Lingo Data / Embodied Data Infrastructure

把多模态具身数据炼成可训练资产Refine multimodal embodied data into trainable assets

Lingo Data 位于真实采集、合成数据、模型训练和部署反馈之间，负责数据接入、处理、标注、治理、训练集构建和闭环反馈，让每一段操作经验都可追踪、可复用、可持续供给世界动作模型。Lingo Data sits between real-world capture, synthetic data, model training, and deployment feedback, turning every operational episode into traceable, reusable, and trainable data assets for the World Action Model.

Core capabilities

从原始 Episode 到模型资产From raw episodes to model assets

Lingo Data 不只是存储数据。它把来自真实采集、仿真生成和真实部署的多模态数据，组织成可治理、可标注、可评估、可版本化的训练资产。Lingo Data is not just storage. It organizes multimodal data from capture, simulation, and deployment into governable, annotatable, evaluable, and versioned training assets.

Data Ingestion

接入 Trace 采集数据、机器人日志、多模态传感器流、仿真数据和部署反馈，统一进入具身数据湖。Ingests Trace capture, robot logs, multimodal sensor streams, simulation data, and deployment feedback into one embodied data lake.

Data Refinery

完成时间同步、动作分割、轨迹提取、自动标注、质量评分和异常检测，将原始片段转化为可用样本。Performs time alignment, action segmentation, trajectory extraction, auto-labeling, quality scoring, and anomaly detection.

Dataset Engine

管理训练集版本、失败样本池、数据血缘和训练接口，让数据持续供给 Brain、World 和评测流程。Manages dataset versions, failure-case pools, lineage, and training interfaces for Brain, World, and evaluation workflows.

Platform architecture

Data 中枢的数据生产架构Data-centered production architecture

接入、处理、资产和训练闭环不是孤立模块，而是一条围绕具身数据持续生产的链路。Ingestion, workflow, assets, and training loops form one continuous production chain for embodied data.

Lingo Data platform architecture diagram

Data workflow

从数据流到训练集From data flow to datasets

一条 Episode 进入平台后，经过标准化、标注、治理、版本化，最终回到训练和评测。Each episode is standardized, annotated, governed, versioned, and routed back to training and evaluation.

Ingest

接入 Trace 采集、机器人日志、多模态传感器流和仿真数据，统一汇入具身数据湖。

Ingest Trace captures, robot logs, sensor streams, and simulation data into a unified embodied data lake.

Capture

Standardize

时间同步、格式统一、多模态对齐，将原始片段转化为标准化数据记录。

Time alignment, format unification, and multimodal synchronization into standardized records.

Align

Annotate

自动标注引擎 + 人机协同标注 + 动作分割与事件提取。

Auto-labeling engine, human-in-the-loop annotation, action segmentation, and event extraction.

Label

Govern

权限审计、数据脱敏、血缘追踪、安全合规与元数据管理。

Access audit, data masking, lineage tracking, compliance, and metadata governance.

Secure

Build Dataset

训练集构建、版本管理、失败样本池维护与训练接口对接。

Dataset building, versioning, failure-case pool management, and training API integration.

Version

Train & Feedback

模型训练接入、部署反馈回流、持续迭代优化闭环。

Model training, deployment feedback loop, and continuous iteration pipeline.

Loop

Data operations console

围绕轨迹、标注、质检和版本的运营工作台An operations console for trajectories, annotation, QA, and versions

同一个上下文里查看轨迹、时间轴、质量评分、版本和失败样本，避免训练数据散落在不同工具之间。Trajectories, timelines, QA scores, versions, and failure cases stay in one context instead of scattering across tools.

轨迹库Trajectory DB

多模态操作轨迹的索引、回放和检索Index, replay, and search multimodal trajectories

标注时间轴Annotation timeline

按时间线浏览、标注和编辑操作片段Browse, annotate, and edit episodes on a timeline

质量评分QA score

自动和人工评估数据质量，标记失败样本Automated and human QA scoring with failure flagging

版本矩阵Version matrix

数据集版本、数据血缘和训练集对比管理Dataset versions, lineage, and training-set diffs

Lingo Data console visual with trajectory database and annotation timeline

Enterprise delivery

为私有化和规模化数据生产而交付Delivered for private and scaled data production

面向科研、机器人和行业客户，Lingo Data 将数据治理、资源管理、系统监控和训练评估集成到可交付的平台能力中。For research, robotics, and industry teams, Lingo Data integrates governance, resource management, monitoring, training, and evaluation into a deliverable platform.

IAM & Governance

账户管理、身份验证、审计日志、RBAC/ABAC、数据脱敏和隐私合规。Account management, authentication, audit logs, RBAC/ABAC, data masking, and privacy compliance.

Monitoring

指标查询、告警管理、日志聚合、可视化和链路追踪，支撑持续运营。Metrics, alerts, log aggregation, visualization, and tracing for ongoing operations.

Resource Management

Kubernetes 编排、GPU/CPU/存储节点、弹性伸缩、存储编排和网络策略。Kubernetes orchestration, GPU/CPU/storage nodes, autoscaling, storage orchestration, and network policy.

Training & Evaluation

模型训练、分布式训练、Sim2Real 评估、策略验证、模型版本和实验追踪。Training, distributed training, Sim2Real evaluation, policy validation, model registry, and experiment tracking.

Private Platform私有化部署的数据平台与权限治理模块Private data platform with governance modules

Processing Pipeline数据处理、标注、质检和训练集构建流水线Processing, annotation, QA, and dataset-building pipelines

API Integration对接 Trace、World、Brain 和外部数据系统的 APIAPIs for Trace, World, Brain, and external data systems