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.
显著提升定位与验证闭环效率，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。Significantly improved 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.
工位复制效率显著提升，跨工厂部署周期大幅缩短，形成可授权的工位模板资产库。Significantly improved workstation replication efficiency, greatly shortened cross-factory deployment cycle, 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.
动力电池头部供应商需要高效的电池包测试验证系统，支持标准工况和自定义工况仿真，积累电池衰减数据和SOH预测数据集。Leading battery supplier needs efficient battery pack testing system supporting standard and custom condition simulation, accumulating degradation data and SOH prediction datasets.
部署电池包在环测试系统，支持 NEDC/WLTP 标准工况和自定义工况仿真，积累电池衰减曲线和 SOH 预测数据集。Deployed battery pack-in-loop testing system supporting NEDC/WLTP and custom condition simulation, accumulating battery degradation curves and SOH prediction datasets.
电池异常预测准确率 92%，SOH 预测误差 < 3%，形成高质量电池健康数据资产。Battery anomaly prediction accuracy 92%, SOH prediction error < 3%, forming high-quality battery health data assets.
新能源汽车检测机构需要建立规模化终端检测网络，部署年检产品和售后诊断工具，积累不同品牌、不同年限的真实车辆健康状态数据。Testing institution needs to build scaled terminal inspection network, deploying inspection equipment and aftermarket diagnostic tools, accumulating real vehicle health data across brands and ages.
部署新能源汽车年检产品和售后诊断工具，建立规模化终端检测网络，积累不同品牌、不同年限的真实车辆健康状态数据。Deployed inspection equipment and aftermarket diagnostic tools, building scaled terminal inspection network and accumulating real vehicle health data across different brands and ages.
年检覆盖车辆 5 万台+，健康档案数据库持续扩充，形成跨品牌、跨年限的车辆健康数据资产。Annual inspection coverage 50,000+ vehicles, health database continuously expanding, forming cross-brand, cross-age vehicle health data assets.
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