMATION",[218,219,220,221,222,223,224],{"risk":75,"tech":74,"type":9,"status":76,"bottleneck":61,"domestic_status":86},{"risk":100,"tech":99,"type":101,"status":76,"bottleneck":61,"domestic_status":118},{"risk":75,"tech":129,"type":9,"status":130,"bottleneck":61,"domestic_status":134},{"risk":75,"tech":147,"type":9,"status":148,"bottleneck":61,"domestic_status":152},{"risk":100,"tech":163,"type":101,"status":148,"bottleneck":61,"domestic_status":174},{"risk":100,"tech":184,"type":9,"status":148,"bottleneck":61,"domestic_status":189},{"risk":75,"tech":200,"type":9,"status":201,"bottleneck":61,"domestic_status":205},[226,227,228,229,230,236,242],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},{"raw":107,"date":108,"name":109,"doc_no":9,"issuer":110,"summary":111},{"raw":168,"date":169,"name":170,"doc_no":9,"issuer":171,"summary":172},{"raw":113,"date":114,"name":115,"doc_no":9,"issuer":116,"summary":117},{"raw":231,"date":232,"name":233,"doc_no":9,"issuer":234,"summary":235},"《陕西省钢铁深加工产业链提升方案》（陕西省工业和信息化厅，2021-12）：推进钢铁深加工产业链提升，发展优特钢与钢材深加工","2021-12","陕西省钢铁深加工产业链提升方案","陕西省工业和信息化厅","推进钢铁深加工产业链提升，发展优特钢与钢材深加工",{"raw":237,"date":238,"name":239,"doc_no":9,"issuer":240,"summary":241},"《陕西省“十四五”制造业高质量发展规划》（陕西省人民政府办公厅，2021-11）：将钢铁深加工等传统产业高端化、智能化、绿色化升级列为重点任务","2021-11","陕西省“十四五”制造业高质量发展规划","陕西省人民政府办公厅","将钢铁深加工等传统产业高端化、智能化、绿色化升级列为重点任务",{"raw":243,"date":244,"name":245,"doc_no":246,"issuer":247,"summary":248},"《关于建立省重点产业链“链长制”工作机制的通知》（陕办字〔2024〕20号）（陕西省委办公厅、省政府办公厅，2024）：省级重点产业链优化拓展为34条并建立链长制，含钢铁深加工产业链","2024","关于建立省重点产业链“链长制”工作机制的通知","陕办字〔2024〕20号","陕西省委办公厅、省政府办公厅","省级重点产业链优化拓展为34条并建立链长制，含钢铁深加工产业链","VERIFIED",1,"2026-10-11T22:08:50.411Z","2026-10-12T14:28:53.874Z",[254,279,309,330,358,378,408,431,456],{"id":255,"chainId":4,"layer":256,"segment":257,"name":257,"keyPoints":9,"companies":258,"sortOrder":270,"isKey":271,"checkState":249,"meta":272,"createdAt":277,"links":278},"cnH_T01_01","上游","铁矿石采选",[259,261,262,263,265,267],{"name":260,"note":210},"中国宝武",{"name":212,"note":9},{"name":214,"note":9},{"name":264,"note":9},"首钢矿业",{"name":266,"note":9},"海南矿业",{"name":268,"note":269},"陕西大西沟矿业有限公司","商洛柞水，菱铁矿采选，陕钢集团旗下",0,false,{"source":19,"experts":273,"policies":274,"chainCode":13,"aggregationZones":276},[],[275],{"raw":113,"date":114,"name":115,"doc_no":9,"issuer":116,"summary":117},[],"2026-10-12T14:28:53.882Z",[],{"id":280,"chainId":4,"layer":256,"segment":281,"name":281,"keyPoints":9,"companies":282,"sortOrder":250,"isKey":271,"checkState":249,"meta":300,"createdAt":307,"links":308},"cnH_T01_02","焦化与烧结",[283,286,289,292,294,297],{"name":284,"note":285},"山西焦煤","炼焦煤龙头",{"name":287,"note":288},"旭阳集团","独立焦化龙头企业",{"name":290,"note":291},"宝武碳业","焦化与碳材料",{"name":293,"note":9},"中国平煤神马集团",{"name":295,"note":296},"陕西黑猫焦化股份有限公司","601015，韩城，焦炭与化工",{"name":298,"note":299},"陕西陕焦化工有限公司","韩城，焦化",{"source":19,"experts":301,"policies":302,"chainCode":13,"aggregationZones":304},[],[303],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},[305],{"city":65,"name":66,"note":306,"level":67},"钢铁与焦化产业集聚区","2026-10-12T14:28:53.885Z",[],{"id":310,"chainId":4,"layer":311,"segment":312,"name":312,"keyPoints":9,"companies":313,"sortOrder":319,"isKey":271,"checkState":249,"meta":320,"createdAt":328,"links":329},"cnH_T01_03","中游","炼铁",[314,315,316,317,318],{"name":89,"note":9},{"name":92,"note":9},{"name":194,"note":9},{"name":179,"note":9},{"name":123,"note":9},2,{"source":19,"experts":321,"policies":324,"chainCode":13,"aggregationZones":327},[322,323],{"org":28,"raw":29,"name":30,"note":31,"title":26},{"org":22,"raw":23,"name":24,"note":25,"title":26},[325,326],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},{"raw":107,"date":108,"name":109,"doc_no":9,"issuer":110,"summary":111},[],"2026-10-12T14:28:53.887Z",[],{"id":331,"chainId":4,"layer":311,"segment":332,"name":332,"keyPoints":9,"companies":333,"sortOrder":347,"isKey":271,"checkState":249,"meta":348,"createdAt":356,"links":357},"cnH_T01_04","炼钢",[334,335,336,337,338,341,344],{"name":89,"note":9},{"name":179,"note":9},{"name":155,"note":9},{"name":123,"note":9},{"name":339,"note":340},"陕西钢铁集团有限公司","陕西省最大钢铁企业，总部西安",{"name":342,"note":343},"陕西龙门钢铁有限责任公司","韩城，陕钢集团旗下长流程钢企",{"name":345,"note":346},"陕钢集团汉中钢铁有限责任公司","汉中勉县",3,{"source":19,"experts":349,"policies":352,"chainCode":13,"aggregationZones":355},[350,351],{"org":28,"raw":29,"name":30,"note":31,"title":26},{"org":38,"raw":43,"name":44,"note":45,"title":26},[353,354],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},{"raw":107,"date":108,"name":109,"doc_no":9,"issuer":110,"summary":111},[],"2026-10-12T14:28:53.891Z",[],{"id":359,"chainId":4,"layer":360,"segment":361,"name":361,"keyPoints":9,"companies":362,"sortOrder":368,"isKey":271,"checkState":249,"meta":369,"createdAt":376,"links":377},"cnH_T01_05","下游","轧钢与深加工",[363,364,365,366],{"name":89,"note":9},{"name":194,"note":9},{"name":92,"note":9},{"name":367,"note":9},"南钢股份",4,{"source":19,"experts":370,"policies":373,"chainCode":13,"aggregationZones":375},[371,372],{"org":33,"raw":34,"name":35,"note":36,"title":26},{"org":38,"raw":39,"name":40,"note":41,"title":26},[374],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},[],"2026-10-12T14:28:53.895Z",[],{"id":379,"chainId":4,"layer":360,"segment":380,"name":380,"keyPoints":9,"companies":381,"sortOrder":398,"isKey":271,"checkState":249,"meta":399,"createdAt":406,"links":407},"cnH_T01_06","特种钢与高端钢材",[382,383,384,385,387,389,392,395],{"name":155,"note":9},{"name":137,"note":9},{"name":161,"note":9},{"name":386,"note":9},"太钢不锈",{"name":388,"note":9},"久立特材",{"name":390,"note":391},"宝钛集团有限公司","宝鸡，钛及钛合金加工龙头",{"name":393,"note":394},"西部超导材料科技股份有限公司","688122，西安经开区，高端钛合金与超导材料",{"name":396,"note":397},"西安三角防务股份有限公司","300775，阎良，航空模锻件",5,{"source":19,"experts":400,"policies":401,"chainCode":13,"aggregationZones":403},[],[402],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},[404],{"city":69,"name":70,"note":405,"level":9},"（宝鸡 · 国家级）：钛及钛合金产业集群","2026-10-12T14:28:53.897Z",[],{"id":409,"chainId":4,"layer":410,"segment":411,"name":411,"keyPoints":9,"companies":412,"sortOrder":15,"isKey":271,"checkState":249,"meta":422,"createdAt":429,"links":430},"cnH_T01_07","支撑","废钢回收与短流程",[413,416,419],{"name":414,"note":415},"欧冶链金","中国宝武废钢回收平台",{"name":417,"note":418},"格林美","再生资源回收利用",{"name":420,"note":421},"华宏科技","废钢加工设备",{"source":19,"experts":423,"policies":425,"chainCode":13,"aggregationZones":428},[424],{"org":38,"raw":43,"name":44,"note":45,"title":26},[426,427],{"raw":168,"date":169,"name":170,"doc_no":9,"issuer":171,"summary":172},{"raw":107,"date":108,"name":109,"doc_no":9,"issuer":110,"summary":111},[],"2026-10-12T14:28:53.901Z",[],{"id":432,"chainId":4,"layer":256,"segment":433,"name":433,"keyPoints":434,"companies":435,"sortOrder":18,"isKey":271,"checkState":249,"meta":448,"createdAt":454,"links":455},"cnH_T01_08","铁合金与耐火材料","硅锰、铬铁等铁合金与耐火材料是炼钢重要辅料，部分高端品种仍依赖进口",[436,439,442,444,446],{"name":437,"note":438},"鄂尔多斯","硅铁龙头企业",{"name":440,"note":441},"濮耐股份","耐火材料",{"name":443,"note":441},"北京利尔",{"name":445,"note":441},"瑞泰科技",{"name":447,"note":9},"中钢洛耐",{"source":19,"experts":449,"policies":451,"chainCode":13,"aggregationZones":453},[450],{"org":47,"raw":48,"name":49,"note":50,"title":26},[452],{"raw":81,"date":82,"name":83,"doc_no":9,"issuer":84,"summary":85},[],"2026-10-12T14:28:53.904Z",[],{"id":457,"chainId":4,"layer":360,"segment":458,"name":458,"keyPoints":459,"companies":460,"sortOrder":470,"isKey":271,"checkState":249,"meta":471,"createdAt":476,"links":477},"cnH_T01_09","钢材加工配送与贸易物流","钢材剪切加工、配送与贸易服务，连接钢厂与汽车、家电、建筑等终端用户",[461,464,467],{"name":462,"note":463},"欧冶云商","钢铁电商与加工配送",{"name":465,"note":466},"找钢网","钢铁电商平台",{"name":468,"note":469},"浙商中拓","钢铁供应链服务",8,{"source":19,"experts":472,"policies":473,"chainCode":13,"aggregationZones":475},[],[474],{"raw":168,"date":169,"name":170,"doc_no":9,"issuer":171,"summary":172},[],"2026-10-12T14:28:53.906Z",[],{"chainId":4,"chainName":5,"matchedTagNames":479,"experts":480,"achievements":482,"topUniversities":484,"note":485},[],{"count":270,"items":481},[],{"count":270,"items":483},[],[],"无标签映射",{"hasMapping":61,"chainId":4,"industry":487,"industryCode":488,"confidence":489,"source":490,"totals":493,"segments":500},"钢铁","I01","high",{"source_text":491,"source_url":492},"工信部指导、赛迪研究院《制造业数字化转型参考指引(2025版)》","",{"scenarios":494,"rated":495,"tools":496,"knowledgeModels":497,"dataElements":498,"talentSkills":499},98,76,84,88,116,105,[501,539,578,625,660,888,960,1037,1076,1130,1166,1299,1380],{"segment":502,"segment_code":503,"activity_group":504,"scenarios":505},"业财管理环节","J","经营管理",[506,524,532],{"scenario_code":507,"scenario_name":508,"rating":347,"pain_text":509,"tools":510,"knowledge_models":514,"data_elements":518,"talent_skills":521},"YB01-J-4-1","业财融合","数据无法有效共享和整合，限制业财融合所需的数据资源流通；系统集成可能面临较大的难度，无法充分利用智能化工具对资金业务数据进行深入挖掘和分析",[511,512,513],"NCCloud","EAS","GSCloud",[515,516,517],"风险识别","成本效益分析","智能化资金配置与调度优化",[519,520],"财务收款数据","销售开票数据，采购发票数据，财务付款数据等",[522,523],"财务管理与会计学","金融工程与风险管理",{"scenario_code":525,"scenario_name":526,"rating":368,"pain_text":527,"tools":528,"knowledge_models":529,"data_elements":530,"talent_skills":531},"YB01-J-4-2","财务管控与共享","缺乏自动化的支持，缺乏全方位人才潜能挖掘、基于复杂网络的人脉关系影响分析等智能化分析模型",[],[],[],[],{"scenario_code":533,"scenario_name":534,"rating":9,"pain_text":492,"tools":535,"knowledge_models":536,"data_elements":537,"talent_skills":538},"YB01-J-4-3","资金管理",[],[],[],[],{"segment":540,"segment_code":541,"activity_group":504,"scenarios":542},"人力资源管理环节","E",[543,563,571],{"scenario_code":544,"scenario_name":545,"rating":347,"pain_text":546,"tools":547,"knowledge_models":551,"data_elements":555,"talent_skills":559},"YB01-E-4-1","人力资源规划","由于系统不兼容等原因，无法有效整合来自不同部门和系统的数据",[548,549,550],"HRCloud","U8+HCM","eHR系统",[552,553,554],"人力资源需求预测模型","人才供应链优化模型","绩效评估模型",[556,557,558],"员工基本信息","组织架构与岗位信息","人员编制计划等",[560,561,562],"人力资源管理专业","数据分析与挖掘\u002F商业智能","工业工程（IE）",{"scenario_code":564,"scenario_name":565,"rating":347,"pain_text":566,"tools":567,"knowledge_models":568,"data_elements":569,"talent_skills":570},"YB01-E-4-2","招聘与人才库管理","传统的培训方式，难以激发员工的学习兴趣和动力。尚未应用动态适应性人才培养路径",[],[],[],[],{"scenario_code":572,"scenario_name":573,"rating":9,"pain_text":492,"tools":574,"knowledge_models":575,"data_elements":576,"talent_skills":577},"YB01-E-4-3","人才培训与发展",[],[],[],[],{"segment":579,"segment_code":580,"activity_group":581,"scenarios":582},"仓储物流管理环节","M","供应链管理",[583,602,610,618],{"scenario_code":584,"scenario_name":585,"rating":368,"pain_text":586,"tools":587,"knowledge_models":592,"data_elements":595,"talent_skills":599},"YB01-M-5-1","智能物料管理","信息流与数据共享不顺畅；缺乏数据同步和认证机制，系统间数据的异质性与准确性不好保证；独立系统间的数据传递存在壁垒、系统通用性差，定制成本高、数据安全问题亟待加强",[588,589,590,591],"生产过程信息系统软件平台","一体化ERP","CI.WMS","WMS",[593,594],"库存优化算法","采购优化算法",[596,597,598],"计划与排产类数据","生产过程类数据","配方管理类数据",[600,581,601],"物流管理","数据分析与可视化",{"scenario_code":603,"scenario_name":604,"rating":347,"pain_text":605,"tools":606,"knowledge_models":607,"data_elements":608,"talent_skills":609},"YB01-M-5-2","远程无人管控","现有的路线优化模型不完善，智能化水平较低，缺乏自适应自学习能力",[],[],[],[],{"scenario_code":611,"scenario_name":612,"rating":319,"pain_text":613,"tools":614,"knowledge_models":615,"data_elements":616,"talent_skills":617},"YB01-M-5-3","物流计划与路线优化","不同供应商和合作伙伴的数据格式和标准也不一致，增加了物流数据采集和整合的复杂性",[],[],[],[],{"scenario_code":619,"scenario_name":620,"rating":9,"pain_text":492,"tools":621,"knowledge_models":622,"data_elements":623,"talent_skills":624},"YB01-M-5-4","物流全流程监控降本降本降本降本降本降本降本降本降本降本主要编制单位：中国电子信息产业发展研究院、河钢数字技术股份公司、上海宝信软件股份有限公司、联通雄安产业互联网有限公司、首钢京唐钢铁联合有限责任公司、鞍钢集团经济发展研究院、北京智冶互联科技有限公司、中冶赛迪工程技术股份有限公司、中国钢铁工业协会、南京钢铁集团、中国电信集团E人力资源管理环节F设备管理环节G环保管理环节H能源管理环节I质量管理环节J业财管理环节K安全管理环节L采销管理环节M仓储物流管理环节钢铁行业数字化转型场景图谱（2025版）（2\u002F2）",[],[],[],[],{"segment":626,"segment_code":627,"activity_group":504,"scenarios":628},"安全管理环节","K",[629,637,645,652],{"scenario_code":630,"scenario_name":631,"rating":319,"pain_text":632,"tools":633,"knowledge_models":634,"data_elements":635,"talent_skills":636},"YB01-K-4-1","生产现场安全态势感知预警","部分企业更依赖于传统的应急处理方式，缺乏统一的应急管理系统，各个部门和环节之间的应急处理信息难以共享和协同，导致应急响应的碎片化和混乱",[],[],[],[],{"scenario_code":638,"scenario_name":639,"rating":347,"pain_text":640,"tools":641,"knowledge_models":642,"data_elements":643,"talent_skills":644},"YB01-K-4-2","标准化安全管理","不安全行为识别困难，人工监管存在诸多漏洞",[],[],[],[],{"scenario_code":646,"scenario_name":647,"rating":9,"pain_text":492,"tools":648,"knowledge_models":649,"data_elements":650,"talent_skills":651},"YB01-K-4-3","人员行为监测降本降本降本降本降本降本降本提质降本提质降本提质降本提质降本降本降本降本节能节能节能降本节能降本节能降本节能",[],[],[],[],{"scenario_code":653,"scenario_name":654,"rating":347,"pain_text":655,"tools":656,"knowledge_models":657,"data_elements":658,"talent_skills":659},"YB01-K-4-4","应急处理服务","能耗数据分散在不同的系统和平台中，集成和整合难度大",[],[],[],[],{"segment":661,"segment_code":662,"activity_group":663,"scenarios":664},"炼钢环节","C","生产制造",[665,689,696,717,725,733,740,763,778,786,794,802,810,817,825,833,841,849,872,880],{"scenario_code":666,"scenario_name":667,"rating":347,"pain_text":668,"tools":669,"knowledge_models":677,"data_elements":681,"talent_skills":685},"YB01-C-2-1","炼钢智能控制","对国外图像识别、深度学习算法工具具有依赖性，国内数字化工具的稳定性、准确性不足",[670,671,672,673,674,675,676],"自动炼钢系统","烟气分析系统","声呐化渣系统","自动化出钢系统","炉况红外监测系统","炉衬激光测厚系统","转炉下渣监测系统",[678,679,680],"库存管理模型","物流路径优化模型","料场三维测控模型",[682,683,684],"废钢信息","铁水信息","炉况信息等",[686,687,688],"冶金工程","机械工程","自动化控制等",{"scenario_code":690,"scenario_name":691,"rating":9,"pain_text":492,"tools":692,"knowledge_models":693,"data_elements":694,"talent_skills":695},"YB01-C-2-10","炉后吹氩",[],[],[],[],{"scenario_code":697,"scenario_name":698,"rating":368,"pain_text":699,"tools":700,"knowledge_models":705,"data_elements":711,"talent_skills":713},"YB01-C-2-11","精炼智能控制","缺少冶炼过程中钢水温度、炉内流场、夹杂物去除等模型，钢水温度模型精度不足；底吹寿命短，后期效果差；高温对炉衬侵蚀严重；终渣全铁高残锰低金属收得率差",[701,702,703,704],"精炼过程控制","自动接电极系统","钢包底吹氩自动控制系统","LF终点成分及温度预报系统等",[706,707,708,709,710],"二级精炼模型","终点成分预报模型","LF脱氧合金化模型","合金加入量计算模型","综合成分预测",[712],"RH炉的真空度，钢包炉加热速率等",[714,715,716],"计算机科学与技术","自动化","控制科学等",{"scenario_code":718,"scenario_name":719,"rating":347,"pain_text":720,"tools":721,"knowledge_models":722,"data_elements":723,"talent_skills":724},"YB01-C-2-12","底吹控制优化","传统的测温取样设备其精度逐渐降低，人工测温有滞后性",[],[],[],[],{"scenario_code":726,"scenario_name":727,"rating":347,"pain_text":728,"tools":729,"knowledge_models":730,"data_elements":731,"talent_skills":732},"YB01-C-2-13","自动测温取样","定期检查精度有限具有滞后性，很难实时把握钢包内衬侵蚀情况。钢包监测数据存储的系统分散，不利于掌握钢包的整体状态",[],[],[],[],{"scenario_code":734,"scenario_name":735,"rating":9,"pain_text":492,"tools":736,"knowledge_models":737,"data_elements":738,"talent_skills":739},"YB01-C-2-14","钢包监测",[],[],[],[],{"scenario_code":741,"scenario_name":742,"rating":347,"pain_text":743,"tools":744,"knowledge_models":747,"data_elements":751,"talent_skills":759},"YB01-C-2-15","无人化浇钢","人工浇钢质量和稳定性不高，容易发生安全事故",[745,746],"ERP，连铸机质量判定系统","连铸机在线震动分析工具",[748,749,750],"生产时间模型","凝固模型","温度场模型",[752,753,754,755,756,757,758],"库下渣监测","温度监测","液面监测","气雾冷却","电磁搅拌","视觉测量火焰切割","出站时间等",[760,761,762],"工艺创新设计","三维建模","连铸生产工艺",{"scenario_code":764,"scenario_name":765,"rating":368,"pain_text":766,"tools":767,"knowledge_models":771,"data_elements":772,"talent_skills":776},"YB01-C-2-16","智能钢包调度","人工录入信息无法实时监控钢包位置和热状态，人工调配行车导致作业效率无法评估；结晶器漏钢模型误报率高，缺少结晶器内部流场和温度场模型",[768,769,770],"天车调度系统","钢包路径规划系统","钢包选配系统",[678,679,680],[773,774,775],"钢包实时状态","生产设备状态","生产计划",[714,715,777],"控制科学",{"scenario_code":779,"scenario_name":780,"rating":347,"pain_text":781,"tools":782,"knowledge_models":783,"data_elements":784,"talent_skills":785},"YB01-C-2-17","结晶器生产控制","高温铸坯表面缺陷检测不及时，效率低，检测多以文本记录，数据利用率低",[],[],[],[],{"scenario_code":787,"scenario_name":788,"rating":347,"pain_text":789,"tools":790,"knowledge_models":791,"data_elements":792,"talent_skills":793},"YB01-C-2-18","钢坯在线质量检测","缺乏中间包液位自动采集、流场实时监测等功能",[],[],[],[],{"scenario_code":795,"scenario_name":796,"rating":347,"pain_text":797,"tools":798,"knowledge_models":799,"data_elements":800,"talent_skills":801},"YB01-C-2-19","中间包状态检测与管理","人工喷号字体的不规整，易出现信息录入错误或者录入不全",[],[],[],[],{"scenario_code":803,"scenario_name":804,"rating":368,"pain_text":805,"tools":806,"knowledge_models":807,"data_elements":808,"talent_skills":809},"YB01-C-2-2","铁水预处理","依赖人工经验，影响折铁的质量、效率和安全性",[],[],[],[],{"scenario_code":811,"scenario_name":812,"rating":9,"pain_text":492,"tools":813,"knowledge_models":814,"data_elements":815,"talent_skills":816},"YB01-C-2-20","连铸自动喷号机器人",[],[],[],[],{"scenario_code":818,"scenario_name":819,"rating":368,"pain_text":820,"tools":821,"knowledge_models":822,"data_elements":823,"talent_skills":824},"YB01-C-2-3","智慧折铁","缺乏能够综合考虑所有相关因素的炉况判断模型",[],[],[],[],{"scenario_code":826,"scenario_name":827,"rating":368,"pain_text":828,"tools":829,"knowledge_models":830,"data_elements":831,"talent_skills":832},"YB01-C-2-4","炉况智能监测","工人劳动强度大，扒渣强度、脱硫剂投放不标准化等问题",[],[],[],[],{"scenario_code":834,"scenario_name":835,"rating":319,"pain_text":836,"tools":837,"knowledge_models":838,"data_elements":839,"talent_skills":840},"YB01-C-2-5","智能溅渣出渣","缺乏精确的控制手段，钢包内部温度不均匀，出现钢包过度烘烤或烘烤不足的情况",[],[],[],[],{"scenario_code":842,"scenario_name":843,"rating":347,"pain_text":844,"tools":845,"knowledge_models":846,"data_elements":847,"talent_skills":848},"YB01-C-2-6","钢包智能烘烤","等待时间长、非计划停机多，能源消耗大，易发生安全事故",[],[],[],[],{"scenario_code":850,"scenario_name":851,"rating":368,"pain_text":852,"tools":853,"knowledge_models":858,"data_elements":861,"talent_skills":867},"YB01-C-2-7","废钢AI判级","高端产品冶炼能力不足、供电稳定性和智能控制能力不足、作业效率有待提高；预处理工艺尚不够成熟，影响铁水质量",[854,855,856,857],"电弧炉智能化控制解决方案","下渣检测系统","自动测温取样系统","自动出钢系统",[859,860],"废钢成像检测算法","废钢定级算法模型",[862,863,864,865,866],"生产计划数据","原料信息","冶炼工艺参数","钢种信息","废钢定级图片数据",[868,714,869,870,871],"机械自动化","电子信息工程","数据分析","图像分析等",{"scenario_code":873,"scenario_name":874,"rating":347,"pain_text":875,"tools":876,"knowledge_models":877,"data_elements":878,"talent_skills":879},"YB01-C-2-8","电炉智能控制","难以准确识别废钢边界，多台天车协作路径规划困难",[],[],[],[],{"scenario_code":881,"scenario_name":882,"rating":347,"pain_text":883,"tools":884,"knowledge_models":885,"data_elements":886,"talent_skills":887},"YB01-C-2-9","废钢无人天车","钢水建模和预测能力不足，污染气体排放难以有效控制",[],[],[],[],{"segment":889,"segment_code":890,"activity_group":663,"scenarios":891},"炼铁环节","B",[892,913,929,937,945,953],{"scenario_code":893,"scenario_name":894,"rating":347,"pain_text":895,"tools":896,"knowledge_models":899,"data_elements":905,"talent_skills":908},"YB01-B-2-1","高炉智能控制","现有模型无法准确描述高炉内部实际状况，控制效果不理想。设备故障、原材料波动等复杂工况的应对能力不足",[897,898],"智慧高炉运行平台","高炉自动化控制系统",[900,901,902,903,904],"高炉配料闭环模型","高炉碱度闭环控制模型","高炉炉热闭环控制模型","高炉布料闭环控制模型","优化决策模型",[906,907],"炉况数据","关键设备运行状态",[909,686,910,911,912],"控制科学与工程","系统工程","工程热物理","数据科学",{"scenario_code":914,"scenario_name":915,"rating":368,"pain_text":916,"tools":917,"knowledge_models":919,"data_elements":922,"talent_skills":926},"YB01-B-2-2","远程一键炉前控制","需进一步加快业务高度集成的转变，以实现更流畅工作流程和数据共享；铁水温度传感器由于恶劣的工作环境（高温、粉尘等）造成测量数据准确性和实时性不足，导致铁水调度计划与实际生产情况脱节",[918],"一键高炉炉前智能化系统",[920,921],"生产调度模型","物料平衡模型",[923,924,925],"高炉运行状态数据","炉前设备的运行状态","原料和燃料数据",[909,686,927,687,714,928],"自动化控制","电气工程",{"scenario_code":930,"scenario_name":931,"rating":347,"pain_text":932,"tools":933,"knowledge_models":934,"data_elements":935,"talent_skills":936},"YB01-B-2-3","铁水调度","原材料成分差异和粒度变化，导致配料计划难以精准实施；现有配料系统在动态响应方面存在滞后性，无法快速、精确地根据炉况变化调整配料量",[],[],[],[],{"scenario_code":938,"scenario_name":939,"rating":347,"pain_text":940,"tools":941,"knowledge_models":942,"data_elements":943,"talent_skills":944},"YB01-B-2-4","配料闭环管控","冶炼过程中的炉内温度不能自动采集，海量数据缺乏深度治理，自动采集数据质量不高",[],[],[],[],{"scenario_code":946,"scenario_name":947,"rating":347,"pain_text":948,"tools":949,"knowledge_models":950,"data_elements":951,"talent_skills":952},"YB01-B-2-5","炉况智能监控","部分抓渣系统可能采用格雷母线定位，在受到干扰或系统故障时容易出现定位错位，可能导致行车运行超限",[],[],[],[],{"scenario_code":954,"scenario_name":955,"rating":9,"pain_text":492,"tools":956,"knowledge_models":957,"data_elements":958,"talent_skills":959},"YB01-B-2-6","智能无人抓渣",[],[],[],[],{"segment":961,"segment_code":962,"activity_group":504,"scenarios":963},"环保管理环节","G",[964,984,992,999,1022,1030],{"scenario_code":965,"scenario_name":966,"rating":368,"pain_text":967,"tools":968,"knowledge_models":972,"data_elements":975,"talent_skills":981},"YB01-G-4-1","气体污染物浓度超限预警","监测设备可能受到环境因素的干扰，导致监测数据失真或设备损坏。现有的预警算法和模型无法完全适应钢铁行业特有的污染物排放特点和环境条件；缺乏长期监测和评估机制，导致评价结果缺乏连续性和可靠性。行业缺乏统一的环保质量评价标准，不同企业之间难以比较",[969,970,971],"SINAUTST7cc","SearchlineExcel","LGR-ICOS",[973,974],"排放物监测与预测模型","多元线性回归模型",[976,977,978,979,980],"排放物种类","排放浓度","排放速率","环境参数","跨区域气体污染协同预警机制",[982,983,714],"环境科学与工程","大气物理学与化学",{"scenario_code":985,"scenario_name":986,"rating":347,"pain_text":987,"tools":988,"knowledge_models":989,"data_elements":990,"talent_skills":991},"YB01-G-4-2","环保质量评价","尚未建立起一套完整的追踪和解析系统，以确保废钢等固废资源能够被高效地回收和再利用",[],[],[],[],{"scenario_code":993,"scenario_name":994,"rating":9,"pain_text":492,"tools":995,"knowledge_models":996,"data_elements":997,"talent_skills":998},"YB01-G-4-3","固废循环利用",[],[],[],[],{"scenario_code":1000,"scenario_name":1001,"rating":347,"pain_text":1002,"tools":1003,"knowledge_models":1007,"data_elements":1012,"talent_skills":1019},"YB01-G-4-4","碳资产管理","在碳资产数字化管理方面缺乏统一的数据标准和规范，导致不同企业之间的碳资产数据存在差异和不可比性。这增加了数据整合和分析的难度，限制了碳资产数字化管理的效果；平台建设方面相对滞后，现有的已建设数字化平台缺乏足够的功能和灵活性",[1004,1005,1006],"碳资产信息登记","碳资产管理系统","配额单价登记",[1008,1009,1010,1011],"碳汇评估模型","碳资产管理风险评估模型","碳价波动预测模型","碳资产组合优化模型",[1013,1014,1015,1016,1017,1018],"能源消耗记录","原料来源及碳含量","生产工艺过程参数","废弃物处理方式及利用途径","碳配额分配及交易记录","碳资产注册信息",[982,1020,1021],"工业自动化与智能化","经济学与金融学",{"scenario_code":1023,"scenario_name":1024,"rating":347,"pain_text":1025,"tools":1026,"knowledge_models":1027,"data_elements":1028,"talent_skills":1029},"YB01-G-4-5","碳中和","缺乏完善的数字化管理系统和工具，在碳足迹数据收集和监测方面存在不足，导致数据不准确、不完整",[],[],[],[],{"scenario_code":1031,"scenario_name":1032,"rating":9,"pain_text":492,"tools":1033,"knowledge_models":1034,"data_elements":1035,"talent_skills":1036},"YB01-G-4-6","碳足迹",[],[],[],[],{"segment":1038,"segment_code":1039,"activity_group":504,"scenarios":1040},"能源管理环节","H",[1041,1060,1068],{"scenario_code":1042,"scenario_name":1043,"rating":347,"pain_text":1044,"tools":1045,"knowledge_models":1047,"data_elements":1051,"talent_skills":1056},"YB01-H-4-1","能源诊断分析","钢铁行业在能源智能化诊断分析方面的应用场景可能相对有限，主要集中在能源消耗监测、能效评估和故障预警等方面。对于更复杂的能源优化和决策支持等场景，智能化诊断分析的应用可能还不够成熟",[1046],"能源监控与数据分析平台",[1048,1049,1050],"能源效率优化模型","热能回收与优化模型","能源成本分析模型",[1052,1053,1054,1055],"能耗数据","生产负荷数据","产品质量数据","余热数据",[928,1057,912,1058,1059],"计算机科学","软件工程","能源管理工程",{"scenario_code":1061,"scenario_name":1062,"rating":347,"pain_text":1063,"tools":1064,"knowledge_models":1065,"data_elements":1066,"talent_skills":1067},"YB01-H-4-2","能耗数据监控与优化","能源调度系统可能无法实时获取和处理不同系统的数据",[],[],[],[],{"scenario_code":1069,"scenario_name":1070,"rating":319,"pain_text":1071,"tools":1072,"knowledge_models":1073,"data_elements":1074,"talent_skills":1075},"YB01-H-4-3","能源调度","系统缺乏智能化支持，无法充分利用数据资源进行预测、分析和优化",[],[],[],[],{"segment":1077,"segment_code":1078,"activity_group":504,"scenarios":1079},"设备管理环节","F",[1080,1099,1115,1123],{"scenario_code":1081,"scenario_name":1082,"rating":368,"pain_text":1083,"tools":1084,"knowledge_models":1089,"data_elements":1092,"talent_skills":1097},"YB01-F-4-1","设备全生命周期管理","数据获取和处理存在难度，限制了监测数据的互通和共享",[1085,1086,1087,1088],"智慧设备管理软件","钢铁设备","PHM","设备智能运维平台",[1090,1091],"设备状态监测模型","设备实时状态监测模型",[1093,1094,1095,1096],"连续运行的小时数","关键部位温","度读数","振动频率和幅度",[1098],"机械制造与自动化等",{"scenario_code":1100,"scenario_name":1101,"rating":347,"pain_text":1102,"tools":1103,"knowledge_models":1107,"data_elements":1110,"talent_skills":1112},"YB01-F-4-2","设备预测性维护","数据质量不高、标注不准确，会严重影响模型的训练效果和预测性维护的准确性；多数钢铁企业尚未开展市场利润预测性核算，部分开展的企业模型可能无法充分考虑所有影响因素，预测结果存在偏差",[1104,1105,1106],"智能运维平台","设备预测性维护解决方案","预测性维护解决方案",[1108,1109],"设备状态监测","设备故障预测",[1111],"工业主机等的运行数据和配置",[1113,715,1114],"钢铁冶金","机电一体化",{"scenario_code":1116,"scenario_name":1117,"rating":347,"pain_text":1118,"tools":1119,"knowledge_models":1120,"data_elements":1121,"talent_skills":1122},"YB01-F-4-3","设备运行优化","人工离线监测，实时性差，安全风险高。存在不同程度的过维修及欠维修情况",[],[],[],[],{"scenario_code":1124,"scenario_name":1125,"rating":9,"pain_text":492,"tools":1126,"knowledge_models":1127,"data_elements":1128,"talent_skills":1129},"YB01-F-4-4","智能设备巡检",[],[],[],[],{"segment":1131,"segment_code":1132,"activity_group":504,"scenarios":1133},"质量管理环节","I",[1134,1151,1159],{"scenario_code":1135,"scenario_name":1136,"rating":347,"pain_text":1137,"tools":1138,"knowledge_models":1143,"data_elements":1146,"talent_skills":1150},"YB01-I-4-1","全流程质量追溯","信息系统相互独立，缺乏有效的数据共享接口，导致数据流通不畅。部分环节数据记录不全面，存在关键信息缺失的情况；现有系统缺乏高效的质量追溯、分析与优化技术。工艺设计通常依赖经验驱动，缺乏系统化的工艺信息集成和协同设计机制，使得工艺设计的合理性和可行性难以保障",[1139,1140,1141,1142],"工业互联网平台","云计算平台","数据加工处理工具","质量控制",[1144,1145],"基于物料族谱的多粒度数据融合与统一存储模型","精准在线质量判定与封锁模型",[332,1147,1148,1149],"轧钢","精整","热处理全流程L1工艺质量实时参数",[1113,714,869],{"scenario_code":1152,"scenario_name":1153,"rating":347,"pain_text":1154,"tools":1155,"knowledge_models":1156,"data_elements":1157,"talent_skills":1158},"YB01-I-4-2","质量工艺设计与优化","存在数据孤岛问题，使得跨流程的质量分析难以实现。检测设备陈旧，检测精度和速度无法满足现代生产需求。与质量相关冶金规范要求分布在独立系统或指导文件当中，缺乏有效的关联和协同",[],[],[],[],{"scenario_code":1160,"scenario_name":1161,"rating":9,"pain_text":492,"tools":1162,"knowledge_models":1163,"data_elements":1164,"talent_skills":1165},"YB01-I-4-3","产品质量管理",[],[],[],[],{"segment":1167,"segment_code":1168,"activity_group":663,"scenarios":1169},"轧钢环节","D",[1170,1188,1196,1204,1212,1220,1228,1236,1244,1252,1260,1267,1275,1282],{"scenario_code":1171,"scenario_name":1172,"rating":347,"pain_text":1173,"tools":1174,"knowledge_models":1178,"data_elements":1181,"talent_skills":1184},"YB01-D-2-1","热轧产线无人化生产","国产工业机器人在关节轴承精度控制等方面还存在一定差距；缺乏生产工艺仿真工具，部分场景控制准确度不足",[1175,1176,1177],"智能轧钢控制系统","加热炉智能控制系统","机器人控制系统",[1179,1180],"热轧过程控制模型","基于比色测温法的线材表面温度检测模型等",[1182,1183],"热轧设备状态数据","工业机器人运行数据",[714,715,1185,1186,1187],"人工智能","物联网","工业工程",{"scenario_code":1189,"scenario_name":1190,"rating":347,"pain_text":1191,"tools":1192,"knowledge_models":1193,"data_elements":1194,"talent_skills":1195},"YB01-D-2-10","冷轧生产优化排程","冷轧产线故障诊断的准确性、速度、可靠性有待进一步提高",[],[],[],[],{"scenario_code":1197,"scenario_name":1198,"rating":347,"pain_text":1199,"tools":1200,"knowledge_models":1201,"data_elements":1202,"talent_skills":1203},"YB01-D-2-11","冷轧产线状态自动监测","对于退火炉内的温度、气氛、速度等参数控制不够精确",[],[],[],[],{"scenario_code":1205,"scenario_name":1206,"rating":347,"pain_text":1207,"tools":1208,"knowledge_models":1209,"data_elements":1210,"talent_skills":1211},"YB01-D-2-12","退火炉智能控制","气刀滞后调控导致锌层超厚或减薄，人工不能高精度控制",[],[],[],[],{"scenario_code":1213,"scenario_name":1214,"rating":347,"pain_text":1215,"tools":1216,"knowledge_models":1217,"data_elements":1218,"talent_skills":1219},"YB01-D-2-13","镀锌工艺参数自动调整","检测设备精度有限，对于涂层厚度等关键参数的测量误差较大",[],[],[],[],{"scenario_code":1221,"scenario_name":1222,"rating":347,"pain_text":1223,"tools":1224,"knowledge_models":1225,"data_elements":1226,"talent_skills":1227},"YB01-D-2-14","彩涂智能监测分析","对中厚板形状不规则、表面不平整等情况时适应性差",[],[],[],[],{"scenario_code":1229,"scenario_name":1230,"rating":347,"pain_text":1231,"tools":1232,"knowledge_models":1233,"data_elements":1234,"talent_skills":1235},"YB01-D-2-2","热轧产线状态自动监测","人工检测效率低、成本高、易产生漏检和误判",[],[],[],[],{"scenario_code":1237,"scenario_name":1238,"rating":347,"pain_text":1239,"tools":1240,"knowledge_models":1241,"data_elements":1242,"talent_skills":1243},"YB01-D-2-3","热表面缺陷检测","面对高频变化的订单情况，比如数量、规格等个性化要求，现有排程工具难以应对，热轧产线弹性有待进一步提升",[],[],[],[],{"scenario_code":1245,"scenario_name":1246,"rating":347,"pain_text":1247,"tools":1248,"knowledge_models":1249,"data_elements":1250,"talent_skills":1251},"YB01-D-2-4","热轧生产优化排程","难以准确把握各参数之间的最佳组合，数据采集不足",[],[],[],[],{"scenario_code":1253,"scenario_name":1254,"rating":347,"pain_text":1255,"tools":1256,"knowledge_models":1257,"data_elements":1258,"talent_skills":1259},"YB01-D-2-5","型材生产工艺参数优化","缺乏统一数据存储平台",[],[],[],[],{"scenario_code":1261,"scenario_name":1262,"rating":9,"pain_text":492,"tools":1263,"knowledge_models":1264,"data_elements":1265,"talent_skills":1266},"YB01-D-2-6","无缝钢管全流程质量追溯",[],[],[],[],{"scenario_code":1268,"scenario_name":1269,"rating":347,"pain_text":1270,"tools":1271,"knowledge_models":1272,"data_elements":1273,"talent_skills":1274},"YB01-D-2-7","中厚板超声波探伤","快速生产的测径效果不佳",[],[],[],[],{"scenario_code":1276,"scenario_name":1277,"rating":9,"pain_text":492,"tools":1278,"knowledge_models":1279,"data_elements":1280,"talent_skills":1281},"YB01-D-2-8","棒线材在线测径",[],[],[],[],{"scenario_code":1283,"scenario_name":1284,"rating":347,"pain_text":1285,"tools":1286,"knowledge_models":1289,"data_elements":1294,"talent_skills":1298},"YB01-D-2-9","冷轧产线无人化生产","国产机器人关节轴承、机器人控制电机等在定位精度方面有差距；排产优化过程，降本减排等目标往往相互矛盾",[1177,1287,1288],"带钢表面质量检测","无人行车及库管系统等",[1290,1291,1292,1090,1293],"生产过程优化模型","质量控制模型","能源优化模型","锌层厚度控制模型",[1295,1296,1297],"无人化冷轧设备振动","温度","应力等状态数据，生产工艺数据等",[714,715,1185,1186,1187],{"segment":1300,"segment_code":1301,"activity_group":581,"scenarios":1302},"采销管理环节","L",[1303,1328,1336,1343,1351,1358],{"scenario_code":1304,"scenario_name":1305,"rating":347,"pain_text":1306,"tools":1307,"knowledge_models":1312,"data_elements":1319,"talent_skills":1325},"YB01-L-5-1","需求预测与产能平衡","钢铁生产难以快速响应市场需求的变化，产能调整的灵活性不足；画像维度单一且不精准，缺乏动态画像能力。与业务流程脱节",[1308,1309,1310,1311],"供应链管理（SCM）系统","企业资源规划（ERP）系统","数据分析与可视化软件","人工智能平台",[1313,1314,1315,1316,1317,1318],"价格预测模型","分析决策模型","采购模型","需求预测模型","产能平衡模型","供应链协同模型",[1320,1321,1322,1323,1324],"历史销售数据","客户订单数据","市场调研数据","生产数据","原材料供应链数据",[1326,1185,1327],"数据挖掘，大数据分析","网络运维等",{"scenario_code":1329,"scenario_name":1330,"rating":347,"pain_text":1331,"tools":1332,"knowledge_models":1333,"data_elements":1334,"talent_skills":1335},"YB01-L-5-2","客户画像","传统的采购管理流程繁琐，缺乏规范的管理制度，效率低下",[],[],[],[],{"scenario_code":1337,"scenario_name":1338,"rating":9,"pain_text":492,"tools":1339,"knowledge_models":1340,"data_elements":1341,"talent_skills":1342},"YB01-L-5-3","合同跟踪管理",[],[],[],[],{"scenario_code":1344,"scenario_name":1345,"rating":347,"pain_text":1346,"tools":1347,"knowledge_models":1348,"data_elements":1349,"talent_skills":1350},"YB01-L-5-4","采购执行","缺乏统一共享平台，结算过程透明度低，难以实时跟踪资金情况",[],[],[],[],{"scenario_code":1352,"scenario_name":1353,"rating":9,"pain_text":492,"tools":1354,"knowledge_models":1355,"data_elements":1356,"talent_skills":1357},"YB01-L-5-5","采购结算",[],[],[],[],{"scenario_code":1359,"scenario_name":1360,"rating":368,"pain_text":1361,"tools":1362,"knowledge_models":1367,"data_elements":1371,"talent_skills":1377},"YB01-L-5-6","供应商管理","供应链环境风险管控方面存在不足，难以应对市场波动和突发事件；功能单一，缺少风险预测等功能，无法满足复杂的业务需求",[1363,1364,1365,1366],"ERP系统","CRM系统","数据分析平台","电子采购平台",[1368,1369,1370],"供应商评级模型","采购优化模型","风险预测模型",[1372,1373,1374,1375,1376],"供应商信息","采购数据","质量数据","物流数据","财务数据",[870,1378,1379],"信息技术","供应链管理等",{"segment":1381,"segment_code":1382,"activity_group":663,"scenarios":1383},"铁前环节","A",[1384,1391,1410,1418,1426,1433,1461,1468,1476,1484,1492,1499,1518,1526,1534,1555,1579,1599,1607,1614,1639,1646],{"scenario_code":1385,"scenario_name":1386,"rating":9,"pain_text":492,"tools":1387,"knowledge_models":1388,"data_elements":1389,"talent_skills":1390},"YB01-A-2-08","一体化优化配矿",[],[],[],[],{"scenario_code":1392,"scenario_name":1393,"rating":319,"pain_text":1394,"tools":1395,"knowledge_models":1398,"data_elements":1399,"talent_skills":1409},"YB01-A-2-1","智能料场","物料不断移动和变化增加精确计量难度，难以实时跟踪和测量，导致计量结果存在误差；环境差、安全风险高、人力劳动强度大，偶有安全事故发生，料场利用率和人均劳动效率低，生产清洁化水平低",[1396,1397],"智能原料场系统","图扑软件",[678,679,680],[1400,1401,1402,1403,1404,1405,1406,687,1407,1408],"库存量","库存位置","库存时间","环境温湿度","粉尘浓度","有害气体浓度","设备运行状态人才技能:自动化","安全工程","项目管理",[715,687,1407,1408],{"scenario_code":1411,"scenario_name":1412,"rating":319,"pain_text":1413,"tools":1414,"knowledge_models":1415,"data_elements":1416,"talent_skills":1417},"YB01-A-2-10","优化配煤","人工测温存在测量误差且不连续，导致炉温波动较大，不利于焦炭质量的稳定；焦炉加热煤气的调节控制不及时，造成能耗高，浪费煤气",[],[],[],[],{"scenario_code":1419,"scenario_name":1420,"rating":347,"pain_text":1421,"tools":1422,"knowledge_models":1423,"data_elements":1424,"talent_skills":1425},"YB01-A-2-11","四大车无人驾驶","部分软件的模型适应性不强，加热控制过程存在偏差；对复杂工艺过程的优化和控制能力不足，无法基于数据提高炼焦效率和产品质量",[],[],[],[],{"scenario_code":1427,"scenario_name":1428,"rating":9,"pain_text":492,"tools":1429,"knowledge_models":1430,"data_elements":1431,"talent_skills":1432},"YB01-A-2-12","智能巡检",[],[],[],[],{"scenario_code":1434,"scenario_name":1435,"rating":319,"pain_text":1436,"tools":1437,"knowledge_models":1441,"data_elements":1448,"talent_skills":1458},"YB01-A-2-13","球团智能控制","监测参数有限，缺乏预测和优化功能，特别是回转窑监测只能监测窑头，缺乏窑尾监测；部分系统灵活性不够，无法快速调整配料方案应对原料质量波动、生产计划变更等情况。配料人为干预较多，影响配料准确性和一致性，无法满足高精度配料需求",[1438,1439,1440],"回转窑智能监测系统","生球粒度在线识别系统","球团生产数字孪生",[1442,1443,1444,1445,1446,1447],"智能配混模型","布料","干燥预热","焙烧","冷却等工序的智能控制模型","工序关联模型",[1449,1450,1451,1452,1453,1454,1455,1456,1457],"视野范围温度","回转窑设备参数","材料导热系数","球盘转速数据","新增图像采集数据","盘内加水量数据","流量检测数据","生球粒径数据","生球合格率",[1459,1460,1057,912,1058],"自动化技术","工艺优化技术",{"scenario_code":1462,"scenario_name":1463,"rating":9,"pain_text":492,"tools":1464,"knowledge_models":1465,"data_elements":1466,"talent_skills":1467},"YB01-A-2-14","球团质量过程监测",[],[],[],[],{"scenario_code":1469,"scenario_name":1470,"rating":347,"pain_text":1471,"tools":1472,"knowledge_models":1473,"data_elements":1474,"talent_skills":1475},"YB01-A-2-15","智能配料","部分系统实时处理能力不够，无法对生球生产实时图像进行检测和粒径分析。无法根据球团良率和粒径检测结果，对圆盘造球机转速、加水量等进行自动优化",[],[],[],[],{"scenario_code":1477,"scenario_name":1478,"rating":319,"pain_text":1479,"tools":1480,"knowledge_models":1481,"data_elements":1482,"talent_skills":1483},"YB01-A-2-2","堆取料自动作业","传统皮带机运输作业在生产环境、流程、安全等诸多方面均存在对人力依赖性高、智能化升级困难的问题",[],[],[],[],{"scenario_code":1485,"scenario_name":1486,"rating":319,"pain_text":1487,"tools":1488,"knowledge_models":1489,"data_elements":1490,"talent_skills":1491},"YB01-A-2-3","料场智能调度","部分工序控制精度不足，导致堆料形状不规范，取料效率低下；自动化系统的适应性和灵活性不足，难以提供个性化服务",[],[],[],[],{"scenario_code":1493,"scenario_name":1494,"rating":9,"pain_text":492,"tools":1495,"knowledge_models":1496,"data_elements":1497,"talent_skills":1498},"YB01-A-2-4","皮带机运行监测",[],[],[],[],{"scenario_code":1500,"scenario_name":1501,"rating":319,"pain_text":1502,"tools":1503,"knowledge_models":1506,"data_elements":1511,"talent_skills":1516},"YB01-A-2-5","烧结过程闭环控制","无法对烧结过程化学反应、热-流-固耦合等实现数字化建模，无法实现精准预判和控制；需要提高配料过程的稳定性和混合料的成球性，需要提升加水精度和点火效果，降低返矿率和烧结能耗",[1504,1505],"智能烧结控制系统","数字化烧结平台",[1507,1508,1509,1510],"烧结过程热平衡模型","烧结过程物料流动模型","烧结配比优化模型","成本控制模型",[1512,1513,1514,1515],"烧结机的温度","压力","气氛和物料流动","混合料和燃料粒度",[909,686,911,1517,714],"化学工程",{"scenario_code":1519,"scenario_name":1520,"rating":319,"pain_text":1521,"tools":1522,"knowledge_models":1523,"data_elements":1524,"talent_skills":1525},"YB01-A-2-6","智能计划排产","面对配矿过程的复杂工艺和多个变量，多目标优化能力不足；软件输入数据质量难以把控，计算分析结果不准确；原料内部化学成本难以建模",[],[],[],[],{"scenario_code":1527,"scenario_name":1528,"rating":347,"pain_text":1529,"tools":1530,"knowledge_models":1531,"data_elements":1532,"talent_skills":1533},"YB01-A-2-7","烧结过程智能监测诊断","：工具软件功能较单一，未充分考虑工艺约束和设备限制；部分软件接口不兼容、数据格式不统一；无法预测优化排产计划",[],[],[],[],{"scenario_code":1535,"scenario_name":1536,"rating":319,"pain_text":1537,"tools":1538,"knowledge_models":1541,"data_elements":1545,"talent_skills":1551},"YB01-A-2-9","炼焦过程控制","部分软件只能提供基于固定规则的加热控制，而无法根据实时数据和炉况情况对配煤方案进行灵活调整；季节变化和恶劣气候，导致异常工况、操作强度大，系统异常、复杂的现场环境导致安全事故频发",[1539,1540],"焦炉智能加热优化控制系统","5G智能化操控系统",[1542,1543,1544],"总管煤气优化控制模型","支管煤气优化控制模型","分烟道吸力优化控制模型",[1546,1547,1548,1549,1550],"直行温度数据","荒煤气温度数据","焦饼温度数据","废气氧含量数据","炼焦工艺数据",[1058,714,1552,1553,1554],"煤化工工艺学","炼焦工艺","现代煤化工生产技术",{"scenario_code":1556,"scenario_name":1557,"rating":319,"pain_text":1558,"tools":1559,"knowledge_models":1563,"data_elements":1568,"talent_skills":1573},"YB01-A-1-1","新钢种研发","钢铁行业中，不同部门、不同项目之间存在数据孤岛，数据无法有效共享和整合，影响研发效率和成果；钢铁工艺和新材料的研发需要产业链上下游企业的协同合作，然而原材料供应商、设备制造商、用户等利益诉求不同、信息沟通不畅，导致协同研发管理难度大。数字化协同研发降本新模式降本；试验过程缺乏有效的实时监控手段，难以及时掌握试验进度。不同试验团队数据记录格式多样，需要花费大量时间进行格式转换和数据清洗。试验数据存储在不同位置，没有集中管理，数据容易丢失",[1560,1561,1562],"工业互联网云平台","数字化研发平台","金相分析系统",[1564,1565,1566,1567],"材料基因组工程模型","服役环境-性能退化模型","成分-工艺-性能关联模型","冶金过程模拟与优化模型等",[1569,1570,1571,1572],"原料成分数据","冶金过程参数","生产设备状态数据","产品质量检测数据",[1574,1575,1576,1577,1578],"冶金工程与材料科学","大数据与数据分析","计算机科学与信息技术","软件开发与编程","人工智能与自动化",{"scenario_code":1580,"scenario_name":1581,"rating":347,"pain_text":1582,"tools":1583,"knowledge_models":1587,"data_elements":1590,"talent_skills":1595},"YB01-A-1-2","工艺数字化设计","数字化设计工具和技术还不够成熟，在处理复杂工艺和大型设备时可能存在性能不足或稳定性问题。由于技术差异和系统之间的不兼容性，不同生产环节技术集成难度较大",[1584,1585,1586],"炼钢工艺模拟软件","钢铁生产工艺优化软件","钢铁工艺流程设计系统",[1588,1589],"质量控制与缺陷预测模型","微观组织演变模拟，动态非线性系统仿真",[863,1591,1592,1572,1593,1594],"生产过程参数","设备状态数据","环境因素数据","经济成本数据",[1596,1597,1598],"材料科学与工程","工业工程与自动化控制","仿真模拟技术",{"scenario_code":1600,"scenario_name":1601,"rating":319,"pain_text":1602,"tools":1603,"knowledge_models":1604,"data_elements":1605,"talent_skills":1606},"YB01-A-1-3","试验管理","钢铁产业链数字化系统集成难度高，主要是因为钢铁生产流程复杂，多原料采购、生产制造、物流配送等环节都需要相应的数字化系统进行支持。然而，这些系统之间的数据交换和集成面临巨大的挑战，导致企业难以实现全面数字化的协同制造；钢铁行业的工厂设计涉及大量的设备、管道\u002F电气系统等，设计元素众多，相互之间的关联复杂。这导致数字化设计过程中的建模仿真和优化变得非常复杂和困难",[],[],[],[],{"scenario_code":1608,"scenario_name":1609,"rating":9,"pain_text":492,"tools":1610,"knowledge_models":1611,"data_elements":1612,"talent_skills":1613},"YB01-A-1-4","三维工厂数字化设计与交付",[],[],[],[],{"scenario_code":1615,"scenario_name":1616,"rating":319,"pain_text":1617,"tools":1618,"knowledge_models":1621,"data_elements":1626,"talent_skills":1634},"YB01-A-3-1","产品个性化定制","缺乏必要的感知设备，导致无法准确感知生产现场的安全态势。部分企业的生产现场安全预警系统不完善，无法及时、准确地发出预警信号。数字化协同服务n；用户满意度和反馈数据无法传递到研发、生产等环节，影响钢铁行业的产品和服务改进；客户的需求往往具有不确定性和变化性，这给钢铁企业的生产和计划带来了很大的挑战。企业需要具备高度灵活的生产计划和调度能力，以适应客户需求的变化；尽管许多钢铁企业已经开始尝试数字化转型，但在售后服务方面，智能化水平仍然较低，例如，缺乏智能化的故障诊断系统、自动化的查户服务流程等，导致服务效率不高，客户体验不佳；尽管许多钢铁企业已经开始尝试数字化转型，但在售后服务方面，智能化水平仍然较低。例如，缺乏智能化的故障诊断系统、自动化的客户服务流程等，导致服务效率不高，客户体验不佳",[1619,1620,588,589],"5G无线网络平台","视觉AI仿真平台和应用平台",[1622,1623,1624,1625],"人的不安全行为识别模型","风险预警模型","钢铁一体化生产调度模型与优化算法","客户个性化需求模型",[1627,1628,1629,1630,1631,1632,1633],"高炉等关键设备运行数据","高风险区域实时智能监控数据","产品订单信息","客户信息","生产操作参数","产品编码信息","技术评审信息",[928,1635,1636,912,686,1637,912,1638],"材料科学","质量管理","控制工程","机器学习系统设计",{"scenario_code":1640,"scenario_name":1641,"rating":9,"pain_text":492,"tools":1642,"knowledge_models":1643,"data_elements":1644,"talent_skills":1645},"YB01-A-3-2","技术支持、咨询与售后服务",[],[],[],[],{"scenario_code":1647,"scenario_name":1648,"rating":319,"pain_text":1649,"tools":1650,"knowledge_models":1651,"data_elements":1652,"talent_skills":1653},"YB01-A-3-3","一体化电商平台","钢铁行业供应链上下游缺乏有效的协同决策机制和平台，信息共享程度不高，导致市场需求预测不准确，部分供应环节不稳定，影响数据的实时传输，限制供应链协同工作的开展。数字化协同管理n；数据孤岛和信息壁垒是钢铁数字化协同管理的最严重痛点问题，由于缺乏协同平台和统一的标准，数据系统相对独立，数据标准不一致，数据共享困难，企业间在生产计划、资源分配、市场响应等方面难以快速达成共识，导致协同效率低下；目前一些模型在泛化能力方面还存在不足，无法能够适应不同设备和故障模式的分析难以应对复杂多变的故障情况",[],[],[],[],[1655,1657,1659,1661,1663,1665,1667,1669],{"name":24,"id":1656},"cmv7rhq30014yhikyjju7kolc",{"name":30,"id":1658},"cmv7rhq350153hikyo5gkt30s",{"name":35,"id":1660},"ftxe_913af962f5a26def",{"name":40,"id":1662},"cmv7rhq3a0158hikyuq171aky",{"name":44,"id":1664},"cmv7rhq3g015dhikyy2e8zyhs",{"name":49,"id":1666},"cmv7rhq3l015ihikynpohca77",{"name":54,"id":1668},"cmv7rhp07005ehikyxuxvmhxf",{"name":58,"id":1670},"cmv7rhq3q015nhiky2d5klh1n",{"list":1672,"total":1708,"page":250,"pageSize":470,"totalPages":347},[1673,1678,1684,1690,1696,1701],{"id":1674,"name":1675,"category":6,"groupCategory":7,"intro":1676,"scale":9,"maturity":10,"checkState":249,"nodeCount":18,"companyCount":1677},"ch60_T08","传统汽车产业链","燃油车整车及零部件，向新能源汽车转型",119,{"id":1679,"name":1680,"category":6,"groupCategory":7,"intro":1681,"scale":1682,"maturity":10,"checkState":249,"nodeCount":16,"companyCount":1683},"fa3b59f4-0fc7-4f9d-bff0-94d4498b3e7c","医药制造产业链（传统）","化学药、中药、生物药传统制造","2025年规模以上医药工业企业营业收入约2.96万亿元(国家统计局);中国生物医药市场规模约2.24万亿元(行业研究机构口径,2025)",59,{"id":1685,"name":1686,"category":6,"groupCategory":7,"intro":1687,"scale":9,"maturity":1688,"checkState":249,"nodeCount":15,"companyCount":1689},"ch60_T11","家电制造产业链","家用电器制造，向智能家电升级","成熟期",28,{"id":1691,"name":1692,"category":6,"groupCategory":7,"intro":1693,"scale":1694,"maturity":1688,"checkState":249,"nodeCount":18,"companyCount":1695},"9f188ad6-e927-4dda-afc4-dde4683089eb","工程机械产业链","工程机械、矿山机械、农业机械制造","2024年装备制造业增加值占规上工业34.6%；工业机器人产量55.6万套(+14.2%)、销量30.2万台占全球54%，机器人行业营收2378.9亿元(国家统计局\u002F工信部,2024)",57,{"id":1697,"name":1698,"category":6,"groupCategory":7,"intro":1699,"scale":9,"maturity":10,"checkState":249,"nodeCount":470,"companyCount":1700},"ch60_T04","建材产业链","水泥、玻璃、陶瓷、新型建材",49,{"id":1702,"name":1703,"category":6,"groupCategory":7,"intro":1704,"scale":1705,"maturity":10,"checkState":249,"nodeCount":1706,"companyCount":1707},"52294d28-07dc-4f86-844f-6b5bea542726","建筑与房地产产业链","房屋建筑、土木工程建筑、建筑安装装饰","2024年全国建筑业总产值32.7万亿元、增加值约9万亿元，装配式建筑占新建建筑比例达30%以上(住建部,2024\u002F十四五)",10,72,19]