INDUSTRIAL COGNITION SYSTEM · 工业认知系统

让数据更可学习让模型更能泛化 Making data more learnableMaking models more generalizable

工业 Physical AI 不能只靠堆数据。清研精准在真实工业数据之上，引入先进的物理约束建模、多尺度时间建模和自监督状态重建，让模型从"拟合单一场景"走向"理解一类物理过程"。 Industrial AI can't rely on data alone. Tsing Standard introduces advanced physical-constraint modeling, multi-scale temporal modeling and self-supervised state reconstruction atop real industrial data — moving models from "fitting a single scenario" to "understanding a class of physical processes."

工业场景的过拟合问题Overfitting problem 清研的破局思路Our approach

物理约束建模Physical Constraint Modeling 物理因果约束Physical-Causal Constraints 多尺度时间建模Multi-Scale Temporal 认知增强Cognitive-Enhanced

工业场景为什么容易过拟合Why Industrial Scenarios Overfit

单一工位高精度 ≠ 跨工况强泛化High accuracy on one station ≠ strong cross-condition generalization

工业场景的核心矛盾：单一工位高度依赖产线、夹具、光照、材料、设备状态，模型只靠数据拟合，容易学到场景相关性而非物理因果关系。换工况、换产线、换本体后性能下降，高精度与强泛化存在天然矛盾。 The core contradiction in industrial scenarios: a single workstation is highly dependent on production line, fixtures, lighting, materials and device status. Models relying solely on data fitting tend to learn scenario correlations rather than physical-causal relationships. Performance degrades when conditions, lines or embodiments change — high accuracy and strong generalization are naturally at odds.

ISSUE 01

场景依赖性强Strong scenario dependency

单一工位高度依赖产线、夹具、光照、材料、设备状态。模型容易学到"在这个产线、这个光照下有效"，而非"这类任务的物理规律"。A single workstation is highly dependent on line, fixtures, lighting, materials and device status. Models learn "works on this line, this lighting" rather than "the physics of this task class."

ISSUE 02

数据拟合陷阱Data fitting trap

模型只靠数据拟合，容易学到场景相关性（spurious correlation）而非物理因果关系。换工况后，相关性失效，性能急剧下降。Models relying solely on data fitting tend to learn spurious correlations rather than physical-causal relationships. When conditions change, correlations fail and performance drops sharply.

ISSUE 03

长尾异常难覆盖Long-tail anomalies hard to cover

工业现场的异常样本分布极度长尾。靠人工采集很难覆盖所有边界 case，模型在未见过的异常场景下容易失效。Anomaly samples in industrial sites have extremely long-tail distributions. Manual collection struggles to cover all edge cases, and models fail on unseen anomalies.

ISSUE 04

精度与泛化矛盾Accuracy-generalization trade-off

追求单一工位高精度，往往需要过拟合到具体场景；追求跨工况泛化，又会牺牲单点精度。高精度与强泛化存在天然矛盾。Pursuing high accuracy on a single station often requires overfitting to specific scenarios; pursuing cross-condition generalization sacrifices single-point accuracy. High accuracy and strong generalization are naturally at odds.

清研的破局思路Our Breakthrough Approach

从拟合场景到理解物理From fitting scenarios to understanding physics

清研精准不是简单堆数据，而是在真实工业数据之上，引入先进的物理约束建模、多尺度时间建模和自监督状态重建，让模型学习物理过程的本质规律，而非场景表象。 Tsing Standard doesn't simply pile up data, but introduces advanced physical-constraint modeling, multi-scale temporal modeling and self-supervised state reconstruction atop real industrial data — enabling models to learn the essential laws of physical processes, not scenario appearances.

APPROACH 01

物理约束建模Physical Constraint Modeling

把多模态感知、认知状态和行为策略映射到统一低维表示空间。不同工况、不同本体的数据在流形空间中共享相似的拓扑结构，提升跨场景泛化能力。灵感来源于神经科学中大脑皮层的低维流形结构。Mapping multimodal perception, cognitive states and behavioral strategies to a unified low-dimensional representation space. Data from different conditions and embodiments share similar topological structures in the manifold space, enhancing cross-scenario generalization. Inspired by the low-dimensional manifold structure of the cerebral cortex.

APPROACH 02

物理因果约束 Physical-Causal ConstraintsPhysical-Causal Constraints

让模型学习动作与结果之间的稳定因果关系，而非场景相关性。引入物理先验（接触力学、运动学、动力学）作为约束，过滤掉虚假相关性，使模型在新工况下仍能保持稳定推理。Enabling models to learn stable causal relationships between actions and results, not scenario correlations. Introducing physical priors (contact mechanics, kinematics, dynamics) as constraints to filter out spurious correlations, maintaining stable reasoning under new conditions.

APPROACH 03

多尺度时间建模 Multi-Scale Temporal ModelingMulti-Scale Temporal Modeling

覆盖毫秒级接触、秒级动作、分钟级任务流程的多尺度时间建模。让模型理解从瞬时物理接触到长程任务规划的完整时间层次，支撑复杂工业任务的序列决策。Multi-scale temporal modeling covering millisecond-level contact, second-level actions and minute-level task flows. Enabling models to understand the complete temporal hierarchy from instantaneous physical contact to long-term task planning, supporting sequential decision-making in complex industrial tasks.

APPROACH 04

自监督状态重建 Self-Supervised State ReconstructionSelf-Supervised State Reconstruction

降低人工标注依赖，增强低标注场景下的鲁棒性。通过预测未来状态、重建遮挡物体、推理接触关系，让模型自主学习物理世界的内在结构，大幅降低数据标注成本。Reducing reliance on manual labeling, enhancing robustness in low-annotation scenarios. By predicting future states, reconstructing occluded objects and inferring contact relationships, models autonomously learn the intrinsic structure of the physical world, greatly reducing annotation costs.

APPROACH 05

主动采样 Active SamplingActive Sampling

找到最有价值的长尾异常和边界样本。模型主动识别不确定性高、信息量大的场景，引导数据采集优先覆盖这些高价值样本，以最少的采集成本覆盖最广的异常分布。Identifying the most valuable long-tail anomalies and edge samples. Models actively identify high-uncertainty, high-information scenarios, guiding data collection to prioritize these high-value samples — covering the widest anomaly distribution with minimum collection cost.

认知增强数据层Cognitive-Enhanced Data Layer

超越视觉与力觉：专家意图与认知数据Beyond vision and force: Expert intent and cognitive data

清研精准独家认知数据采集能力，在专家操作时同步捕获意图信号、注意力焦点和决策过程，为智能系统提供普通传感器数据无法提供的认知维度。 Tsing Standard's exclusive cognitive data capture capability simultaneously captures intent signals, attention focus and decision processes during expert operations — providing cognitive dimensions that ordinary sensor data cannot offer.

专家认知同步采集Expert Cognitive Synchronous Capture

在专家示教过程中，通过先进的认知捕获技术，记录操作意图、注意力焦点和决策过程。这些认知信号与视觉、力觉、位置数据融合，构建完整的专家操作数据集，让AI学习到专家的思维方式，而非仅仅是动作轨迹。During expert demonstrations, advanced cognitive capture technology records operational intent, attention focus and decision processes. These cognitive signals are fused with visual, force and position data to build complete expert operation datasets — enabling AI to learn expert thinking patterns, not just motion trajectories.

数据维度：视觉 + 力觉 + 位置 + 意图 + 注意力Data dimensions: Vision + Force + Position + Intent + Attention

3×

认知增强后模型泛化能力提升Model generalization improvement with cognitive enhancement

独家

工业场景认知同步采集能力Industrial cognitive synchronous capture capability

六步闭环Six-Step Loop

认知系统是闭环的核心引擎Cognitive system is the core engine of the loop

工业认知系统不是孤立模块，而是嵌入在完整工业 AI 闭环中，通过六步循环持续自我强化。 The industrial cognition system is not an isolated module, but embedded in the complete Physical AI loop, continuously self-reinforcing through a six-step cycle.

STEP 01

真实工位接入Real Workstation Access

进入高壁垒工业现场，建立长期数据通道，获取真实物理过程数据。Entering high-barrier industrial sites, establishing long-term data channels, obtaining real physical process data.

STEP 02

5D 多模态采集5D Multimodal Capture

EGO+UMI 工具链采集视觉、力觉、位置、意图、注意力五维数据。EGO+UMI toolchain captures five-dimensional data: vision, force, position, intent, attention.

STEP 03

Cross-Embodiment 映射Cross-Embodiment Mapping

统一任务语义和物理状态表示，让数据跨本体可复用。Unifying task semantics and physical state representation, making data reusable across embodiments.

STEP 04

数据资产沉淀Data Asset Accumulation

形成工位数据包、异常样本库、技能库、评测集，为智能系统提供高质量训练数据。Forming workstation packs, anomaly libraries, skill libraries, evaluation sets as high-quality training data for intelligent systems.

STEP 05

仿真评测验证Simulation Evaluation

在仿真环境和测试平台中验证智能系统的预测准确性、泛化能力和异常处理。Validating intelligent system prediction accuracy, generalization capability and anomaly handling in simulation and test platforms.

STEP 06

现场反馈迭代On-Site Feedback Iteration

现场执行结果、异常样本和边界 case 回流，形成自增强飞轮，边际成本持续递减。On-site execution results, anomaly samples and edge cases flow back, forming a self-reinforcing flywheel with continuously decreasing marginal costs.

七大核心能力Seven Core Capabilities

从感知到推理，完整认知闭环From perception to reasoning, complete cognitive loop

工业认知系统不仅能感知当前状态，还能预测未来、推理异常、生成策略，形成完整的感知-认知-决策闭环。 The industrial cognition system not only perceives current states, but also predicts the future, reasons about anomalies and generates strategies — forming a complete perception-cognition-decision loop.

状态理解State Understanding

从多模态传感器信号中提取物体、环境、设备状态和接触关系的统一表示。Extracting unified representations of objects, environment, device status and contact relationships from multimodal sensor signals.

动态预测Dynamic Prediction

预测未来状态演化、物体运动轨迹和接触结果，支撑前瞻性决策。Predicting future state evolution, object motion trajectories and contact results to support forward-looking decisions.

异常推理Anomaly Reasoning

识别偏离正常物理过程的异常样本，推理异常原因和影响范围。Identifying anomaly samples deviating from normal physical processes, reasoning about anomaly causes and impact scope.

长程推演Long-Horizon Rollout

从当前状态推演未来多步演化，评估不同策略的长期影响。Rolling out multi-step future evolution from current state, evaluating long-term impacts of different strategies.

工况泛化Condition Generalization

在未见过的工况、材料、光照、夹具下保持性能，不被场景表象绑架。Maintaining performance under unseen conditions, materials, lighting and fixtures — not bound by scenario appearances.

本体迁移Embodiment Transfer

配合 Cross-Embodiment 平台，让模型能力跨机器人本体迁移，80%+ 复用率。Working with the Cross-Embodiment platform to transfer model capabilities across robot embodiments with 80%+ reuse rate.

策略生成Strategy Generation

基于状态理解和动态预测，生成适应当前工况的执行策略。Generating execution strategies adapted to current conditions based on state understanding and dynamic prediction.

∞

持续迭代Continuous Iteration

现场执行结果、异常样本和边界 case 回流，持续优化模型性能，形成自增强飞轮。On-site execution results, anomaly samples and edge cases flow back, continuously optimizing model performance, forming a self-reinforcing flywheel.

技术基础Technical Foundation

为什么清研能做工业认知系统Why Tsing Standard can build industrial cognition systems

清研精准团队罕见地结合了自动驾驶、脑科学、具身智能和工业物理系统四个领域的深厚积累，这是构建工业认知系统的必要条件。 The Tsing Standard team uniquely combines deep expertise in autonomous driving, computational imaging, embodied intelligence and industrial physical systems — the necessary conditions for building an industrial cognition system.

FOUNDATION 01 · 核心渊源

清华汽车工程系 · 李克强院士团队Tsinghua Automotive Eng. · Acad. Li Keqiang

创始人董汉师从中国工程院院士、清华大学汽车工程系李克强教授，在智能汽车、自动驾驶感知与控制、复杂物理系统建模方向有近十年深厚积累，直接构成 Physical AI 工程化底座的核心技术基础。Founder Dong Han studied under Academician Li Keqiang, Professor at Tsinghua's Department of Automotive Engineering and member of the Chinese Academy of Engineering. Nearly a decade of deep expertise in intelligent vehicles, autonomous driving perception and control, and complex physical system modeling — directly forming the core technical foundation of the Physical AI engineering base.

中国工程院院士CAE Academician

FOUNDATION 02 · 脑科学

计算成像 · 工业智能Computational Imaging · Industrial Intelligence

战略科学家卢志助理教授（清华大学），在计算成像、工业智能和具身智能方向有深厚积累，将先进的多尺度建模方法引入工业 AI，构建工业认知系统的理论基础。Strategic scientist Asst. Prof. Lu Zhi (Tsinghua University) has deep expertise in computational imaging, industrial intelligence and embodied AI — introducing advanced multi-scale modeling methods to industrial AI, building the theoretical foundation of the industrial cognition system.

清华大学助理教授Tsinghua Asst. Professor

FOUNDATION 03 · 工程化

新能源复杂物理系统建模New Energy Complex Physical System Modeling

在电化学、热管理、安全控制等复杂物理系统中，已经跑通状态感知、动态建模、异常识别和策略反馈闭环，积累了大量工业物理过程建模的工程化经验，为世界模型提供真实验证场景。In complex physical systems including electrochemistry, thermal management and safety control, the complete loop of state sensing, dynamic modeling, anomaly identification and strategy feedback has been validated — accumulating extensive engineering experience in industrial physical process modeling as real validation scenarios for the world model.

已验证工程闭环Validated Engineering Loop