9月24日-25日，由中汽中心工程院、中国汽车工程学会振动噪声分会共同主办的"2025年第二十一届汽车NVH先进控制技术研讨会"在天津举 行。大会以"智绘声境·让声音服务艺术"为主题，设置了主题演讲、专题研讨、互动沙龙、沉浸式路演等多种活动形式，全方位展现了NVH控制技术 的最新动态。来自全国的各汽车厂商、零部件企业、科研机构和高等院校等120余家单位的500多名专家、学者和工程师参加会议。 中汽中心工程院党委书记、董事长张志国也指出，汽车产业正经历一场由"智能"与"新能源"驱动的深刻变革。这场变革不仅重塑了汽车的动力系统和交 互方式，更在重新定义NVH的内涵与价值。过去，行业更多地关注如何"消除"不悦的声音；而今天，大家的任务已经升级为如何为智能座舱"管理"和"设 计"声音，如何将NVH从一个减法问题，转变为一个融合了声学设计、软件算法、电子工程乃至心理声学的加法艺术。 中汽中心党委委员、副总经理李洧 中汽中心工程院党委书记、董事长张志国 中国传媒大学音乐与录音艺术学院党委副书记、听觉艺术博士李洋红琳指出，我们早已生活在被精心设计的"情感音景"中，汽车正从交通工具进化 为"车轮上的智能空间"，用户需求从功能 ...

Core Viewpoint - The article discusses the evolution of autonomous driving technology, highlighting the competitive landscape among major tech companies, automakers, and startups, and how advancements are reshaping transportation methods [2][3]. Group 1: Tesla's Development - Tesla is recognized as a pioneer in autonomous driving, with its aggressive data-driven approach that discards traditional methods like LiDAR and high-definition maps in favor of pure visual perception [6]. - The development path includes the transition from modular designs to end-to-end neural networks, aiming to make AI think and drive like humans [6]. - Key technologies introduced include BEV (Bird's Eye View) and Occupancy Network, enhancing spatial awareness and reducing reliance on high-definition maps [8][12]. Group 2: Huawei's Progress - Huawei's ADS technology has evolved from multi-sensor fusion and high-definition map reliance to a "mapless" approach, enhancing perception algorithms and ultimately leading to end-to-end model applications [23]. - The ADS 1.0 version relied on multiple sensors and high-definition maps, while ADS 2.0 marked a breakthrough in "mapless" driving [25][26]. - The latest ADS 3.0 aims for full scene intelligent driving, integrating advanced perception networks and optimizing hardware for better performance [28]. Group 3: Momenta's Strategy - Momenta employs a dual strategy of data-driven algorithms and mass production of autonomous driving products, creating a feedback loop for continuous improvement [33]. - The company focuses on low-cost automated mapping and crowd-sourced map updates, enhancing its capabilities in complex environments [35]. Group 4: Horizon's Path - Horizon has developed a unique path from automotive-grade AI chips to full-stack solutions, emphasizing software and hardware collaboration for efficiency [47]. - The company has progressively advanced from early ADAS prototypes to L2+ and L3 capabilities, with plans for broader applications in 2025 [49][50]. Group 5: Xiaopeng's Evolution - Xiaopeng's autonomous driving journey reflects a shift from multi-sensor fusion and high-definition maps to a "mapless" approach, driven by AI large models [79]. - The XPILOT series has evolved from basic parking assistance to advanced highway and urban navigation capabilities, with significant improvements in system generalization [81][90]. Group 6: NIO's Development - NIO's approach is characterized by a cautious evolution from collaborative development to full-stack self-research, focusing on safety and reliability [98]. - The introduction of the World Model NWM in 2025 signifies a new phase in NIO's autonomous driving capabilities, enhancing cognitive and reasoning abilities [110].

Core Viewpoint - The article discusses the ongoing debate between two approaches in the autonomous driving sector: VLA (Vision-Language Action) and WA (World Model), highlighting that both are fundamentally reliant on data, but differ in their methodologies and implications for the future of autonomous driving [1][2]. Summary by Sections VLA vs. WA - The autonomous driving landscape is splitting into two camps by 2025: companies like Xiaopeng, Li Auto, and Yuanrong Qixing are betting on the VLA approach, while Huawei and NIO are advocating for the WA model [1]. - WA is claimed to be the ultimate solution for achieving true autonomous driving, but the article argues that it is merely a rebranding of data dependency [1]. Data Dependency - Both VLA and WA are based on the premise that "data determines the upper limit" of capabilities [2]. - VLA relies on real-world multimodal data to train reasoning abilities, while WA requires a combination of real data and simulated data to enhance its capabilities [2]. - The industry is confused about the distinction between "data form" and "data essence," leading to misconceptions about the reliance on data [2]. Industry Misconceptions - The article emphasizes that the discussion should not focus on whether data is needed, but rather on how to efficiently utilize data [2]. - VLA and WA represent different methods of data collection and usage, with data remaining the core competitive advantage in autonomous driving until true artificial intelligence is realized [2]. Community and Resources - The "Autonomous Driving Knowledge Planet" community has over 4,000 members and aims to grow to nearly 10,000 in two years, providing a platform for technical exchange and sharing of knowledge in the autonomous driving field [4][10]. - The community offers resources such as learning routes, technical discussions, and access to industry experts, facilitating knowledge sharing among newcomers and advanced practitioners [4][11].

Core Insights - The article emphasizes the necessity for companies to transition from a "single-point breakthrough" approach to a "full-network penetration" strategy in media promotion to effectively capture consumer attention across various platforms [1][2]. Group 1: Single-Point Breakthrough - Single-point breakthrough is the initial stage of media promotion, focusing resources on a key platform to establish brand recognition and influence [1]. - Companies should analyze their products, target audience, and market competition to select the most promising media platform for initial promotion [1]. - An example provided is a beauty product targeting young women, which can effectively utilize platforms like Xiaohongshu for focused marketing efforts [1]. Group 2: Full-Network Penetration - After achieving success on a single platform, companies need to expand their reach through full-network penetration, which involves personalized strategies tailored to different platforms [2]. - This approach is not merely about posting the same content across multiple platforms but creating a collaborative communication matrix [2]. Group 3: Social Media Platforms - Social media platforms like WeChat and Weibo offer strong social attributes and user engagement, allowing companies to create official accounts and interact with users in real-time [3]. - Engaging users through online activities and discussions can foster organic sharing and enhance brand reputation [3]. Group 4: Video Platforms - Video platforms such as Douyin and Bilibili attract users with engaging video content, where companies can produce high-quality promotional videos that resonate with viewers [4]. - Utilizing the algorithmic recommendation systems of these platforms can significantly increase exposure and conversion rates [4]. Group 5: Industry Vertical Platforms - For specialized products or services, industry vertical platforms are essential for reaching professional users [6]. - Companies can share industry news, technical articles, and case studies to showcase expertise and build credibility within the industry [6]. Group 6: Continuous Optimization - Transitioning from single-point breakthrough to full-network penetration is a dynamic process requiring ongoing adjustments based on market changes and user feedback [7]. - Data analysis tools can help companies monitor promotional effectiveness and optimize strategies to enhance efficiency and return on investment [7].

本届泰达汽车论坛上，来自政产学研界的嘉宾回顾"十四五"汽车业取得的业绩，共同为"十五五"产业发展建言献策。本报梳理了四个热点话题，以飨读 者。 13 William the state 3 3 2 1 2 1.85 Kiss 17 1-57 1 150 are 1 20 B - 4/12/2 194 P 12 t 2015 A I 195 Children y W 东风汽车集团有限公司首席专家、东风汽车研发总院副院长陈涛表示，AI技术正推动汽车从"功能机"向"智能体"转变，通过多模态大模型实时捕捉驾驶员生 理和情绪状态，自动调节车内环境，实现"共情式"出行体验。在企业端，AI能力体系已应用于研发、制造、供应链和服务全链条，显著提升生产效率和质量 控制水平。 智能化转型成为本届泰达汽车论坛重点讨论的话题，在多场关于智能化的专场论坛中，技术创新、安全底线、政策法规等问题被反复提起，成为汽车智 能化进程的重要挑战。 数据驱动产业生态重构 北京理工大学副教授、新能源汽车国家大数据联盟副秘书长刘鹏指出，数据作为新生产要素，正深刻改变汽车产业的研发、生产与服务模式。数据已成为驱 动产业全面升级的新动力，如何系统性挖掘数据 ...

Core Viewpoint - The article highlights the transformation of agriculture in Shandong province through the integration of technology, moving from traditional farming methods to smart, data-driven agricultural practices, thereby enhancing productivity and sustainability [1][12]. Group 1: Technological Advancements in Agriculture - Smart agricultural machinery is reshaping production scenes, transitioning from "human-led" to "cloud-based" farming [1]. - The use of drones for fertilization and pest control, along with AI systems for monitoring, is becoming standard in Shandong's agricultural practices [1][4]. - As of now, Shandong has established over 1,000 smart agriculture scenarios, including more than 20,000 acres of intelligent greenhouses and 1.8 million agricultural drones, covering over 170 million acres of operational area annually [6][12]. Group 2: Data-Driven Management - A comprehensive digital management system is being developed, allowing for real-time data collection and analysis, which enhances decision-making in agricultural practices [7][11]. - The integration of satellite navigation and AI technology enables precise operations in farming, such as autonomous harvesting and planting [8][11]. - The "Qilu Agricultural Cloud" platform consolidates agricultural data resources, totaling 2.94 billion entries, facilitating a data-driven management approach [11]. Group 3: Industry Collaboration and Ecosystem Development - Shandong is promoting a collaborative ecosystem that integrates technology, industry, and talent to enhance agricultural productivity [12][13]. - The province is breaking down silos in the agricultural supply chain, ensuring a seamless connection from production to market [12]. - Initiatives like the "High Tang Agricultural Brain" project digitize local agricultural knowledge, providing farmers with easy access to expert advice and resources [9][12]. Group 4: Future Outlook - Future plans include expanding the application of "space-ground" technology in agriculture and promoting AI models across various crop types [14]. - Continued investment in digital agriculture is expected to enhance connectivity between provincial and local systems, making smart agriculture more accessible [14].

9月13日，国家建筑绿色低碳技术创新中心建筑运维智慧化方向联合实验室建设合作协议签约仪式、科技项目与成果发布、"绿色医院与智慧运营"中日国际 合作揭榜挂帅项目签约仪式暨建筑智慧运维与节能低碳技术交流会在北京成功举办。 本次会议由国家建筑绿色低碳技术创新中心、建科公共设施运营管理有限公司主办，中国建设科技集团中央研究院建筑智慧运维研究中心等多家单位联合主 办，中国建筑一局（集团）有限公司等协办，《暖通空调》杂志社承办，新华网、光明网等媒体支持报道。 开幕式上，国家建筑绿色低碳技术创新中心主任、中国建设科技集团党委书记、董事长孙英，中国建筑西南设计研究院有限公司党委书记、董事长陈勇等致 辞，建科公共设施运营管理有限公司副总经理刘志国主持。大会举行"国家建筑绿色低碳技术创新中心建筑智能感知与自主运维装备联合实验室""国家建筑 绿色低碳技术创新中心建筑运维大数据技术联合实验室"建设合作协议签约仪式，还进行了"绿色医院与智慧运营"中日国际合作揭榜挂帅项目签约及特聘专 家证书颁发。 下GB|国家建筑绿色低碳技术创新中心 "绿色医院与智慧运营"中日国际合作揭榜挂帅项目签约仪式暨特聘专家证书颁发仪式 医院智慧运维与绿色发展科 ...

Core Viewpoint - The automotive industry is shifting from horsepower and leather to computing power and user experience, moving away from brand premium to technology premium [1][3] Group 1: Industry Transformation - The automotive industry is undergoing a significant transformation driven by a technological revolution, leading to a reshaping of the value chain [3] - Advanced technologies such as intelligent networking, autonomous driving, and electric systems are rapidly spreading from luxury vehicles to the mainstream market [3] - Level 2 driver assistance has become standard, and intelligent cockpits are now available in vehicles priced around 100,000 yuan [3] Group 2: Challenges for High-End Brands - High-end brands must break away from technological homogenization and seek differentiated technological anchors to maintain their premium status [3] - The challenge lies in the accelerated competition of innovation, where the technology diffusion cycle has shortened to one to two years [3] - High-end brands need to establish agile R&D systems to quickly adopt mature technologies while also investing in high-risk, long-cycle foundational research [3] Group 3: Strategies for Domestic Brands - Domestic brands have successfully made strides in the fields of new energy and intelligent networking, leading to the emergence of several high-end new energy brands [4] - The essence of automobiles as transportation tools necessitates a focus on safety and reliability, avoiding excessive promotion and misleading users [4] - To build technological competitiveness, domestic brands should follow four pathways: 1. Soft-hard collaboration to integrate chips, operating systems, and algorithms vertically [4] 2. Data-driven approaches to establish a digital intelligence foundation [4] 3. Enhanced security to create a new intelligent defense system [4] 4. Ecological co-construction to develop a comprehensive intelligent networking ecosystem [4] Group 4: Competitive Landscape - Traditional international automotive giants are responding vigorously, leveraging decades of technology, capital, and talent accumulation [4] - Companies like Mercedes-Benz, BMW, and Volkswagen are forming hardware and software alliances with firms like Bosch, inviting companies like NVIDIA and Qualcomm to build a "chip + operating system" alliance [4] - True leadership in the industry depends not only on market scale but also on achieving breakthroughs in core technologies such as chips, algorithms, and operating systems [4] Group 5: Strategic Framework - The strategic framework for the high-end breakthrough of Chinese automotive brands consists of four interconnected elements: soft-hard collaboration, data-driven value closure, enhanced security, and ecological co-construction [5] - This framework aims to transition domestic brands from being technology followers to rule definers in the automotive industry [5]

Core Insights - The automotive industry is undergoing a significant transformation driven by data, which is reshaping the ecosystem and enhancing R&D, production, service, and management [3][4][12] - The need for collaboration among government, industry, academia, and research institutions is emphasized to establish data standards and a secure, shareable data ecosystem [3][4] Group 1: Data as a Driving Force - Data is identified as a new engine for the automotive industry, driving comprehensive upgrades across various sectors [3] - The transition from a "product-driven" to a "user-driven" era is highlighted, necessitating a shift in product definition, marketing strategies, and service models [4] - Companies are urged to leverage big data analytics to better understand user behavior and enhance product and service offerings [4] Group 2: Breaking Down Data Silos - The issue of data fragmentation and siloed systems in the bus industry is addressed, with a focus on creating a comprehensive big data system that connects all aspects of operations [6] - A case study from Xiamen King Long Bus Company illustrates how data-driven approaches have reduced order delivery times from 60 days to under 35 days and improved operational efficiency [6] Group 3: Emerging Data Infrastructure - The concept of a "data space" is introduced as a new infrastructure that allows for secure and efficient data sharing across enterprises and industries [7] - The data space is characterized by its ability to maintain data sovereignty while facilitating trusted data flow, particularly in sensitive scenarios [7] Group 4: AI Applications in the Industry - AI is being utilized to enhance efficiency across the battery lifecycle, addressing challenges in research, manufacturing, and recycling [8] - The development of an integrated platform for intelligent product design and real-time monitoring of battery health is highlighted as a significant advancement [8] Group 5: Data Security and Governance - The importance of establishing a collaborative governance framework for data security and compliance is stressed, particularly in the context of smart connected vehicles [11] - A report on data governance in the automotive industry outlines current challenges and offers recommendations for improving data security and cross-border data flow [12] Group 6: Industry Consensus and Future Directions - The forum reached a consensus that data-driven approaches are essential for the digital transformation of the automotive industry [12] - Emphasis is placed on breaking down data barriers and enhancing cross-industry collaboration to maximize data resource utilization while ensuring security and privacy [12]

Core Insights - JD Supermarket celebrated its 11th anniversary in Beijing, gathering over 400 representatives from the fast-moving consumer goods (FMCG) industry to discuss new trends and opportunities in retail [1] - Coca-Cola's collaboration with JD has lasted for 14 years, with JD being a crucial partner in understanding Chinese consumers and driving localized innovation [1] Group 1: Strategic Collaboration - Gilles Leclerc emphasized that the partnership will deepen in three areas: scenario marketing, data-driven strategies, and a comprehensive ecosystem [3] - In scenario marketing, the focus will be on creating impactful brand activities around major national events like the Spring Festival and FIFA, leveraging JD's PLUS membership system to enhance customer loyalty and repurchase rates [3] - The data-driven approach will utilize JD's real-time data insights and AI technology to predict consumer demand and improve business decision-making efficiency [3] Group 2: Ecosystem Development - The collaboration will extend beyond online retail into areas like instant delivery and dining, enhancing channel coverage and consumer reach [5] - Gilles Leclerc expressed confidence in JD Supermarket's user-centric approach and its retail innovation capabilities, aiming to provide consumers with a more convenient and personalized experience [6] - The deepened strategic cooperation between JD Supermarket and Coca-Cola sets a benchmark for collaboration between FMCG brands and retail platforms, promoting sustainable growth in a complex market environment [6]