清研精准在新能源测试验证、工业现场接入与工程交付中积累场景经验，将项目交付转化为数据资产、工位模板与行业方法论。 Tsing Standard accumulates scenario experience through new energy testing, industrial site access and engineering delivery — transforming project delivery into data assets, workstation templates and industry methodologies.
清研精准的案例不是"完成了一个项目"，而是"跑通了一个闭环"。每个案例都体现：真实工位 → 数据资产 → 模型验证 → 现场反馈 → 持续迭代。 Tsing Standard's cases are not "completed a project," but "proven a loop." Each case demonstrates: real workstation → data asset → model validation → on-site feedback → continuous iteration.
进入高壁垒工业现场，完成多模态数据采集和边缘部署，建立长期数据通道。Entering high-barrier industrial sites, completing multimodal data capture and edge deployment, establishing long-term data channels.
形成工位数据包、异常样本库、技能库、评测集和工位模板，可授权、可订阅、可交易。Forming workstation packs, anomaly libraries, skill libraries, evaluation sets and workstation templates — licensable, subscribable, tradable.
通过仿真评测、HIL 测试和真机验证，持续优化模型性能，覆盖长尾异常和边界 case。Continuously optimizing model performance through simulation, HIL testing and real-machine validation, covering long-tail anomalies and edge cases.
现场执行结果、异常样本回流，形成自增强飞轮，边际成本持续递减。On-site execution results and anomaly samples flow back, forming a self-reinforcing flywheel with continuously decreasing marginal costs.
以下案例均来自真实工业现场，每个案例均已跑通"真实工位 → 数据资产 → 模型验证 → 现场反馈"完整闭环。 The following cases are all validated in real industrial sites. Each case has completed the full loop: real workstation → data asset → model validation → on-site feedback.
全球头部动力电池企业北美基地建设，需快速搭建覆盖研发—生产—售后全链路的动力电池测试产线，适配当地弱电网环境，满足北美本地化制造要求。A global top battery manufacturer's North America facility required rapid deployment of a battery testing line covering R&D-production-after-sales, adapted to weak grid environment and NA localization requirements.
高电压大功率场景多，传统方案对电网冲击大；验证与定位效率不足，无法支撑量产节拍；缺乏能量回收机制，运营成本高。Multiple high-voltage high-power scenarios causing large grid impact; insufficient validation efficiency; lack of energy recovery mechanism.
部署动力电池测试系统与绿电直连方案（GPD），实现能量闭环与数据闭环。HIL + EOL 全链路覆盖，支持 BMS/VCU/MCU 联调，异常样本自动入库。Deployed battery testing system with green power direct connection (GPD), achieving energy loop and data loop. Full HIL + EOL coverage, BMS/VCU/MCU joint debugging, automatic anomaly archiving.
定位与验证闭环效率提升 10 倍以上，成功支撑北美基地量产需求，为客户全球化制造与弱电网适配提供长期能力基础。10× improvement in validation loop efficiency, successfully supporting mass production, providing long-term capability foundation for global manufacturing and weak grid adaptation.
多家头部主机厂，整车控制器迭代频繁，需要高效率 HIL 测试平台支撑快速研发节奏，同时降低回归测试成本。Multiple leading OEMs with frequent controller iterations require high-efficiency HIL testing platforms to support rapid R&D pace while reducing regression testing costs.
部署 VCU/BMS/MCU 组合测试平台，实现自动化仿真验证与多控制器联调。全自动测试序列生成，覆盖极限工况与典型工况，异常样本自动入库。Deployed VCU/BMS/MCU combined testing platform with automated simulation validation and multi-controller joint debugging. Automated test sequence generation covering extreme and typical conditions.
研发测试效率显著提升，验证周期大幅缩短。为整车平台持续迭代提供可复用测试资产，异常样本自动入库形成数据闭环。Significantly improved R&D testing efficiency, greatly shortened validation cycle. Reusable testing assets for continuous vehicle platform iteration with automatic anomaly data loop.
某头部车企，整车总装线工位数量多、工艺复杂，工位数据分散、无法复用，跨工厂复制效率极低，亟需构建标准化工位数据资产体系。A leading OEM with many assembly workstations, complex processes, scattered data, and extremely low cross-factory replication efficiency — urgently needs standardized workstation data asset system.
构建整车总装工位数字化资产系统：工位模板标准化、动作过程采集、质量结果标注、跨工厂复制部署，实现工位数据资产化。Built assembly workstation digital asset system: workstation template standardization, motion process capture, quality result annotation, cross-factory replication deployment.
工位复制效率提升 5×，跨工厂部署周期从 3 个月缩短到 2 周，形成可授权的工位模板资产库。5× workstation replication efficiency, cross-factory deployment cycle reduced from 3 months to 2 weeks, forming licensable workstation template asset library.
某工业机器人厂商，需快速为客户提供插接、拧紧等工业操作技能，但每个客户场景不同，传统方案需大量重复开发，成本高、周期长。An industrial robot manufacturer needs to quickly provide insertion, tightening and other industrial skills, but each customer scenario differs — traditional approach requires extensive repeated development.
构建原子技能数据库：EGO+UMI 工具链采集多场景数据，Cross-Embodiment 平台跨本体映射，仿真评测验证技能泛化性，形成可订阅的技能库。Built atomic skill database: EGO+UMI toolchain capturing multi-scenario data, Cross-Embodiment platform for cross-embodiment mapping, simulation evaluation validating skill generalization.
技能开发周期从 6 个月缩短到 2 周，跨本体复用率 80%+，形成 1000+ 条可订阅原子技能，支持多品牌机器人快速获取工业操作能力。Skill development cycle reduced from 6 months to 2 weeks, 80%+ cross-embodiment reuse rate, 1000+ subscribable atomic skills supporting multiple robot brands.
壳牌润滑油工业客户，设备润滑油状态监测依赖人工定期检查，异常发现滞后，维护成本高，亟需构建智能化状态监测体系。Shell lubricant industrial customers rely on manual periodic inspection for oil state monitoring — delayed anomaly discovery and high maintenance costs.
部署多模态传感器数据采集系统，构建润滑油状态监测模型，实现实时状态感知、异常预警和更换预测，降低设备维护成本。Deployed multimodal sensor data capture system, built lubricant state monitoring model for real-time state awareness, anomaly warning and replacement prediction.
设备维护成本降低 25%，异常预警准确率 92%+，计划外停机减少 40%，形成可复用的工业设备状态监测数据资产。25% reduction in maintenance costs, 92%+ anomaly warning accuracy, 40% reduction in unplanned downtime, forming reusable industrial equipment state monitoring data assets.
蔚来换电站大规模扩张，换电电池包数量庞大，需实现电池包 EOL 检测、全生命周期状态追踪和异常预警，保障换电安全与效率。NIO's large-scale battery swap station expansion requires battery pack EOL inspection, full lifecycle state tracking and anomaly early warning to ensure swap safety and efficiency.
构建电池包 EOL 检测与数据管理系统，实现换电电池全生命周期状态追踪、SOH 评估、异常预警和维护建议，数据持续沉淀为电池健康档案。Built battery pack EOL inspection and data management system for full lifecycle state tracking, SOH assessment, anomaly early warning and maintenance recommendations.
电池安全事故率降低 60%，EOL 检测效率提升 3×，形成大规模电池健康档案数据资产，为蔚来换电网络持续扩张提供数据基础。60% reduction in battery safety incidents, 3× EOL inspection efficiency, forming large-scale battery health record data assets for NIO's continuous swap network expansion.
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