底层架构 Edge × TestOps × Data Hub × AI Platform。5D 数据管线 · Cross-Embodiment 跨本体 · 工业认知系统。 Underlying architecture Edge × TestOps × Data Hub × AI Platform. 5D data pipeline · Cross-Embodiment · Brain-inspired world model.
Edge × TestOps × Data Hub × AI Platform，每一层都是真实工业场景中被验证的工程能力。Edge × TestOps × Data Hub × AI Platform — each layer is engineering capability validated in real industrial scenarios.
采集 · 同步 · 调理 · 控制 · 执行。工业现场的神经末梢，连接真实设备与数字世界。Capture · Sync · Conditioning · Control · Execution. The neural endpoints of industrial sites, connecting real equipment to the digital world.
环境搭建 · 用例管理 · 自动执行 · 结果追溯。将测试验证工程化，形成可复现的质量闭环。Environment setup · Test case management · Automated execution · Result traceability. Engineering test validation into reproducible quality loops.
多源异构 · 统一建模 · 治理 · 沉淀。将碎片化工业数据转化为可训练、可评测、可迁移的数据资产。Multi-source heterogeneous · Unified modeling · Governance · Accumulation. Transforming fragmented industrial data into trainable, evaluable, transferable data assets.
异常检测 · 问题定位 · 覆盖推荐 · 报告生成 · 策略更新。AI 驱动的工业智能闭环，持续自进化。Anomaly detection · Issue localization · Coverage recommendation · Report generation · Strategy update. AI-driven industrial intelligence loop, continuously self-evolving.
7步完整闭环，每一步都在真实工业场景中被验证。数据采集 → 跨本体映射 → 资产沉淀 → 仿真评测 → 世界模型 → 现场反馈。7-step complete loop, each step validated in real industrial scenarios. Data capture → Cross-embodiment mapping → Asset accumulation → Simulation evaluation → World model → Field feedback.
汽车 / 新能源 / 整车总装 / 矿山 / 电网 / 工程机械Automotive / New Energy / Assembly / Mining / Grid / Machinery
EGO / DEX-UMI / FLEX-UMI / 工业感知节点 / EEG 脑电EGO / DEX-UMI / FLEX-UMI / Industrial sensing nodes / EEG
跨本体映射 / 任务对齐 / 控制接口适配 / 数据标准化Cross-embodiment mapping / Task alignment / Interface adaptation / Data standardization
脑-眼-手轨迹 / 认知增强数据包 / 技能库 / 评测集Brain-eye-hand trajectories / Cognitive data packs / Skill libraries / Eval sets
场景复现 / HIL / 真机验证 / 策略评估Scenario replay / HIL / Real-machine validation / Strategy evaluation
感知理解 / 神经流形 / 世界建模 / 动作推演 / 任务规划Perception / Neural manifold / World modeling / Action inference / Task planning
模型更新 / 策略下发 / 数据回流 / 持续迭代Model updates / Strategy deployment / Data return / Continuous iteration
清研精准独创的 5D 数据管线，从场景准入到持续迭代，沉淀脑-眼-手协同轨迹与认知增强数据包。EEG 脑电采集是核心特色，让数据不仅有"做了什么"，更有"为什么这么做"。Tsing Standard's proprietary 5D data pipeline, from scene entry to continuous iteration, accumulates brain-eye-hand trajectories and cognitive-enhanced data packs. EEG capture is a core feature — data includes not just "what was done" but "why it was done".
工位建模 / 任务分解 / 约束定义 / 安全边界Workstation modeling / Task decomposition / Constraint definition / Safety boundaries
传感器布局 / 设备接入 / 同步校准 / 工装定制Sensor layout / Device integration / Sync calibration / Fixture customization
视觉 / 力觉 / 触觉 / 设备状态 / 认知增强 / 工艺参数Vision / Force / Tactile / Device state / Cognitive / Process params
多模态对齐 / 标注增强 / 质量过滤 / 资产封装Multimodal alignment / Annotation enhancement / Quality filtering / Asset packaging
模型反馈 / 异常补采 / 策略更新 / 版本管理Model feedback / Anomaly re-capture / Strategy update / Version management
传统数据采集只记录"状态—动作—结果"，清研精准通过先进的认知捕获技术，额外记录操作者的"意图—注意力—决策过程"，让训练数据包含人类认知维度，大幅提升模型在复杂工况下的泛化能力。Traditional data capture only records "state-action-result." Tsing Standard's advanced cognitive capture technology additionally records the operator's "intent-attention-decision process," enabling training data to include human cognitive dimensions, significantly improving model generalization in complex conditions.
将人类操作者的视觉注意力、手部动作轨迹（DEX-UMI/FLEX-UMI）和认知状态三路同步，形成完整的专家操作数据包，是机器人学习人类技能的高质量训练素材。Synchronizing the visual attention, hand motion trajectories (DEX-UMI/FLEX-UMI), and cognitive state of human operators into complete expert operation data packs — high-quality training material for robots learning human skills.
5层架构，从统一任务语义到跨本体执行，让同一份数据资产可以驱动不同机器人、不同设备、不同场景。5-layer architecture from unified task semantics to cross-embodiment execution, enabling the same data assets to drive different robots, devices and scenarios.
用自然语言和任务图谱统一描述工业任务，让不同本体能理解同一任务指令，消除本体间的语义鸿沟。Unified description of industrial tasks using natural language and task graphs, enabling different embodiments to understand the same task instructions, eliminating semantic gaps between embodiments.
将来自不同传感器、不同本体的物理状态统一表示，形成跨本体可共享的世界状态空间。Unified representation of physical states from different sensors and embodiments, forming a cross-embodiment shareable world state space.
将工业操作分解为原子技能（抓取、插接、拧紧、检测等），形成跨本体可复用的技能库。Decomposing industrial operations into atomic skills (grasping, insertion, tightening, inspection, etc.), forming a cross-embodiment reusable skill library.
针对不同机器人本体（人形、四足、协作臂、工业机器人）的运动学差异，自动完成动作重定向和控制接口适配。Automatic motion retargeting and control interface adaptation for different robot embodiments (humanoid, quadruped, collaborative arm, industrial robot) with varying kinematics.
同一份技能数据，驱动不同本体在不同场景中执行。数据资产的价值随本体数量指数级增长。The same skill data drives different embodiments across different scenarios. The value of data assets grows exponentially with the number of embodiments.
6步闭环，从感知理解到执行反馈。仿生约束层让模型从表面拟合走向物理规律学习，实现跨工况、跨产线、跨本体的泛化能力。6-step loop from perception to execution feedback. Bio-inspired constraint layer moves models from surface fitting to physical law learning, achieving cross-condition, cross-production-line, cross-embodiment generalization.
多模态输入融合 / 场景语义解析 / 物体状态识别Multimodal input fusion / Scene semantic parsing / Object state recognition
高维状态压缩 / 流形学习 / 物理约束嵌入High-dim state compression / Manifold learning / Physical constraint embedding
物理因果建模 / 动态预测 / 异常推理Physical causal modeling / Dynamic prediction / Anomaly reasoning
策略生成 / 轨迹规划 / 约束满足Strategy generation / Trajectory planning / Constraint satisfaction
多步任务分解 / 技能调度 / 异常处理Multi-step task decomposition / Skill scheduling / Anomaly handling
结果评估 / 模型更新 / 数据回流 / 持续进化Result evaluation / Model update / Data return / Continuous evolution
标准化接口，支持第三方机器人和工业系统接入世界模型能力Standardized interfaces for third-party robots and industrial systems to access world model capabilities
高维状态压缩与物理约束嵌入，实现跨工况泛化High-dimensional state compression with physical constraint embedding for cross-condition generalization
工业原子技能的预训练模型，可快速迁移到新场景Pre-trained models for industrial atomic skills, quickly transferable to new scenarios
从脑科学研究中提炼的物理规律约束，让模型学习而非记忆Physical law constraints distilled from brain science research, enabling models to learn rather than memorize
标准化评测集，量化模型在工业场景中的真实能力Standardized evaluation sets quantifying model capabilities in real industrial scenarios
现场数据回流驱动模型自动更新，越用越聪明Field data return drives automatic model updates — the more it's used, the smarter it gets
为机器人时代提供数据、技能、评测和工业世界模型。Providing data, skills, evaluation and industrial world models for the robotics era.