On May 13, our school organized students to visit the R&D Headquarters of Dongfeng Motor Group Co., Ltd. for an in-depth exchange on industry-university-research collaboration regarding scientific research topics in statistics and the establishment of student internship and practice bases, injecting new vitality into collaborative innovation. Participants from our school included Vice Dean Liu Wei, Deputy Party Secretary Wang Siming, Director of the Department of Probability and Statistics Science Rang Guanglin, along with faculty representatives, undergraduate, and postgraduate students. Representatives from Dongfeng R&D Headquarters included Cui Yuxin, Director of Digitalization and Capability Assurance Department; Sun Wei, Chief Digital Engineer; and Chen Kongwu, Senior Engineer in charge of Digital Platform Technology, among others.
Representatives from Dongfeng R&D Headquarters began by introducing the company's development overview and scientific research progress to our faculty and students. They first explained the R&D layout and main R&D products of Dongfeng R&D Headquarters, analyzed the overall data situation and strategy of the R&D center, and then introduced several highly valuable collaborative research topics. Topic 1, "Smart Strategy (Panoramic Decision Cloud)," analyzes user driving habits using AI and big data technologies to provide strong support for vehicle project decision-making. Topic 2, "Smart Drawing - Vehicle Definition Workshop," focuses on customer segmentation, vehicle usage journeys, and customer complaints to mine key clues for product definition. Topic 3, "Smart Connection - Vehicle Infotainment Ecosystem Hub," concentrates on user usage of vehicle infotainment systems and mobile phone functions to accurately identify strengths and cost-reduction opportunities, enabling function optimization. Topic 4, "Smart Connection - Smart Drive - Scenario Efficiency Verification," utilizes algorithms like machine learning to aid R&D improvements, fault resolution, and cost reduction. Topic 5, "Smart View - Full Domain Sales Channel Map," combines AI marketing recommendation algorithms to provide precise direction for marketing activity locations and secondary network development. Topic 6, "Smart Care - Vehicle Service Ecosystem Compass," constructs various algorithms to achieve predictive fault diagnosis and maintenance, identify potential customers, and increase aftermarket revenue. These topics fully demonstrate the broad application prospects of statistics in automotive R&D, marketing, after-sales service, and other fields, laying a solid foundation for in-depth cooperation between the university and the enterprise.
During the discussion session, both sides engaged in lively discussions on the application of statistics in automotive R&D, models for university-enterprise cooperation, and the construction of internship and practice bases. Student Hu Guoqing raised a question about the application of clustering algorithms in enterprise data processing. Experts from Dongfeng Motor Group responded that current projects use machine learning models for clustering, achieving 95% accuracy, and future efforts will explore better algorithms in both depth and breadth. This response not only provided students with insight into the current state of technological application in actual enterprise projects but also pointed out directions for statistical research. Student Yang Guang focused on internships and practical work, inquiring about the content of internship tasks, tools used, project outputs, and whether it involves front-end and back-end collaboration. Company representative Chen Kongwu explained that the enterprise has a complete product and development collaboration process, and interns will quickly integrate into the enterprise under professional guidance and team collaboration, participating in actual project work to contribute to both personal growth and corporate innovation.
This exchange activity built a bridge for our students to connect with enterprise practice, providing them with a clearer understanding of future career development. Simultaneously, it laid a solid foundation for both sides in scientific research cooperation and talent cultivation, promising to promote the deep application of statistics in the automotive industry and achieve mutual benefits in industry-university-research collaboration. In the future, both parties will further deepen their cooperation to jointly cultivate more outstanding talents for the industry's development.
(Contributors: Liu Yingjun Photographers: Hu Guoqing, Wang Mengxuan, Song Yanwei)
