证券研究报告 | 行业周报 2025 年 08 月 25 日 通信 机器人产业持续催化，国产算力芯片迎来突破，持续看好相关产业 投资机会 | 股票 | 股票 | 投资 | EPS （元） | | PE | | | --- | --- | --- | --- | --- | --- | --- | | 代码 | 名称 | 评级 | 2025E | 2026E | 2025E | 2026E | | 002463.SZ | 沪电股份 | 买入 | 1.85 | 2.45 | 30.07 | 22.71 | | 002881.SZ | 美格智能 | 买入 | 0.73 | 0.92 | 73.33 | 58.18 | | 300308.SZ | 中际旭创 | 买入 | 7.6 | 8.29 | 36.7 | 33.64 | | 300394.SZ | 天孚通信 | 买入 | 3.68 | 4.53 | 33.4 | 27.13 | | 301285.SZ | 鸿日达 | 买入 | 0.21 | 0.7 | 231.86 | 69.56 | | 600941.SH | 中国移动 | 买入 | 6.79 | 7.23 ...

一周大事（2025年8月18日-24日） 头条 火热的A股，吸引谁来了？ 图为南都N+智绘平台AI生成 8月22日沪指高开，此后指数一路攀升，午后越过3800点，创2015年8月19日以来新高。截至收盘，三大 指数全线飘红。 专家分析，股市不仅是经济的晴雨表，更是推动经济增长的重要引擎。在当前经济环境下，牛市堪称经 济增长的第四驾马车，作用尤为突出。展望后市，多个机构认为，中期A股市场仍有充足空间和机会。 场内资金虽有分歧，但市场的走势显示韧性十足，且场外资金有望延续入市之势，接下来市场大概率延 续震荡上行的走势。 3800！沪指再创十年新高，专家：牛市是经济增长重要引擎 上证指数创近十年新高，股民、基民、机构的"热"和"冷" 广州楼市重磅！"商转公贷款"细则公布 8月20日，广州住房公积金管理中心印发广州商业性个人住房贷款转住房公积金个人住房贷款实施办法 （暂行）。中指研究院华南分院研究主管陈雪强分析认为，本次新政主要是为了减轻购房者住房贷款利 A股市值总和首次突破100万亿元，沪指创10年来新高！ 持续上涨，三大指数均创近年新高！专家：正逐步转向全面牛市 A 股火热引年轻投资者争相入市！券商揽客费率低破万 ...

Core Insights - The commercialization path for embodied intelligence has been clearly outlined at the inaugural partner conference of Zhiyuan Robotics, emphasizing the importance of collaboration with various industry leaders [1][2] - Zhiyuan Robotics has achieved significant breakthroughs in autonomous mobility, extreme environment adaptability, reliability, and rapid task deployment, marking the beginning of its commercialization journey [1][2] - The company aims to focus on seven key application scenarios, including exhibition reception, entertainment performances, material handling, and scientific education, while also exploring international markets [1][4] Group 1: Strategic Partnerships - Zhiyuan Robotics has a clear and diverse logic for selecting partners, including forming joint ventures with leading companies like Fulian Precision Engineering to refine products in real environments [2][6] - Collaborations with technology service giants like Softcom Power leverage their extensive customer networks to expand into vertical markets such as government services [2][9] - Partnerships with technical experts like Lingxi Technology enhance Zhiyuan's capabilities in industrial vision, which is crucial for precise robotic operations [2][6] Group 2: Technological Breakthroughs - Key technological advancements include fully autonomous movement, adaptability to extreme conditions, reliability demonstrated through extensive testing, and ease of task deployment [8][14] - The company predicts that humanoid robots will soon surpass human speed in running, with aspirations for robots to serve as personal assistants in home care scenarios [2][18] Group 3: Commercialization Metrics - A clear metric for successful commercialization is achieving over 1,000 units operating in a single scenario while maintaining high daily activity rates, indicating maturity in technology, product, and customer demand [3][16] - The company is currently experiencing rapid growth, with expectations of delivering thousands of units this year [16] Group 4: International Expansion - Zhiyuan Robotics is focusing on international expansion through partnerships in North America, Southeast Asia, Japan, and the Middle East, with initial progress noted in Malaysia [1][4] - The company is leveraging educational collaborations as a gateway to enter the North American market [4] Group 5: Industry Challenges - The company disagrees with the notion that hardware is not a bottleneck, emphasizing the need for hardware improvements in areas such as tactile feedback, joint performance, and safety [2][14] - The ROI for industrial applications is targeted to be within three years, contingent on optimizing operational efficiency and reducing costs [14][15]

Group 1: AI Infrastructure Investment - OpenAI plans to invest trillions of dollars in AI infrastructure development and operation [1] - Guosen Securities indicates that AI infrastructure capital expenditure is at an inflection point, signaling a long-term upcycle in the industry [1] - Key segments such as power supply and cooling systems are expected to benefit significantly from this trend [1] Group 2: Securities Industry Consolidation - The China Securities Regulatory Commission has approved the merger of Western Securities and Guorong Securities, aiming to create a new securities firm with nearly 120 billion yuan in total assets [2] - Post-merger, Western Securities is expected to see significant improvements in its rankings across brokerage, investment banking, asset management, and proprietary trading [2] Group 3: Monetary Policy Outlook - The People's Bank of China plans to implement a moderately loose monetary policy, focusing on supply-side financial policies to stimulate effective demand [4] - The upcoming monetary policy will prioritize promoting reasonable price recovery as a key consideration [4] Group 4: Market Trends and Opportunities - The U.S. clean energy tax credit regulations have led to a surge in the solar photovoltaic sector, with prices beginning to rise and profitability expected to recover [8] - Shanghai has launched its first batch of autonomous smart connected taxis, marking a significant step towards the commercialization of autonomous driving in China [9] - The robot industry is entering a phase of expansion, with expectations of significant demand growth for humanoid robots by 2030 [10]

Core Viewpoint - The article discusses the evolution of robot navigation technology from traditional mapping and localization to large model-based navigation, which includes visual language navigation (VLN) and goal navigation. VLN focuses on following instructions, while goal navigation emphasizes autonomous exploration and pathfinding based on environmental understanding [1][5]. Group 1: Visual Language Navigation (VLN) - VLN is fundamentally a task of following instructions, which involves understanding language commands, perceiving the environment, and planning movement strategies. The VLN robot system consists of a visual language encoder, historical environmental representation, and action strategy modules [2][4]. - The learning process for the strategy network has shifted from extracting patterns from labeled datasets to leveraging large language models (LLMs) for effective planning information extraction [4] - The architecture of VLN robots requires them to accumulate visual observations and execute actions in a loop, making it crucial to determine the current task stage for informed decision-making [4]. Group 2: Goal Navigation - Goal navigation extends VLN by enabling agents to autonomously explore and plan paths in unfamiliar 3D environments based solely on target descriptions, such as coordinates or images [5][7]. - Unlike traditional VLN, goal-driven navigation systems must transition from understanding commands to independently interpreting the environment and making decisions, integrating computer vision, reinforcement learning, and 3D semantic understanding [7]. Group 3: Commercial Applications and Demand - Goal-driven navigation technology has been successfully implemented in various verticals, such as terminal delivery, where it combines with social navigation algorithms to handle dynamic environments and human interactions [9]. - Companies like Meituan and Starship Technologies have deployed delivery robots in complex urban settings, while others like Aethon have developed service robots for medical and hospitality sectors, enhancing service efficiency [9][10]. - The growth of humanoid robots has led to an increased focus on adapting navigation technology for applications in home services, healthcare, and industrial logistics, creating significant job demand in the navigation sector [10]. Group 4: Learning and Knowledge Challenges - Both VLN and goal navigation require knowledge across multiple domains, including natural language processing, computer vision, reinforcement learning, and graph neural networks, making it challenging for newcomers to gain comprehensive expertise [11]. - The fragmented nature of knowledge in these fields can lead to difficulties in learning, often causing individuals to abandon their studies before achieving a solid understanding [11].

Core Viewpoint - The rapid rise of Upwind New Materials' stock price, driven by the acquisition by Zhiyuan Robotics, has resulted in it becoming the first tenfold stock of 2025 in just 14 trading days, highlighting the significant impact of strategic investments in the A-share market [1][3][6]. Group 1: Stock Performance - Upwind New Materials' stock price dropped from 7.78 yuan before suspension to 6.78 yuan at the end of 2024, but it surged after resuming trading, achieving an increase of over 600% in a short period [1][3]. - Following its resumption on July 9, the stock hit 11 consecutive 20% daily limit-ups, setting a record in the A-share market [3][6]. - The market capitalization of Upwind New Materials exceeded 180 billion yuan after the initial surge, and it reached 319.74 billion yuan later, indicating a significant valuation premium driven by market sentiment [6]. Group 2: Acquisition Details - On July 8, 2025, Upwind New Materials announced that its controlling shareholder signed share transfer agreements with Zhiyuan Robotics, with a transaction value of approximately 2.1 billion yuan [3][4]. - Following the acquisition, Zhiyuan's core team gained 66.99% ownership of Upwind New Materials, effectively achieving absolute control [4]. - The valuation of Zhiyuan Robotics was assessed at 15 billion yuan after a B-round financing led by Tencent, indicating strong investor confidence in the robotics sector [4]. Group 3: Market Reactions and Governance - The stock's rapid increase led to regulatory scrutiny, with Upwind New Materials issuing announcements regarding abnormal trading fluctuations [7]. - Despite regulatory warnings, investor enthusiasm persisted, resulting in continued price surges [7]. - A shareholder meeting on July 24 approved the exemption of certain executives from voluntary share transfer commitments, which caused dissatisfaction among minority shareholders [9].

Group 1 - The core viewpoint of the article highlights the release of the 2024 annual wage price information for employees, which includes 12 new digital occupational wage levels to assist companies and workers in negotiating salary standards [1] Group 2 - As of the end of June, a total of 8 provinces in the country have raised their minimum wage standards [2]

Core Viewpoint - The article discusses the advancements and applications of Goal-Oriented Navigation technology, emphasizing its significance in enabling robots to autonomously navigate and make decisions in unfamiliar environments, moving from traditional instruction-based navigation to a more autonomous understanding of the world [1][2]. Group 1: Technology Overview - Goal-Oriented Navigation is a key area within embodied navigation, relying on three main technological pillars: language understanding, environmental perception, and path planning [1]. - The technology has been successfully implemented in various verticals, including delivery, healthcare, and hospitality, showcasing its ability to adapt to dynamic environments and human interactions [2]. - The evolution of Goal-Oriented Navigation can be categorized into three generations: end-to-end methods, modular approaches, and LLM/VLM integration strategies [4][6]. Group 2: Industry Applications - In delivery scenarios, Goal-Oriented Navigation combined with social navigation algorithms allows robots to perform tasks in complex urban settings, as seen with Meituan's delivery vehicles and Starship Technologies' campus robots [2]. - In healthcare and hospitality, companies like Aethon and Jianneng Technology have deployed service robots for autonomous delivery of medications and meals, enhancing service efficiency [2]. - The integration of Goal-Oriented Navigation in humanoid robots is accelerating their penetration into home services, care, and industrial logistics [2]. Group 3: Technical Progress and Challenges - The development of embodied navigation has seen significant advancements since the introduction of PointNav in 2020, with evaluation systems expanding to include ImageNav and ObjectNav [3]. - Current challenges include achieving human-level performance in open vocabulary object navigation and dynamic obstacle scenarios, despite notable progress in closed-set tasks [3]. - The introduction of frameworks like Sim2Real by Meta AI provides methodologies for transitioning from simulation training to real-world deployment [3]. Group 4: Educational Initiatives - The article highlights the creation of a comprehensive course aimed at addressing the challenges faced by newcomers in the field of Goal-Oriented Navigation, focusing on practical applications and theoretical foundations [9][10][11]. - The course structure includes a systematic approach to understanding the technology's evolution, practical training on simulation platforms, and hands-on projects to bridge theory and practice [14][15][16][18].

Core Viewpoint - Goal-Oriented Navigation empowers robots to autonomously complete navigation tasks based on goal descriptions, marking a significant shift from traditional visual language navigation systems [2][3]. Group 1: Technology Overview - Embodied navigation is a core area of embodied intelligence, relying on three technical pillars: language understanding, environmental perception, and path planning [2]. - Goal-Oriented Navigation requires robots to explore and plan paths in unfamiliar 3D environments using only goal descriptions such as coordinates, images, or natural language [2]. - The technology has been industrialized in various verticals, including delivery, healthcare, and hospitality, enhancing service efficiency [3]. Group 2: Technological Evolution - The evolution of Goal-Oriented Navigation can be categorized into three generations: - First Generation: End-to-end methods focusing on reinforcement learning and imitation learning, achieving breakthroughs in Point Navigation and closed-set image navigation tasks [5]. - Second Generation: Modular methods that explicitly construct semantic maps, breaking tasks into exploration and goal localization [5]. - Third Generation: Integration of large language models (LLMs) and visual language models (VLMs) to enhance knowledge reasoning and open vocabulary target matching [7]. Group 3: Challenges and Learning Path - The complexity of embodied navigation, particularly Goal-Oriented Navigation, necessitates knowledge from multiple fields, making it challenging for newcomers to enter the domain [9]. - A new course has been developed to address these challenges, focusing on quick entry, building a research framework, and combining theory with practice [10][11][12]. Group 4: Course Structure - The course will cover the theoretical foundations and technical lineage of Goal-Oriented Navigation, including task definitions and evaluation benchmarks [15]. - It will also delve into the Habitat simulation ecosystem, end-to-end navigation methodologies, modular navigation architectures, and LLM/VLM-driven navigation systems [16][18][20][22]. - A significant project will focus on the reproduction of VLFM algorithms and their deployment in real-world scenarios [24].

Core Viewpoint - Tencent has officially invested in the robotics company founded by the influential figure in the tech industry, Zhi Hui Jun, marking a significant development in the robotics sector in China [1][2]. Group 1: Investment Details - Shanghai Zhiyuan New Technology Co., Ltd. recently underwent a business change, with original shareholders exiting and new shareholders including Guangxi Tencent Venture Capital Co., Ltd. and Wolong Electric Drive joining [1][2]. - After the investment, Tencent holds approximately 2.06% of the equity in Shanghai Zhiyuan New Technology Co., Ltd. [2]. - The company has attracted investments from notable firms such as BYD and Hillhouse Capital, indicating a strong backing from the industry [4]. Group 2: Company Background - Zhiyuan Robotics, a well-known domestic robotics brand, is headquartered in Shanghai and focuses on developing intelligent robots [1][4]. - The company was co-founded by Zhi Hui Jun, who previously worked at Huawei and was part of the "Huawei Genius Youth Program" [1][5]. - Zhi Hui Jun, born in 1993, has gained recognition as a prominent figure in the tech community, known for his DIY hardware projects and contributions to AI and robotics [5][6]. Group 3: Future Prospects - Zhiyuan Robotics plans to launch its general-purpose robots for commercial production by December 2024, with significant milestones already achieved in its development [6]. - The company has introduced its first general-purpose embodiment model, the Zhiyuan Qiyuan large model, which is expected to enhance the accessibility of embodied intelligence in various sectors [6].