今天分享的是：2025中国大数据产业白皮书 报告共计：141页 《2025中国大数据产业白皮书》指出，大数据作为国家基础性战略资源与关键生产要素，已成为驱动数字时代变革的核心力 量，其规模性、多样性、高速性、价值性特征日益凸显，2025年全球数据流量将达数百EB级别，未来十年存储量将实现十倍 增长。全球大数据市场稳健扩张，IDC预测2025年全球大数据IT总投资规模约4134亿美元，亚太地区以22%的增速成为全球增 长最快市场，中国占亚太地区总量的65%，政策层面形成"中央统筹、地方落实"的完善体系，《"十四五"大数据产业发展规 划》明确2025年产业测算规模突破3万亿元的目标。技术层面，5G、物联网、云计算、人工智能等技术的深度融合，推动数 据采集、存储、处理全链条效能提升，AI大模型让数据价值转化率提升3-5倍。应用场景已广泛渗透到金融、医疗、农业、制 造、交通等多个行业，在金融领域实现精准风控与普惠金融，医疗领域助力精准诊疗与健康管理，制造业推动智能生产与质 量管控，有效解决各行业核心痛点。产业生态方面，中国大数据产业链已形成38个细分环节，从业企业达49248家，涌现出阿 里云计算、华为、海康威视等一 ...

□ 孙希佳 赋能学科生态的重塑：打破壁垒，激发文科交叉活力 （作者单位：金陵科技学院外国语学院） 数智技术赋能价值的核心在于以技术为纽带打破传统文科的学科边界，构建起集"文科内核+数智支撑 +产业联动"于一体的新文科学科生态体系。高校可运用大数据技术搭建学科交叉需求平台，实时抓取 社会治理、文化传承、经济决策等领域内的实际需求。如乡村文化振兴中涉及的文化数字化传播困境 等，可借助算法匹配技术将其与文史哲等文科专业教学内容进行有机结合，以生成更具现实意义的跨学 科研究课题。同时，各高校可借助AI工具重构传统文科课程体系，为学生带来跨学科教学内容。如在 历史学中嵌入"数字史学"模块，教师可运用大数据技术分析各史料之间的关联，可视化呈现其具体脉 络。 数智时代新文科人才的核心竞争力在于其兼具人文思辨力和数智应用力。高校需依据新文科建设的具体 进展和实际需求，重新制定和完善新文科人才培养方案。面向全体文科生，各高校可通过开设数智素养 必修课的方式，重点培养其辨别数据真实性的能力，会使用AI工具生成分析报告等基础数智工具的应 用能力，避免其陷入"谈数色变"的困境之中。基于此，依据不同专业新文科人才素养提升需求，为学生 ...

Core Points - Shanghai Municipal Education Commission announced the addition of 40 vocational education programs under the integrated training model by 2026 [1] - The programs will be jointly offered by various vocational schools and higher education institutions [1] Group 1: New Programs - The new programs include Artificial Intelligence Technology Application, HVAC Engineering Technology, and Smart Health Care Management among others [3][4] - The management of student enrollment and fee standards will follow the respective regulations for vocational and higher education [1] Group 2: Institutions Involved - Shanghai Information Technology School and Shanghai Urban Construction Vocational College are among the institutions involved in the new programs [3] - Other participating institutions include Shanghai Publishing and Printing College, Shanghai Electronic Information Vocational Technology College, and Shanghai Agricultural and Forestry Vocational College [3][4] Group 3: Program Structure - The first three years of the programs will be managed according to vocational school regulations, while the higher education phase will follow the regulations of the respective colleges [1]

Core Viewpoint - The integration of red culture into ideological and political education in universities is essential for enhancing educational quality and fostering a sense of belief among students, while also promoting the inheritance of red genes [1] Group 1: Systematic Integration of Red Culture Resources - Digital technology enables the systematic and comprehensive organization of red culture resources across different regions, transforming them from isolated storage to a structured supply model [2] - The establishment of a national-level "Red Culture Digital Resource Library" is proposed to categorize and digitize dispersed red culture resources, facilitating easy access and retrieval [3] Group 2: Innovation in Ideological Education Models - The traditional one-way teaching model is inadequate for contemporary students, who prefer interactive and immersive learning experiences; digital technologies can create engaging environments for red culture education [4] - Utilizing technologies like VR and AR can enhance the learning experience by providing interactive and immersive educational scenarios [4] Group 3: Extending Educational Scenarios - Digital dissemination allows for the sharing of red culture resources across regions and institutions, creating a multi-dimensional ideological education environment [5] - Online platforms can host various modules such as cloud lectures, exhibitions, and volunteer activities to engage students in red culture [5] Group 4: Offline Integration and Practical Applications - The integration of digital technology in campus spaces can create an immersive red culture educational atmosphere, with interactive screens and digital reading areas [6] - Local red resources can be utilized to develop digital navigation systems for field studies, enhancing the depth of learning experiences [6] Group 5: Comprehensive Coupling of Red Culture and Ideological Education - Achieving deep integration of red culture and ideological education requires a multi-faceted approach, focusing on resource reconstruction, teaching innovation, and scenario extension [7] - Future efforts should leverage AI and big data for precise interpretation and intelligent resource delivery, while fostering a collaborative educational mechanism [7]

Group 1 - The core viewpoint of the articles emphasizes the transformation of China's agricultural sector towards a high-tech, market-oriented, and eco-friendly model, with a mechanization and automation rate exceeding 75% by the end of 2024 [1][2] - The integration of IoT, AI, and big data technologies in various agricultural processes, including planting, storage, and processing, is enhancing the technical level of food production and modernizing the agricultural management system [1][2] - Mechanization and automation are effectively addressing the challenges posed by urbanization and labor shortages in agriculture, particularly the aging workforce, by improving production efficiency and reducing reliance on human labor [1][2] Group 2 - Intelligent agricultural technologies, such as smart seeding, water-saving techniques, and AI-based fertilization, are enhancing the value of existing agricultural resources and mitigating ecological risks like extreme weather and pest outbreaks [2] - The demand for diversified and quality food consumption is driving the need for a market-oriented agricultural supply system, which is being met through the optimization of the entire production, circulation, and consumption chain using digital technologies [2] - The establishment of a comprehensive agricultural technology application support system is crucial to adapt to the diverse agricultural conditions across different regions in China, ensuring that mechanization and digitalization meet local needs [3]

Core Insights - The article emphasizes the unprecedented growth opportunities in the big data profession, likening it to "the new oil" of the digital economy [1] Industry Demand: Explosive Talent Shortage - The big data industry in China surpassed 1.3 trillion yuan in 2023, with a year-on-year growth rate of 30% [4] - There is a staggering talent gap of 1.5 million in big data-related fields, with a supply-demand ratio of 1:10 [4] - Major cities like Beijing, Shanghai, Guangzhou, Shenzhen, and Hangzhou account for 65% of the total demand, while new first-tier cities like Chengdu and Wuhan see growth rates exceeding 40% [4] - Average starting salary for fresh graduates is 12,000 yuan, and those with three years of experience typically earn over 300,000 yuan annually [4] - The top three in-demand positions are algorithm engineers, data analysts, and big data developers [4] - Applications of big data are expanding across finance, healthcare, retail, and manufacturing sectors [4] Job Market Dynamics - A recruitment platform indicates that the resume submission ratio for big data positions is 1:3, significantly lower than other tech roles at 1:15, suggesting more choices for job seekers [5] - ByteDance's 2023 campus recruitment data shows a competitive ratio of 1:50 for big data-related positions [5] Core Skills: Key Competencies Employers Value - Employers prioritize five core skills in big data professionals, including: - Technical skills: Proficiency in programming languages (Java/Python/Scala), mastery of big data frameworks (Hadoop/Spark/Flink), database management (SQL optimization and NoSQL), and machine learning [7] - Business acumen: Ability to identify value points from large datasets, deep understanding of industry-specific business logic, data visualization skills, and effective communication and collaboration [7] - A data expert from Alibaba highlights the importance of candidates' ability to solve real-world problems, combining technical skills with business thinking [7] Career Pathways: Diverse Development Opportunities - The career trajectory in big data is not limited to technical roles, with various growth paths available: - Technical expert route: Junior engineer → Mid-level developer → Technical expert → Architect (potential annual salary can reach one million yuan) [9] - Management route: Data analyst → Data manager → Data director → Chief Data Officer (CDO) [9] - Cross-industry transition: Opportunities in fintech, healthcare, smart manufacturing, and consulting [9] Future Trends: Key Directions to Watch - The big data profession is expected to evolve in three significant ways: - Real-time processing: Shift from batch processing to stream processing, with increased demand in financial risk control and IoT [10] - Intelligent integration: Deep fusion of AI and big data, with AutoML technologies lowering analysis barriers [10] - Vertical specialization: Growing preference for industry-specific solutions, with sectors like agricultural and energy big data emerging [10] Actionable Recommendations: Enhancing Competitiveness - For those entering the field, four practical suggestions are provided: - Combine education with certifications, such as CDA data analyst and Alibaba Cloud big data certifications [10] - Engage in data competitions like Kaggle to gain practical experience [10] - Focus on emerging industries like digital economy, smart manufacturing, and smart healthcare [10] - Build a portfolio by maintaining personal data projects on platforms like GitHub and writing technical blogs [11]

Core Insights - The reform of state-owned enterprises (SOEs) in China has entered a new phase, with significant actions being taken to deepen and enhance the reform process [1][8] - Over 20 new companies have been established by central SOEs in the construction sector in the first half of this year, aimed at optimizing industry layout and enhancing competitiveness [1][2] Group 1: Establishment of New Companies - In May alone, six new construction SOEs were established, including China State Construction Fifth Engineering Division (Zhejiang) Investment Construction Co., Ltd. and China Urban Construction Group (Shanghai) Technology Co., Ltd. [2][3] - The highest registered capital among these new companies is 900 million yuan for China Resources New Energy (Bama) Co., Ltd., which focuses on power generation and renewable energy technology [2][3] Group 2: Industry Transformation - The establishment of new companies is seen as a move to optimize industry layout, allowing SOEs to focus on specific regions or business areas, thus improving resource allocation [3][5] - The new companies are expected to respond to domestic market opportunities such as urban renewal and new infrastructure, while also expanding into international markets [3][5] Group 3: Green and Intelligent Transition - The government has set a development tone for the construction industry emphasizing "steady progress, green transformation, and innovation-driven" growth [6] - SOEs are expected to promote high-quality, intelligent, and green transformations in the construction industry through various means, including the adoption of advanced technologies like BIM and IoT [6][7] Group 4: Future Outlook - The construction industry is anticipated to face both opportunities and challenges in the second half of the year, with potential support from policies and market demand [7][8] - The ongoing reforms and establishment of new companies are expected to play a crucial role in enhancing the industry's contribution to economic and social development [8]

Core Insights - The soil remediation industry is transitioning from traditional methods to low-carbon, precise governance driven by global climate change and carbon neutrality goals [1] - The rapid development of digital technologies is ushering the industry into a new phase of digitalization and intelligence [1] Summary by Sections Digital Transformation in Soil Remediation - Big data and smart technologies are driving the soil pollution governance system towards precision and low carbon [2] - Traditional remediation methods are limited by reliance on experience and static assessments, leading to inefficiencies and high hidden costs [2] - New technologies enable breakthroughs in pollution spatial analysis, remediation process control, and carbon footprint tracing [2] Pollution Identification and Cost Reduction - Pollution identification has shifted from experience-based judgment to data-driven approaches, significantly improving accuracy and reducing costs [2] - For instance, a case study in a lead-zinc mining area achieved an identification accuracy of 89% and reduced investigation costs by 40% through the use of satellite remote sensing and machine learning [2] Intelligent Upgrades in Remediation Processes - The remediation process is evolving towards intelligent dynamic control, reducing energy consumption and material waste [3] - A project in Tianjin reduced the use of persulfate by 22% and energy consumption by 18% through an intelligent decision-making system [3] - Digital twin technology has been used to optimize carbon emissions, achieving a 31% reduction in lifecycle carbon emissions [3] Comprehensive Evaluation of Remediation Effects - The evaluation of remediation effects is transitioning from terminal detection to a full lifecycle carbon footprint tracking model [3] - A blockchain-based tracing platform recorded carbon footprint data, revealing a 43% difference in carbon emissions from different sources of bentonite [3] Domestic and International Practices - Big data technology has shown irreplaceable advantages in pollution remediation, enhancing precision, efficiency, and sustainability [4] - Domestic applications emphasize technology integration and innovation, achieving significant reductions in repair cycles and carbon emissions [4] - Internationally, there is a focus on interdisciplinary integration and data-driven innovation, with successful case studies demonstrating effective pollution source identification and remediation [6] Recommendations for Future Development - It is recommended to integrate various data sources to build a unified soil environment big data platform for quantitative management [7] - The industry should focus on developing intelligent systems to overcome key technological bottlenecks and enhance carbon monitoring capabilities [7] - Emphasis on cultivating interdisciplinary talent to foster deep integration of environmental science and big data technology [8] Conclusion - Big data technology is reshaping the value logic of soil remediation, transitioning from mere pollution removal to ecological enhancement and carbon asset creation [8] - The collaboration of technological breakthroughs and institutional innovation is essential for advancing the industry towards intelligent and precise remediation solutions [8]