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Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-17ll.com/label.php on line 82

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Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-17ll.com/label.php on line 82

Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-17ll.com/label.php on line 82

Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-17ll.com/label.php on line 82

Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-17ll.com/label.php on line 82

Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-17ll.com/label.php on line 82

Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-17ll.com/label.php on line 82

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Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-17ll.com/label.php on line 82
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  当地时间4月24日,世界银行在美国华盛顿举行发展委员会第111次会议,审议通过了《就业:通往繁荣之路》报告。财政部部长、世行中国理事蓝佛安与会并发言。

  蓝佛安表示,中方赞赏世行以促进就业为目标,为发展中国家摆脱贫困、实现共享繁荣奠定坚实基础。习近平主席指出,我们要在贸易投资、发展合作等领域增加资源投入、做强发展机构,多一些合作桥梁,少一些“小院高墙”,让越来越多发展中国家过上好日子、实现现代化。当前,贸易保护政策为全球减贫与发展事业带来了巨大风险。中方呼吁世行等国际组织倡导非歧视原则、自由贸易原则等,共同维护开放合作的国际环境。

  中国是全球经济一体化的受益者也是贡献者,近年来中国对全球经济增长的贡献率保持在30%左右。在当前复杂外部环境下,中国将采取更加积极有为的宏观政策,推动实现全年预期增长目标,继续为全球经济带来稳定和动力。中国坚持改革开放的基本国策,已对所有建交的最不发达国家产品给予零关税待遇,并愿进一步敞开大门,与全球共享中国的超大规模市场,实现互利共赢。(总台央视记者田琪永)

 

  3月末,云知声智能科技股份有限公司向港交所递交招股书,拟于香港主板IPO上市。

  招股书信息显示,云知声成立于2012年,于2018年推出边缘交互式AI芯片UniOne系列,2023年推出拥有600亿参数的自有大语言模型山海大模型,目前主要为智慧生活、智慧医疗两大应用场景提供产品与解决方案。

  云知声创始团队成员分别为首席执行官兼总经理黄伟、董事长兼首席技术官梁家恩、执行董事兼物联网部副总裁康恒。其中,梁家恩和黄伟均毕业于中国科学技术大学。招股书显示,云知声的三位创始成员黄伟、梁家恩、康恒共同在已发行股本总额中拥有约33.93%的权益。

  这是云知声第三次冲击IPO,中国国际金融香港证券有限公司、海通国际资本有限公司为联席保荐人。天眼查信息显示,截至目前,公司共完成11轮融资,启明创投、高通创投、京东科技、三六零、中金资本、东方证券等曾参与融资。

  收入持续增长应用场景拓展

  招股书显示,根据咨询公司弗若斯特沙利文的资料,云知声是2024年中国第四大AI解决方案提供商,市场份额为0.6%。按收入计,云知声在中国生活AI解决方案排名第三,在医疗AI服务及解决方案排名第四。

  公司收入主要来自销售AI产品及解决方案,过去三年收入逐年增长:2022年为6.01亿元,2023年为7.27亿元,2024年为9.39亿元。增长原因是业务扩展至新的垂直行业并提供更广泛的AI解决方案。

  在生活场景方面,云知声开发了应用于深圳地铁20号线自动售票机的语音售票系统,乘客可通过语音选择目的地,提高购票效率。2022年、2023年及2024年,公司生活解决方案服务客户数量分别为373名、389名、411名,包括中国前三大保险集团、深圳地铁20号线以及厦门软件园。此外,公司通过MaaS向开发者和企业提供AI大语言模型能力,并在2022年、2023年及2024年分别向开发者和客户销售了1280万、2450万、3600万颗AI芯片。

  在医疗领域,云知声的AI解决方案包括病历语音输入、病历质控、单一疾病质控及医疗保险支付管理,旨在提升诊疗效率。招股书介绍:“与打字输入相比,病历语音输入系统将病历输入的速度加快四至六倍;与人工审核相比,病历质控系统将质量审核所需的时间减少80%。”2024年,云知声已向166名医疗客户提供AI解决方案。

  从产品收入明细看,过去三年,云知声生活板块的解决方案与产品营收占比在八成左右,医疗板块营收占比约两成,有逐步上升趋势。

  2022年到2024年,云知声毛利分别为2.4亿元、2.95亿元、3.6亿元,毛利率分别为39.9%、40.5%及38.8%。同时,公司2022年到2024年三年净亏损分别为3.75亿元、3.76亿元和4.54亿元。 

来源:招股书

  持续投入研发经营现金流承压

  招股书显示,云知声净亏损扩大主要由于大量研发产生的开支。

  公司研发开支占企业经营开支主要部分。2022年、2023年、2024年,云知声研发开支分别占企业经营开支总额的65.4%、63%、67.3%。2024年,公司在行政开支同比减少的情况下,研发开支增长到3.7亿元。

  招股书表示,研发开支增长主要由于第三方服务费用增加,用于开发适用于新场景的应用软件及升级现有产品和数据处理服务,以及云计算服务需求增加导致服务器成本及基于云服务费有所上升。

  AI应用领域面临激烈竞争,使得企业需要持续投入支持研发工作。云知声招股书提示的风险因素中包括“我们经营所在行业具有不断变化的特点。我们未必能持续开发及创新技术并提供符合客户预期的产品及解决方案”“我们可能无法成功地与当前或未来的竞争对手竞争”等。公司表示,计划继续大力投资于研发工作。

  此外,招股书显示,公司流动资产净额由截至2023年底的6.65亿元减少至截至2024年底的4.64亿元;净负债由2022年底的19.11亿元增加至2023年底的22.99亿元,2024年底公司净负债进一步增长到27.53亿元,主要由于赎回负债增加以及现金及现金等价物减少。

  由于庞大的研发开支,2022年、2023年及2024年,公司的经营现金流出净额逐年增加,分别为1.66亿元、2.84亿元、3.19亿元。公司贸易应收款项周转率也影响了现金状况。

  客户集中度高、应收账款比例高,给公司带来现金流压力。

  招股书显示,截至2022年底、2023年底及2024年底,公司贸易应收款项分别为3.69亿元、4.11亿元、5.59亿元。公司面临与客户延迟付款及违约相关的信贷风险。

  2022年、2023年、2024年,云知声客户数量分别为538名、555名、576名,来自最大客户的收入分别占当年度总收入的13.1%、9.3%及7.0%,来自五个最大客户的收入则分别占当年度总收入的30.8%、27.4%及26.7%。

  公司表示:“目前预计这种情况可能会持续,直到实现更大规模的收入。”而公司未来的收入增长取决于能否开发新技术、成功将AI技术商业化、提升客户体验、有效竞争以及扩大客户群。

  云知声表示,以真实场景数据和持续的用户反馈为指导,公司AI技术不断迭代。例如,AI语义理解的准确性从2018年的BERT前深度学习模型的88.04%提高到2019年的UniCore的91.02%,并进一步提升到2023年的山海大模型的96.07%。“这些技术进步使我们能够将我公司AI解决方案的能力从感知扩展到理解、响应和生成。”云知声表示。

  截至2024年12月31日,云知声的AI解决方案已在多个垂直行业获得采用,包括医疗、家居、商业空间、酒店及交通。

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