[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fnkJ0z80mmgJ5ZANsstWNGnsq3mPORMhxHncdiQ8jdEI":3},{"code":4,"msg":5,"data":6},200,"操作成功",{"id":7,"title":8,"content":9,"digest":10,"source":10,"coverPath":11,"thumbsCoverPath":12,"isTop":13,"isShow":14,"baseClick":13,"clickCount":15,"createTime":16,"typeId":17,"isNewest":18,"newsInfoTypeRespVo":19,"voiceUrl":22,"voiceSize":23,"taskId":24,"releaseTime":25,"titleEn":26,"contentEn":27,"voiceUrlEn":28,"taskIdEn":29,"voiceSizeEn":30},1628,"当庞大市场需求遭遇算力资源紧张——国产AI如何补上“关键一环”","\u003Cimg alt=\"\" src=\"https:\u002F\u002Fimage.51xinwei.com\u002F2026\u002F03\u002Fhistory\u002Fd0575b684ad64599842647af2467ce12.png\" width=\"761\" height=\"null\" style=\"display: block; margin: auto;\">\u003Cp class=\"ql-align-center\">\u003Cspan style=\"color: rgb(136, 136, 136);\" class=\"ql-lineHeight-1-75\">当庞大市场需求遭遇算力资源紧张——国产AI如何补上“关键一环”\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cstrong class=\"ql-lineHeight-1-75\" style=\"font-size: 18px; color: rgb(255, 153, 0);\">日前，北京智谱华章科技股份有限公司通过其官方公众号发布GLM Coding Plan限售公告。公告指出，随着GLM-4.7系列模型上线，用户数量迅速增长，导致算力资源出现阶段性紧张。\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">这是AI产业算力吃紧的一个缩影。随着GPT、DeepSeek等大模型的算法突破和应用普及，算力需求水涨船高。数据显示，我国AI芯片市场规模预计2028年将超一万亿元，约占全球市场的30%。面对庞大的市场需求，自主可控的高质量AI算力供给已成为我国抢占人工智能产业应用制高点、全方位赋能千行百业的前提条件。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">算力缺口从何而来？国产AI算力又当如何破局？记者就此采访了有关专家。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">第一问：当前是否存在行业性算力缺口？\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">“目前，算力供需矛盾依然突出，在全球范围内都是如此，但国内算力缺口尤为明显。”燧原科技创始人、董事长兼CEO赵立东1月22日对科技日报记者直言。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">智能算力是支撑人工智能等计算任务需求的计算能力，是人工智能产业的“水和电”。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">目前，国外厂商依然是全球AI算力的标准制定者和行业主导者，硬件性能、软件生态等较国产厂商均有不同程度领先。有行业人士透露，2024年，国外厂商占据中国AI芯片市场近七成的市场份额，形成我国庞大的自给缺口。尤其是在大模型训练领域，自给不足问题更为突出，对我国人工智能产业发展造成一定限制。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">不过，国产AI算力近两年也在持续突破。据咨询公司摩根士丹利2025年发布的研究报告估算，中国人工智能GPU自给率已从2020年不足10%提升至2024年约34%，并有望在2027年升至约82%。在这一趋势下，算力产业发展正从过去单点硬件的性能追赶，转向更加务实、高效的系统级创新。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">第二问：算力资源供需矛盾如何造成？\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">目前，我国算力资源供给面临多重限制。高端芯片进口受限，国产GPU芯片在绝对计算性能、能效比、工艺方面与国际旗舰产品仍有差距；技术创新能力不足，如在芯片设计工具、底层算法框架等方面与国际先进水平仍有差距。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">同时，我国算力资源分散，存在“碎片化”问题。各服务商算力资源接口和协议不统一，跨区域跨主体算力调度能力较弱，导致算力资源利用率偏低。此外，产业发展制度环境仍待完善，数据确权、使用和交易等方面的规则有待细化，企业标准与合规方面的挑战日益凸显。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">与此同时，人工智能正在加速落地千行百业，带来算力需求激增。目前，全国已落地的算力应用项目超过1.3万个，建成的各级智能工厂超过3万家，并覆盖工业、金融、交通、医疗、教育等重点行业。“随着我国人工智能应用加速落地，算力需求持续爆发式增长，算力供给不足的问题将会更加显著。”赵立东说，伴随大模型技术的成熟及开源普及，算力应用门槛进一步降低，保障算力供给至关重要。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">第三问：如何持续破解算力之围？\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">在赵立东看来，破解这一难题的核心在于充分释放国产算力的潜力：一方面需大力支持国产算力的应用推广，将现有国产算力资源“用足用好”；另一方面要加快推进国产芯片供应链建设，推动技术落地与产能提升。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">近年来，国家发展改革委、工业和信息化部等部门积极部署算力发展政策，推动智能算力优化建设布局、提升服务水平，破解智能算力供需难题。最新数据显示，我国企业发布多款人工智能芯片产品，在算力设施方面，建成万卡智算集群42个，智能算力规模超过1590EFLOPS，位居全球前列。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">浙江大学光华法学院教授程乐指出，当前算效低下问题普遍存在，部分智算中心GPU实际利用率偏低，造成资源浪费和供需结构失衡。未来应进一步完善算力利用率、任务完成效率、单位能耗产出等实际效能指标，引导产业转向精细化效率竞争。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">是石科技联合创始人兼副总裁毛运航也提到，要通过更精细的调度、资源池化和弹性部署，把现有算力用到极致。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">“国产算力生态已具备良好基础，但仍需产业链各方真正凝聚合力，尤其是算力方、模型方、应用方的协同创新。”赵立东说，当模型、应用与算力实现深度适配，国产算力逐步支撑起从训练到推理的全流程时，我们才能真正在全球人工智能竞争中掌握主动权，才能拥有人工智能产业行稳致远的“压舱石”。（记者 崔 爽）\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(136, 136, 136);\">【新闻来源】MSN \u003C\u002Fspan>\u003Ca href=\"https:\u002F\u002Fwww.msn.cn\u002Fzh-cn\u002Fnews\u002Fother\u002Far-AA1UM9K8?ocid=msedgdhphdr&cvid=6972e1174d08428bbbe3b32719055210&ei=83\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(136, 136, 136);\"> https:\u002F\u002Fwww.msn.cn\u002Fzh-cn\u002Fnews\u002Fother\u002Far-AA1RTDQy?ocid=BingHp01&cvid=6936317f054647a2afcd53fafcde084a&ei\u003C\u002Fa>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"color: rgb(136, 136, 136);\">（本网转发此文章，旨在为读者提供更多的信息资讯，所涉内容不构成投资、消费建议。文章事实如有疑问，请与有关方核实，文章观点非本网观点，仅供读者参考。）\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>","","https:\u002F\u002Fimage.51xinwei.com\u002F2026\u002F01\u002Fbc36ed754fc04ab499db0c0f13fe12b9\u002FAI领域.jpg","https:\u002F\u002Fimage.51xinwei.com\u002F2026\u002F01\u002Fthumbs\u002Fbc36ed754fc04ab499db0c0f13fe12b9\u002FAI领域.jpg",0,1,85,"2026-01-28 14:59",2,false,{"id":17,"name":20,"enName":21},"芯位视野","Xinwei Vision","https:\u002F\u002Fxinwei-dev-test.oss-cn-shenzhen.aliyuncs.com\u002Fintelligent\u002Faudio%3A4209326d-2ceb-4f93-ade5-fb02d9c1ecda%3A0.wav?Expires=1769604903&OSSAccessKeyId=LTAI5tNvY2RkKjZw4LLWsrPK&Signature=2H4bKx1hMz2%2BpEYJQwTc846kWLU%3D",9364214,"4209326d-2ceb-4f93-ade5-fb02d9c1ecda","2026-01-28 14:56","When massive market demand meets a shortage of computing power resources - how can domestic AI make up for the \"critical link\"?","\u003Cimg alt=\"\" src=\"https:\u002F\u002Fimage.51xinwei.com\u002F2026\u002F03\u002Fhistory\u002Fd0575b684ad64599842647af2467ce12.png\" width=\"761\" height=\"null\" style=\"display: block; margin: auto;\">\u003Cp class=\"ql-align-center\">\u003Cspan style=\"color: rgb(136, 136, 136);\" class=\"ql-lineHeight-1-75\">When massive market demand meets a shortage of computing power resources - how can domestic AI make up for the \"critical link\"?\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cstrong class=\"ql-lineHeight-1-75\" style=\"font-size: 18px; color: rgb(255, 153, 0);\">Recently, Beijing Zhitu Hua Zhang Technology Co., Ltd. released a restricted share announcement through its official WeChat account. The announcement pointed out that with the launch of the GLM-4.7 series model, the number of users has grown rapidly, leading to a temporary shortage of computing power resources.\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">This is a microcosm of the tightness in AI industry computing power. With the algorithm breakthroughs and popularization of large models such as GPT and DeepSeek, the demand for computing power has risen sharply. Data shows that the scale of China's AI chip market is expected to exceed one trillion yuan by 2028, accounting for about 30% of the global market. Facing the huge market demand, the supply of self-controlled high-quality AI computing power has become a prerequisite for China to seize the application peak of the artificial intelligence industry and comprehensively empower all industries.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Where does the computing power gap come from? How should domestic AI break through the deadlock? Journalists have interviewed relevant experts on this issue.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">First question: Is there an industry-wide computing power gap at present?\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">\"Currently, the contradiction between computing power supply and demand remains prominent, which is the case globally, but the computing power gap in China is particularly obvious,\" Zhao Lidong, founder, chairman, and CEO of Suiyuan Technology, said directly to the Science and Technology Daily journalist on January 22.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Intelligent computing power is the computing capacity that supports the demand for artificial intelligence and other computing tasks, it is the \"water and electricity\" of the artificial intelligence industry.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">At present, foreign companies are still the standard setters and industry leaders of global AI computing power, and they have varying degrees of advantages in hardware performance, software ecology, etc., compared to domestic companies. Industry insiders revealed that in 2024, foreign companies occupied nearly 70% of the Chinese AI chip market, forming a significant self-sufficiency gap in China. Especially in the field of large model training, the problem of insufficient self-sufficiency is more prominent, which has imposed certain restrictions on the development of China's artificial intelligence industry.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">However, domestic AI computing power has been continuously breaking through in recent years. According to a research report published by Morgan Stanley in 2025, the self-sufficiency rate of China's artificial intelligence GPU has increased from less than 10% in 2020 to about 34% in 2024, and is expected to reach about 82% by 2027. Under this trend, the development of computing power industry is shifting from past single-point hardware performance chasing to more practical and efficient system-level innovation.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Second question: How does the imbalance between computing power supply and demand arise?\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Currently, the supply of computing power resources in China faces multiple constraints. High-end chips are restricted in imports, and domestic GPU chips still lag behind international flagship products in absolute computing performance, energy efficiency ratio, and process technology. In addition, technological innovation capabilities are insufficient, such as in chip design tools and low-level algorithm frameworks, where there are still gaps compared to international advanced levels.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">At the same time, China's computing power resources are scattered, facing a \"fragmentation\" issue. The interfaces and protocols of computing power resources among different service providers are not unified, and cross-regional and cross-subject computing power scheduling capabilities are weak, leading to low utilization of computing power resources. Moreover, the industrial development institutional environment still needs improvement, and rules regarding data rights, usage, and trading need to be further refined, with increasing challenges in enterprise standards and compliance.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Meanwhile, artificial intelligence is accelerating its penetration into various industries, bringing a surge in computing power demand. Currently, over 13,000 computing power application projects have been implemented nationwide, and more than 30,000 intelligent factories at all levels have been built, covering key industries such as industry, finance, transportation, healthcare, and education. \"With the acceleration of AI applications in China, the demand for computing power will continue to grow explosively, and the problem of insufficient computing power supply will become even more pronounced,\" Zhao Lidong said. With the maturity and open-source popularity of large model technology, the threshold for computing power application is further lowered, making it crucial to ensure computing power supply.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Third question: How to continuously solve the computing power dilemma?\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">In Zhao Lidong's view, the key to solving this problem lies in fully unleashing the potential of domestic computing power: on one hand, it is necessary to strongly support the application promotion of domestic computing power, making full use of existing domestic computing power resources; on the other hand, it is necessary to accelerate the construction of the domestic chip supply chain, promoting the implementation of technology and capacity enhancement.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">In recent years, departments such as the National Development and Reform Commission and the Ministry of Industry and Information Technology have actively deployed policies for computing power development, promoting the optimization of intelligent computing power construction layout and improving service levels to solve the problems of intelligent computing power supply and demand. Recent data shows that Chinese enterprises have launched several artificial intelligence chip products. In terms of computing power facilities, 42 thousand-card smart computing clusters have been built, and the scale of intelligent computing power has exceeded 1590 EFLOPS, ranking among the world's top.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Professor Cheng Le from the Guanghua School of Law, Zhejiang University, pointed out that the problem of low computing efficiency is widespread. The actual utilization rate of GPUs in some intelligent computing centers is low, causing resource waste and imbalanced supply and demand structure. In the future, we should further improve practical efficiency indicators such as computing power utilization, task completion efficiency, and output per unit of energy consumption, guiding the industry towards refined efficiency competition.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Maoyunhang, co-founder and vice president of Shishi Technology, also mentioned that we should maximize the use of existing computing power through more precise scheduling, resource pooling, and elastic deployment.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">\"The domestic computing power ecosystem already has a good foundation, but it still requires the joint efforts of all parties in the industrial chain, especially the collaborative innovation of computing power providers, model providers, and application providers,\" Zhao Lidong said. When models, applications, and computing power achieve deep compatibility, domestic computing power gradually supports the entire process from training to inference, only then can we truly grasp the initiative in global artificial intelligence competition, and have the \"ballast stone\" for the steady and long-term development of the artificial intelligence industry.\" (Reporter Cui Shuang)\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(136, 136, 136);\">【News Source】MSN \u003C\u002Fspan>\u003Ca href=\"https:\u002F\u002Fwww.msn.cn\u002Fzh-cn\u002Fnews\u002Fother\u002Far-AA1UM9K8?ocid=msedgdhphdr&cvid=6972e1174d08428bbbe3b32719055210&ei=83\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(136, 136, 136);\"> https:\u002F\u002Fwww.msn.cn\u002Fzh-cn\u002Fnews\u002Fother\u002Far-AA1RTDQy?ocid=BingHp01&cvid=6936317f054647a2afcd53fafcde084a&ei\u003C\u002Fa>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"color: rgb(136, 136, 136);\"> (This article is reprinted by this website to provide readers with more information and news. The content involved does not constitute investment or consumption advice. If you have any questions about the facts of the article, please verify with the relevant party. The views expressed in the article are not the views of this website and are for reference only.)\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>","https:\u002F\u002Fxinwei-dev-test.oss-cn-shenzhen.aliyuncs.com\u002Fintelligent\u002Faudio%3Ae8834596-26ea-4624-ba09-82b205f0dba5%3A0.wav?Expires=1774838422&OSSAccessKeyId=LTAI5tNvY2RkKjZw4LLWsrPK&Signature=EvnJHoM60Vdn5iq6J8TIONQsDF4%3D","e8834596-26ea-4624-ba09-82b205f0dba5",14244586]