[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"chains-groupcat":3,"tech-results-list":383,"achievements-hero-experts":697,"tags-groups":710},[4,15,22,27,34,42,51,58,65,71,77,85,90,96,104,110,115,122,126,131,136,142,147,153,158,164,170,176,181,187,193,198,203,208,213,218,224,230,235,241,245,251,255,260,266,272,278,283,289,296,302,306,311,317,323,329,335,340,345,350,356,360,366,371,375,379],{"id":5,"name":6,"category":7,"groupCategory":8,"intro":9,"scale":10,"maturity":11,"checkState":12,"nodeCount":13,"companyCount":14},"ch60_E11","LED与第三代半导体产业链","战略性新兴产业","EMERGING","碳化硅、氮化镓、LED照明、功率器件",null,"成长期","VERIFIED",8,92,{"id":16,"name":17,"category":7,"groupCategory":8,"intro":18,"scale":19,"maturity":11,"checkState":12,"nodeCount":20,"companyCount":21},"e0c81c4e-878c-4944-9633-2c202497ef16","云计算与大数据产业链","云服务、数据中心、数据处理、数据交易","中国云计算市场规模约8288亿元（信通院，2024年，同比+34.4%），其中公有云6216亿元；智能算力规模超1590 EFLOPS（工信部，2026-01）",12,163,{"id":23,"name":24,"category":7,"groupCategory":8,"intro":25,"scale":26,"maturity":11,"checkState":12,"nodeCount":13,"companyCount":14},"00ee3af3-fe37-4d60-81ef-2833c238866c","人工智能产业链","AI芯片、大模型、算法框架、AI应用","中国人工智能核心产业规模超1.2万亿元(工信部，2025)",{"id":28,"name":29,"category":7,"groupCategory":8,"intro":30,"scale":10,"maturity":31,"checkState":12,"nodeCount":32,"companyCount":33},"ch60_S02","人才与教育供给链","高等教育、职业教育、人才引进、技能培训","成熟期",7,28,{"id":35,"name":36,"category":37,"groupCategory":38,"intro":39,"scale":10,"maturity":40,"checkState":12,"nodeCount":32,"companyCount":41},"ch60_T08","传统汽车产业链","传统产业","TRADITIONAL","燃油车整车及零部件，向新能源汽车转型","转型期",119,{"id":43,"name":44,"category":45,"groupCategory":46,"intro":47,"scale":10,"maturity":48,"checkState":12,"nodeCount":49,"companyCount":50},"ch60_F12","元宇宙与XR产业链","未来产业","FUTURE","VR\u002FAR\u002FMR、数字孪生、数字人、空间计算","孕育期",6,79,{"id":52,"name":53,"category":54,"groupCategory":8,"intro":55,"scale":10,"maturity":11,"checkState":12,"nodeCount":56,"companyCount":57},"ch60_E26","光子（光电子）产业链","新兴产业","从光电子材料、光芯片、光器件\u002F模块，到激光装备、光显示、光传感、光计算与光子集成，是AI算力与信息时代的“光速动脉”。",10,34,{"id":59,"name":60,"category":45,"groupCategory":46,"intro":61,"scale":62,"maturity":48,"checkState":12,"nodeCount":63,"companyCount":64},"48f1ae82-a07c-4d67-93c0-abb71a5d1e0d","具身智能与人形机器人产业链","人形机器人本体、具身大模型、灵巧手、核心零部件","中国人形机器人整机企业超140家、发布产品超330款(工信部，2025)；产业整体处量产初期，市场规模待权威口径",9,138,{"id":66,"name":67,"category":7,"groupCategory":8,"intro":68,"scale":69,"maturity":48,"checkState":12,"nodeCount":13,"companyCount":70},"d5c035fb-13ce-442e-b94e-6ac79b0b5b85","区块链与Web3产业链","区块链底层、联盟链、数字资产、分布式应用","中国区块链市场规模IDC口径2025年超32亿美元；国内区块链信息服务备案编号累计发放20批次4223个（国家网信办《国家信息化发展报告2025》）",44,{"id":72,"name":73,"category":37,"groupCategory":38,"intro":74,"scale":75,"maturity":40,"checkState":12,"nodeCount":63,"companyCount":76},"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":78,"name":79,"category":7,"groupCategory":46,"intro":80,"scale":81,"maturity":82,"checkState":12,"nodeCount":83,"companyCount":84},"90644f83-8a70-4b3a-ad53-8a2b0e397d5a","卫星互联网","卫星互联网指以低轨星座为基础，提供宽带通信、导航增强、物联网接入等天地一体化信息服务的产业。本链聚焦卫星组网运营、地面设备与终端及行业应用；火箭与卫星制造发射的运载环节归入'商业航天'链。核心工程包括GW星座(中国星网约1.3万颗)与千帆\u002FG60星座(垣信约1.5万颗)。到2030年产业链规模有望突破1.2万亿元(券商口径)。","中国低轨卫星互联网到2030年带动产业链规模有望突破1.2万亿元(券商\u002F机构口径，2026)；2024年起两大星座进入高密度组网","TRL6-8(组网\u002F试运行)；消费终端TRL5-7",15,40,{"id":86,"name":87,"category":45,"groupCategory":46,"intro":88,"scale":10,"maturity":48,"checkState":12,"nodeCount":13,"companyCount":89},"ch60_F07","可控核聚变产业链","托卡马克、高温超导、聚变堆材料",84,{"id":91,"name":92,"category":45,"groupCategory":46,"intro":93,"scale":94,"maturity":48,"checkState":12,"nodeCount":13,"companyCount":95},"f3e1775a-755b-4ffd-acaf-e4bff5f6d75b","合成生物产业链","合成生物学平台、生物制造、细胞工厂","中国生物制造产业总规模约1.1万亿元,生物发酵产品产量占全球70%以上(工信部,2025);其中食品及添加剂、生物制药两个细分领域年产值均超4000亿元",91,{"id":97,"name":98,"category":7,"groupCategory":46,"intro":99,"scale":100,"maturity":101,"checkState":12,"nodeCount":102,"companyCount":103},"9b4f4311-cda6-4461-b352-ab269ef8688b","商业航天","商业航天指以市场化方式配置资源、由商业主体提供运载火箭研制发射、卫星制造及相关服务的产业。本链聚焦民营\u002F商业运载火箭与商业卫星的研制、发射、测运控及应用；卫星组网运营与通信导航应用的终端\u002F运营部分与'卫星互联网'链交叉。2024年中国商业航天市场规模约2.3万亿元(含带动效应，口径较宽)。","中国商业航天市场规模约2.3万亿元(国家发改委专家口径\u002F中商产业研究院，2024)，预计2025年约2.8万亿元；该口径含产业链带动效应，直接航天制造发射产值远小于此","TRL6-8(火箭发射常态化)；可重复使用火箭TRL4-6；太空旅游TRL3-4",18,57,{"id":105,"name":106,"category":45,"groupCategory":46,"intro":107,"scale":108,"maturity":48,"checkState":12,"nodeCount":63,"companyCount":109},"deccf963-6b65-4294-9291-483b47327169","基因与生物技术产业链","基因编辑、合成生物学、生物制造","中国基因组测序市场规模约328.6亿元(灼识咨询\u002F中国医疗器械行业协会基因检测分会白皮书,2025);中国细胞与基因治疗(CGT)市场规模2025年首次突破500亿元(行业公开统计,2025)",37,{"id":111,"name":112,"category":7,"groupCategory":8,"intro":113,"scale":114,"maturity":11,"checkState":12,"nodeCount":32,"companyCount":109},"3372d10f-1628-4dfb-b341-db35656c5d6d","基础软件与工业软件产业链","操作系统、数据库、工业软件、CAD\u002FEDA","2025年我国工业互联网核心产业规模超1.6万亿元，带动工业增加值约2.5万亿元；具有一定行业影响力的工业互联网平台超360家（工信部，2026）",{"id":116,"name":117,"category":7,"groupCategory":8,"intro":118,"scale":119,"maturity":120,"checkState":12,"nodeCount":83,"companyCount":121},"b6de4fda-3782-483a-a0f8-d2fffa8d8224","大数据\u002F数据要素","大数据\u002F数据要素产业围绕数据资源的采集、治理、存储、流通交易与开发利用，覆盖上游数据采集与数据库\u002F存储、中游数据治理与数据中台\u002F数据交易与公共数据授权运营、下游BI分析与行业数据应用。本链聚焦数据作为生产要素的流通与价值释放，区别于偏算力基础设施的云计算\u002F算力链、偏模型算法的人工智能链。2025年我国数据产业规模达6.78万亿元、数据企业48.2万家（国家数据局，2026-08）。","2025年我国数据产业规模达6.78万亿元，同比增长15.7%，数据企业48.2万家（国家数据局，2026-08）","TRL7-9（大数据平台成熟商用，数据要素流通\u002F授权运营处制度建设早期）",63,{"id":123,"name":124,"category":37,"groupCategory":38,"intro":125,"scale":10,"maturity":31,"checkState":12,"nodeCount":49,"companyCount":33},"ch60_T11","家电制造产业链","家用电器制造，向智能家电升级",{"id":127,"name":128,"category":37,"groupCategory":38,"intro":129,"scale":130,"maturity":31,"checkState":12,"nodeCount":32,"companyCount":103},"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)",{"id":132,"name":133,"category":37,"groupCategory":38,"intro":134,"scale":10,"maturity":40,"checkState":12,"nodeCount":13,"companyCount":135},"ch60_T04","建材产业链","水泥、玻璃、陶瓷、新型建材",49,{"id":137,"name":138,"category":37,"groupCategory":38,"intro":139,"scale":140,"maturity":40,"checkState":12,"nodeCount":56,"companyCount":141},"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)",72,{"id":143,"name":144,"category":7,"groupCategory":8,"intro":145,"scale":10,"maturity":11,"checkState":12,"nodeCount":32,"companyCount":146},"ch60_E24","数字创意产业链","数字内容、创意设计、网络游戏、短视频、元宇宙",90,{"id":148,"name":149,"category":7,"groupCategory":8,"intro":150,"scale":151,"maturity":11,"checkState":12,"nodeCount":13,"companyCount":152},"e2575327-d150-4ced-be3f-d49c6d2ecedb","新一代通信网络产业链","5G\u002F6G、光通信、卫星通信、网络设备","我国已建成5G基站483.8万座、5G-A覆盖超330个城市，移动物联网终端28.88亿户（工信部，2026-01）；新一代信息技术产业整体规模无单一官方口径，待核实",53,{"id":154,"name":155,"category":7,"groupCategory":8,"intro":156,"scale":10,"maturity":11,"checkState":12,"nodeCount":63,"companyCount":157},"ch60_S01","新型基础设施要素链","5G基站、数据中心、工业互联网、算力枢纽",47,{"id":159,"name":160,"category":7,"groupCategory":8,"intro":161,"scale":162,"maturity":31,"checkState":12,"nodeCount":32,"companyCount":163},"8863836c-acbe-4a9e-9321-d68ac6a064d2","新型显示产业链","LCD\u002FOLED\u002FMicroLED面板、显示材料、显示装备","中国新型显示产业产值约7400亿元(中国电子信息产业发展研究院\u002F赛迪，2024)，全球市场占比超49%；2025年总产值突破8000亿元",41,{"id":165,"name":166,"category":7,"groupCategory":8,"intro":167,"scale":168,"maturity":11,"checkState":12,"nodeCount":13,"companyCount":169},"b0614f9a-1b75-4f3d-8c55-ad54651ed608","新材料产业链","先进金属材料、高分子材料、复合材料、纳米材料","中国新材料产业总产值2024年约8.7万亿元,预计2025年约10万亿元(工信部\u002F赛迪口径,2025)",142,{"id":171,"name":172,"category":7,"groupCategory":8,"intro":173,"scale":174,"maturity":11,"checkState":12,"nodeCount":20,"companyCount":175},"91feac65-a1bd-4369-a4e1-7206bb918d5a","新能源发电与储能产业链","光伏、风电、锂电储能、氢能储能、新型储能，首批试点","2025年全国风电光伏新增装机超4.38亿千瓦(风电1.2亿、光伏3.18亿),累计突破18.4亿千瓦(国家能源局,2026-02);新型储能累计突破1亿千瓦(国家发改委,2026-03)",206,{"id":177,"name":178,"category":7,"groupCategory":8,"intro":179,"scale":10,"maturity":11,"checkState":12,"nodeCount":63,"companyCount":180},"ch60_E19","新能源装备产业链","光伏设备、风电设备、锂电设备、氢能设备",80,{"id":182,"name":183,"category":7,"groupCategory":8,"intro":184,"scale":185,"maturity":31,"checkState":12,"nodeCount":20,"companyCount":186},"6cae6b65-a8de-451e-a5af-b47ed1712fcc","智能终端产业链","智能手机、PC、可穿戴设备、智能家电","2025年中国智能手机出货量约2.85亿部，同比-0.6%，华为16.4%份额第一（IDC，2026-01）；全球智能手机出货约12.6亿部，同比+1.9%",225,{"id":188,"name":189,"category":7,"groupCategory":8,"intro":190,"scale":191,"maturity":11,"checkState":12,"nodeCount":20,"companyCount":192},"2eb24ee1-32bc-4b5e-ad92-d6702a67412e","智能网联新能源汽车产业链","新能源整车、三电系统、自动驾驶、车联网","2025年新能源汽车产销分别完成1662.6万辆和1649万辆,新车销量渗透率47.9%(中汽协\u002F工信部,2026-01)",99,{"id":194,"name":195,"category":37,"groupCategory":38,"intro":196,"scale":197,"maturity":31,"checkState":12,"nodeCount":63,"companyCount":64},"1e4e94ff-2688-4052-9a78-0fefebcd78cf","有色金属产业链","有色金属冶炼和压延加工，包含铜铝铅锌和稀有金属","2024年全国煤炭产量47.8亿吨、约占一次能源生产总量66.6%，煤矿智能化产能占比超50%(中国煤炭工业协会\u002F国家能源局,2024)",{"id":199,"name":200,"category":45,"groupCategory":46,"intro":201,"scale":10,"maturity":48,"checkState":12,"nodeCount":63,"companyCount":202},"ch60_F11","未来信息产业链","光计算、存算一体、神经形态芯片",118,{"id":204,"name":205,"category":45,"groupCategory":46,"intro":206,"scale":10,"maturity":48,"checkState":12,"nodeCount":13,"companyCount":207},"ch60_F10","未来健康产业链","再生医学、抗衰老、精准医疗、数字疗法",39,{"id":209,"name":210,"category":45,"groupCategory":46,"intro":211,"scale":10,"maturity":48,"checkState":12,"nodeCount":63,"companyCount":212},"ch60_F15","未来制造产业链","4D打印、自组装制造、分子制造、纳米制造",144,{"id":214,"name":215,"category":45,"groupCategory":46,"intro":216,"scale":10,"maturity":48,"checkState":12,"nodeCount":49,"companyCount":217},"ch60_F08","未来材料产业链","石墨烯、超导材料、超材料、智能材料",70,{"id":219,"name":220,"category":45,"groupCategory":46,"intro":221,"scale":222,"maturity":48,"checkState":12,"nodeCount":49,"companyCount":223},"c14c039a-3453-4e0f-9403-5ae2cdc08612","未来网络产业链","6G、未来互联网架构、空天地一体化网络","6G处预研期，无独立商用市场规模(中国信通院\u002F工信部，2025)；预计2030年前后商用",30,{"id":225,"name":226,"category":45,"groupCategory":46,"intro":227,"scale":228,"maturity":48,"checkState":12,"nodeCount":63,"companyCount":229},"ee14d5c8-2b2c-455d-be3b-cb80a59c445c","未来能源产业链","新型核能、空间太阳能、零碳能源系统","待核实(未来能源无统一市场规模口径;参照:2025年我国新型储能累计装机突破1亿千瓦,占全球40%以上)",100,{"id":231,"name":232,"category":37,"groupCategory":38,"intro":233,"scale":10,"maturity":31,"checkState":12,"nodeCount":32,"companyCount":234},"ch60_T15","橡胶塑料制品产业链","橡胶和塑料制品业，支撑性材料",75,{"id":236,"name":237,"category":45,"groupCategory":46,"intro":238,"scale":239,"maturity":48,"checkState":12,"nodeCount":32,"companyCount":240},"29473ba5-a97f-4dc4-bf08-62a009d87501","氢能与燃料电池产业链","制氢储氢运氢加氢、燃料电池","待核实(氢能产业链整体规模无统一口径;截至2024年底可再生能源电解水制氢已建成产能约12.5万吨\u002F年,国家能源局《中国氢能发展报告2025》)",73,{"id":242,"name":243,"category":7,"groupCategory":8,"intro":244,"scale":10,"maturity":11,"checkState":12,"nodeCount":20,"companyCount":76},"ch60_E15","海洋装备产业链","深海装备、海上风电、海水淡化、海洋生物医药",{"id":246,"name":247,"category":45,"groupCategory":46,"intro":248,"scale":249,"maturity":48,"checkState":12,"nodeCount":20,"companyCount":250},"f33f81a5-0e29-46b3-85a8-883bb20381c3","深海空天开发产业链","深海探测、太空开发、近地轨道经济","待核实(深海科技无统一市场规模口径;2025年海洋工程装备制造业增加值约1858亿元,其中深海高端装备为核心增量)",173,{"id":252,"name":253,"category":37,"groupCategory":38,"intro":254,"scale":10,"maturity":31,"checkState":12,"nodeCount":49,"companyCount":50},"4279a2a3-b216-4dcc-885a-2895599733d5","烟草及其他传统制造产业链","烟草制品业、其他制造业兜底",{"id":256,"name":257,"category":37,"groupCategory":38,"intro":258,"scale":10,"maturity":31,"checkState":12,"nodeCount":20,"companyCount":259},"ch60_T18","煤炭产业链","煤炭勘探、建设、智能化采掘、洗选、储运，延伸燃煤发电、清洁供热与现代煤化工，是国家能源安全的“压舱石”。",35,{"id":261,"name":262,"category":7,"groupCategory":8,"intro":263,"scale":264,"maturity":11,"checkState":12,"nodeCount":32,"companyCount":265},"7f61bb29-c16e-416f-9315-59b78b7b5ea1","物联网产业链","传感器、RFID、物联网模组、物联网平台","我国移动物联网终端用户达28.88亿户，占移动终端连接数61.3%（工信部《2025年通信业统计公报》，2026-01）；物联网整体市场规模待核实",54,{"id":267,"name":268,"category":37,"groupCategory":38,"intro":269,"scale":270,"maturity":11,"checkState":12,"nodeCount":63,"companyCount":271},"38794d83-8b10-4ca3-bff4-398c57e4ceef","现代农业与生物育种产业链","生物育种、智慧农业、农产品深加工、农机装备","2024年全国粮食总产量14130亿斤、农作物耕种收综合机械化率76.7%、农业科技进步贡献率超63%、良种覆盖率超96%(农业农村部\u002F国家统计局,2024-2025)",129,{"id":273,"name":274,"category":37,"groupCategory":38,"intro":275,"scale":276,"maturity":31,"checkState":12,"nodeCount":32,"companyCount":277},"a20c5f10-4477-4739-abca-fb0550ebdcc2","现代商贸物流产业链","批发零售、物流配送、仓储邮政","2024年交通固定资产投资3.79万亿元、营业性货运量568.75亿吨、快递业务量1750.84亿件、高铁营业里程4.8万公里(交通运输部,2024)",32,{"id":279,"name":280,"category":7,"groupCategory":8,"intro":281,"scale":10,"maturity":11,"checkState":12,"nodeCount":63,"companyCount":282},"ch60_E20","生物医药产业链","创新药、生物制品、CXO、基因与细胞治疗",61,{"id":284,"name":285,"category":37,"groupCategory":38,"intro":286,"scale":287,"maturity":40,"checkState":12,"nodeCount":13,"companyCount":288},"14b1e674-0fa9-4436-bcaa-293d5530db1c","电力能源（传统）产业链","火电、水电、传统发电，向新型电力系统转型","2024年全国全社会用电量98521亿千瓦时、发电装机容量约33.5亿千瓦、发电量约10.09万亿千瓦时(国家能源局,2024)",69,{"id":290,"name":291,"category":37,"groupCategory":38,"intro":292,"scale":293,"maturity":31,"checkState":12,"nodeCount":294,"companyCount":295},"c8bc1bbd-78c7-4a80-bb70-736dbee84c96","石化化工产业链","石油加工、炼焦和化学原料和化学制品制造，向精细化工、新材料升级","2024年石化化工行业(不含油气开采)主营业务收入约15万亿元、乙烯产量超5000万吨(工信部,2024)",11,96,{"id":297,"name":298,"category":7,"groupCategory":8,"intro":299,"scale":300,"maturity":31,"checkState":12,"nodeCount":294,"companyCount":301},"6296069d-2d19-4c7b-9e33-5ecd638b9a7f","科技金融服务链","创投、产业基金、科技信贷、上市服务、知识产权金融","国家中小企业发展基金规模357亿元、累计带动股权融资超4800亿元；国家融资担保基金再担保累计4.73万亿元(财政部,2024)",51,{"id":303,"name":304,"category":37,"groupCategory":38,"intro":305,"scale":10,"maturity":31,"checkState":12,"nodeCount":13,"companyCount":146},"ch60_T05","纺织服装产业链","纺织业、纺织服装服饰业，向品牌化功能化升级",{"id":307,"name":308,"category":7,"groupCategory":8,"intro":309,"scale":310,"maturity":11,"checkState":12,"nodeCount":49,"companyCount":223},"b28f45aa-a435-46e4-af93-7a853d95ccda","网络安全产业链","网络安全、数据安全、工控安全、密码技术","2025年我国信息安全产品和服务收入2235亿元，同比+6.7%（工信部《2025年软件业运行情况》）；赛迪口径网络安全市场规模约964.8亿元（2025）",{"id":312,"name":313,"category":45,"groupCategory":46,"intro":314,"scale":315,"maturity":48,"checkState":12,"nodeCount":32,"companyCount":316},"2a253269-c571-41c3-8606-5870e748d81a","脑科学与类脑智能产业链","脑机接口、类脑计算、神经科学","中国脑机接口市场规模约32.0亿元(赛迪顾问，2024)；2025年有望突破38亿元，企业总量超200家",31,{"id":318,"name":319,"category":7,"groupCategory":8,"intro":320,"scale":321,"maturity":48,"checkState":12,"nodeCount":32,"companyCount":322},"55f40c95-c426-4213-9b23-1fefd4dd39ed","自动驾驶与低空经济产业链","L4+自动驾驶、eVTOL、低空飞行、无人系统","中国低空经济规模约6702.5亿元(赛迪顾问，2024)；2025年预计达1.5万亿元(中国民航局)，2035年有望突破3.5万亿元",60,{"id":324,"name":325,"category":7,"groupCategory":8,"intro":326,"scale":327,"maturity":11,"checkState":12,"nodeCount":20,"companyCount":328},"2e3ee32f-a683-4ed6-a055-642829b99f2c","航空航天装备产业链","大飞机、通用航空、商业航天、卫星互联网","航空航天为国家队主导，无统一公开产业规模口径；C919累计交付16架(2024底)、订单超1400架",98,{"id":330,"name":331,"category":37,"groupCategory":38,"intro":332,"scale":333,"maturity":40,"checkState":12,"nodeCount":32,"companyCount":334},"dc6de969-6636-4914-a6cb-7be786597c08","船舶海工装备产业链","船舶制造、海洋工程装备，向高端化转型","2025年海洋工程装备制造业增加值约1858亿元(+10.2%),新接海工订单金额占世界44.6%(连续8年第一);海洋船舶工业增加值1811亿元(+25.1%),造船三大指标国际份额连续16年第一(自然资源部\u002F工信部,2026-03)",33,{"id":336,"name":337,"category":7,"groupCategory":8,"intro":338,"scale":339,"maturity":11,"checkState":12,"nodeCount":294,"companyCount":288},"f4c900b2-e906-4332-a55a-910a870d66e7","节能环保产业链","节能装备、环保装备、资源循环利用、碳达峰碳中和","2024年全国生态环保产业年营收连续三年超2.2万亿元；垃圾焚烧发电市场规模约730亿元(全联环境服务业商会\u002F华经,2024)",{"id":341,"name":342,"category":7,"groupCategory":8,"intro":343,"scale":10,"maturity":31,"checkState":12,"nodeCount":32,"companyCount":344},"ch60_E13","轨道交通装备产业链","高铁、城轨、信号系统、关键零部件",36,{"id":346,"name":347,"category":37,"groupCategory":38,"intro":348,"scale":10,"maturity":31,"checkState":12,"nodeCount":13,"companyCount":349},"ch60_T06","轻工造纸产业链","造纸、家具、印刷、包装、日用消费品",107,{"id":351,"name":352,"category":45,"groupCategory":46,"intro":353,"scale":354,"maturity":48,"checkState":12,"nodeCount":13,"companyCount":355},"0605ff18-376d-4ef9-b9bd-c8d2d767fbf1","量子信息产业链","量子计算、量子通信、量子测量","2025年我国量子计算产业规模约115.6亿元、年增速超30%（赛迪，转引自人民网2026-08）；量子科技整体规模有望突破200亿元（东方财富研报\u002F赛迪，2025）",58,{"id":357,"name":358,"category":37,"groupCategory":38,"intro":359,"scale":10,"maturity":40,"checkState":12,"nodeCount":63,"companyCount":157},"ch60_T01","钢铁产业链","黑色金属冶炼及压延加工业，向特种钢、绿色低碳转型",{"id":361,"name":362,"category":7,"groupCategory":8,"intro":363,"scale":364,"maturity":11,"checkState":12,"nodeCount":20,"companyCount":365},"be5c17ae-9a06-4828-8111-6b0c0b4c7c2a","集成电路产业链","集成电路设计、制造、封装测试、装备材料，首批试点","中国集成电路全产业销售额约1.43万亿元(中国半导体行业协会，2024)；其中设计业约6460亿元(+11.9%)、制造业增长约14.5%、封测业增长约6.6%",196,{"id":367,"name":368,"category":37,"groupCategory":38,"intro":369,"scale":10,"maturity":31,"checkState":12,"nodeCount":32,"companyCount":370},"ch60_T07","食品加工产业链","农副食品加工、食品制造、饮料制造",109,{"id":372,"name":373,"category":7,"groupCategory":8,"intro":374,"scale":10,"maturity":11,"checkState":12,"nodeCount":32,"companyCount":157},"ch60_E21","高性能医疗器械产业链","高端影像设备、体外诊断、植介入器械、手术机器人",{"id":376,"name":377,"category":54,"groupCategory":8,"intro":378,"scale":10,"maturity":11,"checkState":12,"nodeCount":56,"companyCount":259},"ch60_E27","高端专用装备产业链","面向矿山、冶金、石化、建材、能源等领域的大型化、重型化、智能化成套专用装备与工程成套，是高端装备制造与进口替代的核心。",{"id":380,"name":381,"category":7,"groupCategory":8,"intro":382,"scale":10,"maturity":11,"checkState":12,"nodeCount":49,"companyCount":265},"ch60_E12","高端数控机床产业链","五轴机床、数控系统、伺服电机、精密刀具",{"list":384,"total":694,"page":695,"pageSize":223,"totalPages":696},[385,402,413,424,434,445,455,466,477,487,498,507,518,528,537,548,557,566,575,584,595,605,616,625,634,643,652,661,673,684],{"resultType":386,"id":387,"title":388,"abstract":389,"orgName":390,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":394,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":397,"ipc":398,"inventors":399,"tags":400,"tagNames":401},"patent","gpi2_0227","一种上下文剥离与双轨隔离的大语言模型防御方法及系统","一种上下文剥离与双轨隔离的大语言模型防御方法及系统，涉及大语言模型安全防护领域。解决现有的单轨推理架构下的大模型安全防护方案，仅依靠文本语义规则与模型微调实现风险识别，内存资源占用高、算力传输开销大等计算机底层硬件缺陷，所述方法包括：搭建双GPU显存物理隔离推理架构，接收用户输入token流存入进程内存缓冲区，经硬件路由分发至第一显存分区的目标生成轨进程，加载KV‑cache张量推理生成初始token文本并暂存于流式分片内存缓存。上下文剥离硬件模块剥离诱导上下文内存数据块，提取核心语义数据送入物理隔离的第二显存分区独立审查轨，基于核心语义完成安全判定，根据结果对缓存文本执行放行、清空或改写操作，优化推理硬件资源开销。","长春大学","220000;吉林省","2026-10-12T01:10:42.529Z","发明专利","2026.08.21#公开;2026.09.08#实质审查的生效;2026.09.18#授权",false,true,"CN202611095756.8","G06F21\u002F57;G06N5\u002F04;G06N3\u002F045","刘妍秀;赵俊嵩;徐大伟;王一力;林潇",[],[],{"resultType":386,"id":403,"title":404,"abstract":405,"orgName":406,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":407,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":408,"ipc":409,"inventors":410,"tags":411,"tagNames":412},"gpi2_0551","一种仿生柔性触觉传感器阵列及其制备方法","本发明公开了一种仿生柔性触觉传感器阵列及其制备方法，涉及柔性触觉传感技术领域，包括柔性单电极、压敏导电层、仿生结构层、缓冲层以及柔性阵列电极，压敏导电层的表面间隔阵列分布有多个凸起，仿生结构层的表面对应凸起设置有仿生结构，凸起嵌入仿生结构内部且在未受力状态下，凸起的端面与仿生结构的端面存在预设间隙。本发明采用压敏导电层与仿生结构层嵌套式初始间隙设计，形成阈值触发式导电通路，传感器在未受外力时使得电极单元与压敏导电层完全隔离，传感器处于零功耗待机状态，区别于传统常通型压阻传感器，显著降低了静态功耗；同时，从根源上避免了传统传感器因预压缩导致的有效灵敏度提前损耗问题，保证了全量程内的灵敏度稳定性。","吉林大学;辽宁材料实验室","2026.09.04#公开;2026.09.22#实质审查的生效;2026.09.29#授权","CN202611170523.X","G01L1\u002F18;G01L5\u002F22","刘燕;李加琪;李杰;陈士豪;丁振民;杨凯",[],[],{"resultType":386,"id":414,"title":415,"abstract":416,"orgName":417,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":418,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":419,"ipc":420,"inventors":421,"tags":422,"tagNames":423},"gpi2_0317","一种光伏发电供热系统用光伏板激光切割装置","本发明涉及光伏板加工技术领域，具体为一种光伏发电供热系统用光伏板激光切割装置，包括作业仓、栅栏架、定位组件、吸附件、调节组件和保护组件。本发明中通过定位组件配合CCD相机形成机械定位配合视觉定位的双重定位，并通过机械定位提高切割时的稳定性，减少因应力变化导致工件边缘形变以及影响切割稳定的问题，并为后续吸附动作提供基准导向，其次借助吸附件的吸附过程降低电池片切割分离时释放应力的影响，提高稳定性和切割精度，同时通过保护组件形成矩形区域保护电池片，避免出现过度切割导致电池片出现损坏的问题，综上，通过加强定位基准、吸附动作减小应力影响和跟随基准保护三重作业相互配合，确保电池片精准切割。","吉林省清洁能源开发利用有限公司","2026.08.25#公开;2026.09.11#实质审查的生效;2026.09.25#授权","CN202611098943.1","B23K26\u002F02;B23K26\u002F08;B23K26\u002F70;B23K26\u002F38;B23K101\u002F36","王璘;王梓屹;公维康;孙生亮;阚丽媛",[],[],{"resultType":386,"id":425,"title":426,"abstract":427,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":394,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":429,"ipc":430,"inventors":431,"tags":432,"tagNames":433},"gpi2_0222","一种基于X射线CT三维孔隙重构的土壤导水能力评价方法","本发明公开了一种基于X射线CT三维孔隙重构的土壤导水能力评价方法，属于土壤物理学与农业土壤结构评价技术领域。该方法包括：土壤样品采集与培养、X射线CT扫描、图像预处理、孔隙分割、三维孔隙结构重构及孔隙结构参数提取。利用Avizo软件获取CT孔隙率及孔隙与固体界面比表面积，并在同源土壤体系条件下对Kozeny–Carman模型进行归一化处理，构建基于CT结构参数的无量纲结构导水能力指数，通过对不同处理条件下土壤潜在导水能力进行定量评价，并识别结构导水能力优化阈值。本发明能够提高复杂土壤体系导水能力评价的准确性，可应用于农田土壤结构评价、水分调控及农业土壤优化管理等领域。","吉林大学","CN202611097619.8","G01N15\u002F08;G01N23\u002F046;G06T12\u002F10","吴宝广;叶俊廷;胡伟;王秋菊;许顺;殷志鹏;聂晓雨;刘玉萍;谢岩松;王振宇",[],[],{"resultType":386,"id":435,"title":436,"abstract":437,"orgName":438,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":439,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":440,"ipc":441,"inventors":442,"tags":443,"tagNames":444},"gpi2_0081","一种基于扩张状态观测器的三维高斯泼溅自适应重建方法","本发明公开了一种基于扩张状态观测器的三维高斯泼溅自适应重建方法。针对现有三维高斯泼溅同步定位与地图构建方法中不确定性方差作为静态权重、后端优化为开环结构的缺陷，本发明将所述不确定性方差建模为逐像素动态状态量，构建离散时间状态空间方程，设计逐像素二阶扩张状态观测器，引入非线性误差反馈函数区分高频测量噪声与低频环境退化信号，动态估计逐像素方差估计值与扰动估计值，以所述方差估计值构建渲染损失自适应像素权重，对所述扰动估计值进行空间聚合得到全局退化指标，动态调制优化学习率，当所述扰动估计值超过动态阈值时暂停对应区域致密化操作，从权重、学习率、密度三个维度实现闭环补偿，提升了退化环境下的建图精度。","长春工业大学","2026.08.25#公开;2026.09.11#实质审查的生效;2026.09.18#授权","CN202611110444.X","G06T17\u002F00","张秀梅;胡俊佑;李慧;程艳妍;王大楠;荣井涛;张立建;贾延逊",[],[],{"resultType":386,"id":446,"title":447,"abstract":448,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":449,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":450,"ipc":451,"inventors":452,"tags":453,"tagNames":454},"gpi2_0169","一种基于物理信息生成对抗网络的长尾事故场景生成方法","本发明属于自动驾驶场景生成技术领域，具体的说是一种基于物理信息生成对抗网络的长尾事故场景生成方法。本发明以少量真实事故场景为原型，围绕原型采样车辆初始状态、道路条件和目标碰撞条件；构建条件生成器与条件判别器，生成器同时作为物理信息神经网络，输出多交通参与者的连续状态轨迹与控制量；联合真实轨迹监督、对抗损失、运动学残差、道路边界、控制范围、目标首次碰撞及多样性约束进行训练；最后经物理筛选与相似性去重，形成长尾事故场景数据库。本发明可构建数量丰富且多样化的长尾事故场景数据库，提升自动驾驶汽车对长尾事故的适应能力与行驶安全性，助力其产业化落地。","2026.08.28#公开;2026.09.15#实质审查的生效;2026.09.29#授权","CN202611107354.5","G06N3\u002F094;G06N3\u002F098;G06F18\u002F15;G06F18\u002F214;G06F18\u002F24;G06F18\u002F213;G06F18\u002F25","赵健;李文旭;朱冰;张培兴;黄殷梓",[],[],{"resultType":386,"id":456,"title":457,"abstract":458,"orgName":459,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":460,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":461,"ipc":462,"inventors":463,"tags":464,"tagNames":465},"gpi2_0892","一种多阶段漏洞挖掘与威胁溯源方法及系统","一种多阶段漏洞挖掘与威胁溯源方法及系统，涉及网络安全技术领域。解决了攻防演练中现有技术使用的工具之间数据孤立、全量日志扫描资源浪费、测试引导缺乏战术视角的问题。所述方法包括：获取隔离沙箱中的系统调用日志和权限变更日志，进行语义分析得到测试用例；执行测试用例；提取系统调用序列，生成攻击原语第一阶段记录；从全量日志中检索全量日志子集；通过图神经网络进行二分类得到攻击路径候选集，生成攻击原语第二阶段记录；从攻击原语第二阶段记录中提取关键实体，检索情报，将攻击路径、检索情报和MITRE ATT&amp;CK映射结果拼接为推理上下文；生成攻击者画像，标记未覆盖的测试盲区，生成攻击原语第三阶段记录，并将所述测试盲区写入盲区列表。","华易数安科技(吉林省)有限公司","2026.08.25#公开;2026.09.11#实质审查的生效;2026.09.29#授权","CN202611122166.X","H04L9\u002F40;G06N5\u002F04","朱琳;李佳桐;李忆平;宋涛;李雷;王宪辉;刘洋;陈兴钰;姜洋;李志权",[],[],{"resultType":386,"id":467,"title":468,"abstract":469,"orgName":470,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":471,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":472,"ipc":473,"inventors":474,"tags":475,"tagNames":476},"gpi2_0275","一种大豆种子缺陷检测模型的构建方法","一种大豆种子缺陷检测模型的构建方法，涉及种子检测领域。解决了现有用于大豆种子检测的模型缺乏对智能精选过程中多目标密集分布、局部接触与局部遮挡条件下的处理能力，容易发生漏检与类别混淆的问题。所述构建方法包括：采集包含大豆种子的原始图像，对所述原始图像进行筛选和类别标注，并按比例划分为训练集、验证集和测试集，得到原始图像数据集；对原始图像数据集进行离线数据增强，得到最终数据集；从最终数据集中读取批量图像进行在线数据增强；将在线数据增强后的训练图像输入至SWDSoybeanNet模型中，计算损失函数并更新模型参数，得到训练后的SWDSoybeanNet模型。本发明所述的方法还可应用于玉米、麦穗、花生等其他作物种子的检测。","吉林农业大学","2026.08.21#公开;2026.09.08#实质审查的生效;2026.09.22#授权","CN202611098855.1","G06V20\u002F60;G06V10\u002F44;G06V10\u002F774;G06V10\u002F776;G06V10\u002F82;G06N3\u002F0464;G06N3\u002F08","于合龙;李延松;石磊;吕肇聪;郭靖",[],[],{"resultType":386,"id":478,"title":479,"abstract":480,"orgName":481,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":460,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":482,"ipc":483,"inventors":484,"tags":485,"tagNames":486},"gpi2_0778","一种结构视觉监测中的伪位移识别与数据有效性判定方法","本发明属于光学计量与图像处理技术领域，尤其涉及一种结构视觉监测中的伪位移识别与数据有效性判定方法，包括如下步骤：S1：获取监测图像序列、稳定参考区域和辅助状态信息；S2：提取监测测点位移、图像质量和多源状态特征；S3：构建多维一致性指标；S4：判断多维一致性指标是否满足真实结构形变判据；S5：根据可信度和异常类型生成数据有效性等级，并控制数据是否参与安全预警；S6：存储原始图像、伪位移标签、处置过程、最终位移和有效性等级，返回持续监测。本方案能通过联合分析图像、空间、时间、结构、波段及设备状态信息，识别伪位移来源并执行差异化处置，最终输出带有效性等级的结构形变数据。","中国科学院长春光学精密机械与物理研究所","CN202611136668.8","G06F18\u002F20;G06F18\u002F2433;G06T7\u002F246;G06V10\u002F98;G01M11\u002F00;G01C11\u002F04;G01B11\u002F16","孙健维;刘帅凝;宋来运;袁东明;刘学吉",[],[],{"resultType":386,"id":488,"title":489,"abstract":490,"orgName":491,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":492,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":493,"ipc":494,"inventors":495,"tags":496,"tagNames":497},"gpi2_0210","一种脑部表面点云重建方法、电子设备及存储介质","本发明属于医学影像处理技术领域，尤其为一种脑部表面点云重建方法、电子设备及存储介质。包括以下步骤：S1：获取样本集，所述点云标签用于指示对应的三向正交MRI切片样本的真实表面特征点云；S2：构建神经网络框架；S3：采用多重几何损失函数训练所述神经网络框架，所述损失函数包括Chamfer距离、改进Hausdorff距离和法向一致性损失；S4：将待重建的三向正交MRI切片输入训练完成的框架，输出包含2048个点的脑部表面点云。本发明利用基于深度学习的脑部表面点云重建模型对三向正交MRI切片图像进行处理，能够充分联合利用多视图MRI数据，有效发掘不同解剖平面之间的潜在空间关联，从而显著提升重建效果和临床适用性。","长春理工大学","2026.08.18#公开;2026.09.04#实质审查的生效;2026.09.18#授权","CN202611097197.4","G06T17\u002F00;G06N3\u002F042;G06N3\u002F0455;G06N3\u002F0464;G06T7\u002F50;G06T19\u002F00;G06V10\u002F70;G06V10\u002F80;G06V10\u002F82","赵家石;王文豪;方旭;程新异;申秀贤;田浩;梁世奇",[],[],{"resultType":386,"id":499,"title":500,"abstract":501,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":471,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":502,"ipc":503,"inventors":504,"tags":505,"tagNames":506},"gpi2_0947","一种自平衡可变姿态可回收锚固装置","本发明公开了一种自平衡可变姿态可回收锚固装置，属于海洋工程锚固装备技术领域，包括自平衡螺旋桨系统、变向系统、持力增强系统、动力系统和自平衡双钻头系统；持力增强系统由第一持力增强系统和第二持力增强系统组成；自平衡螺旋桨系统用于提供推进力或回收升力；变向系统用于在锚固装置完成钻进锚固后，使变向关节一上部连接的结构能够随缆绳受力方向相对于下部锚固装置偏转；持力增强系统用于在锚固装置钻进至目标土层后向外展开并与周围土体形成相互作用；动力系统用于提供可控的轴向自给进补偿推力；自平衡双钻头系统用于通过内钻头和外钻头的协同反向旋转对目标土层进行破土和排土。","CN202611114407.6","B63B21\u002F26;B63B21\u002F50","高科;袁井泉;赵研;于永平;王治刚;贾瑞;谢晓波;李旭;闻育民;张丛珊;刘奥;刘松赫",[],[],{"resultType":386,"id":508,"title":509,"abstract":510,"orgName":511,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":512,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":513,"ipc":514,"inventors":515,"tags":516,"tagNames":517},"gpi2_0463","一种轨道车辆冷弯型钢矫平处理工艺","本发明为一种轨道车辆冷弯型钢矫平处理工艺，涉及冷弯型钢矫平处理技术领域，解决了上矫平辊和下矫平辊之间辊缝磨损补偿效果差的技术问题，包括原材料准备与分条，选取冷轧高耐候钢板作为原材料，辊压冷弯成型，将分条后的条料送入冷弯成型机组的开卷机上，将条料依次通过成型模具，按照先矫平、后逐道次横向弯曲的顺序进行辊压成型，辊缝在线补偿矫平，在矫平作业过程中，实时监测下矫平辊表面磨损状态，四个下矫平辊各自升高与磨损量对应的不同高度，恢复与上矫平辊之间的原始辊缝，本发明通过在下矫平辊因磨损导致上矫平辊和下矫平辊间辊缝增大时，驱动四个下矫平辊同步上升，自动补偿辊缝增量，可实时响应磨损变化，无需停机调整。","长春市新金享冷弯型钢有限公司","2026.08.18#公开;2026.09.04#实质审查的生效;2026.09.25#授权","CN202611092195.6","B21D1\u002F02;B21D43\u002F09","温杰;温博阁",[],[],{"resultType":386,"id":519,"title":520,"abstract":521,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":522,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":523,"ipc":524,"inventors":525,"tags":526,"tagNames":527},"gpi2_0381","一种轨道车辆管接头松动检测方法及系统","本发明公开了一种轨道车辆管接头松动检测方法及系统，属于轨道交通车辆管接头检测领域；该方法包括：局部姿态解耦，检测管接头结构特征并建立局部坐标系、估计局部变换；标记线骨架提取，提取防松标记线候选区域并骨架化得到待检测标记线轨迹和模板标记线轨迹；同源轨迹比较，根据接缝边界划分固定侧、活动侧及跨缝邻域段，将模板标记线轨迹映射为参考标记线轨迹，计算与待检测标记线轨迹的接缝偏差特征；松动判定输出，根据接缝偏差特征及配准可信度计算松动风险评分，输出管接头状态结果。通过模板图像同源参照、局部坐标系消除视角偏差、曲线拓扑结构表征防松标记线，区分画线不规范与真实松动，避免误报和漏报，提升检测准确性和鲁棒性。","2026.08.18#公开;2026.09.04#实质审查的生效;2026.09.15#授权","CN202611091071.6","G06T7\u002F00;G06V10\u002F75;G06V10\u002F34;G06V10\u002F40;G06V10\u002F26;G06T7\u002F136;G06T5\u002F70;G06T5\u002F92","蒋尚佑;田海龙;李鑫;陈晓彬;李修昱",[],[],{"resultType":386,"id":529,"title":530,"abstract":531,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":439,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":532,"ipc":533,"inventors":534,"tags":535,"tagNames":536},"gpi2_0652","一种轮速缺失下双驱动桥电动商用车转矩协调控制方法","本发明适用于电动商用车驱动转矩协调控制技术领域，提供了一种轮速缺失下双驱动桥电动商用车转矩协调控制方法，包括：获取车辆状态信息；利用驱动桥电机转速折算得到轮边等效转速，利用前桥左、右轮速计算车辆基准车速，构建驱动桥滑转率；建立驱动桥地面驱动力估计模型，通过滚动窗口法拟合得到局部斜率，并确定滑转状态；根据滑转状态对参考滑转率和轮边可传递转矩上限进行更新或保持；根据中、后桥滑转状态组合对驾驶员需求轮边驱动转矩进行分配与约束；对过度滑转的驱动桥进行防滑转矩修正；生成并输出目标驱动转矩。本发明实现了桥间驱动转矩的合理分配与过度滑转抑制，适用于部分驱动桥未配备轮速传感器的双驱动桥电动商用车。","CN202611141180.4","B60L15\u002F20;B60L15\u002F32","管欣;薛鹏程;詹军;卢萍萍;田培华;丁淼;杨利国;吴楠;徐国彭",[],[],{"resultType":386,"id":538,"title":539,"abstract":540,"orgName":541,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":542,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":543,"ipc":544,"inventors":545,"tags":546,"tagNames":547},"gpi2_0738","偶极子阵列的电磁波相控阵聚焦能量调控方法及系统","本发明涉及相控阵聚焦调控领域，更具体地，本发明公开了一种偶极子阵列的电磁波相控阵聚焦能量调控方法及系统。首先基于医学影像数据及生物组织弛豫参数，构建反映非线性色散特性的宽带复频域介电属性空间分布，基于局部空间信息熵特征的动态边界平滑，抑制影像伪影导致的参数突变，获得修正后的介电属性分布。以此分布通过强制反相替换静态电导率引入虚拟负热力学耗散因子，在目标靶区注入激励并进行拟逆向电磁演化，从而自靶区反向推算抵达各辐射端口截面的波前信息，得到用于精确控制相控阵各辐射通道的延迟相位补偿量与能量分配权重系数。该方法能有效提升对生物组织深部靶区的能量聚焦精度与效率。","吉林省众驰医疗科技有限公司","2026.08.25#公开;2026.09.11#实质审查的生效;2026.09.22#授权","CN202611131805.9","A61N5\u002F00;A61N5\u002F02;G16H20\u002F30;G16H40\u002F60","姜新;王身云;高杨;刘忠刚;李超贵",[],[],{"resultType":386,"id":549,"title":550,"abstract":551,"orgName":481,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":439,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":552,"ipc":553,"inventors":554,"tags":555,"tagNames":556},"gpi2_0655","基于GaNFET的高速紫外单光子探测器淬灭复位电路及方法","本发明涉及单光子探测技术领域，尤其涉及一种基于GaNFET的高速紫外单光子探测器淬灭复位电路及方法。电路包括淬灭复位模块、TIA跨阻放大模块、比较器整形模块、FPGA主控、数模转换模块及电源模块；淬灭复位模块由SPAD、氮化镓场效应管、第一电容及两只分压电阻构成两级淬灭结构，TIA模块并联RC相位补偿支路抑制环路振荡，比较器输出端配置差分匹配电阻消除信号反射；FPGA分别控制氮化镓场效应管与两路数模转换器，数模转换器调控电源输出两路独立可调高压，分别为SPAD提供静态偏压、为淬灭开关提供淬灭电压。优点在于：实现纳秒级快速淬灭复位；有效减小了单光子探测的死时间，提高了计数率与探测效率。","CN202611139916.4","G01J1\u002F44;G01J1\u002F42;H03K17\u002F22;H03K17\u002F081;H03K17\u002F687","黎大兵;朱虹宇;蒋科;隋延林;陈一仁;孙晓娟",[],[],{"resultType":386,"id":558,"title":559,"abstract":560,"orgName":481,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":449,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":561,"ipc":562,"inventors":563,"tags":564,"tagNames":565},"gpi2_0714","基于云边端协同的数字孪生系统性能预测与故障自愈方法","本公开涉及基于云边端协同的数字孪生系统性能预测与故障自愈方法，属于数字孪生领域，包括：在云端构建数字孪生体的全局性能预测模型；在边缘节点部署故障检测模块，采用自适应滑动窗口技术检测传感器数据流中的异常特征，并通过多尺度特征提取算法将异常特征与预测结果比对；建立端设备与边缘节点的双向认证通道；边缘节点识别故障特征，向端设备注入动态修复策略代码包；云端启动虚拟仿真引擎进行故障推演，采用蒙特卡洛树搜索算法生成若干种修复方案，进行仿真验证筛选最优修复方案；将最优修复方案通过区块链下发至边缘节点，采用差分更新机制同步至端设备进行修复；云端动态重构模型的损失函数。本公开的性能预测准确与故障自愈能力强。","CN202611144398.5","H04L41\u002F0654;H04L9\u002F40;H04L67\u002F10;H04L9\u002F32;H04L41\u002F14;H04L41\u002F147;H04L41\u002F149;H04L41\u002F16","赵伟超;孙鸽;齐翌辰;李泽",[],[],{"resultType":386,"id":567,"title":568,"abstract":569,"orgName":481,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":460,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":570,"ipc":571,"inventors":572,"tags":573,"tagNames":574},"gpi2_0022","基于时空交互融合的双时相图像变化检测系统及方法","本发明涉及图像处理技术领域，尤其涉及一种基于时空交互融合的双时相图像变化检测系统及方法，首先生成双时相图像的伪视频序列，并对伪视频序列进行多尺度特征提取；对双时相图像的多尺度特征进行空间方向上的差异信息的提取与增强，得到对应尺度的空间特征；将时间特征进行时间方向上的聚合，再将聚合后的特征与空间特征进行融合和门控调制；对得到的时空融合特征进行频域和空域的增强；对得到的每种尺度的多维度增强特征进行逐级上采样解码操作，得到双时相图像的变化概率图。本发明通过时空特征双分支提取与增强机制，实现时序细节增强、空间精细定位与频率域干扰抑制的统一建模，提升复杂场景下变化检测的精度与可靠性。","CN202611120547.4","G06V20\u002F40;G06V10\u002F44;G06V10\u002F52;G06V10\u002F62;G06V10\u002F77;G06V10\u002F80;G06V10\u002F82;G06N3\u002F045;G06N3\u002F0455;G06N3\u002F0464;G06N3\u002F084;G06N3\u002F09","孙海江;郭超凡;刘巧元;陈周长",[],[],{"resultType":386,"id":576,"title":577,"abstract":578,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":460,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":579,"ipc":580,"inventors":581,"tags":582,"tagNames":583},"gpi2_0780","基于机器学习的减隔震桥梁震致残余位移预测方法及系统","本发明涉及基于机器学习的减隔震桥梁震致残余位移预测方法及系统，属于桥梁性能分析技术领域，解决现有预测方法成本高、速度慢、精度低的问题。首先获取减隔震桥梁基本信息及减隔震装置参数信息；建立减隔震桥梁在地震作用下的仿真模型；采用拉丁超立方抽样方法对表征参数进行抽样，并进行特征处理和仿真模型计算，构建特征参数‑震致残余位移样本集；基于样本集训练Stacking集成模型；采用训练后的Stacking集成模型预测减隔震桥梁的震致残余位移。本发明无需布设大量传感器，能够快速、准确地预测罕遇地震作用下的桥梁震致残余位移，具有成本低、速度快、精度高的优点，为桥梁抗震韧性评估和灾后快速鉴定提供了有效技术手段。","CN202611133281.7","G06F30\u002F27;G06F30\u002F23;G06F18\u002F27;G06F18\u002F214;G06F18\u002F213;G06N20\u002F00;G06F18\u002F25;G06F18\u002F21;G06N7\u002F01;G06N3\u002F0985;G06N3\u002F096;G06F18\u002F22;G06F18\u002F2433;G06N5\u002F01;G06F111\u002F04;G06F119\u002F14","顾正伟;刘朔宬;谭国金;吴春利;梁春雨;周培蕾;秦绪喜;艾永明;王文盛;何昕;都基委;赵鹤松;万肖音",[],[],{"resultType":386,"id":585,"title":586,"abstract":587,"orgName":588,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":589,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":590,"ipc":591,"inventors":592,"tags":593,"tagNames":594},"gpi2_0357","基于电网时序约束的新能源消纳能力优化方法","本发明涉及电力系统技术领域，具体涉及一种基于电网时序约束的新能源消纳能力优化方法，通过获取电网运行的多源数据，并提取在不同时间尺度下的多源时序特征；基于在历史时间段内的多源时序特征，生成含多种场景在内的不确定性场景集合；基于未来预测时间段内在不同时间尺度下的多源时序特征，确定动态约束条件，并将不确定性场景集合代入以新能源弃电量最小和电网运行成本最低为目标的随机优化模型中进行求解，得到在不同时间尺度下的新能源消纳基础调度计划，并在不同新能源消纳基础调度计划之间进行限幅协同，获得最终的新能源消纳调节计划。本发明有效提升了调度计划应对极端波动的鲁棒性与跨时间尺度执行的平稳性。","国网吉林省电力有限公司经济技术研究院","2026.08.21#公开;2026.09.08#实质审查的生效;2026.09.29#授权","CN202611097399.9","H02J3\u002F466;H02J3\u002F28;H02J3\u002F175;G06F18\u002F15;G06F18\u002F20;H02J101\u002F24;H02J101\u002F28;H02J103\u002F30;H02J105\u002F52;G06F123\u002F02","杜秋实;孟繁波;时雨;姜明磊;吕长会;时圣尧;张圆美;王博闻;张文宝;谌骏哲;赵博;杨爽;张亮;柳禾丰;姚新光;张艳",[],[],{"resultType":386,"id":596,"title":597,"abstract":598,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":599,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":600,"ipc":601,"inventors":602,"tags":603,"tagNames":604},"gpi2_0650","基于自适应字典约束的面波频散三维层析成像方法","本申请属于地震学与地球物理层析成像技术领域，涉及一种基于自适应字典约束的面波频散三维层析成像方法，包括：对研究区域进行三维网格剖分，构建用于表征三维剪切波速度场的模型参数向量，构建走时残差与剪切波速度扰动量之间的线性化灵敏度矩阵方程，获得灵敏度矩阵及灵敏度矩阵的列范数分布，依据贝叶斯定理将最大化联合后验概率密度函数等价转化为最小化，得到由走时数据拟合项、全局‑辅助耦合约束项、图像块稀疏表示误差项三项加权平方和构成的联合反演目标函数，并采用交替最小化策略对联合反演目标函数进行求解，迭代收敛后，输出最终三维剪切波速度结构模型。实现了数据约束与结构先验的最优平衡，实现三维剪切波速度结构的高保真成像。","2026.08.25#公开;2026.09.11#实质审查的生效;2026.09.15#授权","CN202611143380.3","G01V1\u002F28;G01V1\u002F34;G01V1\u002F30;G06F18\u002F20;G06F18\u002F15","陈祖斌;李志文;丁健洋;周晓华;张林行",[],[],{"resultType":386,"id":606,"title":607,"abstract":608,"orgName":609,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":610,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":611,"ipc":612,"inventors":613,"tags":614,"tagNames":615},"gpi2_0532","新能源弱送端异质边界外送安全校核方法","一种新能源弱送端异质边界外送安全校核方法，属于新能源电力系统技术领域。本发明的目的是能够在同一外送延拓路径和同一运行平衡点基础上，统一表征异质安全边界，确定其沿给定外送方向临界触发位置的新能源弱送端异质边界外送安全校核方法。本发明构造外送延拓平衡点轨迹和安全裕度，确定对象级临界延拓值、家族级临界延拓值及族内首触发对象集合，构造有效边界集合确定确定安全送出能力参数和最大安全外送功率，并构造共触发判别带，确定唯一首触发边界族和原始次近边界族，统计各边界族的唯一主导边界频率、条件参与频率及共触发或窄间隔状态出现比例。本发明提高新能源外送安全校核结果的准确性、可比性和工程解释性。","东北电力大学","2026.09.01#公开;2026.09.18#实质审查的生效;2026.09.29#授权","CN202611176506.7","H02J3\u002F38;G06F17\u002F10;H02J3\u002F50;H02J3\u002F1842;H02J3\u002F06;G06F123\u002F02;H02J103\u002F30;H02J101\u002F28","成龙;孟繁宇;王延旭;张鸿杨;礼元杰;王翀;杨悦;王燕辉;杨文博",[],[],{"resultType":386,"id":617,"title":618,"abstract":619,"orgName":481,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":439,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":620,"ipc":621,"inventors":622,"tags":623,"tagNames":624},"gpi2_0934","用于高精度光学系统的计算机辅助装调去噪方法","本发明涉及一种用于高精度光学系统的计算机辅助装调去噪方法，首先搭建失调光学系统仿真模型，通过差分法计算灵敏度矩阵，构建成像质量评判目标函数；采用干涉仪多个视场多次采集系统波像差，构建多组像差矩阵并拼接生成观测矩阵；依次对观测矩阵实施密集噪声去噪、矩阵行平均处理，拼接得到全视场像差矩阵后进一步完成稀疏噪声去噪；依托灵敏度矩阵求解补偿量，结合目标函数筛选有效补偿量并进行系统装调，装调后测试成像质量，未满足指标则重复波像差测量与去噪流程迭代校正。优点在于：能够有效降低干涉仪测量噪声对装调精度的负面影响，提高高精度光学系统装调精度，且该方法在不同噪声强度下均能保持稳定的去噪性能。","CN202611120542.1","G02B27\u002F62;G02B27\u002F00;G06F30\u002F20;G06F18\u002F10","赵尚男;郑青青;张新;吴庆;叶昊坤;史广维;回佳轩",[],[],{"resultType":386,"id":626,"title":627,"abstract":628,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":589,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":629,"ipc":630,"inventors":631,"tags":632,"tagNames":633},"gpi2_0001","融合超宽带非视距检测与锚点自适应选择的室内定位方法","本发明涉及融合超宽带非视距检测与锚点自适应选择的室内定位方法，属于室内定位技术领域，解决现有定位方法难以实时检测NLOS误差以及锚点缺失导致定位中断的问题。首先获取原始测距数据及采样数据并对齐时间；进行短时惯性积分后得到三维相对位移预测值；根据当前帧和上一帧各锚点的有效测距值计算测距差分序列和方向向量序列；对三维相对位移预测值与实测距离变化量进行一致性校验，判定锚点的NLOS等级；自适应选择定位策略；执行定位策略进行定位解算，得到目标位置估计值；卡尔曼滤波平滑后输出最终定位位置。本发明能够实时检测NLOS误差，并在部分锚点失效时动态调整定位策略，提高复杂遮挡环境下室内定位的鲁棒性与连续性。","CN202611114283.1","G01C21\u002F20;G01C21\u002F16;H04W64\u002F00;H04W4\u002F02;H04W4\u002F029;H04W4\u002F33;G06F18\u002F24;G06F18\u002F10;G06F18\u002F25","李云烽;刘鹏;陈玮峥;樊飞虎;李浩源;魏来;刘芸;赵峥;席嘉璐;邱云圣浩;罗永哲;彭楚尧",[],[],{"resultType":386,"id":635,"title":636,"abstract":637,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":492,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":638,"ipc":639,"inventors":640,"tags":641,"tagNames":642},"gpi2_0396","长波红外可调谐光学模拟计算器件及其空间频率调控方法","本发明公开了长波红外可调谐光学模拟计算器件及其空间频率调控方法，属于长波红外光学图像处理技术领域，所述长波红外可调谐光学模拟计算器件沿光路依次包括上分布式布拉格反射镜、液晶腔层和下分布式布拉格反射镜，形成可调谐法布里‑珀罗谐振腔；通过外加电场调节液晶腔层的有效折射率，改变谐振腔的光学厚度与相位匹配条件，使透射峰或阻带在空间频率域中动态移动，从而获得低通、高通、带通或带阻等可切换的空间频率传递函数。本发明利用角度透射谱与空间频率的映射关系，在光传输过程中直接对长波红外图像实现去噪、边缘增强、特定频段抑制或尺度选择等处理，具有结构紧凑、可调谐、多模式切换和易于集成于透射式红外成像系统的优点。","CN202611089068.0","G02F1\u002F1335;G02F1\u002F1333;G02F1\u002F133;G02B27\u002F00","靳淳淇;韦雨婷;陈岐岱",[],[],{"resultType":386,"id":644,"title":645,"abstract":646,"orgName":428,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":439,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":647,"ipc":648,"inventors":649,"tags":650,"tagNames":651},"gpi2_0729","面向侧风工况的商用车尾板分区独立偏转控制装置及方法","本发明适用于车辆空气动力学控制技术领域，提供了面向侧风工况的商用车尾板分区独立偏转控制装置及方法，装置包括：三分区尾板总成、独立设置的执行单元、风速风向传感器、车辆状态感知模块及电子控制单元。在离线阶段通过CFD仿真与高斯过程回归构建气动响应离线映射模型，并生成离线预测表；在线阶段根据实时车速与侧风偏航角，在满足侧向力安全阈值下快速查表寻优，输出减阻最优的尾板角度指令，并驱动各尾板独立执行。本发明能够根据侧风主动调整各尾板姿态，在保证侧风稳定性的同时持续降低气动阻力，具有决策快、鲁棒性强、节油效果明显的优点。","CN202611134103.6","B62D37\u002F02;G06F30\u002F28;G06F30\u002F27;G06F30\u002F15;G05B13\u002F04;G06F111\u002F04;G06F111\u002F06;G06F113\u002F08;G06F119\u002F14;G06F18\u002F27","王靖宇;王子瑞;史珂源;胡兴军;董春波",[],[],{"resultType":386,"id":653,"title":654,"abstract":655,"orgName":491,"province":391,"date":392,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":449,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":656,"ipc":657,"inventors":658,"tags":659,"tagNames":660},"gpi2_0642","面向泛在网络生产者移动性的数据平面转发增强方法","本发明涉及命名数据网络的数据平面转发技术领域，具体为面向泛在网络生产者移动性的数据平面转发增强方法。该方法用于解决生产者在泛在网络中切换接入节点时兴趣包仍转向旧路径、PIT回传路径不一致及路由收敛期间兴趣包回溯丢弃的问题；通过在数据平面建立移动绑定表，旧接入节点基于本地控制兴趣包进入代理转发模式，对未满足兴趣条目生成恢复性兴趣包，并将恢复性兴趣包及新到达兴趣包按新接入节点二维坐标标识转发至新接入节点；新接入节点经生产者接入链路转发兴趣包并基于PIT记录回传数据包，同时通过NACK触发前一跳节点重表达回溯兴趣包，适用于低轨卫星网络等生产者移动场景。","CN202611149114.1","H04L45\u002F50;H04L45\u002F645;H04L45\u002F748","祁晖;从立钢;底晓强;刘旭;孙苏鹏;毕琳;李锦青",[],[],{"resultType":386,"id":662,"title":663,"abstract":664,"orgName":665,"province":391,"date":666,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":667,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":668,"ipc":669,"inventors":670,"tags":671,"tagNames":672},"gpi_00449","一种人参保健品冲剂的制备方法","本发明提供一种人参保健品冲剂的制作方法。将人参皂苷提取物先进行处理，从而将人参中的天然的人参皂苷包括二醇型皂苷的Rb1、Rb2、Rb3、Rc、Rd等转化成20R－Rg3、20S－Rg3、Rk1、Rg5。将人参总皂苷、人参多糖及精氨酸双糖苷混合，加入蔗糖粉、人参总皂苷、人参多糖及精氨酸双糖苷和蔗糖，混合均匀后用颗粒机制成颗粒状冲剂。本发明提供的制备方法得到的人参保健品冲剂，可以起到辅助治疗肿瘤的作用，有利于肿瘤患者的康复。本发明提供的制备方法得到的人参保健品冲剂，服用方便，口感良好，溶液黄棕色澄清透明没有沉淀。","中国科学院长春应用化学研究所","2026-10-12T00:54:35.109Z","2009.01.28#公开;2009.03.25#实质审查的生效;2011.08.03#授权","CN200810051162.2","A61K36\u002F258;A61K9\u002F16;A61P1\u002F14;A61P35\u002F00","宋凤瑞;张语迟;刘志强;王淑敏;刘淑莹",[],[],{"resultType":386,"id":674,"title":675,"abstract":676,"orgName":677,"province":391,"date":666,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":678,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":679,"ipc":680,"inventors":681,"tags":682,"tagNames":683},"gpi_01550","一种人参酸性多糖的降血糖应用","本发明公开一种人参酸性多糖在降血糖方面的应用，以人参根为原材料，经过提取，纯化，精制，制备出具有降血糖作用的人参酸性多糖。本发明涉及的人参酸性多糖可制备具有降血糖作用人参酸性多糖的各种制剂。本发明保留了人参多糖的活性，并提高了人参多糖降血糖活性成分的纯度。","东北师范大学","2010.10.20#公开;2010.12.01#实质审查的生效;2011.11.02#授权","CN200910066819.7","A61K31\u002F715;A61P3\u002F10","台桂花;周义发;陈艳;孙成新;倪维华;韩翰",[],[],{"resultType":386,"id":685,"title":686,"abstract":687,"orgName":665,"province":391,"date":666,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":393,"legalStatus":688,"isOpenLicense":395,"isRevitalized":396,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":689,"ipc":690,"inventors":691,"tags":692,"tagNames":693},"gpi_00742","一种医用快速吸液泡沫材料的制备方法","本发明涉及一种医用快速吸液泡沫材料的制备方法。将聚乙烯醇水溶液，醛，成孔剂，混合搅拌均匀；再加入酸，继续搅拌，将混合液倒入模具中，固化成型；洗涤，干燥，得到目标物。采用机械打泡和成孔剂配合使用的办法使材料具有丰富的相互贯通的开孔结构；制备的材料生物相容性好，具有优良的亲水性，吸液能力远高于一般的棉纱和纸张，对蒸馏水、生理盐水、尿液具有快速而大量吸收的能力，吸液后成为柔软、强韧、有弹性的海绵。材料耐热、耐紫外线，可用多种方式进行消毒。制备的泡沫材料干态时为硬质泡沫材料，湿态时为柔软的海绵状材料，表观密度0.04－0.09g\u002Fcm3，吸液率1800－2800％，可在医科手术中应用。","2009.08.19#公开;2009.10.14#实质审查的生效;2012.11.14#授权","CN200910066679.3","A61L15\u002F24;C08F8\u002F28;C08J9\u002F04","潘艳雄;王伟财;彭超;罗云霞;姬相玲",[],[],126,1,5,{"list":698,"total":709,"page":695,"pageSize":695,"totalPages":709},[699],{"id":700,"name":701,"orgName":702,"school":10,"gender":10,"title":10,"degree":10,"honor":703,"position":704,"researchDirection":705,"intro":10,"province":10,"completeness":180,"avatarAuthorized":395,"sourceUrl":706,"nameValid":396,"tags":707,"tagNames":708},"cmv7rn1er000jddmmau8qulzd","应汉杰","苏州大学","ACADEMICIAN","苏州大学校长","生物制造与酶催化方向专家","chaingraph:\u002F\u002F应汉杰",[],[],35346,[711,948,1027,1704],{"group":712,"code":712,"name":713,"count":207,"tags":714},"CHAIN","产业链环节",[715,722,729,736,742,748,754,760,767,773,780,787,792,798,801,806,810,816,822,827,833,839,846,851,858,865,870,877,883,889,896,902,906,912,919,925,930,935,941],{"code":716,"name":717,"group":712,"gb4754":10,"parent":10,"aliases":718,"expertCount":721},"CH-01","量子科技",[719,720],"量子","量子计算",179,{"code":723,"name":724,"group":712,"gb4754":10,"parent":10,"aliases":725,"expertCount":728},"CH-02","生物制造",[726,727],"合成生物制造","生物基制造",16,{"code":730,"name":731,"group":712,"gb4754":10,"parent":10,"aliases":732,"expertCount":735},"CH-03","氢能与核聚变能",[733,734],"氢能","核聚变",67,{"code":737,"name":738,"group":712,"gb4754":10,"parent":10,"aliases":739,"expertCount":33},"CH-04","脑机接口",[740,741],"脑机","脑机交互",{"code":743,"name":744,"group":712,"gb4754":10,"parent":10,"aliases":745,"expertCount":747},"CH-05","具身智能",[746],"具身智能机器人",48,{"code":749,"name":750,"group":712,"gb4754":10,"parent":10,"aliases":751,"expertCount":753},"CH-06","6G",[752],"第六代通信",25,{"code":755,"name":756,"group":712,"gb4754":10,"parent":10,"aliases":757,"expertCount":759},"CH-07","未来能源",[758],"未来能源技术",13,{"code":761,"name":762,"group":712,"gb4754":10,"parent":10,"aliases":763,"expertCount":766},"CH-08","集成电路",[764,765],"芯片","半导体",666,{"code":768,"name":769,"group":712,"gb4754":10,"parent":10,"aliases":770,"expertCount":772},"CH-09","航空航天",[771],"航空航天制造",706,{"code":774,"name":775,"group":712,"gb4754":10,"parent":10,"aliases":776,"expertCount":779},"CH-10","生物医药",[777,778],"医药","生物药",1016,{"code":781,"name":782,"group":712,"gb4754":10,"parent":10,"aliases":783,"expertCount":786},"CH-11","低空经济",[784,785],"低空","低空飞行",26,{"code":788,"name":789,"group":712,"gb4754":10,"parent":10,"aliases":790,"expertCount":696},"CH-12","新型显示",[791],"显示面板",{"code":793,"name":794,"group":712,"gb4754":10,"parent":10,"aliases":795,"expertCount":797},"CH-13","新材料",[796],"先进材料",1263,{"code":799,"name":98,"group":712,"gb4754":10,"parent":10,"aliases":800,"expertCount":695},"CH-14",[98],{"code":802,"name":803,"group":712,"gb4754":10,"parent":10,"aliases":804,"expertCount":20},"CH-15","深海科技",[805],"深海",{"code":807,"name":79,"group":712,"gb4754":10,"parent":10,"aliases":808,"expertCount":56},"CH-16",[79,809],"星座",{"code":811,"name":812,"group":712,"gb4754":10,"parent":10,"aliases":813,"expertCount":815},"CH-17","新一代信息技术",[814],"新一代IT",321,{"code":817,"name":818,"group":712,"gb4754":10,"parent":10,"aliases":819,"expertCount":821},"CH-18","生物技术",[820],"生物科技",201,{"code":823,"name":824,"group":712,"gb4754":10,"parent":10,"aliases":825,"expertCount":826},"CH-19","新能源",[824],581,{"code":828,"name":829,"group":712,"gb4754":10,"parent":10,"aliases":830,"expertCount":832},"CH-20","高端装备",[831],"高端装备制造",587,{"code":834,"name":835,"group":712,"gb4754":10,"parent":10,"aliases":836,"expertCount":70},"CH-21","新能源汽车",[837,838],"电动车","EV",{"code":840,"name":841,"group":712,"gb4754":10,"parent":10,"aliases":842,"expertCount":845},"CH-22","绿色环保",[843,844],"环保","绿色低碳",1146,{"code":847,"name":848,"group":712,"gb4754":10,"parent":10,"aliases":849,"expertCount":146},"CH-23","海洋装备",[850],"海洋工程装备",{"code":852,"name":853,"group":712,"gb4754":10,"parent":10,"aliases":854,"expertCount":857},"CH-24","现代农业",[855,856],"智慧农业","农业",2433,{"code":859,"name":860,"group":712,"gb4754":10,"parent":10,"aliases":861,"expertCount":864},"CH-25","采矿",[862,863],"矿业","矿山",378,{"code":866,"name":867,"group":712,"gb4754":10,"parent":10,"aliases":868,"expertCount":202},"CH-26","电力",[869],"电力行业",{"code":871,"name":872,"group":712,"gb4754":10,"parent":10,"aliases":873,"expertCount":876},"CH-27","建筑",[874,875],"建筑业","建造",976,{"code":878,"name":879,"group":712,"gb4754":10,"parent":10,"aliases":880,"expertCount":882},"CH-28","交通",[881],"交通运输",3618,{"code":884,"name":885,"group":712,"gb4754":10,"parent":10,"aliases":886,"expertCount":888},"CH-29","金融",[887],"金融业",77,{"code":890,"name":891,"group":712,"gb4754":10,"parent":10,"aliases":892,"expertCount":895},"CH-30","制造",[893,894],"制造业","先进制造",668,{"code":897,"name":898,"group":712,"gb4754":10,"parent":10,"aliases":899,"expertCount":901},"CH-31","石油化工",[900],"石化",253,{"code":903,"name":904,"group":712,"gb4754":10,"parent":10,"aliases":905,"expertCount":33},"CH-32","消费电子",[904],{"code":907,"name":908,"group":712,"gb4754":10,"parent":10,"aliases":909,"expertCount":911},"CH-33","人工智能",[910],"AI",1668,{"code":913,"name":914,"group":712,"gb4754":10,"parent":10,"aliases":915,"expertCount":918},"CH-34","云计算\u002F算力",[916,917],"云计算","算力",56,{"code":920,"name":117,"group":712,"gb4754":10,"parent":10,"aliases":921,"expertCount":924},"CH-35",[922,923],"大数据","数据要素",271,{"code":926,"name":927,"group":712,"gb4754":10,"parent":10,"aliases":928,"expertCount":929},"CH-36","工业互联网",[927],14,{"code":931,"name":932,"group":712,"gb4754":10,"parent":10,"aliases":933,"expertCount":234},"CH-37","物联网",[934],"IoT",{"code":936,"name":937,"group":712,"gb4754":10,"parent":10,"aliases":938,"expertCount":940},"CH-38","区块链",[939],"Blockchain",19,{"code":942,"name":943,"group":712,"gb4754":10,"parent":10,"aliases":944,"expertCount":947},"CH-39","网络与数据安全",[945,946],"网络安全","安全",480,{"group":46,"code":46,"name":45,"count":929,"tags":949},[950,956,962,967,970,975,981,987,995,1000,1006,1012,1017,1022],{"code":951,"name":952,"group":46,"gb4754":10,"parent":10,"aliases":953,"expertCount":955},"FU-01","未来制造",[954],"未来制造技术",23,{"code":957,"name":958,"group":46,"gb4754":10,"parent":10,"aliases":959,"expertCount":961},"FU-02","未来信息",[960],"未来信息技术",29,{"code":963,"name":964,"group":46,"gb4754":10,"parent":10,"aliases":965,"expertCount":966},"FU-03","未来材料",[964],184,{"code":968,"name":756,"group":46,"gb4754":10,"parent":10,"aliases":969,"expertCount":955},"FU-04",[758],{"code":971,"name":972,"group":46,"gb4754":10,"parent":10,"aliases":973,"expertCount":696},"FU-05","未来空间",[974],"未来空间技术",{"code":976,"name":977,"group":46,"gb4754":10,"parent":10,"aliases":978,"expertCount":980},"FU-06","未来健康",[979],"未来健康技术",0,{"code":982,"name":983,"group":46,"gb4754":10,"parent":10,"aliases":984,"expertCount":986},"FU-07","元宇宙",[985],"Metaverse",68,{"code":988,"name":989,"group":46,"gb4754":10,"parent":10,"aliases":990,"expertCount":994},"FU-08","生成式人工智能",[991,992,993],"AIGC","生成式AI","通用人工智能",151,{"code":996,"name":997,"group":46,"gb4754":10,"parent":10,"aliases":998,"expertCount":32},"FU-09","合成生物学",[999],"合成生物",{"code":1001,"name":1002,"group":46,"gb4754":10,"parent":10,"aliases":1003,"expertCount":940},"FU-10","基因与细胞治疗",[1004,1005],"细胞治疗","基因治疗",{"code":1007,"name":1008,"group":46,"gb4754":10,"parent":10,"aliases":1009,"expertCount":1011},"FU-11","新型储能",[1010],"储能",106,{"code":1013,"name":1014,"group":46,"gb4754":10,"parent":10,"aliases":1015,"expertCount":83},"FU-12","钙钛矿光伏",[1016],"钙钛矿",{"code":1018,"name":1019,"group":46,"gb4754":10,"parent":10,"aliases":1020,"expertCount":163},"FU-13","第三代半导体",[1019,1021],"宽禁带半导体",{"code":1023,"name":1024,"group":46,"gb4754":10,"parent":10,"aliases":1025,"expertCount":83},"FU-14","类脑智能",[1026],"类脑计算",{"group":1028,"code":1028,"name":1029,"count":1030,"tags":1031},"GB4754","国民经济行业分类",97,[1032,1040,1048,1055,1062,1068,1076,1083,1090,1096,1102,1108,1114,1122,1128,1135,1141,1148,1155,1161,1167,1173,1179,1185,1192,1200,1208,1215,1221,1227,1234,1241,1249,1255,1261,1267,1274,1281,1289,1298,1306,1312,1319,1326,1334,1341,1348,1356,1364,1370,1377,1385,1391,1399,1406,1413,1421,1427,1435,1442,1449,1457,1463,1471,1477,1485,1493,1501,1507,1513,1520,1527,1535,1544,1551,1558,1566,1573,1580,1586,1593,1599,1605,1613,1621,1627,1634,1640,1647,1652,1658,1665,1673,1679,1685,1692,1698],{"code":1033,"name":856,"group":1028,"gb4754":1034,"parent":1035,"aliases":1036,"expertCount":1039},"GB-01","01","A",[1037,1038],"农林种植","种植业",2436,{"code":1041,"name":1042,"group":1028,"gb4754":1043,"parent":1035,"aliases":1044,"expertCount":1047},"GB-02","林业","02",[1045,1046],"营林","造林",131,{"code":1049,"name":1050,"group":1028,"gb4754":1051,"parent":1035,"aliases":1052,"expertCount":13},"GB-03","畜牧业","03",[1053,1054],"养殖","畜禽养殖",{"code":1056,"name":1057,"group":1028,"gb4754":1058,"parent":1035,"aliases":1059,"expertCount":83},"GB-04","渔业","04",[1060,1061],"水产","水产养殖",{"code":1063,"name":1064,"group":1028,"gb4754":1065,"parent":1035,"aliases":1066,"expertCount":980},"GB-05","农、林、牧、渔专业及辅助性活动","05",[1067],"农林牧渔服务",{"code":1069,"name":1070,"group":1028,"gb4754":1071,"parent":1072,"aliases":1073,"expertCount":259},"GB-06","煤炭开采和洗选业","06","B",[1074,1075],"煤炭","煤矿",{"code":1077,"name":1078,"group":1028,"gb4754":1079,"parent":1072,"aliases":1080,"expertCount":240},"GB-07","石油和天然气开采业","07",[1081,1082],"油气开采","油气",{"code":1084,"name":1085,"group":1028,"gb4754":1086,"parent":1072,"aliases":1087,"expertCount":1089},"GB-08","黑色金属矿采选业","08",[1088],"铁矿采选",3,{"code":1091,"name":1092,"group":1028,"gb4754":1093,"parent":1072,"aliases":1094,"expertCount":259},"GB-09","有色金属矿采选业","09",[1095],"有色矿采选",{"code":1097,"name":1098,"group":1028,"gb4754":1099,"parent":1072,"aliases":1100,"expertCount":1089},"GB-10","非金属矿采选业","10",[1101],"非金属矿",{"code":1103,"name":1104,"group":1028,"gb4754":1105,"parent":1072,"aliases":1106,"expertCount":980},"GB-11","开采专业及辅助性活动","11",[1107],"矿山服务",{"code":1109,"name":1110,"group":1028,"gb4754":1111,"parent":1072,"aliases":1112,"expertCount":980},"GB-12","其他采矿业","12",[1113],"其他采矿",{"code":1115,"name":1116,"group":1028,"gb4754":1117,"parent":1118,"aliases":1119,"expertCount":1121},"GB-13","农副食品加工业","13","C",[1120],"农副食品",4,{"code":1123,"name":1124,"group":1028,"gb4754":1125,"parent":1118,"aliases":1126,"expertCount":202},"GB-14","食品制造业","14",[1127],"食品制造",{"code":1129,"name":1130,"group":1028,"gb4754":1131,"parent":1118,"aliases":1132,"expertCount":695},"GB-15","酒、饮料和精制茶制造业","15",[1133,1134],"酒类饮料","茶饮",{"code":1136,"name":1137,"group":1028,"gb4754":1138,"parent":1118,"aliases":1139,"expertCount":695},"GB-16","烟草制品业","16",[1140],"烟草",{"code":1142,"name":1143,"group":1028,"gb4754":1144,"parent":1118,"aliases":1145,"expertCount":980},"GB-17","纺织业","17",[1146,1147],"纺织","棉纺",{"code":1149,"name":1150,"group":1028,"gb4754":1151,"parent":1118,"aliases":1152,"expertCount":980},"GB-18","纺织服装、服饰业","18",[1153,1154],"服装","服饰",{"code":1156,"name":1157,"group":1028,"gb4754":1158,"parent":1118,"aliases":1159,"expertCount":980},"GB-19","皮革、毛皮、羽毛及其制品和制鞋业","19",[1160],"皮革制鞋",{"code":1162,"name":1163,"group":1028,"gb4754":1164,"parent":1118,"aliases":1165,"expertCount":1089},"GB-20","木材加工和木、竹、藤、棕、草制品业","20",[1166],"木材加工",{"code":1168,"name":1169,"group":1028,"gb4754":1170,"parent":1118,"aliases":1171,"expertCount":980},"GB-21","家具制造业","21",[1172],"家具",{"code":1174,"name":1175,"group":1028,"gb4754":1176,"parent":1118,"aliases":1177,"expertCount":980},"GB-22","造纸和纸制品业","22",[1178],"造纸",{"code":1180,"name":1181,"group":1028,"gb4754":1182,"parent":1118,"aliases":1183,"expertCount":63},"GB-23","印刷和记录媒介复制业","23",[1184],"印刷复制",{"code":1186,"name":1187,"group":1028,"gb4754":1188,"parent":1118,"aliases":1189,"expertCount":980},"GB-24","文教、工美、体育和娱乐用品制造业","24",[1190,1191],"文体用品","工美",{"code":1193,"name":1194,"group":1028,"gb4754":1195,"parent":1118,"aliases":1196,"expertCount":1089},"GB-25","石油、煤炭及其他燃料加工业","25",[1197,1198,1199],"石油加工","炼油","煤化工",{"code":1201,"name":1202,"group":1028,"gb4754":1203,"parent":1118,"aliases":1204,"expertCount":1207},"GB-26","化学原料和化学制品制造业","26",[1205,1206],"化工","化学原料",283,{"code":1209,"name":1210,"group":1028,"gb4754":1211,"parent":1118,"aliases":1212,"expertCount":1214},"GB-27","医药制造业","27",[777,1213],"制药",988,{"code":1216,"name":1217,"group":1028,"gb4754":1218,"parent":1118,"aliases":1219,"expertCount":1089},"GB-28","化学纤维制造业","28",[1220],"化纤",{"code":1222,"name":1223,"group":1028,"gb4754":1224,"parent":1118,"aliases":1225,"expertCount":294},"GB-29","橡胶和塑料制品业","29",[1226],"橡胶塑料",{"code":1228,"name":1229,"group":1028,"gb4754":1230,"parent":1118,"aliases":1231,"expertCount":265},"GB-30","非金属矿物制品业","30",[1232,1233],"建材","非金属矿物",{"code":1235,"name":1236,"group":1028,"gb4754":1237,"parent":1118,"aliases":1238,"expertCount":13},"GB-31","黑色金属冶炼和压延加工业","31",[1239,1240],"钢铁","黑色冶金",{"code":1242,"name":1243,"group":1028,"gb4754":1244,"parent":1118,"aliases":1245,"expertCount":1248},"GB-32","有色金属冶炼和压延加工业","32",[1246,1247],"有色冶金","有色金属",42,{"code":1250,"name":1251,"group":1028,"gb4754":1252,"parent":1118,"aliases":1253,"expertCount":56},"GB-33","金属制品业","33",[1254],"金属制品",{"code":1256,"name":1257,"group":1028,"gb4754":1258,"parent":1118,"aliases":1259,"expertCount":157},"GB-34","通用设备制造业","34",[1260],"通用设备",{"code":1262,"name":1263,"group":1028,"gb4754":1264,"parent":1118,"aliases":1265,"expertCount":695},"GB-35","专用设备制造业","35",[1266],"专用设备",{"code":1268,"name":1269,"group":1028,"gb4754":1270,"parent":1118,"aliases":1271,"expertCount":328},"GB-36","汽车制造业","36",[1272,1273],"汽车","整车",{"code":1275,"name":1276,"group":1028,"gb4754":1277,"parent":1118,"aliases":1278,"expertCount":1280},"GB-37","铁路、船舶、航空航天和其他运输设备制造业","37",[1279],"轨道船舶航空航天",707,{"code":1282,"name":1283,"group":1028,"gb4754":1284,"parent":1118,"aliases":1285,"expertCount":1288},"GB-38","电气机械和器材制造业","38",[1286,1287],"电气机械","电工电器",867,{"code":1290,"name":1291,"group":1028,"gb4754":1292,"parent":1118,"aliases":1293,"expertCount":1297},"GB-39","计算机、通信和其他电子设备制造业","39",[1294,1295,1296],"电子设备","电子制造","通信设备",1192,{"code":1299,"name":1300,"group":1028,"gb4754":1301,"parent":1118,"aliases":1302,"expertCount":1305},"GB-40","仪器仪表制造业","40",[1303,1304],"仪器仪表","仪表",130,{"code":1307,"name":1308,"group":1028,"gb4754":1309,"parent":1118,"aliases":1310,"expertCount":980},"GB-41","其他制造业","41",[1311],"其他制造",{"code":1313,"name":1314,"group":1028,"gb4754":1315,"parent":1118,"aliases":1316,"expertCount":728},"GB-42","废弃资源综合利用业","42",[1317,1318],"资源综合利用","再生资源",{"code":1320,"name":1321,"group":1028,"gb4754":1322,"parent":1118,"aliases":1323,"expertCount":980},"GB-43","金属制品、机械和设备修理业","43",[1324,1325],"设备修理","机电维修",{"code":1327,"name":1328,"group":1028,"gb4754":1329,"parent":1330,"aliases":1331,"expertCount":1333},"GB-44","电力、热力生产和供应业","44","D",[867,1332],"热电",164,{"code":1335,"name":1336,"group":1028,"gb4754":1337,"parent":1330,"aliases":1338,"expertCount":929},"GB-45","燃气生产和供应业","45",[1339,1340],"燃气","城市燃气",{"code":1342,"name":1343,"group":1028,"gb4754":1344,"parent":1330,"aliases":1345,"expertCount":32},"GB-46","水的生产和供应业","46",[1346,1347],"供水","水务",{"code":1349,"name":1350,"group":1028,"gb4754":1351,"parent":1352,"aliases":1353,"expertCount":980},"GB-47","房屋建筑业","47","E",[1354,1355],"房屋建筑","房建",{"code":1357,"name":1358,"group":1028,"gb4754":1359,"parent":1352,"aliases":1360,"expertCount":1363},"GB-48","土木工程建筑业","48",[1361,1362],"土木建筑","基建",284,{"code":1365,"name":1366,"group":1028,"gb4754":1367,"parent":1352,"aliases":1368,"expertCount":980},"GB-49","建筑安装业","49",[1369],"建筑安装",{"code":1371,"name":1372,"group":1028,"gb4754":1373,"parent":1352,"aliases":1374,"expertCount":980},"GB-50","建筑装饰、装修和其他建筑业","50",[1375,1376],"建筑装饰","装修",{"code":1378,"name":1379,"group":1028,"gb4754":1380,"parent":1381,"aliases":1382,"expertCount":695},"GB-51","批发业","51","F",[1383,1384],"批发","商贸批发",{"code":1386,"name":1387,"group":1028,"gb4754":1388,"parent":1381,"aliases":1389,"expertCount":1121},"GB-52","零售业","52",[1390],"零售",{"code":1392,"name":1393,"group":1028,"gb4754":1394,"parent":1395,"aliases":1396,"expertCount":961},"GB-53","铁路运输业","53","G",[1397,1398],"铁路运输","铁路",{"code":1400,"name":1401,"group":1028,"gb4754":1402,"parent":1395,"aliases":1403,"expertCount":759},"GB-54","道路运输业","54",[1404,1405],"道路运输","公路运输",{"code":1407,"name":1408,"group":1028,"gb4754":1409,"parent":1395,"aliases":1410,"expertCount":32},"GB-55","水上运输业","55",[1411,1412],"水运","航运",{"code":1414,"name":1415,"group":1028,"gb4754":1416,"parent":1395,"aliases":1417,"expertCount":1420},"GB-56","航空运输业","56",[1418,1419],"航空运输","民航",642,{"code":1422,"name":1423,"group":1028,"gb4754":1424,"parent":1395,"aliases":1425,"expertCount":940},"GB-57","管道运输业","57",[1426],"管道运输",{"code":1428,"name":1429,"group":1028,"gb4754":1430,"parent":1395,"aliases":1431,"expertCount":1434},"GB-58","装卸搬运和仓储业","58",[1432,1433],"仓储","物流仓储",2,{"code":1436,"name":1437,"group":1028,"gb4754":1438,"parent":1395,"aliases":1439,"expertCount":32},"GB-59","邮政业","59",[1440,1441],"邮政","快递",{"code":1443,"name":1444,"group":1028,"gb4754":1445,"parent":1395,"aliases":1446,"expertCount":695},"GB-60","多式联运和运输代理业","60",[1447,1448],"多式联运","货运代理",{"code":1450,"name":1451,"group":1028,"gb4754":1452,"parent":1453,"aliases":1454,"expertCount":980},"GB-61","住宿业","61","H",[1455,1456],"住宿","酒店",{"code":1458,"name":1459,"group":1028,"gb4754":1460,"parent":1453,"aliases":1461,"expertCount":980},"GB-62","餐饮业","62",[1462],"餐饮",{"code":1464,"name":1465,"group":1028,"gb4754":1466,"parent":1467,"aliases":1468,"expertCount":929},"GB-63","电信、广播电视和卫星传输服务","63","I",[1469,1470],"电信","通信运营",{"code":1472,"name":1473,"group":1028,"gb4754":1474,"parent":1467,"aliases":1475,"expertCount":316},"GB-64","互联网和相关服务","64",[1476],"互联网服务",{"code":1478,"name":1479,"group":1028,"gb4754":1480,"parent":1467,"aliases":1481,"expertCount":1484},"GB-65","软件和信息技术服务业","65",[1482,1483],"软件","IT服务",451,{"code":1486,"name":1487,"group":1028,"gb4754":1488,"parent":1489,"aliases":1490,"expertCount":49},"GB-66","货币金融服务","66","J",[1491,1492],"银行","货币金融",{"code":1494,"name":1495,"group":1028,"gb4754":1496,"parent":1489,"aliases":1497,"expertCount":1500},"GB-67","资本市场服务","67",[1498,1499],"资本市场","证券基金",238,{"code":1502,"name":1503,"group":1028,"gb4754":1504,"parent":1489,"aliases":1505,"expertCount":32},"GB-68","保险业","68",[1506],"保险",{"code":1508,"name":1509,"group":1028,"gb4754":1510,"parent":1489,"aliases":1511,"expertCount":695},"GB-69","其他金融业","69",[1512],"其他金融",{"code":1514,"name":1515,"group":1028,"gb4754":1516,"parent":1517,"aliases":1518,"expertCount":1089},"GB-70","房地产业","70","K",[1519],"房地产",{"code":1521,"name":1522,"group":1028,"gb4754":1523,"parent":1524,"aliases":1525,"expertCount":695},"GB-71","租赁业","71","L",[1526],"租赁",{"code":1528,"name":1529,"group":1028,"gb4754":1530,"parent":1524,"aliases":1531,"expertCount":1534},"GB-72","商务服务业","72",[1532,1533],"商务服务","专业服务",66,{"code":1536,"name":1537,"group":1028,"gb4754":1538,"parent":1539,"aliases":1540,"expertCount":1543},"GB-73","研究和试验发展","73","M",[1541,1542],"研发","研究试验",2855,{"code":1545,"name":1546,"group":1028,"gb4754":1547,"parent":1539,"aliases":1548,"expertCount":1550},"GB-74","专业技术服务业","74",[1549],"专业技术服务",358,{"code":1552,"name":1553,"group":1028,"gb4754":1554,"parent":1539,"aliases":1555,"expertCount":980},"GB-75","科技推广和应用服务业","75",[1556,1557],"科技服务","技术推广",{"code":1559,"name":1560,"group":1028,"gb4754":1561,"parent":1562,"aliases":1563,"expertCount":1565},"GB-76","水利管理业","76","N",[1564],"水利",127,{"code":1567,"name":1568,"group":1028,"gb4754":1569,"parent":1562,"aliases":1570,"expertCount":1572},"GB-77","生态保护和环境治理业","77",[1571,843],"环境治理",1071,{"code":1574,"name":1575,"group":1028,"gb4754":1576,"parent":1562,"aliases":1577,"expertCount":1579},"GB-78","公共设施管理业","78",[1578],"市政公用设施",17,{"code":1581,"name":1582,"group":1028,"gb4754":1583,"parent":1562,"aliases":1584,"expertCount":980},"GB-79","土地管理业","79",[1585],"土地管理",{"code":1587,"name":1588,"group":1028,"gb4754":1589,"parent":1590,"aliases":1591,"expertCount":980},"GB-80","居民服务业","80","O",[1592],"居民服务",{"code":1594,"name":1595,"group":1028,"gb4754":1596,"parent":1590,"aliases":1597,"expertCount":980},"GB-81","机动车、电子产品和日用产品修理业","81",[1598],"修理维护",{"code":1600,"name":1601,"group":1028,"gb4754":1602,"parent":1590,"aliases":1603,"expertCount":980},"GB-82","其他服务业","82",[1604],"其他服务",{"code":1606,"name":1607,"group":1028,"gb4754":1608,"parent":1609,"aliases":1610,"expertCount":1612},"GB-83","教育","83","P",[1607,1611],"教育培训",339,{"code":1614,"name":1615,"group":1028,"gb4754":1616,"parent":1617,"aliases":1618,"expertCount":1620},"GB-84","卫生","84","Q",[1615,1619],"医疗",456,{"code":1622,"name":1623,"group":1028,"gb4754":1624,"parent":1617,"aliases":1625,"expertCount":728},"GB-85","社会工作","85",[1623,1626],"社工",{"code":1628,"name":1629,"group":1028,"gb4754":1630,"parent":1631,"aliases":1632,"expertCount":192},"GB-86","新闻和出版业","86","R",[1633],"新闻出版",{"code":1635,"name":1636,"group":1028,"gb4754":1637,"parent":1631,"aliases":1638,"expertCount":294},"GB-87","广播、电视、电影和录音制作业","87",[1639],"广电影视",{"code":1641,"name":1642,"group":1028,"gb4754":1643,"parent":1631,"aliases":1644,"expertCount":1646},"GB-88","文化艺术业","88",[1645],"文化艺术",45,{"code":1648,"name":1649,"group":1028,"gb4754":1650,"parent":1631,"aliases":1651,"expertCount":21},"GB-89","体育","89",[1649],{"code":1653,"name":1654,"group":1028,"gb4754":1655,"parent":1631,"aliases":1656,"expertCount":1089},"GB-90","娱乐业","90",[1657],"娱乐",{"code":1659,"name":1660,"group":1028,"gb4754":1661,"parent":1662,"aliases":1663,"expertCount":980},"GB-91","中国共产党机关","91","S",[1664],"党政机关",{"code":1666,"name":1667,"group":1028,"gb4754":1668,"parent":1662,"aliases":1669,"expertCount":1672},"GB-92","国家机构","92",[1670,1671],"政府机构","国家机关",22,{"code":1674,"name":1675,"group":1028,"gb4754":1676,"parent":1662,"aliases":1677,"expertCount":1434},"GB-93","人民政协、民主党派","93",[1678],"政协民主党派",{"code":1680,"name":1681,"group":1028,"gb4754":1682,"parent":1662,"aliases":1683,"expertCount":49},"GB-94","社会保障","94",[1684,1681],"社保",{"code":1686,"name":1687,"group":1028,"gb4754":1688,"parent":1662,"aliases":1689,"expertCount":282},"GB-95","群众团体、社会团体和其他成员组织","95",[1690,1691],"社会团体","社会组织",{"code":1693,"name":1694,"group":1028,"gb4754":1695,"parent":1662,"aliases":1696,"expertCount":980},"GB-96","基层群众自治组织及其他组织","96",[1697],"基层自治组织",{"code":1699,"name":1700,"group":1028,"gb4754":1701,"parent":1702,"aliases":1703,"expertCount":980},"GB-97","国际组织","97","T",[1700],{"group":1705,"code":1705,"name":7,"count":63,"tags":1706},"SEI",[1707,1712,1717,1722,1728,1732,1737,1744,1749],{"code":1708,"name":1709,"group":1705,"gb4754":10,"parent":10,"aliases":1710,"expertCount":1711},"SEI-01","新一代信息技术产业",[812,814],2228,{"code":1713,"name":1714,"group":1705,"gb4754":10,"parent":10,"aliases":1715,"expertCount":1716},"SEI-02","高端装备制造产业",[829],691,{"code":1718,"name":1719,"group":1705,"gb4754":10,"parent":10,"aliases":1720,"expertCount":1721},"SEI-03","新材料产业",[794],1247,{"code":1723,"name":1724,"group":1705,"gb4754":10,"parent":10,"aliases":1725,"expertCount":1727},"SEI-04","生物产业",[1724,1726],"生物医药生物",1085,{"code":1729,"name":1730,"group":1705,"gb4754":10,"parent":10,"aliases":1731,"expertCount":84},"SEI-05","新能源汽车产业",[835,837],{"code":1733,"name":1734,"group":1705,"gb4754":10,"parent":10,"aliases":1735,"expertCount":1736},"SEI-06","新能源产业",[824],633,{"code":1738,"name":1739,"group":1705,"gb4754":10,"parent":10,"aliases":1740,"expertCount":1743},"SEI-07","节能环保产业",[1741,1742],"节能环保","环保产业",166,{"code":1745,"name":1746,"group":1705,"gb4754":10,"parent":10,"aliases":1747,"expertCount":1121},"SEI-08","数字创意产业",[1748],"数字创意",{"code":1750,"name":1751,"group":1705,"gb4754":10,"parent":10,"aliases":1752,"expertCount":56},"SEI-09","相关服务业",[1753],"战略性新兴服务业"]