Academicians Appraise Innovations, Nationwide First in the Energy Storage Industry | Excellence Ruixin (02687) Discipline Large Model Lands at Tianjin University

Title context: Academicians Appraise Innovations, Nationwide First in the Energy Storage Industry | Excellence Ruixin (02687) Discipline Large Model Lands at Tianjin University Text: On the morning of October 20, the Tianjin University Energy Storage Discipline Vertical Domain Large Model Launch Event and AI-Empowered New Engineering Education Reform Seminar was held at Tianjin University. At the event, the nation's first energy storage discipline vertical domain large model version 1.0 was officially unveiled, pressing the "accelerator key" for the digital and intelligent cultivation of interdisciplinary engineering talent. Wang Chengshan, academician of the Chinese Academy of Engineering and dean of the School of Energy at Tianjin University, and Li Bin, member of the Standing Committee of the Party Committee and vice president of Tianjin University, attended the launch event. The technical support provider for this high-level conference was Hong Kong-listed knowledge technology enterprise Excellence Ruixin (02687). The AI capabilities of a listed company thus entered the core arena of discipline construction at a top university. The "question of the era" in the eyes of academicians, the technical answer given by the enterprise As the dual carbon strategy advances in depth and the new power system is accelerated in construction, the energy storage industry is becoming a key support for the energy transition. In the final analysis, competition in the industry is competition for talent. The energy storage discipline spans many fields such as materials, electric power, safety, and management, with intricate knowledge coupling. Under traditional teaching models, knowledge is transmitted in a scattered way by course unit, making it difficult for students to build a systematic and complete cognitive picture of the discipline. "The energy storage discipline is highly interdisciplinary and its knowledge system is complex. AI has brought complex disciplinary knowledge to 'life.'" Academician Wang Chengshan said at the launch event that artificial intelligence can make tangled disciplinary knowledge explicit and interactive, accurately identify students' learning weaknesses, and carry out deduction and verification for complex engineering scenarios. He also introduced that the positioning of the energy storage discipline vertical domain large model is to empower teachers, students, and discipline construction in an all-round way, promoting the upgrade of energy storage talent cultivation from the traditional teacher experience-driven model to a new "teacher-student-AI" collaborative-driven model. Behind the academician's judgment is a technical system that has already been implemented. According to reports, unlike general large models, the energy storage discipline vertical domain large model released this time is a discipline-specific knowledge engine built exclusively for the energy storage field, with Excellence Ruixin providing core technical support. The model is built on four core datasets: basic knowledge, course teaching, scientific research innovation, and engineering cases. Downward, it anchors more than 40 professional courses, including "Principles of Energy Storage," "Foundations of Energy Storage Chemistry," "Foundations of Energy Storage Physics," "Foundations of Electrochemistry," and the "Energy Storage Maker" series of project-based courses. Upward, it connects the full-chain application scenarios of self-study tutoring, scientific research assistance, project management, and career development. The disciplinary knowledge foundation formed by the four major datasets transforms the complex knowledge system in which materials, devices, systems, safety, and management are interwoven into teaching capabilities that are computable, interactive, and iterable. Relying on multiple dedicated intelligent agents such as the self-study little tutor, scientific research little helper, and internship and employment assistant, together with featured functions such as the course center, interdisciplinary knowledge workshop, and student profiles, the model covers the entire talent cultivation cycle of "teaching, learning, research, studying, and career." Industry observers pointed out that general large models solve the question of "whether one can converse," while discipline vertical domain large models solve the question of "whether one understands the profession." The implementation of the nation's first energy storage discipline vertical domain large model marks that the application of AI in higher education is advancing in depth from the general tool level to the core teaching of disciplines. This step imposes requirements on the technology provider's data accumulation, model capabilities, and industry understanding that are far higher than those of general scenarios. "Teacher-student-AI" triadic symbiosis redefines the engineering classroom The value of this launch that has attracted even more attention lies in the "teacher-student-AI" triadic symbiotic education ecosystem it proposes. This model is in sync with Tianjin University's New Engineering Construction Plan 3.0the plan explicitly proposes building a human-machine collaborative innovation ecosystem, promoting the shift of teaching relations from the traditional "teacher-led" model to "teacher-student-AI symbiosis," and transforming the classroom paradigm toward intelligent integration. On the front lines of teaching, triadic symbiosis is first reflected in the simultaneous efficiency gains at both the teacher and student ends. For frontline teachers, the large model takes over a large amount of repetitive work such as knowledge organization and routine Q&A, freeing teachers from cumbersome affairs so that they can focus more on inspiring students' higher-order thinking, carrying out project guidance, and providing value leadership. When teachers input teaching requirements, the model can produce a complete instructional design plan including basic course information, teaching objectives, and content outlines. For the broad body of students, the system combines individual learning trajectories to intelligently push knowledge point navigation and personalized learning plans, and provides services such as literature assistance, project control, and internship and employment profiles, enabling students with different foundations to obtain customized support and truly realize teaching according to aptitude. In scientific research scenarios, the value of the large model extends further. Taking the complex engineering problem of a campus microgrid power outage under extreme weather as an example, students use the scientific research little helper to obtain system parameters and scenario information, sort out the technical context and timeline, deduce multiple feasible power supply schemes, and ultimately form a decision conclusion. The complete closed loop of "analysis-deduction-decision-verification" allows students to temper systematic thinking in engineering situations close to reality. Beyond the classroom, the internship and employment assistant builds a standardized management system for the entire internship process, covering links such as the school-enterprise resource database, task management, process monitoring, and daily and weekly reports, and outputs comprehensive evaluations and employment profiles after the internship ends. Data from the four training modules of the course center, scientific research practice, project implementation, and enterprise internship ultimately flows into student profiles and is automatically deposited into a visualized professional growth trajectory, realizing a leap from "recording growth" to "guiding growth." Vice President Li Bin said at the event that the emergence of this energy storage discipline vertical domain large model is a breakthrough practice in the deep embedding of artificial intelligence into engineering education and teaching, and he looks forward to polishing out a replicable and promotable sample of digital and intelligent engineering education reform. Behind the benchmark project, the capability foundation of a knowledge technology company Discipline construction projects at top universities have always been the highest-threshold examination room in the education technology field. The reason why the nation's first energy storage discipline vertical domain large model was supported and implemented by Excellence Ruixin is inseparable from its long-accumulated core capabilities. Public information shows that Excellence Ruixin positions itself as a knowledge technology company and builds its capability system around "letting data carry knowledge and letting AI understand knowledge," forming three core pillars: data assets, self-developed full-modal large models, and a nationwide service network. At the data level, relying on deep resources accumulated from serving more than a thousand universities and research institutes, the company builds cross-domain structured knowledge graphs. At the model level, the company has formed a full-modal large model matrix covering text, voice, vision, and 3D generation. At the service level, its nationwide network supports projects in deeply entering frontline teaching scenarios for continuous iteration. Specifically for this cooperation, the construction of the four core datasets tests the enterprise's structured governance capability for the energy storage discipline knowledge system; the deep anchoring of more than 40 courses tests the integration capability of AI with real teaching processes; and the formation of the "teacher-student-AI" triadic symbiotic ecosystem tests the comprehensive strength of the technology provider in moving from a single-point tool to a systematic platform. Under these three thresholds, the demonstration effect of the benchmark project is emerging. According to the plan, Tianjin University will next continue to polish and iterate the energy storage discipline vertical domain large model, promote the deep implementation of AI throughout the entire classroom teaching process, improve the intelligent talent cultivation system in the energy storage field, and deliver top-notch innovative energy storage talent for the country's "dual carbon" goals and the building of an energy powerhouse. As this "Tianjin University sample" matures and is promoted, discipline vertical domain large models are expected to move from energy storage to more interdisciplinary engineering fields, opening up broader industrial space. At the intersection of the waves of education digitalization and new engineering construction, the role of AI companies is evolving from "tool suppliers" to "partners in educational transformation." The release of the nation's first energy storage discipline vertical domain large model is both a key move in Tianjin University's new engineering reform and another footnote to Excellence Ruixin's vertical deep-cultivation strategy. The academicians' endorsement and the university's choice are themselves the most persuasive vote by the market for the quality of its technology.