Core Points - The Shandong Provincial Science and Technology Conference highlighted significant achievements in technology and innovation, with awards given to various projects and individuals for their contributions to science and technology [1][3][4]. Group 1: Awards and Recognitions - Shandong University and Shandong First Medical University received the highest provincial science and technology awards for their groundbreaking work in carbon capture and other key technologies [1]. - A total of 51 natural science award achievements showcased interdisciplinary collaboration, with 21 projects demonstrating clear cross-disciplinary characteristics [3]. - The conference awarded 27 projects in the first prize category, representing 43% of the total, across 18 strategic fields including artificial intelligence and integrated circuits [4]. Group 2: Technological Innovations - The establishment of the first domestic million-ton CCUS demonstration project at Shengli Oilfield supports China's dual carbon strategy and green transition [4]. - The development of a high-performance broadband signal analyzer addresses high-precision measurement challenges for advanced signal technologies like 5G and 6G [4]. Group 3: Talent Development - The average age of primary contributors to awarded projects is 42.8 years, indicating a younger talent pool compared to previous years, with a notable increase in the number of contributors under 45 [4]. - The province has seen a significant increase in high-level talent, with 168 academicians residing in Shandong and over 8,700 national and provincial leading talents [5]. Group 4: Investment and Economic Impact - In 2023, Shandong's total R&D investment reached 238.6 billion yuan, a 9.4% increase, surpassing the national growth rate [5]. - The proportion of high-tech industry output value in the province's industrial output is projected to reach 53.32% in 2024, exceeding the "14th Five-Year Plan" target by 3.3 percentage points [5]. Group 5: Policy and Infrastructure - Shandong is a pilot province for several reforms in technology evaluation and talent classification, aiming to enhance the innovation ecosystem [5]. - The establishment of the Shandong Technology Market provides a one-stop service for technology transfer, facilitating collaboration with over 100 universities and research institutions [5].

Group 1 - The core viewpoint emphasizes the Industrial and Commercial Bank of China (ICBC) Guigang Branch's commitment to supporting key areas of the "Five Major Articles" by providing financial assistance to the real economy [1] - The bank has utilized digital supply chain financing products to assist small and micro enterprises, addressing their financing difficulties and high costs [1] - As of May 2025, the bank's outstanding digital supply chain financing business involved 72 corporate clients totaling 30.28 million yuan, with 93 transactions amounting to 17.76 million yuan completed in the current year, benefiting 72 small and micro enterprises [1] Group 2 - The bank is accelerating the development of digital supply chain financing by enhancing online business scenarios and leveraging its "online + offline" product advantages to offer personalized financial services [2] - The implementation of the "One Chain, One Policy" system aims to deepen bank-enterprise connections and reduce reliance on traditional collateral by providing credit enhancement measures from core enterprises [2] - The bank is focusing on technology-driven digital finance development by integrating big data, cloud computing, blockchain, and artificial intelligence to improve customer acquisition, operations, and risk control capabilities [2]

Group 1: AI and Big Data in Finance - The integration of artificial intelligence and big data is reshaping the core operational models of the financial industry, with significant developments in China's fintech sector following the global AI wave initiated by ChatGPT [2] - Major financial institutions like ICBC and China Merchants Bank are leading the application of AI in finance, while Tencent Cloud and Ant Group excel in technology output [2] - Ant Group has developed a leading AI risk control system that supports real-time transactions and compliance for hundreds of millions of users [2] Group 2: Evolution of Credit Risk Assessment - The credit risk evaluation system in banks has evolved from information-based to data-driven and intelligent systems, driven by the deep integration of data and technology [3] - Traditional credit risk assessment relied heavily on customer-provided information and internal data, limiting the use of external data [3] - The rise of digital finance allows financial institutions to access a broader range of external data, enhancing the comprehensiveness of risk assessments [3] Group 3: Innovation in Banking Services - Banks are innovating their service models by integrating online and offline channels, enabling personalized services anytime and anywhere [4] - The application of technologies like Intelligent Process Automation (IPA) has significantly improved operational efficiency, reducing processing times from days to minutes [4] - The focus of banking innovation has shifted from product-centric to ecosystem-centric approaches, integrating business, data, and technology [5] Group 4: Challenges in Inclusive Finance - Financial services for small and micro enterprises face challenges due to high service costs and the inherent risk characteristics of these customer segments [6] - Information asymmetry exacerbates the difficulties in risk identification and control in these segments [6] - Data is recognized as a key production factor in the digital transformation, with its marginal utility increasing as it is reused [6] Group 5: Enhancements in Risk Control Models - Traditional risk control models are limited by the narrow scope of data used, often leading to inadequate risk assessments [7] - By integrating diverse data sources, including user behavior and environmental factors, a more comprehensive risk management system can be developed [7] - The value of data increases with volume and reduced application barriers, enhancing both social and economic value [7] Group 6: AI in Anti-Money Laundering - Ant Group's anti-money laundering system combines AI and graph computing to enhance the identification of complex relationships [9] - The system utilizes heterogeneous graph modeling to depict various entities and their relationships, enabling effective tracking of fund flows [9] - AI plays a crucial role in analyzing suspicious transactions and automating report generation, improving decision-making efficiency [10]

Group 1: AI Integration in Healthcare Services - The rapid development of AI technology is being integrated into healthcare insurance services, enhancing convenience and improving service quality for the public [1][2] - Various local healthcare insurance bureaus have adopted AI models like DeepSeek, enabling instant responses to frequently asked questions regarding insurance policies and reimbursement processes [2] - The AI assistant "依保儿" has been launched in multiple regions, significantly improving service efficiency and user experience, with an average monthly access of 37,500 visits in Hangzhou [2][3] Group 2: Challenges in Fund Supervision - The healthcare fund supervision faces significant challenges due to a lack of personnel compared to the vast number of insured individuals and healthcare institutions [4] - Traditional supervision methods are inefficient, with less than 1% of medical expense documents being manually audited, leading to difficulties in identifying fraudulent activities [4] - The need for modernized supervision through big data and AI applications is emphasized to address the extensive regulatory tasks and improve efficiency [4] Group 3: Smart Supervision Initiatives - The National Healthcare Security Administration has initiated the first batch of intelligent supervision rules and is promoting pilot reforms to enhance fund supervision [5][7] - By the end of 2024, the goal is to achieve full coverage of the intelligent monitoring subsystem across all healthcare institutions, particularly benefiting secondary hospitals and grassroots medical facilities [7] - The intelligent supervision system has been implemented in over 90% of the national healthcare regions, enhancing the monitoring capabilities of healthcare funds [5][7] Group 4: Data Utilization and Analysis - The healthcare sector possesses rich data resources that can optimize resource allocation, fund supervision, and drive innovation in the pharmaceutical industry [9] - In-depth data analysis has revealed unusual patterns, such as male patients receiving gynecological treatments, highlighting the importance of data scrutiny in preventing fund misuse [9][10] - Local healthcare departments are leveraging technology to improve data dissemination and analysis, with initiatives like the "医保高铁" platform in Nanjing, which integrates data from over 2,148 healthcare institutions [11] Group 5: Future Prospects - The application of AI and big data in healthcare is expected to expand, enhancing fund prediction models and improving efficiency [12] - Combining AI with blockchain technology could increase data transparency and traceability, thereby strengthening the credibility of the healthcare system [12] - AI and big data will play a significant role in chronic disease management, telemedicine, and personalized healthcare services in the future [12]

以下文章来源于富国基金 ，作者填志愿 富国基金 . 因此，最近各个社交平台都上演着"志愿填报血泪史"，小编看到不少家长和考生线上发帖，有纠 结"专业如何选"、"热门专业是哪些"，也有焦虑"冲一冲会不会滑档"、"985有点坑的专业VS双非王 牌专业如何选"……刷着这些充满困惑的讨论，小编深有感触：无论是选学校选专业，还是选基金， 一定程度上可以说都是对未来的投资，细细品味，其实这志愿填报和买基金的弯弯绕绕颇有异曲同 工之处~ ★ 第一波 ★ 难选程度——眼花缭乱？ 高考选择院校、专业之难，就如同选基一样，通过下面这组数据可见数量之繁多。 志愿填报： 根据教育部2025年最新发布的普通高等学校本科专业目录，共包含93个专业类，845种专业， 其中今年新设了29种新专业，分别涉及区域国别学、碳中和科学与工程、海洋科学与技术、 航空运动、智能分子工程、人工智能教育等方向。同时，截至2024年6月20日，全国高等学校 共计3117所，其中普通高等学校2868所。可见院校林立，选择维度之复杂。 选基： 截至2025年6月25日，数据显示，共有12869只公募基金（仅含主代码），其中非货12499 只，基金经理共有4043 ...

随着互联网的飞速发展，大数据的应用价值在电信运营商领域日益凸显。上海联通通过一期二期的建设，已打造出内容丰富的数据仓库。然而，面对数据量 的爆发式增长，基础架构的长远规划迫在眉睫。深入挖掘数据价值，探索新的商业模式，将成本中心转化为利润中心，成为电信运营商的必然选择。 电信运营商的数据集中化趋势愈发明显。业务运营的集中化要求、对数据架构的集中化需求，推动着企业级数据中心的形成。同时，高性能、动态资源共 享、标准化功能组件等特性，成为大数据平台不可或缺的一部分。在移动互联网时代，实时数据获取、处理、分析以及智能化主动事件触发，正助力电信运 营商实现实时、智能化运营。 然而，电信运营商也面临着诸多大数据挑战。联通总部 3G 互联网访问记录查询及分析系统的海量数据，对存储和查询速度提出了极高的要求。移动互联网 流量的井喷、业务融合带来的数据互通、ICT 融合催生的海量用户画像等，都对大数据的关联分析计算效能带来了前所未有的挑战。 上海联通的大数据平台规划，旨在打造一个分层清晰、功能完备、高效协同的数据生态系统。其目标架构涵盖了数据采集、Hadoop 云平台、分布式数据 库、主数据仓库等多个关键层级。 本文引用的参考 ...

Core Viewpoint - The company, Hehe Information, known for its "Scan All-in-One" app, has submitted an IPO application to the Hong Kong Stock Exchange after less than a year of its listing on the STAR Market, raising concerns due to previous fundraising shortfalls and project delays [2][3][5]. Group 1: Company Overview - Hehe Information is an AI and big data technology enterprise that provides digital and intelligent products and services to global C-end users and diverse B-end clients [3]. - The company's C-end business includes major apps like Scan All-in-One, Business Card All-in-One, and Qixinbao, while its B-end services focus on cost reduction, risk management, and opportunity exploration [3]. Group 2: Financial Performance - Revenue growth from 2022 to 2024 shows a consistent upward trend: 2022 revenue was 989 million RMB, increasing to 1.187 billion RMB in 2023 (20% growth), and projected to reach 1.438 billion RMB in 2024 (21.2% growth), resulting in a three-year compound growth rate of 20.6% [5]. - Net profit also increased during the same period: from 284 million RMB in 2022 to 323 million RMB in 2023 (14% growth), and expected to rise to 401 million RMB in 2024 (24% growth), with a three-year compound growth rate of 18.8% [5]. Group 3: IPO and Fundraising - Hehe Information's IPO on the STAR Market raised 1.38 billion RMB, but the actual amount was lower than planned, leading to adjustments in the allocation of raised funds [7][10]. - The company adjusted its planned investment from 1.49 billion RMB to 1.27 billion RMB for various projects, including AI and big data product development [10][11]. Group 4: Project Delays - Several fundraising projects have been delayed, with the timeline for completion extended due to macroeconomic factors and the fast-paced nature of the software industry [12][13]. - Key projects originally scheduled for completion by December 2024 have been postponed to 2026, reflecting the need for more thorough market research and user demand analysis [13]. Group 5: Risk and Compliance - The company acknowledges potential risks associated with user data protection and the rapid development of AI technology, emphasizing compliance with data protection laws and the implementation of relevant policies [15]. - The funds raised from the IPO will be allocated to the development of cutting-edge AI technologies, product development, and the establishment of global operational and marketing networks [15].

6月26日晚间，成都苑东生物制药股份有限公司(以下简称"苑东生物")发布公告称，为加快推进公司创 新转型战略的实施步伐，公司董事会同意由公司全资子公司苑东生物投资管理(上海)有限公司作为投资 主体，拟以自有资金810万元和1158万元分别受让吴汉超所持有的上海超阳药业有限公司(以下简称"上 海超阳")约7.95%的股权和北京齐力佳科技有限公司所持有的上海超阳约11.36%的股权，合计受让上海 超阳约19.32%的股权。 公告显示，此次交易完成后，苑东生物间接持有上海超阳的股权比例将由11.36%增加至30.68%，上海 超阳将成为公司施加重大影响的参股公司。苑东生物相关负责人表示，上海超阳拥有专业的创新药研发 技术和团队，此次受让上海超阳股权是基于看好上海超阳创新药管线及创新药团队的发展，符合公司战 略发展规划，有利于加快推进公司创新转型战略的实施步伐。 公开资料显示，上海超阳成立于2021年，是一家致力于创新药研发的生物科技公司。公司专注于抗肿瘤 与自身免疫性疾病领域，依托蛋白质稳态技术和计算机辅助药物设计两大技术平台，不断推进 MolecularGlue(分子胶)、PROTAC(蛋白降解靶向嵌合体)和DA ...

数据 ETF（516000）紧密锚定中证大数据产业指数，其样本精准覆盖大数据存储、分析、应用等全产 业链上市公司，堪称大数据产业发展的 "晴雨表"。当下，算力互联互通浪潮正重塑产业格局。一方 面，算力的高效调度与整合，为产业各环节注入强劲动能。在存储端，可优化架构，大幅提升数据存储 效率与容量；于分析领域，则能加速模型训练与算法迭代，显著提升数据洞察的精度与深度。另一方 面，算力保障有力推动创新应用落地，如金融投资、智能驾驶等对实时数据处理要求严苛的前沿场景， 得以突破技术瓶颈，实现从概念到实践的跨越，持续拓宽大数据产业的发展边界。 截至2025年6月27日 14点31分，中证大数据产业指数（930902）上涨0.17%，成分股东华软件上涨 4.18%，奥飞数据上涨2.50%，光环新网上涨2.42%，创业慧康上涨1.79%，科华数据上涨1.36%。数据 ETF（516000）上涨0.22%，冲击5连涨，最新价报0.93元。拉长时间看，截至2025年6月26日，数据ETF 近1周累计上涨4.64%。流动性方面，数据ETF盘中换手4.62%，成交1934.06万元。 消息方面，近日，首批数据中心REITs项目—— ...

Group 1 - The 2025 China Future Finance Summit focused on reshaping global order and accelerating smart finance, addressing topics such as global trade impacts, AI in finance, big data-driven operations, customer experience enhancement, and resilience building in financial institutions [1] - The current global economic landscape is undergoing profound adjustments, with the Chinese financial industry at a critical transformation phase, necessitating accelerated digital transformation and globalization in the banking sector [1] - The global economy has entered an era where macroeconomic resilience relies more on policy agility and institutional trust, leading to a reshaping of trade and finance [1] Group 2 - The EU-China cooperation can deepen in capital markets, with Chinese capital aiding Europe's green transition, emphasizing the need for dialogue to build trust and promote sustainable financial development [2] - China's service consumption potential is significant but remains lower than that of countries like the US, with challenges in demand strength and supply quality, necessitating income increases for low- and middle-income groups and optimized service supply [2] - China needs to deepen reforms and open up, accelerate technological progress, and strengthen cooperation with partners like Europe to address global economic governance challenges [2] Group 3 - The discussion on "Financial Transformation in the Age of AI" highlighted that large language models are shifting financial AI from internal data-driven approaches to external data training, enhancing information processing capabilities while exposing security risks like deep forgery and data pollution [3] - A mechanism of "centralized model control + scenario-based fine-tuning" is needed to balance data privacy and model effectiveness in cross-border finance, alongside strengthening internal AI ethics education and sharing model results among multiple institutions [3]