极兔、京东物流、圆通、顺丰、申通、菜鸟…快递行业的大模型应用，落地到哪一步了？_老虎社区_美港股上老虎

极兔、京东物流、圆通、顺丰、申通、菜鸟…快递行业的大模型应用，落地到哪一步了？

驿站01-27

老鬼邀您阅前思考AI时代，各行各业、各大企业都在抢占大模型所带来的新一代技术红利。具体到快递，又都做了什么？文/驿站珍珍2026.01.27快递企业已经玩转“大模型”？去年大模型新秀DeepSeek、Manus等陆续火爆出圈，不仅加速了AI在各行业的落地速度，也开启了企业间AI投入与应用的较量。比如互联网领域，火山引擎发布豆包大模型及阿里通义大模型体系、腾讯混元、百度文心一言持续迭代；再比如快递...

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implemented?","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"89988":0.6,"02618":1.93,"HBBD.SI":0.6,"ALBmain":0.6,"06936":1.77,"002352":1.77,"01519":1.95},"content_text":"老鬼邀您阅前思考AI时代，各行各业、各大企业都在抢占大模型所带来的新一代技术红利。具体到快递，又都做了什么？文/驿站珍珍2026.01.27快递企业已经玩转“大模型”？去年大模型新秀DeepSeek、Manus等陆续火爆出圈，不仅加速了AI在各行业的落地速度，也开启了企业间AI投入与应用的较量。比如互联网领域，火山引擎发布豆包大模型及阿里通义大模型体系、腾讯混元、百度文心一言持续迭代；再比如快递物流行业，京东物流发布“超脑大模型2.0”、极兔采用“外部集成+自研系统”的混合模型等等，都在抢占大模型所带来的新一代技术红利。AI在快递物流行业可以被全面应用在仓储、运输、分拣、末端配送等全链路物流环节，不仅可以产生多维度数据信息，通过智能感知、智能预测、智能决策等方面能力，还能够挖掘出新的增值价值，同时提高运营效率，减少物流成本，更有效地满足客户服务要求。理想确实很丰满，但现实呢？今天我们就来聊一聊大模型在快递行业是如何具体落地，以及都带来了哪些改变和成效的。01因“递”制宜布局大模型据驿站老鬼了解，多数快递企业在AI的投入与布局都是渐进式的。例如，极兔方面就表示：他们在AI上的投入落地采取的不是激进的方式，而是有的放矢，阶段式的投入，先试点、打磨，然后根据需求升级，进而推广以至全面应用，一边摸索一边前进。无独有偶。实际上，其他快递企业在AI产品方面的投入同样采取了渐进式布局——2018年菜鸟推出物流客服AI大脑；2021年申通推出大模型运营管理数智化平台“昆仑”；2023年菜鸟又推出基于大模型的数字供应链产品“天机π”；京东物流推出物流大模型产品“京东物流超脑”；圆通推出垂直大模型“智多星（YTO-GPT）”；2024年顺丰推出“丰知”物流决策大模型…从各家上线的产品来看，AI在快递物流领域的应用确实涉及到全链路、全环节。譬如，在运营决策上，AI可帮助企业内部实现运营数据分析与决策支持；在数字化管理上，基于AI和大数据所构建的供应链数字孪生模型，可以帮助客户模拟实际运营场景，从而做出更加精准的成本分析与风险预警，同时也能为仓储、运输等环节提供更优的解决方案。然而，对于现阶段的快递企业需求而言，并非所有环节都要用到大模型。极兔方面称，基于业务场景和需求做的调研显示，有很多要求较为严苛的的核心垂直场景非常依赖小模型，像是客户下单时生成的电子面单就依赖于小模型。其中一个关键原因在于，大模型应用主要集中在对延迟要求没有那么高且涉及到语义理解的场景，如AI助手、智能客服、经营分析等；其他要求严苛的场景，小模型更适配，大模型精确度还需要优化，必须契合场景需求才行。大模型应用落地中，极兔采用“外部集成+自研系统”的混合模型方式，外部集成了阿里通义、火山引擎豆包、DeepSeek等大模型，海外市场依据合规要求选用其他大模型，自研系统则打造了整合揽收、运输、客服等全链路以提升协同效率的物流AI决策大脑、融合图像识别、OCR、人脸识别等技术的异常件识别系统，以及基于自研AI平台或飞书平台搭建的智能体工作流平台。京东物流的大模型落地走的是“另一条路”——2023年推出大模型产品“京东物流超脑”，之后其对该产品一直做持续迭代与业务场景渗透，2025年已升至“超脑大模型2.0”。全新升级的超脑大模型2.0，是基于数亿用户、千万级SKU和智能仓储群中流转产生的海量真实数据，能在实战中充分淬炼。值得注意的是，“超脑大模型2.0”还可以在1:1映射物流世界的全链路数字孪生物流网络中，不断进行自我博弈与推演，成为一个可以“不断自我进化”的供应链生命体。同时，“超脑大模型2.0”构建了多智能设备交互机制，实现京东物流“狼族”系列产品（如“独狼”无人车、“飞狼”无人机、“异狼”机械臂）之间的互联互通，通过群体智能协作，形成了高度协同的“人工智能+”物流应用生态系统。这一生态也已展现出强大的自我生长与商业化能力。其标志性产物便是“与图”数智时空这一商业化平台。它由京东物流内部亿级供应链数据和上千个供应链场景打磨，可为客户提供全景GIS、精智地址、位域洞察三大方向的赋能。时空大模型的落地，使得“与图”平台更加可视可智，变身“上帝之眼”看得更清晰，也刷新了地址“新”精度。02降本增效到底如何？目前，极兔通过“外部集成+自研系统”的混合模型策略方式，正在构建适合公司网络体系的物流AI决策大脑，目的就是为了整合揽收、中心操作、运输、末端、客服等原本独立性相对较强的环节，提升环节间的协作能力，从而为集团整体提效。具体效果如何呢？根据极兔方面给出的案例信息，极兔市场营销部在制作企业宣传片时，便将流程拆解为脚本生成、分镜设计、故事校验、物料整合等多个节点，每个环节由对应的智能体调用相应大模型完成任务，并通过链式调用确保输出内容符合商标色号、形制等企业规范，这一混合模型策略让极兔既充分发挥了通用大模型的技术能力，又能通过自研系统满足核心业务的定制化需求，最终实现了AI技术与物流场景的有效融合。如此提效，肉眼可见。快递物流业务端，京东物流全新升级的物流“超脑大模型2.0”，全面走向Agentic，实现智能设备的自主决策。基于数字孪生的智能决策体系，超脑大模型2.0可将千万级别变量模型求解时间缩短至2小时内，动态调整物流最优规划。通过多个智能设备协作，大幅提升物流供应链作业效率，可全场景监控，及时发现异常并优化流程，将员工操作标准化水平提升15%；可对人、车、货、场等运力进行最佳调度，根据实时作业数据动态优化算法，将一线效率提升近20%；同时，超脑大模型2.0可自主学习灵活调整执行策略，将人机协作作业效率提升超20%。超脑大模型2.0与“与图”商业化平台结合后，使得“与图”平台更加可视可智，实现地理编码准确率超98%、分单准确率99.98%；以及打造的选址、营销智能体，实现精准投放场景订单增长25%。该模式正为零售、家电、金融、保险和电力等多行业提供解决方案，并为百余品牌实现了降本提效。除此之外，京东物流也在通过大模型解决一些在揽收、派送、验收等各环节常见却棘手的问题。京东物流大模型相关负责人介绍，他们正在用大模型解决一些看似很“低级”的错误——比如，当快递员将原本送往26号楼的快递误送到29号楼时，系统会在几秒内判断这一行为“违背常识”：快递员刚刚在26号楼完成了两单派件，却在一两分钟后，突然出现在29号楼，时间和路径都不合理。模型会立即向快递小哥推送“派送异常”提醒，提示其及时纠正。“当大模型有了‘常识’，就能用人类的眼光发现问题，帮我们解决更多物流场景下的问题。”该负责人表示，“实际上相当于给每一位物流小哥都配备了一名智慧员工助手，无论是包裹状态异常、运单明细查询、通话记录判责还是录入订单校验，大模型都能把小哥从繁琐重复的流程中解放出来，减少沟通成本，提升配送量与收入。而在全国几十万小哥高频次使用和反馈下，大模型也会快速‘进化’。“据了解，目前京东物流智慧员工大模型覆盖全国近10000个快递站点，助力站点在揽派等8个场景实现自动化，直接为几十万快递小哥服务，助力终端提效10%。圆通使用垂直大模型智多星（YTO-GPT）后，构建起覆盖揽派、中转、运输、客服及管理等全链路的智能化矩阵“最强大脑”——如揽派环节，圆通自主研发的“行者助手”可以实现批量外呼、分堆播报、智能优化配送路径等功能，并主动给业务员推送相关指示，帮助和提醒业务员在旺季期间改善和提升服务质量、高效完成工作任务。据测算，该系统每日帮助业务员节省30-60分钟工作时间。在运输环节，圆通的“智能路由”系统通过结合大数据、AI技术和GIS地理信息，不仅能迅速计算出最优路线，还能够根据实时情况进行动态调整。与传统人工模式相比，该系统将路由分析效率从过去的5天缩短至实时，大幅提升运力调度响应速度。未来的市场竞争，一定是技术主导型企业占据上风。相信，随着各企业大模型产品的不断迭代与深化，一场由AI带来物流科技革命必将上演。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"hasMoreComments":false,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"isCrawlerRequest":true}