科思科技(688788)8月27日披露了2025年半年报，上半年公司实现营业收入15,445.69万元，较上年同期 增长40.54%；实现归属于母公司所有者的净利润-10,872.15万元，较上年同期有所减亏。受最终用户的 具体需求或其年度采购计划等的影响，公司营业收入有明显提升，财务状况也有所改善。 半年报显示，2025年上半年公司营业收入同比大幅上升，但公司仍然保持较高的研发投入，上半年研发 投入占营业收入的比例高达84.88%，同时公司业务的发展也致使相关费用同比增加，此外公司还计提 了适当的资产减值准备，导致公司上半年利润亏损。 据悉，科思科技的芯片研发取得重要进展，自主研发的第一代智能无线电基带处理芯片也已进入商业化 推广阶段，第二代智能无线电基带处理芯片已完成试产流片及测试工作，正在全力推进产品化落地；射 频收发芯片已经完成流片，正在进行封装以及测试。科思科技高度重视研发工作，继续保持对AI、云 计算、虚拟现实、智能决策等领域前沿技术的研发投入。 ...

Core Viewpoint - The Ministry of Industry and Information Technology of China has initiated a push for the deployment of large models in key manufacturing sectors, marking a transition from experimental AI development to industrial application, with manufacturing becoming a core area for technology transformation [1][2]. Group 1: Challenges in Manufacturing - Traditional manufacturing enterprises face three main challenges: data silos, difficulty in knowledge retention, and slow decision-making responses [1]. - The automotive industry has experienced significant losses due to supply chain disruptions, highlighting the limitations of traditional ERP systems in predicting component shortages [1][2]. Group 2: Demand for Intelligent Decision-Making - There is a pressing need for intelligent decision-making capabilities in manufacturing, with large models offering a breakthrough through their integrated cognitive, reasoning, and generative abilities [2]. - A case in the steel industry demonstrated that the deployment of a large model improved scheduling efficiency by 40%, reduced turnaround time by 12%, and generated annual savings exceeding 10 million yuan [2]. Group 3: Technical Implementation Features - The implementation of large models in manufacturing is characterized by data-driven intelligent decision-making, utilizing vast amounts of production data for deep analysis [2][3]. - Multi-modal integration allows large models to process diverse data types, significantly enhancing quality inspection efficiency, as evidenced by a 300% increase in detection efficiency for an electronics company [3]. - A hybrid deployment model combining edge computing and cloud optimization addresses the real-time processing needs of manufacturing [3]. Group 4: Barriers to Adoption - The adoption of large models faces three significant barriers: data fragmentation across various systems, a shortage of skilled professionals who understand both manufacturing processes and AI modeling, and long investment return cycles [3][4]. - Initiatives such as the establishment of industry-level data exchanges and the promotion of federated learning are being explored to overcome data barriers [3]. Group 5: Policy Innovations - Policy innovations should focus on targeted support, such as promoting "AI micro-factory" models for discrete manufacturing to lower transformation costs and creating industry model libraries for shared algorithm resources [4]. - The unique Chinese approach to AI in manufacturing leverages a vast array of industrial scenarios to drive the evolution of large models [4]. Group 6: Future Prospects - The deep integration of large models with manufacturing is expected to facilitate three major transitions: from scale expansion to quality enhancement, from factor-driven to innovation-driven growth, and from following industry standards to leading them [5]. - The penetration of large model technology into every production unit and the application of digital twin technology will enable Chinese manufacturing to transition from a follower to a leader in the global market [5].

Core Viewpoint - The article discusses the challenges and opportunities faced by the restaurant industry in the digital age, particularly focusing on the application of generative BI (Business Intelligence) to enhance decision-making and operational efficiency [2][3]. Group 1: Digital Transformation in the Restaurant Industry - The restaurant industry is experiencing a dual challenge of "data flood" and "decision thirst," which generative BI and AI technologies aim to address [3]. - XIBEI has been on a digital journey since 2010, establishing a comprehensive digital network that connects the supply chain to service endpoints [3]. - The goal is to transform data visualization into intelligent decision-making through the application of generative BI [3][4]. Group 2: Generative BI Implementation Challenges - XIBEI's core objective in generative BI is to deliver the right data at the right time, in the right way, to the right people, which presents significant challenges in practical implementation [4][5]. - The main difficulty lies in balancing information density; too much information can overwhelm users, while too little can hinder decision-making [5]. - Data governance is identified as a prerequisite for BI implementation, with a focus on ensuring data quality and standardization across various business processes [9]. Group 3: User-Centric Data Strategies - XIBEI has developed a three-tier user profile system to tailor data push strategies for different roles within the organization, such as store managers and chefs [7]. - The company is exploring the potential of large models for data correlation analysis and intelligent algorithm optimization [8]. Group 4: Practical Applications and Future Plans - Current applications of generative BI at XIBEI include intelligent customer service and activity effectiveness prediction [10]. - The company faces challenges in standardizing operational procedures, such as inventory management, to ensure compliance and effective use of tools [11][12]. - Future plans involve creating two intelligent systems: a marketing activity library for ROI prediction and an operational AI system for real-time strategy recommendations [16]. Group 5: Industry Insights and Recommendations - XIBEI advises against blindly pursuing new technologies without first ensuring data accuracy and measuring the return on investment [17]. - The article emphasizes the importance of establishing a closed-loop system of "data → insight → action" to help restaurant businesses navigate market uncertainties [17].

Investment Rating - The report does not explicitly state an investment rating for the communication equipment industry or the specific company involved [1]. Core Insights - The communication equipment industry is experiencing a dual-driven demand from traditional equipment delivery acceleration and rapid development in emerging fields [17]. - The company, Shenzhen Kesi Technology Co., Ltd., has a significant first-mover advantage and deep technological accumulation in the electronic information industry, focusing on continuous R&D investment across various fields including AI and cloud computing [17]. - The company is actively pursuing intelligent and unmanned product trends, integrating AI, smart wireless communication, and virtual reality technologies into its offerings [17]. Summary by Sections 1. Basic Research Information - The research was conducted on March 13, 2025, focusing on Shenzhen Kesi Technology, which operates in the communication equipment sector [13]. 2. Detailed Research Institutions - Participating institutions included Ping An Fund, Southern Fund, Xinda Australia Fund, and Rongtong Fund, all of which are fund management companies [14]. 3. Research Institution Proportions - The report does not provide specific data on the proportions of research institutions involved [16]. 4. Main Content Information - The company was established in 2004 and listed on the Sci-Tech Innovation Board in 2020, emphasizing its commitment to R&D in various advanced technology fields [17]. - The company has established a complete chip R&D team and has made significant investments in chip development, with successful trials of its first-generation smart wireless communication baseband chip [17][19]. - R&D expenses are primarily composed of employee salaries, materials, depreciation, and design/testing fees, with a high proportion allocated to chip development [19].