Group 1 - The core viewpoint emphasizes the transformation of data assets and the need for effective governance and value conversion in various industries [1][2][3] - The "Data Element × Three-Year Action Plan (2024-2026)" aims to optimize resource allocation and empower the real economy through data utilization, highlighting its significance for high-quality development [1][3] - The increasing importance of data as a core production factor necessitates a focus on governance and value conversion, with data governance being a key link for realizing data value enhancement [1][2] Group 2 - The marketization of data elements is advancing, with nearly 500 central enterprises establishing digital technology companies and about 66% of industry leaders purchasing data, indicating a growing enthusiasm for data utilization [3][4] - The "2025 Digital Economy Work Points" emphasizes accelerating the release of data element value and promoting the integration and sharing of data resources [3] - The ultimate goal of data element marketization is to achieve value through innovative applications by enterprises, which is crucial for unlocking the potential of data elements [3][5] Group 3 - Technology companies are innovating solutions and platforms to enhance the integration of data elements with business scenarios, as seen with Shenzhou Information's "Smart Brain" solution and Tencent Cloud's intelligent agent development platform [4][5] - The financial industry is embracing a collaborative model of software development involving both human modelers and AI programmers, aiming to break traditional departmental silos and enhance efficiency [5] - The shift from a product-oriented mindset to a service ecosystem in the financial sector is essential for improving funding circulation and fostering collaboration within the industry [5]

来自航空公司、机场、民航保障企业、科研院所的相关负责人与网络安全领域的近300位专家学者参加 此次论坛。与会代表表示，论坛主题鲜明，内容丰富，具有很强的战略性、政策性、针对性和指导性。 既有对网络安全新挑战的深度剖析，也有对数据治理与技术创新的前瞻洞察；既有政策的权威解读，也 有实战案例的经验分享。同时见证了国产化适配成果的落地，探讨了技术创新与安全治理的融合路径。 从架构创新到场景实践，从政策解读到技术落地，论坛内容既有高度，又有温度，充分展现了民航网络 安全领域的"中国智慧"与"行业担当"。 中国航协民航科技和信息化工作委员会在论坛上发出确保行业网络和数据安全的四点倡议：遵循总体国 家安全观，着力提升民航网络和数据安全现代化治理水平；拥抱人工智能新技术，全面强化民航网络和 数据安全潜在风险应对能力；加强多层次合作共享，共同构建民航网络和数据安全协同联动响应机制； 全方位加强人才队伍建设，不断提升多层次网络和数据安全专业人员实战化能力。 本次论坛由南航数智科技（广东）有限公司、中国民航信息网络股份有限公司承办，中国民航大学协 办。南航数智科技有限公司新近获批广州市工业和信息化局"广州市信息技术应用创新（交通 ...

近日，"数云原力2025.AlxFinTech拓界平行论坛"正式举行。论坛以"问道AI科技进阶"为主题，多位业务专家围绕AI赋能智慧核心、信贷创新、财资云服务、 电子渠道进化及数据资产转化等前沿议题，为金融业数智化转型与高质量发展提供可落地方案。 为破解区域性银行转型难题，中国信息通信研究院在本次论坛期间发起"区域性银行新一代核心系统研究课题"，旨在形成兼具落地性与可推广性的建设范 式，全面提升区域性银行核心竞争力，推动技术标准与行业实践深度融合。 数据作为核心生产要素，其治理与价值转化成为论坛焦点。神州信息数据研究中心总经理李庆刚指出，数据资产化需以高质量供给为基石，依托复合人工智 能、智能问数等技术打通"数据—知识—业务"链路。结合研究公司Gartner发布的2025年数据趋势，他提出九大关键方向，凸显"数据治理"作为数据资产化的 核心使能力量。系统阐述了人工智能与数据治理深度融合的实践路径，通过构建复合AI构建知识库、加速AI智能体落地业务场景，推动数据编织驱动研发 智能化三大路径，打通数据闭环，构建企业级数字孪生体，实现数据驱动的精准决策。 面对商业银行利差持续收窄的挑战，神州信息资产负债管理解决方案 ...

Core Viewpoint - The article discusses the "Beijing Action Plan for AI Empowerment in New Industrialization (2025)", which includes 16 measures to support the integration of artificial intelligence with industrial development, aiming to enhance productivity and foster new production capabilities [1][2]. Group 1: Data and Model Development - The plan emphasizes the construction of high-quality industry data sets to support manufacturing enterprises and research institutions in data collection and processing, with rewards for data registration and transactions [2]. - It aims to improve public data governance services by establishing data governance platforms and supporting the creation of high-quality open-source data sets [2]. - Enterprises are encouraged to participate in model training using AI data sandboxes, ensuring data privacy and compliance, with free services for first-time users [2][3]. Group 2: AI Model and Ecosystem - The initiative supports the development of industry-leading large models by collaborating with key enterprises and software companies, providing up to 30 million yuan for those achieving top domestic or international standards [3]. - It promotes the creation of high-performance general intelligent agents that integrate industrial mechanisms and data, with financial support for projects that significantly enhance manufacturing efficiency [3]. - The plan encourages the establishment of a manufacturing intelligence ecosystem based on self-protocols, facilitating the integration of large models with external tools and data sources [3][4]. Group 3: Technological Advancements and Support - The action plan includes support for enterprises to build experimental scenarios around their technology centers, focusing on the deployment of AI models and the development of intelligent products with proprietary intellectual property [4]. - It aims to enhance simulation verification capabilities by developing proprietary industrial simulation software and platforms, with financial backing for key projects [4][5]. - The initiative also emphasizes the importance of intelligent safety measures, providing support for the establishment of model safety testing grounds and risk assessment systems [5]. Group 4: Equipment and Talent Development - The plan supports the enhancement of equipment intelligence across various stages of development and operation, with financial assistance for projects that demonstrate significant improvements [5][6]. - It promotes the establishment of intelligent factory benchmarks using advanced technologies like embodied intelligence and 5G, with funding for qualifying demonstration projects [6]. - The initiative includes training programs for cultivating interdisciplinary talent in AI and manufacturing, with support for effective course development and training bases [6][7]. Group 5: Financial Services and Promotion - The action plan encourages financial institutions to innovate financial tools like "AI Smart Manufacturing Loans" to support enterprises in their AI-driven manufacturing upgrades [7]. - It aims to promote successful AI empowerment cases through media and industry events, facilitating knowledge sharing and collaboration among manufacturing enterprises [7].

Core Insights - The company is leveraging advanced technology to enhance emergency management and operational efficiency in oil extraction processes [1][2][3][4][5][6] Data Governance - The deployment of new sensors has improved data collection completeness from 68% to 91%, and maintenance costs for deep-sea equipment have been reduced by 40% [1] - The introduction of a Beidou timing system has synchronized 23,000 monitoring points, reducing timestamp error rates from 15% to 1% [2] - The use of wavelet transform algorithms has decreased data deviation from 12% to 3%, enhancing data accuracy [2] - In emergency drills, geological data sharing has improved leak simulation accuracy, reducing error from 30% to 12% and increasing joint response efficiency by 40% [2] Model Optimization - The integration of AI algorithms has significantly improved fire prediction models, reducing prediction error from 45% to 18% [3] - A dynamic learning mechanism has been established to update models when equipment vibration exceeds 15%, achieving a fault warning accuracy of over 89% for aging equipment [3] Innovative Techniques - Parameter transfer technology allows for model adaptation with only 10% new data, cutting training costs by 70% [4] - The use of Generative Adversarial Networks (GAN) has expanded historical accident data, improving pre-incident warning detection rates from 32% to 78% [4] Workforce Transformation - The introduction of augmented reality (AR) training has increased operational proficiency among older employees from 52% to 89% [5] - A mentorship program has facilitated knowledge transfer between experienced and younger employees, leading to the development of an intelligent valve control system [6] - VR training has improved average scores in emergency simulations by 37%, and collaboration with universities has led to the creation of AI-enhanced predictive maintenance modules [6] - The company has showcased its intelligent emergency system at a national safety conference, demonstrating significant advancements in safety management [6]

在技术合作层面，小花科技展现出前瞻性布局。2025年与华为云达成深度合作，基于昇腾AI云服务与 DeepSeek大模型，将AI技术深度融入互联网金融场景，实现AI机器人、智能客服等多个应用场景的孵 化与商用。未来双方将深化合作，在智能数据核验、智能语音交互及风控等业务场景输出对外技术解决 方案，提供更智能的个人金融科技服务。 与此同时，小花科技与华中科技大学展开产学研合作，聚焦客服系统智能化升级。依托高校学术优势和 自身千万级用户服务数据，突破传统客服痛点。升级后的系统将实现知识库动态匹配与用户情绪精准感 知，通过设备指纹、声纹识别提升黑灰产客诉识别准确率，构建"人机协同、精准响应"的服务新范式。 数据治理闭环：构筑风险防控"铁三角" 在大数据应用领域，小花科技以"数据智能+数据治理"为双轮，构建金融风险防控的立体屏障。一方面 为持牌金融机构精心打造并提供数据评分类产品，提升金融服务的精准度与安全性。另一方面将新市 民、信用白户等传统征信数据覆盖不足人群纳入覆盖范围。截至目前，小花科技已累计服务该类客群 2200万人，协助金融机构实现380亿元授信额度投放，在践行普惠金融的同时，实现风险与收益的动态 平衡。 ...

从小微客户信用评估、零售客户精准画像到供应链金融等业务创新，近年来，数据作为生产要素，正 加速融入银行运营各环节，成为拓展营收的重要引擎。 证券时报·券商中国记者观察到，一方面，近年来有关银行数据报送与治理违规的罚单频现；另一方面，越来 越多的银行将数据管理部从信息科技部门独立出来，并抬升至与后者地位相同的一级部门。 证券时报·券商中国记者梳理各家上市银行2024年年报发现，国有大行中的中国银行、建设银行、交通银行、 邮储银行，股份行中的浦发银行、兴业银行、民生银行，城商行中的青岛银行、贵阳银行、苏州银行等，于近 年均单独设置统筹数据治理的一级部门，与信息科技类部门同级并列，多数命名为"数据管理部"。 如建设银行的科技渠道板块，就有多个一级部门，其中包括"数字化建设办公室""数据管理部"和"金融科技 部"等。 当然，并非统筹数据治理工作的部门全然称为"数据管理部"。如交通银行将原金融科技与产品创新委员会、数 据治理（金融统计标准化）委员会整合为数字金融委员会；在部门设置中，也单独设置了"数据管理与应用 部"，与"金融科技部"并列为一级部门。再如，华夏银行成立的是"数据信息部"，光大银行设置的是"数据资产 管理 ...

Group 1: Core Insights - The new Private Economy Promotion Law, effective from May 20, aims to provide strong legal support for the high-quality development of the private economy [1] - The law's implementation raises questions about how the private economy can achieve leapfrog development [1] Group 2: Financial Transparency and Credit System - Insufficient financial transparency is identified as the core reason for the financing difficulties faced by private enterprises [1] - A credit system based on financial transparency is crucial for promoting high-quality development in private enterprises [1] - High-quality private enterprises, especially those focused on technological innovation, benefit from easier access to public financing due to improved financial transparency [1] Group 3: Data Governance and Asset Value - Data governance is essential for private enterprises to unlock asset value and build core competitiveness [2] - A comprehensive data governance analysis and application system can enhance market responsiveness, cost control, customer experience, and product innovation [2] - Three key areas for private enterprises to focus on include establishing a standardized data collection and management system, expanding intelligent application scenarios, and building a flexible technological foundation [2] Group 4: Value Creation and Innovation - The new productive forces are expected to bring paradigm shifts to the private economy, transitioning from survival competition to value creation [2] - An integrated innovation system focusing on data, technology, and talent is necessary at the enterprise level [2] - Policy optimization and resource allocation through cross-industry cooperation and mergers can provide more motivation for long-term research and development [2] Group 5: Internationalization and Compliance - Private enterprises face strategic choices between "local agents" and "self-construction" in building international supply chains, considering logistics costs and management efficiency [3] - Financial design should aim for cost minimization and maximum gross profit, with a focus on meeting expected cost-performance ratios [3] Group 6: IPO Trends and Recommendations - Five recommendations for companies considering an IPO in Hong Kong include choosing the right listing model, optimizing corporate governance, assessing listing conditions, preparing for financial compliance, and understanding market conditions [3] - Companies should align with Hong Kong's listing rules and enhance financial transparency and information disclosure standards [3]

Core Viewpoint - The financial information technology industry in China is experiencing continuous growth, with a projected market size of 72.602 billion yuan in 2024, reflecting a year-on-year increase of 13.85% [1][10]. Industry Definition and Classification - Financial information technology refers to the extensive application of information technology in the financial sector, promoting the digital, networked, and intelligent transformation of financial services, management, and operations [2]. Industry Chain Analysis - The financial information technology industry chain is a technology-driven ecosystem, with upstream focusing on infrastructure and foundational technology, midstream on system development and solutions, and downstream covering financial institutions and regulatory bodies [4]. Development History - The industry has evolved over four decades, transitioning from initial automation to deep intelligence, with key phases including the establishment of electronic systems, the rise of online banking, the integration of technology, and the current focus on self-control and interconnectedness [6]. Current Market Analysis - The financial information technology market in China has been expanding at a double-digit annual growth rate, with significant investments from financial institutions driven by business expansion, innovation, and regulatory requirements [10]. Downstream Application Areas - The banking sector dominates the market with a 48% share, followed by securities and funds at 26%, and insurance at 16%, each focusing on specific technological needs and innovations [11]. Key Enterprises Analysis - The industry features a diverse landscape with leading companies like Hengsheng Electronics and Kingdee, which dominate their respective segments, while others like Softcom and Zhongke Soft focus on niche markets [14][16]. Future Development Trends - AI technology is increasingly empowering the financial sector, enhancing risk management and customer service, while data governance is becoming crucial for maximizing data value and ensuring compliance [20][21][23].

然而，培养"施工队"却并不容易。魏凯直言，许多企业用户往往需要先干活再立项做预算，这导致 了"脏活累活"难解决、"施工队"动力不足等问题。此外，企业的数据治理能力弱也是制约人工智能产业 发展变革的短板。"数据治理被视为'下水道工程'，这方面做不好，即使把模型引入公司，'施工队'也无 处下手。"魏凯说。 对此，魏凯建议，企业应在大模型落地过程中加大预训练阶段投入，同时提升训练集群算力效率、加快 代理型人工智能研发，充分发挥消费场景"练兵场"作用，以数据闭环优化数据供给，推动大模型优化。 此外，应培养更多具备跨界思维的复合型人才。 （文章来源：科技日报） "大模型就像一个软乎乎的大脑，发挥作用必须有脑壳、眼睛、手、胳膊和腿。这背后必须要搭建多套 软件来支撑。完整的软件栈和工具链才能让人工智能真正变成生产力。"在日前举行的清华大学数字经 济系列沙龙第八期上，中国信息通信研究院人工智能研究所所长魏凯说，算法、数据、算力的规模效应 仍在持续放大，但大模型并非"万能钥匙"。引入大模型只是"买图纸"，而要真正落地，需要配备专 业"施工队"来完成大模型开发、调优、评估、部署及推理等全流程工作。 "以宠物经济为例，大模型可以通 ...