Investment Rating - The industry rating is "Recommended" [1] Core Insights - The trend of integrated development of satellite networks is characterized by the convergence of sensing, computing, and communication, evolving into an intelligent entity capable of perception, reasoning, and action [2][5] - The intelligent entity consists of three layers: 1) Global Connectivity Layer, providing comprehensive and optimal connectivity; 2) Fusion Capability Layer, where "cloud-edge-end collaboration" becomes an intrinsic capability; 3) Intelligent Service Layer, where "network as intelligence" offers cognitive-level information services [5][7] - The evolution of satellite networks from "technical tools and infrastructure" to "intelligent ecological hubs" and "value creation platforms" is highlighted, aiming for seamless loops of global perception, intelligent computation, and on-demand services [2][8] Summary by Sections Integrated Sensing and Computing - The sharing of satellite infrastructure allows for multi-modal functionalities on a single satellite platform, exemplified by the Iridium second-generation system integrating communication, navigation, and situational awareness [3] - The communication network is transitioning from "connective collaboration" to "integrated coexistence," with high and low orbit collaboration and integrated space-ground networking as core directions [3] Dynamic Resource Scheduling - The construction of elastic resource networks is possible through dynamic scheduling, as seen in the Zhijiang Laboratory's "Three-body Computing Constellation," which enables in-orbit computing capabilities and resource allocation [4] Global Connectivity and AI-Driven Services - The global connectivity layer features a three-dimensional backbone network with high, medium, and low orbit satellites, each serving distinct roles in global resource scheduling and data processing [5][6] - The intelligent service layer transforms the network into an intelligent entity capable of understanding user intent and providing cognitive-level responses, facilitating applications across various industries [7][10] Case Study: Palantir's Meta Constellation - Palantir's "Meta Constellation" system exemplifies the integration of satellite data with multi-domain data, enabling real-time situational awareness and decision-making across military, commercial, and governmental sectors [8][9] - The system's core functions include planetary-level situational awareness, tactical target tracking, and autonomous learning capabilities, significantly enhancing operational efficiency and responsiveness [10][14] Beneficiary Companies - Companies such as Putian Technology, Holoway, Guoke Military Industry, and others are positioned to benefit from advancements in satellite technology and the growing demand for integrated satellite services [15][16][17]

Core Viewpoint - MogoMind, launched by MOGOX, is the first physical world cognitive model that aims to enhance urban traffic management through real-time data integration and intelligent decision-making [1][8]. Group 1: MogoMind's Functionality and Role - MogoMind serves three primary roles: central decision-maker for urban traffic, multi-functional assistant for vehicle operation, and invisible foundation for autonomous driving [2]. - The model utilizes an integrated sensing and computing network to capture and analyze vast amounts of heterogeneous data, enabling real-time perception and decision-making [1][4]. Group 2: Improvements Over Traditional Systems - Traditional traffic perception systems rely on isolated devices, leading to information silos and limited coverage, which hampers effective traffic management [3]. - MogoMind's multi-modal sensor collaboration combines LiDAR, high-definition cameras, and millimeter-wave radar to create a continuous sensing network, addressing compatibility issues and enhancing data accuracy [4]. Group 3: Limitations and Challenges - The effectiveness of MogoMind decreases in suburban areas due to deployment and maintenance costs, resulting in a significant drop in data accuracy and update frequency [5]. - The model's reliance on sample vehicle data for road condition estimation presents challenges during low traffic periods, leading to data sparsity and reduced model performance [5]. Group 4: Societal and Technical Implications - MogoMind's focus on efficiency may overlook safety and equity concerns in specific areas, highlighting the need to quantify social values within the model [6]. - The model exposes critical issues in the industry, such as the need for improved physical data collection, human behavior modeling, and balancing multiple objectives [6][7]. Group 5: Future Directions - Addressing the identified challenges requires interdisciplinary collaboration among traffic engineers, sociologists, and policymakers to develop innovative solutions [7]. - MogoMind's development signifies a step towards integrating intelligent transportation systems with urban planning and social governance [7][8].

Ground-based Perception and Computing Network - The breakthrough of 5G-A technology represents a significant leap in communication speed, latency, and positioning accuracy, with peak download rates reaching 10Gbps and latency reduced to milliseconds [2][4] - 5G-A technology supports various applications, including extended reality (XR), cloud gaming, and industrial internet, showcasing its transformative potential in sectors like autonomous driving and smart cities [4][5] - Major Chinese telecom operators are accelerating the deployment of 5G-A networks, with China Mobile planning to complete the smart transformation of 400,000 base stations by 2025, covering over 300 cities [4][5] AI-Driven Smart Traffic Practices - Companies like Mushroom Car Union are building AI-driven perception and computing networks for smart traffic, utilizing roadside intelligent units and cloud AI models for real-time traffic optimization [5][6] - The system enhances traffic efficiency by synchronizing vehicle intentions at intersections, improving traffic flow by over 30% [6][8] - The integration of AI perception networks significantly reduces traffic accident rates, demonstrating the technology's value in public safety [8] Space-based Perception and Computing Network - The recent launch of the "Three-body Computing Constellation" marks a major breakthrough in space-based perception and computing networks, enabling on-orbit data processing and real-time analysis [9][11] - The constellation consists of 12 satellites with a total computing power of 1000POPS, allowing for rapid disaster warning and environmental monitoring [11][12] - The integration of AI into space infrastructure enables autonomous scheduling and execution of multi-source data fusion tasks, enhancing the efficiency of various applications [12][14] Future Outlook: Integrated Perception and Computing Network - The complementary nature of ground-based and space-based networks allows for seamless integration, ensuring continuous navigation and communication in various scenarios [15][17] - Challenges such as standardization, resource allocation, and security need to be addressed, but they also present new opportunities in chip development and software innovation [17][18] - The true value of AI perception networks lies in driving technology integration through scenario-based approaches, enhancing capabilities in autonomous driving and other applications [18][20] Conclusion - The transition from ground-based 5G-A to the space-based "Three-body Constellation" signifies a shift towards an integrated AI perception and computing network, reshaping communication, perception, and computation boundaries [20][21] - China's strategic positioning in 5G-A and space computing networks places it at the forefront of this technological evolution, paving the way for a new era of digital civilization [20]