南昌矿机集团股份有限公司投资者关系活动记录表 3. 公司海外市场的区域拓展优先级如何划分？各区域当前业务进展及规划是什 么？ 证券代码：001360 证券简称：南矿集团 答：公司海外拓展遵循清晰的区域优先级：非洲为第一优先级，已搭建初步 服务网络并与主要中资企业建立稳定合作，未来计划加密网点；俄罗斯及中亚为 第二优先级，已实现设备进入，后续着力培育本地服务能力；南美为第三优先级， 处于策略研究与前期开拓阶段；澳大利亚等成熟市场为第四优先级，将以完善高 端服务配套作为切入点。此外，在东南亚聚焦骨料等领域，在北美已实现向本土 高端客户的直接销售，正在探索后市场服务模式。 4. 公司海外业务的盈利水平如何？与国内业务相比，毛利率差异及定价策略是 什么？ 编号：2026-001 | 投资者关系 | 特定对象调研 分析师会议 媒体采访□业绩说明会 □新闻发布会 □路演 | | --- | --- | | 活动类别 | 活动 现场参观 □其他（请文字说明其他活动内容） | | | 华夏基金 韩霄 刘海泉 时赟凯 卢疆啸 | | 活动参与人 | 长江证券 臧雄 | | 员 | 华安证券 王璐 | | | 北京坤溪 ...

作者 | 李众力 编辑 | 自动驾驶之心 原文链接： https://www.zhihu.com/question/552466858/answer/1973504909879030493 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 2025 年年底了，我也来回答一下。 先说结论： 据我能接触到的一圈国内玩家，大家嘴里的"数据闭环"，绝大多数还是各个算法团队内部的"小闭环"，离当年 PPT 里畅想的那种"数据直接解决问题"的 大闭环，还有好几层台阶。 先简单说下我自己的背景（方便大家判断我是不是在瞎说） 我从事自动驾驶行业大概 7 年多了，从最早那种"开完车工程师拎着硬盘，从工控机上拔下来，抱着去机房拷数据"的年代一路干到现在。 这几年主要在一家 互联网大厂的物流无人车项目 里，从封闭园区到高速公路再到城市公开道路，从载人到拉货都有涉及，负责整车的数据体系和质量体系搭建，带 团队做的事情大致包括： 日常工作基本就是跟各种 log、Trigger、标注平台、仿真平台 ...

以下文章来源于红色星际 ，作者红色星际科技 红色星际 . 让更多人，更深入地了解自动驾驶行业！ 作者 | 钟声 来源 | 红色星际 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 2025年结束了，2026年马上到来。 2025年对于智驾人来说仍旧是非常辛苦的一年，为了技术攻关和量产交付节点，没少过封闭开发的日子。不过，回顾2025，这是智驾承上启下和辞旧 迎新的一年。 2025年智驾最主要的两条线是：向下普及和向上挑战。 比亚迪、吉利、奇瑞等传统车企扮演了向下普及的角色，掀起了全民智驾的浪潮，把中阶的高速NOA功能下放到10W+车型上，26年将会继续推进智 驾普及，把城市NOA功能下放到10W+车型上。 新势力以及头部智驾供应商则是在挑战智驾技术的上限，秉持着一年一代新技术的做法，在端到端之后继续探索新技术，引领技术迭代。 可以说，25年主机厂分化成了两个阵营，一个是负责向下普及的传统车企；一个是负责向上挑战的新势力。 2025年，智驾开始挑战技术"深水区"，核心问 ...

作者 | 李众力 编辑 | 自动驾驶之心 原文链接： https://www.zhihu.com/question/552466858/answer/1973504909879030493 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 2025 年年底了，我也来回答一下。 先说结论： 据我能接触到的一圈国内玩家，大家嘴里的"数据闭环"，绝大多数还是各个算法团队内部的"小闭环"，离当年 PPT 里畅想的那种"数据直接解决问题"的 大闭环，还有好几层台阶。 先简单说下我自己的背景（方便大家判断我是不是在瞎说） 我从事自动驾驶行业大概 7 年多了，从最早那种"开完车工程师拎着硬盘，从工控机上拔下来，抱着去机房拷数据"的年代一路干到现在。 这几年主要在一家 互联网大厂的物流无人车项目 里，从封闭园区到高速公路再到城市公开道路，从载人到拉货都有涉及，负责整车的数据体系和质量体系搭建，带 团队做的事情大致包括： 日常工作基本就是跟各种 log、Trigger、标注平台、仿真平台 ...

12月15日，工信部首次发放L3级"有条件自动驾驶"车型准入许可，长安深蓝SL03与极狐阿尔法S6率先 入围，限定在北京、重庆的高速/快速路开启试点，最高时速50-80km/h，仍要求驾驶员随时接管。同一 天，特斯拉宣布其"无人驾驶"Robotaxi取消安全员，计划2026年直接以L4形态上路运营。两件事叠加， 意味着中国市场的竞争逻辑即将发生三点根本性变化： 一句话：L3准入是"门票"，Robotaxi是"终局"。谁能先把L3卖出去的车变成24小时跑数据的Robotaxi， 谁就拿到下一轮的生存权。 国内传统车企拿到L3"准生证"，但只能在固定路段、低速场景下卖车；特斯拉跳过L3，用L4 Robotaxi 直接切入出行运营，把车变成"会赚钱的资产"。后续车企必须同时拿到"政府准入"与"运营牌照"两张通 行证，否则硬件再先进也只能停留在辅助驾驶层面，无法打开持续收费的出行服务市场。 （文章来源：21世纪经济报道） 3. 行业淘汰赛从"价格战"升级为"资质+资金"双重筛选 1. 竞争焦点从"硬件堆料"转向"法规+运营"双轨能力 政策端已明确：L3准入只给"完成安全评估+具备冗余+能远程监管"的企业，相当于把90 ...

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 理想汽在ICCV'25期间也分享了些新东西！目前还没有视频对外。 VLA团队负责人詹锟老师做了一场世界模型的presentation，名为World Model: Evolving from Data Closed-loop to Training Closed-loop。自动驾驶之心第一时间做了解 读分享给大家~ 首先是介绍下理想VLA司机大模型： 回顾了理想汽车智能驾驶的发展路线，从规则时代的轻图和无图，再到基于AI的E2E+VLM快慢双系统和VLA， 这四个方案中Nav（导航）是重点突出的模块。 下面介绍的是数据闭环的价值。左上角这张图是一个完整的数据闭环流程： 影子模式验证→经由数据触发回传到云端进行数据挖掘→有效样本进行自动标注→生 成训练集训练模型→模型下发验证性能。 这个过程已经可以做到一分钟的数据回传。 目前已经有15亿公里的驾驶数据，200+的Trigger来生产15-45s的Clip数据。 目前理想的端到端量产版本MPI已经到了220+， ...

Core Insights - The article highlights the transition of China's intelligent driving industry from concept to practical application, with Chery's commitment to its intelligent driving strategy serving as a milestone [1][3]. Industry Overview - By 2025, the Chinese intelligent driving industry is expected to shift from "parameter competition" to "real-world validation," with consumer expectations evolving from "availability" to "usability" and "reliability" [3]. - The current stage of the industry is characterized by both technological breakthroughs and challenges in implementation [4]. Chery's Commitment - Chery's chairman publicly committed to equipping all models with the Falcon intelligent driving assistance system within the year, a move that sparked industry discussions due to the previous trend of high-level intelligent driving features being limited to premium models [3][6]. - As of the end of the year, Chery successfully integrated the Falcon system across all models, demonstrating its technical capabilities through real-world testing in complex driving conditions [3][6]. Challenges in Intelligent Driving - Many automakers face issues such as "feature reduction," "delayed functionality," and limitations to high-end models when delivering intelligent driving features [5]. - Current intelligent driving systems exhibit significantly higher error rates on unstructured roads compared to structured ones, with failure rates being 3-5 times higher [5]. Technical Foundation of Falcon Intelligent Driving - The Falcon system's success is attributed to a collaborative foundation of data, algorithms, and hardware, creating a "data loop - algorithm breakthrough - hardware redundancy" structure [7]. - Chery's Tianqiong Intelligent Computing Center has accumulated over 24 billion kilometers of driving assistance data, enhancing the system's adaptability across various road conditions [7][10]. Algorithm and Hardware Integration - The Falcon system utilizes the Momenta R6 reinforcement learning model, which allows for rapid decision-making in unforeseen scenarios, enhancing its performance in complex environments [10][11]. - The hardware setup includes a combination of sensors, ensuring reliable perception in challenging conditions, while the system's computational power is optimized for efficient data processing [12][14]. Long-term Strategy and Collaboration - Chery's approach to intelligent driving is rooted in a long-term commitment to technology development, having invested in intelligent technology since 2010 [17][19]. - The company employs a collaborative ecosystem model, partnering with various tech firms to enhance its capabilities while maintaining core technology independence [19]. Future Outlook - Chery aims to achieve end-to-end integration of its intelligent driving system by 2026, with ongoing updates to enhance functionality [21]. - The intelligent driving industry is moving towards a phase of "refined cultivation," focusing on real-world validation and user-centric solutions [22].

Core Insights - The Chinese intelligent driving industry is undergoing a significant reshuffle, highlighted by the suspension of the once-prominent unicorn, Haomo Zhixing, while competitors like Yuanrong Qixing and Zhuoyu are gaining market share and investment support [1][2][5] Company Analysis - Haomo Zhixing, originally a spin-off from Great Wall Motors, received substantial early-stage funding but has struggled to maintain momentum, with its last financing round occurring in early 2024 without support from its former backer [2][3] - The company's choice of Qualcomm Snapdragon Ride chips over the industry-standard NVIDIA Orin has hindered its ability to adapt to new technological trends, leading to operational inefficiencies [3][4] - Great Wall Motors has shifted its focus to other suppliers, notably investing $100 million in Yuanrong Qixing, indicating a loss of confidence in Haomo Zhixing's capabilities [5][6] Industry Trends - The competitive landscape has evolved, with a focus on achieving a scale of one million vehicles to generate valuable data for algorithm development, moving beyond flashy demonstrations to practical data-driven solutions [7][10] - Companies like Yuanrong Qixing and Horizon Robotics are positioning themselves as strategic partners rather than mere component suppliers, emphasizing the importance of data access and integration [8][9] - The industry is witnessing a consolidation of market share among leading players, with predictions that only a few companies will dominate the market by 2025 [14][15] Future Outlook - The intelligent driving sector is transitioning from an optional feature to a core asset for automotive companies, with a clear divide emerging between those who can leverage large-scale data and those who cannot [14][15] - The ultimate goal for many companies is to develop systems that not only enhance vehicle performance but also contribute to broader applications in robotics and artificial intelligence [12][13]

Investment Rating - The industry investment rating is "Overweight" indicating a positive outlook for the sector compared to the overall market performance [8]. Core Insights - The report emphasizes that intelligence will be a key theme in the market for 2026, with investment opportunities extending beyond smart driving to areas like Robotaxi. A data closed loop is identified as the core starting point for achieving full-stack self-research, which differs fundamentally from mere data collection [1][3]. - The establishment of a data closed loop is crucial for filtering effective information from massive data, enabling machines to understand data, feedback to correct models, and perform OTA updates for secondary verification. This requires not only data ownership but also the ability to identify data gaps and utilize data to enhance models [1][3]. - The report suggests that the scale of the data closed loop team (e.g., whether it reaches a hundred members) and related investments should be key indicators for assessing a company's commitment and capability for self-research [1][3]. Summary by Sections Data Closed Loop - The report highlights that when algorithm models are truly driven by PB-level data, it will create a competitive barrier that is difficult to replicate. Even if competitors acquire model architectures or poach key personnel, lacking a substantial underlying data accumulation will hinder their ability to replicate similar algorithm capabilities in the short term [2][4]. - Building a solid data closed loop is expected to provide companies with a certainty of competitive advantage for six months to a year. Companies like Xiaopeng, Li Auto, and Huawei are noted to have established a leading advantage in the smart driving sector, with a high degree of technical moat [2][4]. Investment Recommendations - The report recommends focusing on domestic strong alpha manufacturers such as BYD, Geely, and Xiaopeng, as well as companies that represent the trend of intelligence like Huawei's HarmonyOS. Attention is also drawn to companies like JAC Motors and Seres, with specific recommendations for Li Auto, Kobot, Desay SV, and Jingwei Hengrun [2]. - For state-owned enterprise integration, the report suggests monitoring SAIC Motor, Dongfeng Motor Group, and Changan Automobile. Additionally, it highlights component companies with strong performance growth and capabilities for overseas expansion, recommending Fuyao Glass, New Spring, Fuda, Shuanghuan Transmission, and Yinlun [2].

当一款健康险产品从构想到上市的周期大幅缩短，当产品经理能够实时洞察市场热点并一键生成营销方 案，当理赔数据能够精准反馈到产品设计的初始环节——一场由AI技术驱动的健康险产业深度变革正 在悄然发生，它不仅重塑着行业的决策方式，更在重构整个健康险产业的价值链条。 在第八届中国国际进口博览会上，南开大学—镁信健康精算科技实验室正式发布商业健康险决策辅助大 模型mind42.ins，集合镁信健康、中再寿险及南开大学在各自领域深耕的行业经验与知识积累，旨在以 人工智能技术重构健康险产品设计、风险定价与理赔管理体系，为保险业提供智能化、可解释的决策辅 助工具。 数据复杂与决策挑战 健康险行业长期面临着数据结构性复杂，标准不统一的挑战。尽管行业每年产生数以亿计的医疗及药品 理赔数据，但这些宝贵的信息却被割裂在不同的系统中——保险公司的核心业务系统、医院的HIS系 统、药店的销售系统，形成了难以逾越的数据鸿沟。这种数据割裂直接导致了三大行业难题：产品创新 滞后、风险定价粗放、运营效率低下。 在产品设计环节，传统模式暴露明显局限。"当我们设计一款慢性病保险时，传统做法只能参考有限的 流行病学数据和再保险公司的费率表，"一位保险 ...