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2025年04月24日

浙江日报要闻版｜之江实验室聚焦智能计算三体计算星座，在太空织张算力网...

（文章转载自浙江日报）像是一个拧紧的发条——近日，之江实验室天基计算系统研究中心负责人李超来回奔走在办公室、实验室和指挥大厅。按照惯例，他与在嘉兴进行整星平台环试和多星联试的同事开了一场线上调度会，召集各项目组负责人部署和协调相关工作。“三体计算星座”首发星座已进入发射倒计时，对他来说，每天的忙碌都只为做好一件事：想方设法尽快在太空中织一张算力网。看上去异想天开的计划，背后却是浙江科学家面向未来、发展新质生产力的思量。时间的指针回拨，在2024年世界互联网大会乌镇峰会“人工智能赋能新质生产力发展”分论坛上，之江实验室首次对外宣布启动“三体计算星座”项目，将协同全球合作伙伴，共建千星规模的天基智能计算基础设施，星座建成后整个太空算力将达到1000P（每秒百亿亿次浮点运算）。通常，卫星把接收到的数据传回地球，再由地面数据处理中心对其进行解析，即“天感地算”。这种模式受带宽限制，数据传输效率低，信息损耗大。解决这一问题正是“三体计算星座”的出发点。李超介绍，把算力带上太空，可以将卫星接收到的数据实时在轨处理，实现“天感天算”。“我们希望将单颗卫星的算力大幅提升，并像互联网连接电脑一样实现卫星之间的互联互通。”中国工程院院士、之江实验室主任王坚表示，“有了计算星座，一颗卫星也能体现算力价值，这对产业变革具有深远意义。”于是，国星宇航、氦星光联、天链测控等二十多家航天领军企业、高校院所、科研机构都来了。它们在之江实验室开展联合攻关，作为团队各小组的一分子，在日调度、周交流、月总结中汇报项目进度、分析问题、提出解决方案，参与全体成员单位之间的资源要素调配。之江实验室7号楼的一楼，正是“三体计算星座”的指挥大厅，推开门就能看到一块映着倒计时的屏幕。面对记者，李超在紧张中带着一种难以抑制的激动：“距离首发星座的日期越来越近了！”倒计时推动着团队以前所未有的加速度前进。2024年以来，他们突破了太空计算系统的关键软硬件技术，成功研制了星载智能计算机、星间激光通信机、星载高速路由器、天基分布式操作系统和天基遥感大模型，并经过了四次太空发射验证。“这是一个极度复杂的系统，一开始大家心里都没有底。”李超坦言，每天睡醒睁眼后的第一件事，就是思考遇上的新问题——卫星上超过100个硬件和200个软件全都是从零开始自研，几乎没有可借鉴的经验。这段科研过程，李超形容是一个不断“踩坑”的过程。比如，预想中可直接使用的地面站基带设备，与太空计算的新环境不适配，需要将传统非网络化的航天设备全部进行网络化改造；比如，随着详细设计的展开，天基分布式操作系统需要完善的内容越来越多，到最终交付团队共计写了24万行代码；再比如，随着几个月的调试，团队已经发现并完善了卫星软件上的上千个BUG（缺陷）……面对新困难，这支平均年龄不到35岁的团队充满了热忱，80%以上的主任设计师都是青年英才。“有机会参与研发太空计算星座，大家都铆足了劲要做成。”青年科研人员张亚兵说。“三体计算星座”首发星座将在全国范围内开创多个方面的“第一次”。之江实验室总工程师赵志峰介绍，包括第一次实现所有卫星搭载太空计算系统，第一次开展异轨卫星互联，第一次用3D打印的方式研制卫星，第一次通过共商、共建、共享的方式构建星座。事实上，该项目酝酿已久。按照浙江省委省政府“锚定一流，对标最好”的要求，2023年7月，之江实验室启动“二次创业”改革，明确“干好智能计算一件事”，对实验室进行了体系性优化重塑。在此基础上，实验室组建智算集群、计算星座、科学模型等重大科研任务总体部，形成了学科交叉、集智攻关和有组织科研的有效探索。该项目便是来自计算星座总体部。“省政府工作报告提出，要以长期主义者的坚定干好难而正确的事，之江实验室同样应该如此。”李超感慨，实验室将在2025年上半年一箭发射12颗卫星，2025年全年将完成超50颗星座布局，推动“三体计算星座”在全球环境治理、防灾减灾、可持续发展等领域作出中国贡献。“人工智能不能因为缺失算力而缺席太空”，在“三体计算星座”指挥大厅，这句标语如同一颗明星，指引着这群勇攀高峰的浙江科学家赓续前进。

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2024年05月26日

United Nations Global Geodetic Centre of Excellence UN cam...

Photo Attribution: "https://www.unbonn.org/news/new-un-organisation-bonn-2021/In 2020 the United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) welcomed and supported the offer from the Federal Republic of Germany to establish and host a Global Geodetic Centre of Excellence (UN-GGCE) at the United Nations Campus in Bonn, Germany. With the conclusion and signing of the Agreement on the Operationalization of the UN-GGCE by the United Nations and the Federal Ministry of the Interior and Community, UN-GGCE is ready to start of work. The State Secretary at the Federal Ministry of the Interior and Community (BMI) Juliane Seifert emphasizes:"With the successful conclusion of the Agreement for the United Nations Global Geodetic Centre of Excellence Germany once more proved itself to be a reliable international partner. The Centre of Excellence is going to provide important contributions to a UN Member State agreed worldwide geodetic infrastructure. This infrastructure is the reliable long-term foundation for applications like satellite navigation, space-borne Earth observation as well as monitoring of the UN Sustainable Development Goals 2030."Recognizing the growing need for a high quality and sustainable Global Geodetic Reference Frame (GGRF) to support good policy development and decision-making for inclusive social progress, increasing environmental sustainability and vibrant economic development, the General Assembly on 26 February 2015 adopted resolution 69/266, entitled 'A Global Geodetic Reference Frame for Sustainable Development'. The resolution recognizes the importance of international cooperation, as no one country can do this alone, to realize the GGRF and services to underpin global navigation satellite systems technology and provide the framework for all geospatial activity, as a key enabler of geospatial data interoperability and data integration, and sustainable development. The resolution also recognizes the economic and scientific importance of and the growing demand for an accurate and stable global geodetic reference frame for the Earth that allows the interrelationship of measurements taken anywhere on the Earth and in space, combining geometric positioning and gravity field-related observations, as the basis and reference in location and height for geospatial information, which is used in many Earth science and societal applications, including sea-level and climate change monitoring, natural hazard and disaster management and a whole series of industrial applications including mining, agriculture, transport, navigation and construction) in which precise positioning introduces efficiencies.UN-GGCE's overarching goal is to assist Member States and geodetic organizations to coordinate and collaborate to sustain, enhance, access and utilise an accurate, accessible and sustainable GGRF to support science, society and global development. The objective is to support, within available resources, the implementation of General Assembly resolution 69/266 through strengthening and advancing: global geodetic cooperation and coordination; worldwide geodetic infrastructure; standards and policies; education, training and capacity development; and communication and awareness.

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2024年05月12日

中国宇航学会编制并发布《中国宇航学会重要学术会议指南（2024）》...

在第八个“全国科技工作者日”来临之际，中国宇航学会围绕“弘扬科学家精神，勇当高水平科技自立自强排头兵”主题，开展“航天科技工作者服务周”系列活动，广泛传播学会服务理念，精准对接航天科技工作者需求，打通“宣传、关心、服务”科技工作者的最后一公里。为促进宇航领域学术会议规范管理，提升学术会议质量，推动学术会议品牌建设，提高科技工作者对高质量学术会议信息获取的便捷性，中国宇航学会编制并发布《中国宇航学会重要学术会议指南（2024）》（以下简称《指南2024》）。中国宇航学会向所属47个分支机构征集高水平年度会议，根据会议的学术影响力、举办的规范性和机制性，遴选出20项宇航领域重要学术会议，其中国际会议7项，国内会议13项。以下分支机构推荐会议入选《指南2024》：深空探测技术专业委员会空间太阳能电站专业委员会空间控制专业委员会空间遥感专业委员会空气动力与飞行力学专业委员会空天动力燃烧与传热专业委员会航天医学与空间生物学专业委员会发射工程与地面设备专业委员会电推进专业委员会质量与可靠性专业委员会计量与测试专业委员会无人飞行器分会临近空间产业工作委员会自2021年起，中国宇航学会开展发布《中国宇航学会重要学术会议指南》工作，旨在通过持续发布指南，与相关单位共同打造权威学术会议，推动宇航领域学术会议质量提升。中国宇航学会将选择优秀会议进一步推荐至中国科协等上级有关部门。

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国际会议

关于举办2025年中国航天大会（CSC2025）的通知

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中国航天大会（China Space Conference）是由中国宇航学会和中国航天基金会联合主办的中国航天具有广泛影响力的综合性行业盛会，已连续六年入选中国科协重要学术会议指南。创办于2018年，中国航天大会,每年一届，至今已成功举办七届。“2025年中国航天大会（CSC2025）”将于4月23-26日在上海市举办。大会指导思想为“立足国内，面向国际，繁荣学术，培养航天下一代”，致力于构建成为“航天领域面向国际的高端学术交流平台、促进航天产学研协同的合作平台、传承航天精神和文化的科普宣传平台”。大会旨在加强学术创新、技术创新及应用创新，促进国内外合作与交流，加强航天系统工程建设，推动航天产业科学发展，将聚集国内外航天领域知名专家、学者、管理者共议航天发展成果。大会将涵盖学术论坛、产业对话、前沿展示、赛事路演、文创体验和科普活动等一系列活动，打造航天领域国际化、专业化的交流合作平台。本次大会以“海上生明月九天揽星河”为主题，广邀国内外航天领域知名专家、学者、管理者，深入探讨航天发展新格局，全面展示宇航前沿技术，广泛推动世界航天深度合作，为增进全人类福祉贡献航天力量。为方便您参会，现将有关事宜通知如下：一、大会时间地点主办单位：中国宇航学会、中国航天基金会时间：4月23-26日地点：上海世博中心、上海交通大学等二、大会及相关活动日程大会包含学术、产业、科普、文创等活动体系，将举办30余场形式多样的活动，全面覆盖空间科学、空间技术、空间应用、空间与社会等多个关键领域。

第15届联合国全球地理空间信息管理专家委员会（UN-GGIM）...

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第14届联合国全球地理空间信息管理专家委员会（UN-GGIM）...

会议时间： 7 -9 August 2024

会议地点：美国纽约联合国总部

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中国航天大会

会议时间： 2024年4月23-26日

会议地点：湖北省武汉市

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成果物

成果介绍

技术文章

白皮书

标准工作

三体计算星座

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“之江智加”系列星载智能计算机

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激光通信机

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A Joint Communication and Computation Framework for Digita...

In this article, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) need to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. For the special case with one physical device, the optimal number of transmission times is revealed. Based on the theoretical findings, the original problem is transformed into a simplified problem and the optimal device scheduling can be found. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2% compared to the conventional schemes.Published in: IEEE Journal of Selected Topics in Signal Processing ( Volume: 18, Issue: 1, January 2024)

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Deep Reinforcement Learning for Energy Efficiency Maximiza...

Federated learning (FL) is a promising solution for preserving user privacy in Internet of Things (IoT) networks thanks to its distributed computing feature. Furthermore, over-the-air FL (AirFL) can leverage the superposition property of wireless channels to achieve fast model aggregation through concurrent analog transmissions. To make AirFL sustainable for energy-constrained IoT devices, we apply simultaneous wireless information and power transfer (SWIPT) at the base station to broadcast the global model and charge local devices during the model training process. To characterize the optimality gap between the aggregated FL model and the ideal FL model brought by signal misalignment, channel fading, and random noise in the model distribution and aggregation processes, we prove the convergence of SWIPT-based AirFL to show the precise impact of up- and down-link communications on the learning performance. We formulate a long-term energy efficiency (EE) maximization problem and propose a deep reinforcement learning algorithm with a collaborative double-agent approach to optimize resource allocation strategies while guaranteeing learning performance. Numerical results demonstrate that the proposed algorithm can achieve a maximum of 41% improvement in EE under various network settings compared with benchmark schemes, and the learning performance of SWIPT-based AirFL can be improved significantly by alleviating transmission errors.Published in: IEEE Transactions on Green Communications and Networking ( Volume: 8, Issue: 1, March 2024)

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Distributed Machine Learning for UAV Swarms: Computing, Se...

The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-generation communication networks with their extraordinary flexibility, affordability, and the ability to collaboratively and autonomously provide Line-of-Sight (LoS) services. However, autonomous collaboration under wireless dynamics is challenging. Distributed learning (DL) provides a chance for the UAV swarms to operate intelligently under sophisticated dynamics, such that they can be applied to wireless communication service scenarios, as well as applications including multidirectional remote surveillance, and target tracking. In this survey, we first introduce several popular DL frameworks that are capable of managing a UAV swarm, these include federated learning (FL), multiagent reinforcement learning (MARL), distributed inference (DI), and split learning (SL). We also present a comprehensive overview of how these DL frameworks manage UAV swarms in regard to trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite–drone integration. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such as reconfigurable intelligent surfaces (RISs), virtual reality (VR), and semantic communications (SemComs), and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL-enabled UAV swarms. In summary, this survey provides a concise survey of various DL applications for UAV swarms in extensive scenarios.Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 5, 01 March 2024)

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Delay-Optimized Edge Caching in Integrated Satellite-Terre...

X. Zhu, C. Jiang, Z. Yang and H. Wang, "Delay-Optimized Edge Caching in Integrated Satellite-Terrestrial Networks With Diverse Content Popularity Distribution and User Access Modes," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2024.3355139.In this paper, we investigate delay-optimized edge caching in the integrated satellite-terrestrial network with diverse content popularity distribution and user access modes. Based on the cooperation among the base stations, the satellite and the gateway, we propose a three-layer caching architecture to provide content service for both base station access users and satellite access users. Considering diverse content preferences for users in different areas, we formulate the content placement problem with the objective to minimize the average content retrieving delay of the network. By introducing the concept of the delay reduction gain and the caching benefit, we first derive the optimal caching strategy for base stations in different areas separately. Then, we propose two algorithms to calculate the cooperative caching strategy of the network, in which reduced search space is applied based on theoretical analysis. While the dynamic programming algorithm can achieve the optimal solution of the content placement problem, the submodular optimization based algorithm can provide guaranteed performance with relatively low complexity. Simulation results show that the proposed caching strategies can effectively improve the network delay performance.Published in: IEEE Internet of Things Journal ( Early Access )链接地址：https://ieeexplore.ieee.org/document/10402004

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“新开源”太空智能应用征集活动

三体计算星座开放太空计算算力平台，启动“新开源”太空智能应用征集活动

智造空天，数算未来

三体计算星座开放太空计算算力平台，启动“新开源”太空智能应用征集活动