真实场景，才是定义胜负的战场。当本体与大脑日趋成熟，下一个胜负手在场景落地的工程化能力。 Real-world scenarios define the winners. As robot bodies and AI brains mature, the next decisive factor is engineering capability for scenario deployment.
在电化学域跑通"感知—推理—执行"闭环。多模态测试诊断 + 工程化闭环数据资产。已规模化部署。Proven "perception-reasoning-execution" loop in the electrochemical domain. Multimodal testing & diagnostics + engineered data assets. Scaled deployment.
横向扩展到动力电池制造、汽车总装线、矿山巡检等场景。工程化底座 + 数据闭环，驱动规模化复制。Horizontal expansion to battery manufacturing, auto assembly, mining inspection. Engineering infrastructure + data loops driving scaled replication.
跨物理域具身智能，统一世界模型支撑未知环境泛化决策。Cross-physical-domain embodied AI, unified world models for unknown-environment generalization.
在电化学域跑通"感知—推理—执行"闭环，做透一个高门槛物理场。电化学多物理状态感知、非线性动态建模、毫秒级云-边协同闭环、复杂工况动态安全控制。Proven "perception-reasoning-execution" loop in the electrochemical domain. Electrochemical multi-physics sensing, nonlinear dynamic modeling, millisecond cloud-edge loop, dynamic safety control in complex conditions.
三电系统全工况验证Full-condition powertrain validation
下线全项检测数据闭环Full-item inspection data loop
长尾异常样本库积累Long-tail anomaly library accumulation
极端工况场景复现Extreme condition scenario replay
NEW ENERGY PHYSICAL AI · VALIDATED SHOWCASE
让电池产线从"试点工位"变成"可训练、可评测、可复制"的数据资产。工业场景成熟、自动化基础好、质量与安全标准清晰，是具身智能落地的最佳首选场景。 Transforming battery production lines from "pilot workstations" into trainable, evaluable, replicable data assets. Mature industrial scenarios with solid automation foundations and clear quality standards — the ideal first deployment scenario for embodied AI.
覆盖上下料、搬运、检测复核、拧紧、插接等核心工位，形成标准化工位数据资产模板，支持单工位到多工序的快速复制。Covering loading/unloading, transport, inspection, tightening, and insertion workstations. Standardized workstation data asset templates enabling rapid replication from single station to multi-process.
RGB-D / 手眼相机、力觉 / 触觉、关节状态、工装 / PLC 信号、异常接管样本。遥操作示教 → 真机采集清洗 → 仿真回放 → 评测回归 → 模型 fine-tune 数据包。RGB-D/eye-in-hand cameras, force/tactile sensors, joint states, tooling/PLC signals, anomaly takeover samples. Teleoperation → real-machine capture → simulation replay → evaluation → model fine-tune data packs.
本体公司进场即可采集真机数据，失败样本自动回流，评测指标体系量化机器人能力，形成单工位到多工序复制的完整闭环。Robot companies can capture real-machine data immediately upon deployment. Failed samples auto-feedback, evaluation metrics quantify robot capabilities, forming a complete loop from single station to multi-process replication.
把总装 / 物流 / 检测 / 返修工位沉淀成可迁移的具身智能数据底座。节拍稳定、工序清晰，易形成单工位验证 → 多工序复制路径，是具身智能规模化落地的理想场景。 Transforming assembly/logistics/inspection/repair workstations into transferable embodied AI data infrastructure. Stable cycle times and clear processes enable the ideal single-station-to-multi-process replication path for embodied AI at scale.
覆盖总装、线边物流、质检复核、返修辅助、核心零部件上下料 / 装配等工位，形成可复制的工位数据资产包。Covering assembly, line-side logistics, quality inspection, repair assistance, and core component loading/assembly workstations, forming replicable workstation data asset packages.
工装夹具适配、PLC / MES 边界、AMR / AGV 协同、安全围栏、工位节拍。20 年汽车 / 电池行业治具与工装积累，工程接口打通是我们的核心壁垒。Fixture adaptation, PLC/MES boundary, AMR/AGV coordination, safety fencing, workstation cycle time. 20 years of automotive fixture and tooling expertise — engineering interface integration is our core barrier.
单工位标定、多工位采集、长尾异常样本、泛化评测、联合样板间。真实工位入口 + 长尾异常样本 + 联合方案，终端订单批量复制。Single-station calibration, multi-station capture, long-tail anomaly samples, generalization evaluation, joint showcases. Real workstation entry + long-tail anomaly samples + joint solutions → batch order replication.
高危繁重、强制少人化无人化政策驱动，替代一名井下工人单点 ROI 极高。已在榆林建成矿山模拟巷道场景，真实矿山数据闭环全面运营中。 High-risk operations with mandatory unmanning policies. Replacing one underground worker delivers extremely high single-point ROI. A mining simulation tunnel has been built in Yulin, with real mining data loops fully operational.
强制少人化无人化，国家政策明确要求降低井下作业人员数量，机器人替代需求刚性。Mandatory unmanning. National policy explicitly requires reducing underground workers — robot replacement demand is rigid.
替代井下高危岗位，提升作业安全性和效率，是具身智能在工业场景的典型应用。Replacing high-risk underground positions, improving operational safety and efficiency — a typical embodied AI application in industrial scenarios.
矿山场景准入门槛高，需要真实场景验证。清研精准已对接榆林等矿山创新中心，具备实际部署经验。Mining scenarios require high entry standards and real-world validation. Tsing Standard has connected with Yulin and other mining innovation centers with practical deployment experience.
依托榆林真实矿山项目现场，复现稳运 / 槽道 / 作业面等典型场景，低照度 / 粉尘 / 水雾 / 温湿等极端环境全覆盖。从单矿试点到集团矿区复制。Built on real Yulin mining project sites, reproducing conveyor/channel/working face scenarios with full coverage of extreme environments: low light, dust, water mist, temperature/humidity. From single mine pilot to group-wide replication.
将新能源样板间的工程方法论复制到更广泛的工业制造场景。核心零部件（电驱 / 转向 / 线控制动 / 座舱）品类多、工艺差异大，利于沉淀跨场景复用能力，形成通用工业 工业 AI 基础设施。Replicating the new energy showcase's engineering methodology to broader industrial manufacturing. Core components (e-drive/steering/brake-by-wire/cockpit) span diverse categories and processes — ideal for accumulating cross-scenario reusable capabilities and forming a general industrial Physical AI foundation.
电驱 / 转向 / 线控制动 / 座舱等核心零部件的工位数据采集与资产化，品类多工艺差异大，沉淀跨场景复用能力。Workstation data capture and assetization for e-drive/steering/brake-by-wire/cockpit components. Diverse categories and processes accumulate cross-scenario reusable capabilities.
视觉检测、尺寸测量、表面缺陷识别。基于真实工况数据训练的高精度质检模型，覆盖多品类工业场景。Visual inspection, dimensional measurement, surface defect detection. High-precision quality inspection models trained on real production data, covering multi-category industrial scenarios.
基于历史工况数据的工艺参数优化和设备故障预测，降低停机率和废品率，形成持续优化的数字孪生闭环。Process parameter optimization and equipment failure prediction based on historical condition data, reducing downtime and defect rates, forming a continuously optimizing digital twin loop.
为机器人时代提供数据、技能、评测和工业世界模型。从真实工业现场采集的高质量具身智能训练数据，是机器人公司最稀缺的资源。清研精准已在动力电池、汽车总装、矿山等场景建立真机数据采集闭环。Providing data, skills, evaluation and industrial world models for the robotics era. High-quality embodied AI training data from real industrial sites is the scarcest resource for robot companies. Tsing Standard has established real-machine data capture loops in battery, auto assembly, and mining scenarios.
插接、拧紧、抓取、检测等工业原子技能的标准化数据包。支持 Cross-Embodiment 跨本体迁移，一份数据驱动多种机器人。Standardized data packs for industrial atomic skills: insertion, tightening, grasping, inspection. Supports cross-embodiment transfer — one dataset drives multiple robots.
专为人形机器人设计的工业场景数据采集方案，含 EEG 脑电的认知增强数据包，让人形机器人真正理解工业操作意图。Industrial scenario data capture solutions designed for humanoid robots, with EEG cognitive-enhanced data packs enabling humanoid robots to truly understand industrial operation intent.
使用真实工业场景评测集，量化机器人在工业任务中的能力。提供 HIL、仿真和真机三种评测模式，覆盖电池、汽车、矿山等多场景。Using real industrial scenario evaluation sets to quantify robot capabilities in industrial tasks. Provides HIL, simulation and real-machine evaluation modes across battery, auto, and mining scenarios.
EGO + DEX-UMI + FLEX-UMI + EEG 全套采集方案，形成完整的脑-眼-手协同轨迹数据包，是机器人学习人类技能的高价值训练素材。Complete EGO + DEX-UMI + FLEX-UMI + EEG capture solution forming complete brain-eye-hand coordination trajectory data packs — high-value training material for robots learning human skills.
将新能源测试验证的工程方法论迁移到能源与电网场景。传感器数据采集、状态监测、异常预测、智能巡检机器人数据采集。Transferring new energy testing methodology to energy and grid scenarios. Sensor data capture, state monitoring, anomaly prediction, smart inspection robot data capture.
巡检机器人数据采集与技能库，支持设备状态识别、异常检测和自主巡检路径规划。Inspection robot data capture and skill libraries supporting equipment state recognition, anomaly detection and autonomous inspection path planning.
多模态传感器数据采集，基于历史数据的故障预测模型，降低停电风险。Multimodal sensor data capture, fault prediction models based on historical data, reducing power outage risks.
将新能源电池测试验证能力迁移到储能系统，电化学状态感知与动态安全控制。Transferring new energy battery testing capabilities to energy storage systems, electrochemical state sensing and dynamic safety control.
中试基地是连接机器人技术供给方与动力电池、汽车产业、矿山化工等场景的中试转化平台。通过复现真实产线场景，为机器人企业、主机厂、电池厂提供从场景验证、数据采集、模型训练、工艺适配到产品中试到客户导入的一体化服务。 The pilot center is a transfer platform connecting robot technology providers with battery, automotive, and mining/chemical scenarios. By reproducing real production line scenarios, it provides integrated services from scene validation, data capture, model training, process adaptation, product piloting to customer onboarding.
扎根动力电池产业链，100+ 全球 TOP 主流车企、电池及零部件企业客户基础，提供进入新工业场景的高效稳健通道。Rooted in the battery supply chain with 100+ global top OEM, battery, and component enterprise clients — providing an efficient, stable channel into new industrial scenarios.
已规划 / 在建多个垂直行业数采中试基地，真实工位覆盖，多模态数据持续输出，工程验证闭环。Multiple vertical industry data capture pilot bases planned/under construction. Real workstation coverage, continuous multimodal data output, engineering validation loops.
标准化数据治理平台、仿真回放系统、行业评测标准体系，降低本体公司进场成本，提升验证效率。Standardized data governance platform, simulation replay system, industry evaluation standard system — reducing robot company entry costs and improving validation efficiency.
场景入口一旦形成，本体公司、算法公司、系统集成商和投资机构会围绕真实工位持续聚集，形成具身智能产业生态。Once the scenario gateway forms, robot companies, algorithm companies, system integrators and investors continuously gather around real workstations, forming an embodied AI industrial ecosystem.
复现典型工位和工艺流程Reproduce typical workstations and processes
多模态数据采集与标注Multimodal data capture and annotation
抓取 / 搬运 / 装配等服务测试Grasping/transport/assembly service testing
适配真实工艺节拍与标准Adapt to real process cycle times and standards
展示验证方案推动订单Showcase validation solutions to drive orders
培养操作员与运维人才Train operators and maintenance personnel
场景入口一旦形成，本体公司、算法公司、系统集成商和投资机构会围绕真实工位持续聚集Once the scenario gateway forms, robot companies, algorithm companies, integrators and investors continuously gather around real workstations
了解中试中心合作Learn About Pilot Center Collaboration →无论您是汽车主机厂、机器人公司、工业企业还是矿山能源集团，清研精准都能提供定制化的 物理AI 工程化解决方案。Whether you're an auto OEM, robot company, industrial enterprise or mining/energy group, Tsing Standard can provide customized Physical AI engineering solutions.