原标题：长七A火箭测发周期再缩6天 为高密度发射提前准备9月13日，长征七号A遥五运载火箭在文昌航天发射场成功发射中星1E卫星。　肖国军、姜文博　摄中新网北京9月13日电 (王冰)由中国航天科技集团一院抓总研制的长征七号A遥五运载火箭(简称“长七A火箭”或“长七改火箭”)13日晚在文昌航天发射场成功发射中星1E卫星。此次发射，长七A火箭的测发周期由32天缩减到26天，火箭发射效率进一步提高，为应对高密度常态化发射奠定基础。“随到随吊” 总装时间减3天吊装芯一级、捆绑助推器、装上芯二级……火箭进场后，试验队队员首先需要将水平躺着的火箭竖立起来，进行总装。         Conceptual diagram of AI Photo: VCG
In a report released on Tuesday, the World Bank made an optimistic assessment that artificial intelligence (AI) could allow developing countries to do in a decade what might otherwise take a century. The report found that AI will throw developing economies a lifeline, and that the technology's greatest promise for developing countries lies not in replacing workers, but in amplifying what they can do.
While advanced economies are still debating whether AI will wipe out white-collar jobs, this report serves as a critical reminder that AI is not merely a force that replaces human labor. More importantly, it acts as an amplifier that unlocks growth potential.
For years, many assumed that AI penetration would primarily erode low- and mid-skilled jobs and widen development gaps among economies. Yet for most developing countries still undergoing digital transformation, AI represents far more than a choice between automation and human labor. It provides a cost-effective shortcut to remedy decades of digital infrastructure shortcomings. 
Many digital capabilities that once required massive capital investment, systematic infrastructure development and professional talent training can now be realized through open-weight AI tools. This dramatic reduction in technological barriers offers developing countries a rare chance to leapfrog stages of technological iteration and catch up with global development trends.
In China, this "amplification effect" is unfolding in various ways. Over the past few years, new professions built around human-AI collaboration have kept emerging, while the skill sets required for traditional jobs are evolving at a rapid pace. 
What matters even more is that China's unique AI development path carries special reference value for other developing countries. China has already integrated AI on a massive scale into core real-life scenarios spanning manufacturing, agriculture, healthcare and education, while continuously driving down the cost of access. 
According to CNBC, Chinese built AI models are gaining ground, and they are gaining traction as they narrow the performance gap with leading American rivals while remaining significantly cheaper to use. This model provides the most practical, accessible entry point for developing economies stepping into the AI era, rather than forcing them to chase unattainable, high-end technical standards that are out of their financial and operational reach.
With the rise of Chinese open-source and open-weight models, AI competition between China and the US has become a hot topic in the global technology landscape. Some in the West often frame this as a technological power tussle between two major countries, measured by model parameters, financing scale and semiconductor manufacturing precision. While such indicators reflect technological advancement, one shouldn't overlook the far more fundamental purpose of technology. The ultimate value of any technology lies in its ability to address shared global challenges and deliver tangible public benefits.
Many developing regions are still in the very early stages of digital transformation. Truly meaningful, impactful AI capability is never cutting-edge technology locked away in a laboratory. It is technology that can step out of the lab, root itself in local realities, and deliver inclusive, accessible solutions that help these countries cross the threshold of digital infrastructure at minimal cost. It is about bringing open-weight, user-friendly AI tools into the hands of ordinary people and companies.
When more developing economies are able to use AI as a lever to bridge their long-standing development gaps, the global digital divide will not be further widened by this new round of technological revolution. Instead, it will be gradually narrowed for the first time in decades. From this perspective, the outcome of global AI competition will not be determined by which country first reaches the ceiling of technological sophistication. The real decisive factor is which country can extend technological benefits to the broadest population groups and leverage AI to drive inclusive global growth.
。在垂直总装及单元测试阶段，试验队通过改进总装模式，将总装时间缩短了3天。一院长七A火箭主任设计师魏远明介绍，作为全低温火箭，长七A火箭是目前中国模块数量最多的新一代火箭，由4个助推器、芯一级、芯二级和芯三级构成，部段较多且复杂。9月13日，长征七号A遥五运载火箭在文昌航天发射场成功发射中星1E卫星。　肖国军、姜文博 摄 以前，队员们需等所有部段都准备齐备，再一鼓作气完成垂直总装。

B | 但火箭芯三级吊装要先完成火工品安装、氦检漏、喷管延伸段安装等多项工作，比助推器和一二级准备时间长。为此，这次任务中，团队将总装模式由“一气呵成”改为“随到随吊”，优化总装时间。“现在，我们先吊装好助推器和一二级，在等待芯三级的过程中，插空进行助推器和芯二级的伺服机构安装工作，等三级具备条件，再进行吊装。再加上仪器设备上箭安装等分系统测试前准备工作优化了1天，算下来，本阶段比以往的模式可以节省3天时间。”魏远明说。合并“同类项” 抢出2天测试时间总装及单元测试后，火箭需要进行分系统匹配测试和总检查测试，验证其技术性能和可靠性，使其达到符合发射状态的要求。在这次任务中，团队将具备类似状态的测试进行了一次合并“同类项”，进一步缩短检测时间。

C | 增补压测试是分系统动力系统测试的最后一项测试，紧接着就是进行第一次总检查测试。总检查测试和增补压测试都会运行模飞程序。研究团队提出，“如果两者合并，是否可以减少总检查测试的一部分工作？合并后能否达到相同测试效果？”经过研究分析，团队找出两个测试存在的差别，在增补压测试中加强了对测量系统的验证，让测试更全面，实现用更少的时间达到相同测试效果。9月13日，长征七号A遥五运载火箭在文昌航天发射场成功发射中星1E卫星。　肖国军、姜文博 摄 此外，团队将火箭控制系统和测量系统检测前的准备工作提前并行开展完成，缩减了分系统匹配测试周期。

D | “通过这些措施，我们在分系统匹配测试和总检查测试阶段共节省了2天时间。”魏远明说。串行变并行 转场及发射区工作省下1天时间转场当天，长七A火箭“站”在活动发射平台上，缓缓“走”向发射塔架。这意味着火箭在技术准备区经检查测试达到了可以进行发射的状态。在以往发射中，长七A火箭从转场加上在发射区的工作，一共需要4天时间。这次团队梳理流程，将测量系统的恢复和测试工作由串行改为并行，使转场与发射区工作缩减为3天，这是目前发射区占位时间最短的液体火箭。

E | 9月13日，长征七号A遥五运载火箭在文昌航天发射场成功发射中星1E卫星。

F | 　肖国军、姜文博 摄 “串行变并行，并不是什么新鲜的措施，其难点在于如何确定这些流程可以实现并行，且在工作过程中不受干扰。”魏远明说，“这对我们的风险识别、计划调度和现场操作都提出了更高的要求。

G | ”此外，魏远明说：“作为一型新火箭，前几次任务，工作安排余量相对大一些，经历前几发任务的考验，我们对各项工作了解更加深入，操作更加熟练，因此对流程优化的协调性和适应性也有了更强的信心和把握。”35天、32天、26天，测发周期的一次次缩短，长七A火箭不断优化。

H | 此次任务是长七A火箭第四次执行发射任务，火箭状态正在逐步固化，为进入高密度发射阶段提前准备。(完)返回搜狐，查看更多责任编辑：