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腾讯云与卡尔动力达成战略合作_我的网站

A |     8月25日，腾讯云与卡尔动力达成战略合作。    

    This photo shows the reusable rocket Nebula-1, developed by Chinese private rocket company Deep Blue Aerospace, blasting off during a high-altitude vertical recovery flight test in north China's Inner Mongolia Autonomous Region, Sept. 22, 2024. (Deep Blue Aerospace/Handout via Xinhua)
China's commercial reusable launch vehicle sector continues to make steady progress, as a company announced on Friday that it had completed a long-duration test firing of an engine for a large recoverable carrier rocket.
Deep Blue Aerospace, a commercial space startup based in Wuxi, east China's Jiangsu Province, said it recently completed a 100-second static-fire test of its Thunder-RS engine, a 130-tonne-class liquid-oxygen kerosene engine designed for the company's large reusable rocket, Nebula-2.
The test reached 1,297.5 kilonewtons of thrust, or about 132.3 tonnes. The company said multiple short-duration test firings had been conducted previously to evaluate engine performance, with no anomalies recorded.
The recent 100‑second run was the engine's first crossing of that steady‑state duration threshold, serving as a comprehensive verification of its performance, the company said.
    This screenshot from a video clip shows the first stage of the Zhuque-3 Y2 rocket making its land-based recovery, Aug. 19, 2026. (LandSpace/Handout via Xinhua)
Rocket reusability can greatly cut launch costs and boosts launch frequency, making it key to achieving large-scale, routine access to space.
China launched the reusable rocket Zhuque-3 on Wednesday morning, achieving its first recovery of a rocket's first stage on land, marking the country's first successful land-based recovery. This mission followed the successful sea-based net recovery of the Long March-10B's first stage on July 10.
More models of Chinese reusable rockets are scheduled to conduct tests this year. 
。双方将进一步深化AI基础设施协同，围绕自动驾驶货运模型的训练、推理、数据处理与持续迭代开展合作，为卡尔动力L4级自动驾驶干线货运的规模化运营提供支撑。通过此次战略合作，腾讯云将在云上算力、数据存储与AI平台三方面，助力提升卡尔动力自研自动驾驶货运模型的训练、推理与持续迭代效率。

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Published on:06:07:20