证券代码：688787 证券简称：海天瑞声 公告编号：2024-032 北京海天瑞声科技股份有限公司 关于收到公司控股股东、实际控制人、董事长 提议回购公司股份的提示性公告 本公司董事会及全体董事保证本公告内容不存在任何虚假记载、误导性陈述 或者重大遗漏，并对其内容的真实性、准确性和完整性依法承担法律责任。 为践行"以投资者为本"的上市公司发展理念，维护北京海天瑞声科技股份 有限公司（以下简称"公司"）全体股东利益，基于对公司未来发展前景的信心 及价值认可，公司将采取措施，落实"提质增效重回报"行动方案，树立公司良 好的市场形象，努力通过良好的业绩表现、规范的公司治理、积极的投资者回报， 切实履行上市公司的责任和义务，回馈投资者的信任，维护公司股价的长期稳定。 公司董事会于2024年8月6日收到公司控股股东、实际控制人、董事长贺琳女 士"关于回购北京海天瑞声科技股份有限公司股份"的提议，具体内容如下： 一、提议人的基本情况及提议时间 1.提议人：公司控股股东、实际控制人、董事长贺琳女士 2.提议时间：2024 年 8 月 6 日 二、提议人提议回购股份的原因和目的 贺琳女士基于对公司未来持续发展的信心和对公司 ...

证券代码：688787 证券简称：海天瑞声 公告编号：2024-030 北京海天瑞声科技股份有限公司 截至本公告披露日，北京海天瑞声科技股份有限公司（以下简称"公司"）董 事、高级管理人员李科持有公司股份 55,262 股，占公司股份总数的 0.0916%； 董事、高级管理人员吕思遥持有公司股份 43,350 股，占公司股份总数的 0.0719%； 董事、高级管理人员黄宇凯持有公司股份 48,450 股，占公司股份总数的 0.0803%； 高级管理人员郝玉峰持有公司股份 2,570 股，占公司股份总数的 0.0043%。上述 股份来源为股权激励归属及公司实施资本公积转增股本，已于 2023 年 5 月 30 日 全部上市流通。 减持计划的主要内容 公司于 2024 年 7 月 30 日收到董事、高级管理人员李科、吕思遥、黄宇凯、 高级管理人员郝玉峰出具的《关于北京海天瑞声科技股份有限公司股份减持计划 的告知函》，因自身资金需求，李科拟通过集中竞价交易方式减持 13,815 股，占 公司股份总数的 0.0229%；吕思遥拟通过集中竞价交易方式减持 10,837 股，占 公司股份总数的 0.0180%；黄宇凯拟通 ...

一、 本次上市流通的限售股类型 重要内容提示： 本次股票上市类型为首发限售股份；股票认购方式为网下，上市股数为 19,073,395 股。 本次股票上市流通总数为 19,073,395 股。 本次股票上市流通日期为 2024 年 8 月 13 日。 经中国证券监督管理委员会《关于同意北京海天瑞声科技股份有限公司首次公 开发行股票注册的批复》（证监许可[2021]2366 号）同意，北京海天瑞声科技股份 有限公司（以下简称"公司"）首次向社会公开发行人民币普通股（A 股）1,070 万 股，并于 2021 年 8 月 13 日在上海证券交易所科创板挂牌上市。公司首次公开发 行 A 股前总股本为 3,210 万股，首次公开发行 A 股后总股本为 4,280 万股，其中 有限售条件流通股 3,409.6898 万股，占本公司发行后总股本的 79.6656%，无限售 条件流通股 870.3102 万股，占本公司发行后总股本的 20.3344%。 证券代码：688787 证券简称：海天瑞声 公告编号：2024-031 北京海天瑞声科技股份有限公司 关于首次公开发行部分限售股上市流通公告 本公司董事会及全体董事保证本公告 ...

一、本次上市流通的限售股类型 经中国证券监督管理委员会《关于同意北京海天瑞声科技股份有限公司首次 公开发行股票注册的批复》（证监许可[2021]2366 号）同意，北京海天瑞声科技 股份有限公司首次向社会公开发行人民币普通股（A 股）1,070 万股，并于 2021 年 8 月 13 日在上海证券交易所科创板挂牌上市。公司首次公开发行 A 股前总股 本为 3,210 万股，首次公开发行 A 股后总股本为 4,280 万股，其中有限售条件流 通股 3,409.6898 万股，占本公司发行后总股本的 79.67%，无限售条件流通股 870.3102 万股，占本公司发行后总股本的 20.33%。 本次上市流通的限售股为公司首次公开发行部分限售股及限售期间实施资 本公积转增股本增加的股份，涉及限售股股东 2 名，限售股股东为贺琳、北京中 瑞安投资中心（有限合伙），限售期限为自公司股票在上交所上市之日起 36 个 月，合计限售股数量为 19,073,395 股，占公司股本总数的 31.62%。现限售期即 将届满，上述限售股将于 2024 年 8 月 13 日上市流通。 二、本次上市流通的限售股形成后至今公司股本数量变化 ...

Summary of Conference Call on Data Processing in Autonomous Driving Industry Overview - The discussion focused on the application of data processing in the autonomous driving sector, highlighting the increasing importance of data handling technologies due to the exponential growth of data volume as autonomous driving technology advances [2][9]. Key Points and Arguments - **Data Processing Demand**: The need for data cleaning, labeling, and preprocessing is rising, necessitating advanced algorithms and technology platforms to enhance processing efficiency and reduce operational costs [2][9]. - **Technical Barriers**: There are significant technical barriers in achieving high-precision data labeling and processing, emphasizing the importance of data quality [2][9]. - **Collaboration with Automakers**: Collaborating with automotive manufacturers is seen as an effective way to address data standardization issues, which is crucial for advancing autonomous driving technology [2][4]. - **Revenue Growth Outlook**: The company anticipates revenue and profit growth in the coming years, reflecting an optimistic market outlook despite previous challenges [2][24]. Data Processing Techniques - **Algorithm Optimization**: The use of clustering algorithms and deep learning techniques is being employed to optimize object detection frameworks, enhancing tracking efficiency and accuracy [3][10]. - **AI Data Services Focus**: The company is expanding its revenue from autonomous driving-related services, aiming to maintain a 15% to 20% share of total revenue from this segment [4]. Industry Trends - **End-to-End Algorithm Development**: The shift towards end-to-end algorithms in autonomous driving is significantly impacting the data services industry, leading to explosive data demand and increased labeling complexity [5][10]. - **Data Accumulation**: Data accumulation is primarily through self-collection and collaboration with clients, with a strong emphasis on data quality and ownership protection [5][10]. Business Model and Revenue Composition - **Service Models**: The company offers two main service models: project-based services tailored to client needs and product-based services through the sale of mature data products, which together account for over 90% of annual revenue [6][16]. - **Revenue Structure**: The revenue is mainly derived from one-time project income and periodic income, with project income constituting about 90% of annual revenue [16]. Data Quality and Compliance - **Real vs. Synthetic Data**: Real data is deemed significantly more valuable than synthetic data for AI model training, although synthetic data can supplement areas where real data is lacking [7][17]. - **Data Compliance Challenges**: Data compliance issues are a major challenge for the application of synthetic data, necessitating exploration of data simulation techniques to address compliance while unlocking new business opportunities [18]. Future Trends and Challenges - **Data Demand Drivers**: Factors driving the increasing data demand in autonomous driving include vehicle diversity, sensor types, and advancements in driving technology [21]. - **Competitive Advantage**: The company possesses competitive advantages in data processing, including platform capabilities, algorithm efficiency, project management, and resource allocation [22]. Market Dynamics - **Collaboration vs. Competition**: As automakers enhance their data collection capabilities, the relationship between the company and automakers is viewed as collaborative rather than competitive, focusing on data processing and compliance [20]. - **Government Initiatives**: Recent government initiatives aim to open up public data, which could facilitate data utilization in the industry [19]. Financial Outlook - **Recovery from Previous Decline**: The company expects to recover from a rare financial decline experienced last year, with Q1 data showing significant revenue and profit rebounds, aiming for stable growth throughout the year [24].

Group 1: Financial Performance - In 2023, the company's revenue was 170 million yuan, a decrease of 35.33% compared to the previous year, primarily due to overseas clients undergoing layoffs and adjustments in business direction and R&D cycles [3] - The domestic business faced challenges despite macroeconomic stabilization, with clients showing caution in centralized R&D investments, leading to a slowdown in budget releases and increased competition in the industry [3][4] Group 2: Revenue Growth Factors - The revenue growth in Q1 2024 was driven by the gradual development and application of large model technology, resulting in increased demand for data in AI research and development [4] - The company is currently engaging with regions such as Beijing, Anhui, Shandong, and Hebei for potential projects, with disclosures to follow if cooperation meets requirements [4][5] Group 3: Data Demand in Large Models - The data demand during the pre-training phase of large models differs from traditional training data in terms of scale, quality, and sources, with pre-training data typically in the tens of billions of tokens compared to around 1 billion for traditional models [5] - Future data demand is expected to continue growing significantly as more large model products are launched and iterated, particularly for high-quality, large-scale copyright data and public data [6][7] Group 4: Trends in Reinforcement Learning - The overall data demand for reinforcement learning is gradually increasing, with a trend towards expanding into more verticals such as law, finance, and healthcare [7] - There is a shift from single-modal to multi-modal data requirements, with a focus on text-video and text-image combinations in annotation tasks [8]

北京市天元律师事务所 关于北京海天瑞声科技股份有限公司 2023 年年度股东大会的法律意见 京天股字（2024）第 369 号 致：北京海天瑞声科技股份有限公司 北京海天瑞声科技股份有限公司（以下简称"公司"）2023 年年度股东大会 （以下简称"本次股东大会"）采取现场投票与网络投票相结合的方式，现场会议 于 2024 年 6 月 7 日 13:30 在北京市海淀区知春路 68 号院 1 号楼 4 层 401 召开。 北京市天元律师事务所（以下简称"本所"）接受公司聘任，指派本所律师参加本 次股东大会现场会议，并根据《中华人民共和国公司法》《中华人民共和国证券 法》（以下简称"《证券法》"）、《上市公司股东大会规则》（以下简称"《股东大会 规则》"）以及《北京海天瑞声科技股份有限公司章程》（以下简称"《公司章程》"） 等有关规定，就本次股东大会的召集、召开程序、出席现场会议人员的资格、召 集人资格、会议表决程序及表决结果等事项出具本法律意见。 为出具本法律意见，本所律师审查了《北京海天瑞声科技股份有限公司第二 届董事会第二十四次会议决议公告》《北京海天瑞声科技股份有限公司第二届监 事会第二十三次会议决议公告 ...

证券代码：688787 证券简称：海天瑞声 公告编号：2024-029 重要内容提示： 本次会议是否有被否决议案：无 北京海天瑞声科技股份有限公司 2023 年年度股东大会决议公告 本公司董事会及全体董事保证公告内容不存在任何虚假记载、误导性陈述 或者重大遗漏，并对其内容的真实性、准确性和完整性依法承担法律责任。 | 1、出席会议的股东和代理人人数 | 10 | | --- | --- | | 普通股股东人数 | 10 | | 2、出席会议的股东所持有的表决权数量 | 23,936,483 | | 普通股股东所持有表决权数量 | 23,936,483 | | 3、出席会议的股东所持有表决权数量占公司表决权数量的 | 39.9706 | | 比例（%） | | | 普通股股东所持有表决权数量占公司表决权数量的比例 | 39.9706 | | （%） | | （四） 表决方式是否符合《公司法》及公司章程的规定，大会主持情况等。 一、 会议召开和出席情况 （三） 出席会议的普通股股东、特别表决权股东、恢复表决权的优先股股东及 其持有表决权数量的情况： （一） 股东大会召开的时间：2024 年 6 月 7 日 （二） ...

Group 1: Data Service Trends - The demand for data services in reinforcement learning is increasing, with a trend towards more verticals such as law, finance, and healthcare [3] - Evaluation metrics for reinforcement learning annotations are becoming more diverse, requiring annotators to assess models from multiple dimensions [3] - The shift from unimodal to multimodal data annotation is evident, with a focus on text-image and text-video combinations [3][4] Group 2: Automation in Data Annotation - Current data annotation tasks in large models primarily focus on supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), with a strong reliance on human input [4] - Some projects have begun to implement algorithmic pre-annotation strategies to enhance the efficiency of manual annotation and verification [4] Group 3: Multimodal Development and Data Needs - The transition to multimodal models will create new data demands, such as generating images from text inputs, requiring machines to understand and map keywords to image tags [5] - The importance of high-quality multimodal training datasets will increase, driving growth in the data service industry [5] Group 4: Synthetic Data Considerations - Synthetic data is viewed as a necessary byproduct of AI development, serving as an effective supplement for data collection, though it has limitations in replicating real-world features [5][6] - Most companies still rely on real-world data for model training, but they will monitor advancements in synthetic data technology to adjust their business strategies accordingly [6] Group 5: Copyright Data and Value Proposition - The value of the company lies in aggregating diverse copyright data, cleaning it, and providing tailored services based on client needs [6] - High-quality cleaning of copyright data is essential before it can be used for model training, ensuring compliance with legal standards [6] Group 6: Differences in Data Requirements - The data requirements for pre-training large models are similar to traditional deep learning but differ in scale, quality, and sources [7][8] - Pre-training data typically involves token counts in the hundreds of billions, compared to around 1 billion for traditional models, necessitating a richer variety of data sources [7][8]

证券代码：688787 证券简称：海天瑞声 北京海天瑞声科技股份有限公司 投资者关系活动记录表 编号：2024-008 投资者关系活动类别 □特定对象调研 □分析师会议 □媒体采访 √业绩说明会 □新闻发布会 □路演活动 □现场参观 □电话会议 □其他 （请文字说明其他活动内容） 参与单位名称及人员姓 参加业绩说明会的广大投资者 名 ...