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泛太科技杨恒博士：扬帆出海正当时

        2025年5月下旬太阳成集团tyc7111cc董事长、新加坡南洋理工大学校友、香港科技大学校友、西北工业大学无锡校友会副会长杨恒博士应约访问了泰国曼谷、新加坡等地。在曼谷期间，杨博士和泰国AI高教、职业教育合作方进行了深入探讨，并介绍了泛太出海 AI教育从实验室、师资培养和校企合作Total Solution，获得合作方好评。


        5月20日傍晚6点，杨恒博士与新加坡共和理工学院的姜力军教授、国立大学吴达军博士、华科大仓储总裁陶明及西北工业大学新加坡校友会王永杰、强大勇、徐磊、陈国超、李宁和吴小伟等一批教授专家在武吉士的麦当劳会晤。

        杨恒博士作为“海外科技领军人才”和一批志同道合合作伙伴回国创办了太阳成集团tyc7111cc。公司成立于2009年，坐落于中国物联网之都无锡新区，拥有一批留英、留美归国博士团队。泛太科技拥有包括发明专利、实用新颖专利以及计算机软件著作权在内的中国完全自主知识产权百余项。近年来与国内500余所院校合作共建了多门类的实验/实训室、实验/实训中心以及实践/实训基地，合作院校类别涵盖本科、高职、中职、技工技师以及普教学校，教培产品与服务受众已超过50万人。公司和清华大学共同获科技创新一等奖，也成功进入华为等一批上市公司头部企业供应商体系。
        近年来，全球AI赛道逐渐呈现中美争霸格局，中国AI、工业互联网、无人驾驶智能座舱、无人工厂、智慧高铁及智慧矿山等前沿技术不断获得海外关注。中国科技产业及教育产品具有物美价廉优势深受海外客户欢迎，杨恒博士此次东南亚行的一个主要目的就是与当地经销商探讨与各海外大专院校合作开办AI专业课程设置。
 
        无锡泛太科技在AI方面产品、教育与产业应用，立即引起了在场教授专家们的浓厚兴趣和热烈讨论。为使方便交流，大家移步到附近的湘聚餐馆，一边聚餐，一边进一步交流。
        第二天早上，杨恒还就相关话题与其他朋友继续探讨，详细阐述泛太主打产品、企业运行模式和出海目标及愿景。
        在新的一波AI教育浪潮下，南洋理工大学和新加坡国立大学一批博士专家支撑下，新加坡合作方和泛太科技正在推出新加坡儿童AI学习品牌。欢迎国内外感兴趣朋友加盟合作。
   
        此次出访是一次有关AI交流、学习和具体应用的有意义碰撞。最后，预祝无锡泛太科技出海乘风破浪！直挂云帆济沧海！（全文完）
        （感谢西北工业大学 新加坡校友会提供部分内容和图片）
 
 
 
附件 1. 泛太科技针对海外市场主打产品

产品1 第二代鸿蒙智能座舱实训车

 

        第二代鸿蒙智能座舱实训车（SeaIOT-CAR-05）是一款基于鸿蒙操作系统定制开发的智能座舱实验实训系统，该系统模拟智能网联汽车大脑，是云计算、大数据、人工智能、智联网、自动驾驶、国产鸿蒙系统等新一代信息技术在智能网联汽车教育领域的创新应用成果，整个智能座舱由一部纯电动汽车改装而成。包括智能驾驶实训系统、鸿蒙智能座舱实训系统、路况模拟虚拟仿真系统和线控底盘数据采集系统4大部分组成。第二代鸿蒙智能座舱实训车既可进行实验实训，也支持开展二次开发，更可完成无人驾驶，是本科人工智能、电子信息工程、车辆工程、自动化以及计算机科学与技术专业，高职高专智能网联汽车技术、汽车电子技术、汽车检测与维修技术、新能源汽车技术、智能交通技术运用、汽车运用与维修技术等专业。

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产品2 C2M2B黑灯工厂智能生产线

 

        C2M2B黑灯工厂智能生产线（SeaIOT-EA-DF04-01），是一款以AI、工业互联网和电气自动化为核心技术的全流程无人值守、甚至无需照明的订单式名片夹装配产线。将工厂产线简化搬进课堂，让学生在课堂上就能学习智能产线的各项技术，与工厂无缝衔接，缩短学生与行业的距离。
        产线由原料码垛、移载输送、机器人装配、雕刻检测共4个工位、以及安全操作罩壳拼装组合形成，结合边缘服务器、工业互联网融合平台，通过工业网络通信技术，用户直接使用微信小程序或移动端APP下单触发生产，实现名片夹上下盖的自动取料、移载输送、机器人自动取针、装针、顶针、合盖、压紧、中英文字符激光雕刻、名片夹表面划痕瑕疵检测、合格品及残次品自动分拣等功能，平台能够实时展示原材料库存状态、设备运行状态、当前及历史工单情况、视觉采集图片与检测结果、产线能量消耗等信息。
        课程方面，提供了丰富的实验资源和应用开发案例，支持职业技能大赛，具有协同分组实验实训、科研开发及应用创新的能力。

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产品3 鸿蒙轨道交通应用场景模型设备

 

        鸿蒙轨道交通应用场景模型设备（型号：SeaIOT-PMST-02），包括轨道交通智能沙盘实训系统、机车控制系统、微机联锁系统、CTC车站系统、CTC调度室系统、智慧轨道软件模块以及配套的课程资源。整套设备模拟真实轨道交通运营环境，实现列车自动控制、列车调度、乘客信息以及智能监控与预警管理等，以及再现智慧轨道交通中的交通流量管理、路径优化、自动驾驶车辆调度等应用场景模拟，搭建轨道交通控制和调度的仿真教学科研平台。

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产品4 物联网技术开发平台

 

        物联网技术开发平台（型号：SeaIOT-FTable-02）是一款基础教学实验开发平台，由1个通用平台，多个系列硬件模块，上位机软件及教学资源三部分组成，主要针对物联网、电子信息、计算机等专业的单片机与传感器、嵌入式接口技术、识别技术、无线通信技术、智能产品、人工智能等课程的教学实验。
SeaIOT-FTable-1A型增加了鸿蒙开发模块。
       平台结构符合人体工学设计，由分离式基座和网板组成。硬件模块采用磁吸方式与基座连接固定，接触式探针进行供电和信号传输，使用方便，不易损坏管脚，易于拓展。场景引入式教学模式和丰富的教学资源，既可以支撑单个模块单一知识点的学习，也支持多个模块自由组合进行多个知识点的综合应用。

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产品5 无线传感网全功能实验箱

 

        SeaIOT-B-WSN-2A型无线传感网全功能实验箱是SeaIOT-B-WSN-02型的升级版，集成Bluetooth、WiFi、IEEE802.15.4、ZigBee等短距离无线通信技术，将6LowPAN(IPv6)互联网协议应用到短距离无线通信网络中，与ZigBee使用的ZStack协议栈并存，支持双协议栈；集成LoRa、NB-IoT等长距离无线通信技术，自定义传感网协议，CoAP应用协议，实现主从机组网应用，平台接入应用。采用三星Cortex-A9 S5P4418四核处理器作为智能网关，支持6LowPAN、Z-Stack、自定义传感网协议等多协议解析，具有1GB内存、8GB大容量存储空间、7寸电容触摸显示屏、丰富的外围接口，可板载GPS定位、WIFI/BT二合一通讯、4G移动通讯等多种模块，内嵌Android、Linux双系统，可一键切换。
        系统提供丰富的实验例程、实验手册、教学视频等课程资源，能够满足嵌入式接口技术、无线通信技术、无线传感器网络、嵌入式系统应用开发、Android移动互联网应用开发等课程的教学与实践。

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产品6 5G人工智能实验箱

  

        5G人工智能实验箱（型号：SeaIOT-B-5GEDK-01），是一款综合5G通信、边缘计算、视觉识别、语音识别、物联网技术、Python应用开发的实验教学产品。
        产品采用高性能AI处理器，内嵌机器视觉库和深度学习框架，外围连接摄像头、麦克风阵列进行图像、语音信号的采集、分析、识别、决策；引出处理器外设接口用于应用扩展；板载物联网传感器和传感网模块，支持通过有线、或无线方式与AI系统进行通信；融合5G移动通信，可将数据、图像、视频等多媒体数据及结构化数据推送到云服务平台；提供5G云端接入、视频流实时推送、图像处理基础、机器学习、深度学习、语音识别、数据预测、以及与物联网模块结合开展综合应用的案例。

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产品7 人工智能物联网实验箱

        
        人工智能物联网实验箱（型号：SeaIOT-B-AIOT-01），是一款综合人工智能物联网技术综合应用、5G通信、边缘计算、视觉识别、语音识别、Python应用开发的实验教学产品。
        产品采用高性能AI处理器，内嵌机器视觉库和深度学习框架，板载摄像头、麦克风阵列进行图像、语音信号的采集、分析、识别、决策；引出处理器外设接口用于应用扩展；板载物联网传感器和传感网模块，支持通过有线、或无线方式与AI系统进行通信；融合5G移动通信，可将数据、图像、视频等多媒体数据及结构化数据推送到云服务平台；提供5G云端接入、视频流 实时推送、图像处理基础、机器学习、深度学习、语音识别、数据预测、以及与物联网模块结合开展综合应用的案例。

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附件 2. 泛太科技针对海外市场的AI 应用型本科教育tyc1286太阳集团

Teaching, Experiment and Graduation Project Plan for the Four-year Undergraduate Program in Artificial Intelligence and Information Technology International（Based on total solution of Fantai. Tech.）

1.1 Freshman Year Courses and Practice Plan

1.1.1 First Semester

· Course Offerings: "Advanced Mathematics", "University Physics", "Introduction to Computer Basics and Programming". "Advanced Mathematics" and "University Physics" provide the mathematical and physical foundation for subsequent professional courses. "Introduction to Computer Basics and Programming" teaches basic computer principles, basic Python programming syntax, data types, control structures, etc., enabling students to get an initial exposure to programming and laying the foundation for in - depth learning of programming languages and development technologies.
· Experiment Arrangements: Relying on the 5G artificial intelligence experiment box, conduct Python basic programming experiments, such as simple numerical calculations, string processing, conditional judgment, and loop structure applications. With the help of the basic development platform for electronics and computer - related majors, carry out basic circuit cognition experiments to familiarize students with the basic structure of the experiment box, circuit connection methods, and the use of common instruments and meters.
· Teaching Objectives: Enable students to understand the importance of basic subject knowledge in the major, master basic Python programming skills and basic circuit experiment operations, cultivate students' logical thinking and hands - on practical ability, and stimulate students' interest in learning professional courses.

1.1.2 Second Semester

· Course Offerings: "Discrete Mathematics", "Digital Electronic Technology", "Advanced Python Programming". "Discrete Mathematics" teaches knowledge such as set theory, mathematical logic, and graph theory, providing theoretical support for artificial intelligence algorithm design. "Digital Electronic Technology" explains digital logic basics, combinational logic circuits, and sequential logic circuits, enabling students to understand the basic principles and design methods of digital circuits. "Advanced Python Programming" delves into Python functions, modules, file operations, object - oriented programming, etc., to improve students' programming ability.
· Experiment Arrangements: Conduct digital circuit experiments on the basic development platform for electronics and computer - related majors, such as function testing and circuit design of digital chips like counters and decoders. Use the 5G artificial intelligence experiment box to carry out advanced Python programming experiments, such as developing simple command - line tools and file management programs.
· Teaching Objectives: Enable students to master the basic concepts and principles of discrete mathematics and digital electronic technology, proficiently use Python for more complex program development, improve students' logical thinking and circuit design capabilities, and cultivate students' programming thinking for solving practical problems.

1.2 Sophomore Year Courses and Practice Plan

1.2.1 First Semester

· Course Offerings: "Data Structure", "Algorithm Analysis and Design", "Introduction to Artificial Intelligence". "Data Structure" teaches data structures such as linear lists, stacks, queues, trees, and graphs, as well as their storage and operation methods, providing a data organization basis for algorithm implementation. "Algorithm Analysis and Design" explains common algorithm design strategies and algorithm complexity analysis methods, cultivating students' ability to design efficient algorithms. "Introduction to Artificial Intelligence" introduces the development history, basic concepts, main research fields, and application scenarios of artificial intelligence, giving students a comprehensive understanding of artificial intelligence.
· Experiment Arrangements: Based on the basic development platform for electronics and computer - related majors and the 5G artificial intelligence experiment box, conduct data structure and algorithm verification experiments, such as implementing the basic operations of linked lists and binary trees, and performance testing of sorting and searching algorithms. Carry out simple artificial intelligence algorithm experiments, such as building rule - based expert systems.
· Teaching Objectives: Enable students to master the core knowledge of data structures and algorithms, understand the basic principles and applications of artificial intelligence, be able to use the learned knowledge for simple algorithm implementation and artificial intelligence system construction, and improve students' algorithm design and practical ability.

1.2.2 Second Semester

· Course Offerings: "Machine Learning", "Computer Networks", "Database Principles and Applications". "Machine Learning" deeply explains machine learning algorithms such as supervised learning, unsupervised learning, and semi - supervised learning, including the principles and applications of models such as linear regression, decision trees, and neural networks. "Computer Networks" introduces the computer network architecture, protocols, and network communication principles, enabling students to understand the network data transmission and communication mechanisms. "Database Principles and Applications" teaches the basic concepts of database systems, relational database design, and SQL language, cultivating students' database design and operation capabilities.
· Experiment Arrangements: Use the 5G artificial intelligence experiment box to carry out machine learning algorithm experiments, such as using the iris dataset for classification algorithm experiments and building a simple neural network using the TensorFlow framework for handwritten digit recognition. Conduct computer network experiments on the basic development platform for electronics and computer - related majors, such as network topology construction, IP address configuration, and network communication testing. Conduct database experiments, such as designing and implementing a small - scale database management system.
· Teaching Objectives: Enable students to proficiently master machine learning algorithms and applications, understand the principles of computer networks and databases, have the ability to use machine learning algorithms to solve practical problems, design and manage databases, and improve students' practical skills in the cross - field of artificial intelligence and information technology.

1.3 Junior Year Courses and Practice Plan

1.3.1 First Semester

· Course Offerings: "Deep Learning", "Machine Vision", "Natural Language Processing". "Deep Learning" deeply studies the structure, training methods, and optimization strategies of deep neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. "Machine Vision" introduces the composition of machine vision systems, image processing algorithms, object detection and recognition technologies, cultivating students' ability to use machine vision technology to solve practical problems. "Natural Language Processing" explains the basic tasks, models, and algorithms of natural language processing, such as text classification, sentiment analysis, and machine translation, enabling students to understand how to make computers understand and process human language.
· Experiment Arrangements: Based on the 5G artificial intelligence experiment box, carry out deep learning experiments, such as using CNNs for image classification and object detection, and using RNNs for text generation. Conduct machine vision experiments, such as industrial product appearance defect detection and face recognition access control system design. Carry out natural language processing experiments, such as news text classification and simple question - answering system development.
· Teaching Objectives: Enable students to master the core technologies of deep learning, machine vision, and natural language processing, be able to use relevant technologies to develop intelligent application systems, and improve students' practical and innovative abilities in the cutting - edge fields of artificial intelligence.

1.3.2 Second Semester

· Course Offerings: "Internet of Things Technology and Applications", "Intelligent Computing Technology", "Principles of Big Data Technology". "Internet of Things Technology and Applications" introduces the architecture, key technologies, and application scenarios of the Internet of Things, including sensor technology, wireless communication technology, Internet of Things platforms, etc., cultivating students' ability to design and develop Internet of Things systems. "Intelligent Computing Technology" explains intelligent computing methods such as genetic algorithms and particle swarm optimization algorithms and their applications in optimization problems, expanding students' thinking for solving complex problems. "Principles of Big Data Technology" teaches the basic technologies of big data collection, storage, processing, and analysis, such as the Hadoop and Spark frameworks, enabling students to understand the big data processing process and technical architecture.
· Experiment Arrangements: Combine the basic development platform for electronics and computer - related majors and the 5G artificial intelligence experiment box to conduct Internet of Things comprehensive experiments, such as smart home system construction and smart agricultural environment monitoring system development. Carry out intelligent computing algorithm experiments, such as using genetic algorithms to solve function optimization problems. Conduct big data technology experiments, such as using Hadoop for large - scale data storage and processing and using Spark for data analysis and mining.
· Teaching Objectives: Enable students to master the basic principles and applications of the Internet of Things, intelligent computing, and big data technologies, be able to comprehensively use multiple technologies to solve practical problems, and improve students' cross - field technology application and system development capabilities.

1.4 Senior Year Courses and Practice Plan

1.4.1 First Semester

· Course Offerings: "Professional Comprehensive Course Design". Oriented by project practice, comprehensively apply the previously learned professional knowledge. Students work in groups to choose comprehensive projects, such as the development of intelligent security monitoring systems and the design of intelligent logistics management systems, covering artificial intelligence algorithms, information technology applications, system integration, and other aspects.
· Experiment Arrangements: Under the guidance of teachers, students use two experimental platforms to complete project requirements analysis, system design, code writing, system testing, and optimization. During the project implementation process, cultivate students' teamwork, project management, and comprehensive technology application capabilities.
· Teaching Objectives: Through the professional comprehensive course design, improve students' ability to comprehensively use professional knowledge to solve practical problems, cultivate students' teamwork spirit and project management capabilities, and lay a foundation for graduation projects and future career development.

1.4.2 Second Semester

· Course Offerings: "Graduation Project". Students determine their graduation project topics according to their interests and professional directions and conduct in - depth research and development. The topics can be sourced from teachers' scientific research projects, actual enterprise needs, or students' independent innovative ideas, such as artificial - intelligence - based medical image diagnosis assistance systems and big - data - based personalized recommendation systems.
· Experiment Arrangements: Students independently complete the graduation project, including project research, scheme design, technology selection, system development, experimental verification, and thesis writing. Teachers provide regular guidance, check the progress and quality of students' graduation projects, and help students solve problems encountered.
· Teaching Objectives: Through the graduation project, cultivate students' independent thinking, innovative practice, and scientific research abilities, enable students to have the ability to comprehensively use the learned knowledge to solve complex engineering problems, and meet the professional level and comprehensive quality requirements of undergraduate graduates.

1.5 Graduation Project Topic Directions

1. Deep - Learning - Based Intelligent Medical Image Diagnosis System: Use deep learning algorithms to analyze medical images (such as X - rays, CTs, MRIs, etc.) to achieve automatic disease disease diagnosis and auxiliary decision - making, improving the accuracy and efficiency of medical diagnosis.
2. Intelligent Environmental Monitoring System Based on the Internet of Things and Artificial Intelligence: Combine Internet of Things sensor technology to collect environmental data (such as air quality, water quality, noise, etc.), and use artificial intelligence algorithms for data analysis and prediction to achieve real - time environmental monitoring and intelligent management, providing support for environmental protection decision - making.
3. Natural - Language - Processing - Based Intelligent Customer Service System: Adopt natural language processing technology to implement an intelligent customer service system that can automatically understand user questions and provide accurate answers, improving customer service efficiency and quality. It can be applied in many fields such as e - commerce and finance.
4. Big - Data - and Machine - Learning - Based Personalized Education Recommendation Platform: Collect and analyze students' learning data, use machine learning algorithms to build personalized learning models, provide students with customized learning resources and learning path recommendations, achieve personalized education, and improve learning effects.