从测试验证系统到工业数据资产，清研精准持续连接真实设备、真实工位与真实反馈，构建面向物理世界的 AI 工程化能力 From testing systems to industrial data assets, Tsing Standard continuously connects real equipment, real workstations and real feedback to build AI engineering capabilities for the physical world
清研精准从真实工业现场出发，提供从数据采集、处理，到模型训练与部署的完整能力体系，让 AI 能力真正扎根于工业物理世界。Tsing Standard starts from real industrial sites, providing a complete capability system from data capture, processing, to model training and deployment — so AI capabilities genuinely take root in the industrial physical world.
近十年汽车测试测量、新能源检测验证、自动驾驶测试与仿真经验，深度理解工业现场的复杂性和工程化要求。Nearly a decade of experience in automotive testing, new energy validation and autonomous driving simulation — deeply understanding industrial site complexity and engineering requirements.
清华大学李克强院士学生领衔，卢志助理教授加盟，在计算成像、认知计算和具身智能方向提供科学指导。Led by a student of Tsinghua Academician Li Keqiang, with Asst. Prof. Lu Zhi providing scientific guidance in computational imaging, cognitive computing and embodied intelligence.
已在动力电池、整车总装、矿山巡检、电网巡检等场景完成数据闭环验证，具备真实工业现场交付能力。Validated data loops in battery PACK, vehicle assembly, mining inspection and grid inspection — proven real industrial site delivery capability.
与国内主要汽车主机厂、新能源企业、科技平台及能源机构建立深度合作关系，共同推动工业 AI 在真实场景中的落地。Deep collaboration with major domestic OEMs, new energy enterprises, tech platforms and energy institutions — jointly advancing industrial AI deployment in real scenarios.
清研精准过去 8 年深耕汽车、新能源、工业检测与测试验证场景，形成了稳定的客户基础与工程交付能力。在工业 AI 时代，我们将真实工业场景中积累的工程能力与数据资产，转化为可复用、可迁移的 AI 基础设施，服务更广泛的工业场景。Tsing Standard has spent 8 years in automotive, new energy, industrial inspection and testing — building a stable customer base and engineering delivery capability. In the industrial AI era, we transform the engineering capabilities and data assets accumulated in real industrial scenarios into reusable, transferable AI infrastructure, serving broader industrial applications.
从汽车测试到工业 AI，持续深耕工业现场From automotive testing to industrial AI, continuously deepening industrial site expertise
已进入国内几乎全部汽车主机厂，真实项目验证Entered nearly all domestic auto OEMs, validated by real projects
覆盖整车 / 电池 / 检测 / 科技平台Covering OEM / Battery / Testing / Tech platforms
清华院士学术基因 · 青年科学家加盟 · 持续迭代Tsinghua academician lineage · Young scientists · Continuous iteration
为机器人时代提供数据、技能、评测和工业智能能力。Providing data, skills, evaluation and industrial intelligence capabilities for the robotics era.
苏州市清研精准汽车科技有限公司成立，聚焦汽车测试测量与数据采集。Suzhou Tsing-standard Automotive Technology Co., Ltd founded, focusing on automotive testing and data capture.
为吉利、一汽、北汽、长城等整车厂提供测试测量服务，积累工业现场工程化经验。Providing testing and measurement services for Geely, FAW, BAIC, Great Wall and other OEMs — accumulating industrial site engineering experience.
切入动力电池 PACK、EOL、BMS、PCS 检测验证，跑通电化学复杂物理系统的状态感知、动态建模、异常识别和策略反馈闭环。Entering battery PACK, EOL, BMS, PCS testing and validation — proving the loop of state sensing, dynamic modeling, anomaly detection and strategy feedback in complex electrochemical systems.
拓展自动驾驶场景库、仿真回放、评测验证能力，积累多模态数据采集和场景复现经验。Expanding autonomous driving scenario libraries, simulation replay and evaluation-validation capabilities — accumulating multimodal data capture and scenario replay experience.
推出多模态数据采集工具链、多机器人协同平台、仿真评测系统，形成覆盖工业 AI 全流程的能力体系。Launching multimodal data capture toolchain, multi-robot collaboration platform and simulation-evaluation system — forming a full-workflow industrial AI capability system.
与清华大学卢志助理教授合作，构建面向工业物理世界的认知系统，提升 AI 在跨工况、跨产线、跨系统条件下的理解与泛化能力。Collaborating with Tsinghua Asst. Prof. Lu Zhi, building a cognition system for the industrial physical world — improving AI understanding and generalization across conditions, production lines and systems.
FOUNDER & CHAIRMAN · DONG HAN
清华大学车辆与运载学院博士，师从李克强院士（中国工程院院士）。曾任清华大学苏州汽车研究院感知检测中心主任，参与国家 863 电动汽车重大专项。PhD from Tsinghua University School of Vehicle and Mobility, supervised by Academician Li Keqiang (Chinese Academy of Engineering). Former Director of Perception & Detection Center at Tsinghua Suzhou Automotive Research Institute. Participated in National 863 Electric Vehicle Major Project.
2018年创立清研精准，8年深耕智能电动汽车检测领域，带领团队服务国内主要汽车主机厂，实现持续发展。Founded Tsing Standard in 2018, 8 years in intelligent EV inspection, led team to serve major domestic auto OEMs with sustained development.
工业 Physical AI 需要的不是"更多数据拟合"，而是"更好的物理状态重建与泛化约束"。清华青年教授在高维动态观测、计算成像、物理驱动重建和自监督鲁棒学习方面的研究，为公司构建"仿生约束层"提供方法论支撑。Industrial Physical AI needs not "more data fitting" but "better physical state reconstruction and generalization constraints." Research in high-dimensional dynamic observation, computational imaging, physics-driven reconstruction and self-supervised robust learning provides methodological support for our bio-inspired constraint layer.
四位核心成员各司其职：陈负责机器人硬件与产业化落地，赵负责具身智能技术研发，曹负责战略融资与全球化，周负责多模态算法与产品。不是从零起步，把已跑通的工程方法论迁移到具身场景——本质是能力外溢，而非转型。Four core members with clear division: Chen leads robotics hardware and industrialization, Zhao leads embodied AI technology, Cao leads strategy and globalization, Zhou leads multimodal algorithms and product. Transferring proven engineering methodologies to embodied scenarios — capability overflow, not transformation.
横跨多个科技与制造领域，深度参与头部机器人公司的本体研发与量产交付，具备从运动控制到整机交付的全流程工程经验。将互联网大厂的产品思维与机器人硬科技深度融合，是推动 Physical AI 从研发走向规模化落地的核心力量。Spanning multiple technology and manufacturing domains, with deep involvement in robot hardware R&D and mass production delivery at a leading robotics company. Full-stack engineering experience from motion control to complete system delivery. Fusing internet product thinking with robotics hard tech, driving Physical AI from R&D to scaled industrial deployment.
具身智能技术方向博士，曾在多家头部具身智能公司承担核心技术职务，深耕具身技术与机器人全链路研发。在工业场景的感知-规划-控制一体化方向有深厚积累，是公司工业 AI 技术架构的核心设计者。PhD in embodied AI, former core technology executive at multiple leading embodied AI companies. Deep expertise in full-stack robotics R&D and perception-planning-control integration for industrial scenarios — core designer of the company's industrial AI technical architecture.
海外顶尖高校硕士，曾就职于知名投资机构，深度参与 AI 与科技领域投融资。后加入 AI 创业公司负责战略与全球业务拓展，具备横跨学术、资本与产业的复合视野，负责公司具身智能板块的整体运营与商业化路径。MS from a top overseas university. Previously at a well-known investment firm with deep involvement in AI and tech investment. Later joined an AI startup to lead strategy and global business development. Brings a composite perspective spanning academia, capital and industry, overseeing the overall operations and commercialization of the company's embodied AI division.
海外顶尖高校博士，在顶级 AI 学术会议发表多篇论文，曾在多家头部科技企业及高校从事访问研究。多模态与解码算法专家，负责公司 VLA 模型与跨模态推理方向的核心算法研发。PhD from a top overseas university, with multiple publications at leading AI conferences. Visiting researcher experience at top tech companies and universities. Expert in multimodal and decoding algorithms, leading core algorithm R&D for the company's VLA models and cross-modal reasoning.
创始团队来自清华大学、中科院及头部整车企业，具备汽车测试测量、新能源检测验证、自动驾驶测试与仿真的深厚工程化经验。Founding team from Tsinghua, CAS and leading automotive enterprises — with deep engineering experience in automotive testing, new energy validation and autonomous driving simulation.
世界模型、多模态学习、VLA、强化学习、自监督学习方向的算法工程师和研究员，来自清华、北大、中科院、CMU、Stanford 等。Algorithm engineers and researchers in world models, multimodal learning, VLA, RL and self-supervised learning — from Tsinghua, PKU, CAS, CMU, Stanford, etc.
机器人控制、遥操作系统、传感器融合、嵌入式系统方向的工程师，具备 EGO+UMI 工具链和 Cross-Embodiment 平台开发经验。Engineers in robot control, teleoperation, sensor fusion and embedded systems — with EGO+UMI toolchain and Cross-Embodiment platform development experience.
数据工程、标注平台、仿真评测、Cross-Embodiment 平台方向的产品经理和工程师，负责数据资产化和平台服务。Product managers and engineers in data engineering, labeling platforms, simulation-evaluation and Cross-Embodiment platform — responsible for data assetization and platform services.
新能源、汽车制造、工业现场交付、测试验证方向的解决方案专家和交付经理，负责真实工业现场的工程化落地。Solution experts and delivery managers in new energy, automotive manufacturing, industrial site delivery and testing-validation — responsible for engineering implementation in real industrial sites.
清华大学卢志助理教授，在计算成像、认知计算和具身智能方向提供科学指导，为工业认知系统提供方法论支撑。Tsinghua Asst. Prof. Lu Zhi provides scientific guidance in computational imaging, cognitive computing and embodied intelligence for industrial cognition system methodology.
覆盖汽车、新能源、能源等多个行业，与行业伙伴共同推动工业 AI 在真实场景中的落地与标准建设。Spanning automotive, new energy and energy industries — jointly advancing industrial AI deployment and standard-building in real scenarios with our industry partners.
清研精准欢迎各行业伙伴一起探索工业 AI 的无限可能。Tsing Standard welcomes partners across industries to explore the unlimited possibilities of industrial AI together.
联系我们Contact Us →清研精准的企业文化围绕工程真实、数据闭环、长期主义和跨学科协作展开。Tsing Standard's culture centers on engineering authenticity, data loops, long-termism and cross-disciplinary collaboration.
不做 demo，不做 PPT 工程。所有技术和产品都必须在真实工业现场验证，经得起现场反馈和持续迭代。No demos, no PPT engineering. All technologies and products must be validated in real industrial sites, withstanding on-site feedback and continuous iteration.
从真实工位到数据资产到模型迭代到现场反馈，形成完整闭环。不做单点数据采集，不做孤立模型训练。From real workstations to data assets to model iteration to on-site feedback — forming complete loops. No point data capture, no isolated model training.
工业 Physical AI 是长期工程，需要持续沉淀数据资产、技能库、评测标准和平台能力。不追求短期爆发，追求长期复利。Industrial Physical AI is long-term engineering, requiring continuous accumulation of data assets, skill libraries, evaluation standards and platform capabilities. Not chasing short-term bursts, but long-term compounding.
工程化团队 + 科学家大脑 + 产业交付能力。AI、机器人、数据平台、工业工程、商业化团队紧密协作。Engineering team + scientific brain + industrial delivery capability. AI, robotics, data platform, industrial engineering and commercialization teams collaborate closely.
世界模型不能只在仿真环境里训练，必须进入真实工业现场。让 AI 理解物理世界的复杂性、不确定性和长尾异常。World models can't just train in simulation — they must enter real industrial sites. Making AI understand the complexity, uncertainty and long-tail anomalies of the physical world.
技术只是辅助工具，吃透业务本质、落地商业闭环，才是核心竞争力。不盲目堆砌技术，先打通商业闭环，再规模化量产。Technology is only a supporting tool. Understanding business essence and closing the commercial loop is the core competitiveness. Don't blindly stack technology — close the commercial loop first, then scale production.
深入了解 Physical AI 闭环、Cross-Embodiment 和职位机会。Dive deeper into Physical AI loop, Cross-Embodiment and career opportunities.