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中通云仓科技发布“云知AI大模型平台” 开启物流AI应用新篇章

过江龙 2026-09-01 09:51
过江龙 2026/09/01 09:51

邦小白快读

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本次文章核心信息是2026年8月中通云仓正式发布物流垂直领域的云知AI大模型平台,同时推出落地成果玖云智能工单系统,标志着中通进入AI-Agent集群与合作伙伴生态建设新阶段,核心干货如下:

1.云知AI大模型是面向物流供应链全链路的智能化技术底座,拥有六大核心能力,搭建了三层完整技术体系,依托中通沉淀的月超百亿级全链路物流数据训练,对物流行业有专业的理解力与判断力。

2.落地成果玖云智能工单系统重构售后客服模式,实现全流程自动化,具备全时段值守、99%问题自动处理、覆盖33个售后场景、降本增效超50%四大核心优势,可对接多类协同平台与主流快递企业。

3.未来中通将持续投入,联合生态伙伴推动物流全链路智能化升级,实现从人找事到事找人的范式转变。

本文披露了物流供应链领域最新的AI智能化成果,能为品牌商优化供应链管理、降本增效提供新的解决方案,相关干货如下:

1.在售后环节,玖云智能工单系统可帮助品牌实现全渠道全链路售后工单自动化处理,释放人工处理重复问题的压力,降低50%以上运营成本,同时保障服务质量,提升消费者售后体验,还能无缝对接客户、仓库、快递等多方角色,实现全链路数据化协同。

2.除售后外,品牌商还可借助现有成熟智能体优化运营,比如包材推荐算法智能体,可基于多维数据推荐最优包装方案,既降低包材浪费、压缩成本,还能降低货物破损率,符合绿色消费趋势。

3.当前中通云仓开放AI生态合作,品牌商可接入成熟技术能力,快速实现供应链全链路智能化升级,不需要投入高额自研成本。

本文分享了物流供应链AI智能化的最新进展,能为广大卖家带来降本增效的新机会,相关干货内容如下:

1.针对卖家普遍面临的售后客服成本高、响应不及时、漏单漏消息的痛点,玖云智能工单系统提供了成熟的解决方案,支持7*24小时全渠道自动值守,99%的重复性规则问题可自动处理,能帮助卖家降低50%以上售后成本,大幅提升响应速度。

2.除售后外,卖家还可享受多个成熟AI智能体的服务,比如包材推荐算法智能体可根据商品特性、订单结构、运输路径等数据优化包装方案,降低包材成本和破损率,提升消费者购物体验。

3.该系列AI工具开放接入,可对接主流沟通平台和快递系统,卖家不需要高额自研投入就能快速接入使用,借助智能化升级提升自身竞争力,是卖家降本增效的可靠新机会。

本文介绍了物流端AI智能化的最新发展,能给工厂推进数字化电商转型、抓住商业机会带来不少启示,核心干货如下:

1.当前物流端已经实现全链路节点的智能化升级,工厂对接智能化供应链的门槛已经大幅降低,工厂可直接接入成熟的AI智能体服务,快速实现仓储、售后、运输等环节的智能化运营,降低自身运营成本。

2.中通云仓已经打造开放的跨组织AI协作生态,对接了多个主流电商平台和快递网络,做To C电商业务的工厂,可以直接借助成熟的智能化供应链体系,减少自身在数字化系统建设上的投入,将更多资源投入到产品生产和设计中。

3.对于想要推进数字化转型的工厂来说,AI驱动的供应链升级已经具备落地可行性,工厂可通过加入开放生态借力成熟技术,依托供应链数据反馈更好地把握市场需求,优化生产和产品设计,匹配消费端需求。

本文透露了物流AI领域的最新行业发展趋势,能给各类相关服务商提供行业参考和方向指引,核心干货如下:

1.当前物流AI行业已经完成了从单点AI工具应用到AI-Agent集群与跨生态协同的阶段跃迁,行业核心需求已经从单一功能的智能化转向全链路全节点的智能决策,开放协同是未来行业发展的核心趋势。

2.当前行业客户的核心痛点集中在供应链各环节协同效率低、人力成本高,尤其是售后工单环节,大量重复性工作占用过多人力,中通云仓推出的云知大模型加玖云智能工单方案,就是针对该痛点的成熟落地方案,依托百亿级行业数据训练,可实现全流程自动化,降本增效效果显著。

3.未来行业会在数据层、模型层、应用层开放互联互通,服务商可抓住机遇,加入开放生态,和平台在数据共享、模型共建、场景共创层面深化合作,共同开发更多垂直场景的AI应用,开拓新的业务增长空间。

本文介绍了中通云仓布局物流AI的最新实践,能给布局供应链智能化的平台商提供多方面参考,核心干货如下:

1.当前平台商家对物流供应链智能化的需求十分突出,核心需求集中在降本增效、全链路协同层面,平台可通过引入成熟的AI智能化工具,为商家提供更多增值服务,提升商家的运营效率,进而增强商家粘性和平台竞争力。

2.中通云仓布局AI的路径,即先打造垂直领域大模型技术底座,沉淀行业数据训练模型,再落地垂直场景应用,最后开放生态合作,对平台布局AI有较高的借鉴价值,该路径可降低技术试错风险,逐步推进落地。

3.在生态建设层面,中通云仓采用开放接入的模式,对接多平台多服务商,这种模式能吸引更多合作伙伴加入,平台可参考该模式开展招商合作,吸纳不同领域的参与者共同完善生态,同时规避封闭发展带来的技术迭代慢、场景覆盖不全的风险。

本文披露了物流垂直领域AI大模型应用的最新产业动向,对研究产业智能化升级的研究者有较高的参考价值,核心干货如下:

1.当前我国物流AI产业已经出现了明确的范式跃迁,从早期的单点AI工具应用,正式进入AI-Agent集群建设和跨组织生态协同的新阶段,实现了从原有的人找事到事找人的模式转变,这是物流产业智能化的最新动向。

2.本次发布的云知AI大模型,是垂直领域大模型落地的新探索,其打造的三层技术体系,依托企业沉淀的百亿级全链路物流数据训练,解决了通用大模型缺乏行业知识的痛点,验证了垂直大模型加场景落地的商业模式可行性。

3.本次落地的玖云智能工单系统,给出了AI赋能物流售后场景的实际效果数据,即AI自动处理率超99%、降本增效超50%,同时开放生态合作模式也为产业智能化研究提供了新的典型案例,具备较高的研究价值。

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Quick Summary

This article covers ZTO Cloud Warehouse's August 2026 launch of Yunzhi AI, a large language model platform built specifically for the logistics industry, alongside its first commercial application, the Jiuyun Intelligent Work Order System. The launch marks ZTO's official entry into a new stage focused on building AI-Agent clusters and partner ecosystems. Key takeaways are as follows:

1. Yunzhi AI is an intelligent technology foundation built to support end-to-end intelligent upgrading of the entire logistics supply chain. It features six core capabilities, a complete three-layer technical architecture, and is trained on more than 10 billion monthly logistics data points accumulated by ZTO, giving it strong specialized understanding and judgment for the logistics industry.

2. The Jiuyun Intelligent Work Order System, the first commercial application of Yunzhi AI, reconstructs the after-sales customer service model to enable full-process automation. It boasts four core advantages: 24/7 operation, 99% automatic problem resolution, coverage of 33 after-sales scenarios, and over 50% improvement in cost reduction and efficiency. It can also integrate with various collaboration platforms and major express delivery companies.

3. Going forward, ZTO will continue investing, and work with ecosystem partners to drive intelligent upgrading across the entire logistics supply chain, achieving a paradigm shift from "people seeking work" to "work finding people".

This article introduces the latest AI-driven intelligent achievements in the logistics and supply chain sector, offering a new solution for brands to optimize supply chain management and cut costs while boosting efficiency. Key insights for brands are as follows:

1. For after-sales operations, the Jiuyun Intelligent Work Order System enables fully automated processing of after-sales work orders across all channels and the entire supply chain. It frees up staff from handling repetitive issues, cuts operating costs by more than 50%, maintains service quality, improves consumer after-sales experience, and seamlessly connects customers, warehouses, couriers and other stakeholders to enable data-driven end-to-end collaboration.

2. Beyond after-sales, brands can leverage existing mature AI agents to optimize operations. For example, the packaging recommendation AI agent can suggest optimal packaging solutions based on multi-dimensional data, reducing packaging waste, cutting costs, lowering product damage rates, and aligning with the trend of green consumption.

3. ZTO Cloud Warehouse has opened its AI ecosystem for cooperation, allowing brands to access mature technical capabilities to quickly achieve end-to-end intelligent upgrading of their supply chains without heavy in-house R&D investment.

This article shares the latest progress in AI-driven intelligence for logistics and supply chains, bringing new cost-reduction and efficiency-boosting opportunities for sellers. Key takeaways are as follows:

1. The Jiuyun Intelligent Work Order System provides a mature solution to common seller pain points including high after-sales customer service costs, delayed responses, and missed orders or messages. It supports 24/7 automatic monitoring across all channels, handles 99% of repetitive rule-based issues automatically, cuts after-sales costs by more than 50% for sellers, and substantially improves response speed.

2. Beyond after-sales, sellers can access services from multiple mature AI agents. For example, the packaging recommendation AI agent can optimize packaging solutions based on product characteristics, order structure, transportation routes and other data, reducing packaging costs and product damage rates while improving consumer shopping experience.

3. This series of AI tools is open for integration, and compatible with mainstream communication platforms and express delivery systems. Sellers can access and use the tools quickly without heavy in-house R&D investment, improving their competitiveness through intelligent upgrading, making this a solid new opportunity to cut costs and boost efficiency.

This article outlines the latest developments in AI-driven intelligence on the logistics side, offering useful insights for factories advancing digital e-commerce transformation and seizing business opportunities. Key takeaways are as follows:

1. The logistics industry has already completed intelligent upgrading across all supply chain nodes, drastically lowering the barrier for factories to connect to intelligent supply chains. Factories can directly access mature AI agent services to quickly achieve intelligent operations for warehousing, after-sales, transportation and other links, reducing their own operating costs.

2. ZTO Cloud Warehouse has built an open cross-organizational AI collaboration ecosystem connected to multiple major e-commerce platforms and express delivery networks. Factories engaged in direct-to-consumer e-commerce can leverage this mature intelligent supply chain system to reduce investment in building their own digital systems, and reallocate more resources to product production and design.

3. For factories pursuing digital transformation, AI-powered supply chain upgrading is now commercially viable. Factories can leverage mature technologies by joining the open ecosystem, use supply chain data feedback to better understand market demand, and optimize production and product design to better match consumer demand.

This article shares the latest industry development trends in logistics AI, providing industry references and strategic guidance for relevant service providers. Key takeaways are as follows:

1. The logistics AI industry has completed a phase transition from deploying standalone AI tools to building AI-Agent clusters and enabling cross-ecosystem collaboration. Core industry demand has shifted from intelligent single functions to intelligent decision-making across the entire supply chain and all nodes, making open collaboration the core trend for future industry development.

2. Currently, the core pain points for industry clients focus on low collaboration efficiency across supply chain links and high labor costs, particularly in the after-sales work order segment where large volumes of repetitive work consume excessive human resources. ZTO Cloud Warehouse's Yunzhi large model paired with the Jiuyun intelligent work order solution is a mature, market-ready solution targeting this pain point. Trained on 10 billion-level industry data, it enables full-process automation and delivers significant cost reduction and efficiency improvement.

3. Going forward, the industry will achieve open interconnection at the data, model, and application layers. Service providers can seize the opportunity to join the open ecosystem, deepen cooperation with platforms in data sharing, joint model development, and scenario co-creation, develop more AI applications for vertical scenarios, and unlock new business growth.

This article introduces ZTO Cloud Warehouse's latest progress in logistics AI, offering multiple insights for marketplace platforms pursuing supply chain intelligence. Key takeaways are as follows:

1. Platform merchants currently have strong demand for intelligent logistics and supply chains, with core needs focused on cost reduction, efficiency improvement, and end-to-end supply chain collaboration. Platforms can integrate mature AI intelligent tools to provide more value-added services for merchants, improve merchant operational efficiency, and in turn strengthen merchant stickiness and platform competitiveness.

2. ZTO Cloud Warehouse's AI development path – first building a vertical large model technology foundation, training the model with accumulated industry data, deploying applications for vertical scenarios, and finally opening up ecosystem cooperation – offers strong reference value for platforms developing their own AI strategies. This approach reduces technical trial-and-error risk and allows for gradual, incremental deployment.

3. In terms of ecosystem building, ZTO Cloud Warehouse adopts an open integration model that connects multiple platforms and multiple service providers. This approach attracts more partners to join, and platforms can reference this model for investment and cooperation, bringing together participants from different fields to jointly improve the ecosystem while avoiding the risks of slow technical iteration and incomplete scenario coverage that come with closed development.

This article discloses the latest industry developments in vertical large model applications for the logistics industry, offering high reference value for researchers studying industrial intelligent upgrading. Key takeaways are as follows:

1. China's logistics AI industry has now achieved a clear paradigm shift, moving from early-stage deployment of standalone AI tools to a new stage of AI-Agent cluster building and cross-organizational ecosystem collaboration, completing the transition from the original "people seeking work" model to a "work finding people" model, which is the latest development in logistics industry intelligence.

2. The newly launched Yunzhi AI large model represents new exploration of vertical large model deployment. Its three-layer technical architecture, trained on 10 billion-level end-to-end logistics data accumulated by the company, solves the pain point of general large models lacking industry-specific knowledge, and verifies the commercial viability of the "vertical large model + scenario deployment" business model.

3. The Jiuyun Intelligent Work Order System provides verifiable real-world performance data for AI-enabled logistics after-sales scenarios: over 99% automatic problem resolution and more than 50% cost reduction and efficiency improvement. Meanwhile, its open ecosystem cooperation model also provides a new typical case for industrial intelligent upgrading research, with high research value.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

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信息技术负责人过江龙:以中通云仓AI-Agent集群+合作伙伴智能体协同,推动云仓合作生态智能化体系建立。

2026年8月28日,中通云仓科技在“2026品牌供应链创新私董会暨‘中通云仓之夜’品牌沙龙”上正式发布“云知AI大模型平台”,并同步推出基于该平台训练的重要成果——“玖云智能工单系统”。这场发布不仅展示了中通云仓在人工智能能力建设上的最新进展,更宣告中通云仓科技正式进入“AI-Agent集群与合作伙伴生态建设”的新阶段,以数智化协同和跨组织智能体协作,开启物流领域AI行业化应用新篇章。

01

云知AI大模型平台正式上线:打造物流垂直领域的智能体技术底座

中通云仓科技信息技术中心负责人过江龙介绍,云知AI大模型平台是公司面向物流供应链全链路打造的智能化技术底座。平台以“数据感知、数据治理、向量记忆、模型调度、工具注册、数据可视化”六大核心能力为支撑,构建了从基础设施与运维管理、应用数据层到场景应用层的完整技术体系。

在基础设施层,云知AI通过数据管理平台、统一计算资源、运维与治理体系,为大模型训练、推理与部署提供稳定、安全、可扩展的算力与数据保障。在应用数据层,平台集成模型网关、本地模型、LoRA微调、RAG知识库等关键技术,实现企业私域数据与大模型能力的深度融合,确保模型既能理解通用语义,又深谙物流行业专属知识。在场景应用层,平台已覆盖山海通SCM、智慧景天、智能客服与运维等核心业务系统,形成“业务数据生产—智能分析决策—自动调度执行”的智能化闭环。

多年来,中通云仓科技沉淀了雄厚的业务数据生产能力:物流轨迹、配送时效、拣货路径、波次作业等月数据超百亿级。这些数据资产覆盖物流轨迹、仓储运营、配送履约、售后服务等全链路场景,为云知AI大模型提供了高质量、持续更新的训练燃料,也让模型在面对复杂物流问题时具备真正的行业理解力与业务判断力。

02

玖云智能工单系统发布:云知AI大模型训练成果落地的标杆

作为云知AI大模型平台的重要训练成果之一,玖云智能工单系统在本次发布会上正式亮相。该系统以“数据员工”协同理念重构售后客服作业模式,覆盖仓库AI客服、物流AI客服、网点AI客服三大场景,实现了从群消息自动采集、AI语义理解、智能分类、工单自动创建与分发,到执行反馈与闭环的全流程自动化。

玖云智能工单系统具备四大核心价值:第一,7×24小时自动采集值守,实现全时段、全渠道服务覆盖,杜绝消息遗漏;第二,AI自动处理率达到99%以上,将重复性、规则化问题从人工客服中释放,大幅提升响应速度与处理效率;第三,系统覆盖33个以上工单自动化场景,横跨拦截、催件、查询、未收到货、发货错误、货物丢失、货物破损、服务类等全物流售后链路,实现AI语义精准解析;第四,通过智能化替代与人工兜底相结合,帮助企业实现50%以上的降本增效,同时保障服务质量与客户体验。

在渠道协同层面,玖云已完成全渠道消息AI托管,无缝接入钉钉、飞书、企业微信、个人微信等主流协同平台,覆盖中通、圆通、申通、韵达、京东、EMS、德邦、顺丰等主流快递物流企业工单操作,同时支持手工自建单、API接口及第三方工单系统。这种开放的接入能力,使得玖云不仅是一个工单处理工具,更成为连接客户、仓库、网点、快递、品牌方等多方角色的数据化协同中枢。

03

进入“AI-Agent集群”与合作伙伴生态建设阶段

随着云知AI大模型平台和玖云智能工单系统的发布,中通云仓科技正式从单点AI工具应用阶段,迈入“AI-Agent集群”与合作伙伴生态协同的新阶段。公司依托山海通SCM系统、产研数字员工平台、包材推荐算法智能体等仓内作业智能体矩阵,推动仓储、运输、售后、产研等每一个作业节点具备智能决策能力,实现从“人找事”到“事找人”的范式跃迁。

在山海通SCM系统中,智能体已深度覆盖电商企业业务场景。以“包材推荐算法智能体”为例,系统能够基于商品特性、订单结构、运输路径、历史破损率等多维数据,智能推荐最优包装方案,实现包材成本优化与履约质量提升的双赢。该智能体不仅降低了仓储运营中的包材浪费,也为绿色低碳物流提供了数据驱动的可行路径。

更为重要的是,中通云仓科技正通过数据化协同,与多个知名品牌、电商平台、快递网络及ISV服务商建立跨组织智能体协作机制。玖云工单系统与主流沟通工具和快递物流平台的无缝对接,正是这一协作机制在售后服务场景中的生动缩影。未来,更多业务智能体将在数据层、模型层、应用层实现互联互通,形成开放、协同、可持续进化的物流AI生态,为行业客户提供端到端的智能化升级能力。

04

未来展望:以AI驱动物流全链路智能化升级

过江龙表示,中通云仓科技将持续加大对AI基础设施与垂直场景模型的投入,把“云知AI大模型平台”打造成物流行业智能化升级的核心引擎。公司将以AI-Agent集群为纽带,与生态伙伴在数据共享、模型共建、场景共创等层面深化合作,共同推动物流全链路从“人找事”向“事找人”的跃迁,为客户创造更高的运营效率、更低的运营成本和更好的服务体验。

中通云仓科技坚信,人工智能不是简单的工具替代,而是物流供应链生产力与生产关系的重构。未来,公司将继续秉持“科技链接天下良仓”的愿景,与合作伙伴携手,共同书写物流行业人工智能应用的新篇章。

注:文/过江龙,文章来源:中通云仓,本文为作者独立观点,不代表亿邦动力立场。

文章来源:中通云仓

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FAQ回顾

云知AI大模型平台是什么?

云知AI大模型平台是中通云仓科技面向物流供应链全链路打造的智能化技术底座,具备六大核心能力,覆盖核心业务系统形成智能化闭环,依托月超百亿级的全链路物流数据训练,具备专业的物流行业理解力与业务判断力。

玖云智能工单系统有哪些核心优势?

玖云智能工单系统是云知AI大模型的落地成果,AI自动处理率达99%以上,覆盖33个以上工单自动化场景,可7×24小时全渠道值守,能帮助企业实现50%以上降本增效,还可无缝接入主流协同平台和快递企业系统。

AI-Agent集群能为物流行业带来什么价值?

物流行业的AI-Agent集群可让仓储、运输、售后、产研等各作业节点具备智能决策能力,实现从“人找事”到“事找人”的范式跃迁,还能实现跨组织智能体协作,打造开放协同的物流AI生态,为行业提供端到端智能化升级能力。

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