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]

思政教师可借助数字技术为学生打造沉浸式教学场景，以更生动的形式将红色文化资源融入课堂，激发 学生学习兴趣。利用VR技术构建虚拟博物馆，使学生在沉浸式体验中感受红色历史；运用AR技术开发 互动课堂，学生仅需扫描教材中的图片，即可在手机上看到文物3D模型及背后的历史故事，真正实现 课本内容与数字场景的联动。同时，依托在线学习平台，向学生推送与课堂教学内容相关的学习资源和 素材。在此基础上，利用大数据技术分析学生资源浏览时长、互动讨论频率、阶段测试表现等自主学习 行为，精准判断学生对红色文化和思政知识的理解程度，并以此为依据为其定点推送学习资源，实 现"千人千策"的精准育人。 延伸融合教育场景，从思政课堂到全场景覆盖 红色文化的传播往往受限于地域空间、传播载体等因素，而数字化传播可让高校思政教育突破时空限 制，实现红色文化资源的跨区域、跨校际共享，为深度耦合奠定资源基础。 □ 焦娜 红色文化是党长期积淀的精神瑰宝，承载着理想信念与爱国情怀。将红色文化融入高校思政教育，是顺 应教育变革的必然选择，也是提升育人质量的关键路径。这一举措不仅能增强教学底蕴，铸牢学生信 仰，还能推动红色基因传承，为培养时代新人提供精神养分。 ...

数据显示，2024年底，全国农作物耕种收综合机械化率超过75%。同时，物联网、AI、大数据等技术也 在粮食种植、仓储、加工等环节广泛应用，粮食生产的技术水平不断提升，推动了我国农业经营体系的 现代化转型。粮食生产已经从传统依靠人力投入转型为高技术导向、高度市场化、绿色生态的新型农 业。 机械化和智能化有效应对了我国城乡社会快速转型对粮食生产经营的影响，有利于打造新型现代农业经 营体系。随着城镇化进程中的人口外流，农业劳动力面临老龄化问题，"谁来种地"成为一个重大现实问 题。许多智能化、无人化设备的广泛应用有效提升了生产效率，降低了农业对人力投入的依赖。此外， 各种无人化、智能化设备有效解决了农业劳动过程分散、劳动贡献不可细分等监管难题，让新型经营模 式成为可能。 机械化和智能化有效克服了资源限制和生态风险对粮食生产的影响，有利于打造绿色高效农业。智能播 种技术、智能节水技术、AI算法施肥方案等智能化手段能有效提升既有资源的农业价值；极端天气、 病虫害扩散等生态风险也可以通过农业智能化解决。以应对复杂气候为例，通过构建基于物联网、云计 算和大数据的平台，整合气象、土壤、作物等数据，提高无人机、收割机等设备的作业 ...

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]

在应对全球气候变化与实现"双碳"目标的双重驱动下，土壤修复行业正在从"传统粗放式治理"向"低碳 精准治理"的模式转变。近年来，随着数字技术的快速发展，土壤修复行业已进入数字化、智能化发展 的新阶段。通过建立涵盖"天—空—地—井"的一体化立体监测体系，结合智能决策支持系统，大数据技 术正在深度改造污染源识别、修复工艺优化和治理效果评估等关键技术环节，为行业绿色低碳发展提供 了新的技术支撑。 大数据技术赋能土壤修复全链条降碳 大数据与智能技术正驱动土壤污染治理体系向精准化、低碳化转型。传统治理模式受限于经验依赖与静 态评估，存在效率低、隐性成本高等瓶颈。新技术通过多维数据融合与智能算法迭代，在污染空间解 析、修复过程调控及碳足迹溯源三个维度实现突破，为精准修复与"双碳"目标协同提供了创新路径。 一是污染识别模式从经验判断向数据驱动转变。传统土壤污染识别主要依靠经验判断和离散采样，检测 周期长、成本高。大数据技术通过多维数据融合，显著提升了污染识别精度。如利用卫星遥感高光谱数 据反演表层重金属空间分布，结合历史工业用地GIS数据训练机器学习模型，在某铅锌矿区案例中识别 精度达到89%，调查成本降低40%。通过部署物 ...