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Building Consensus and Collaborating on Key Challenges | Volatility Technology Actively Participates in the National Key R&D Program Symposium on “Intelligent Management and Maintenance of Airport Runways”
On July 27–28, the quarterly workshop for the key project “Joint Research and Demonstration Application of Key Technologies for an Intelligent Airport Runway Maintenance System,” under the National Key R&D Program’s priority special project on “Strategic Scientific and Technological Innovation Cooperation” (Project No.: 2025YFE0217000), was held at the Science and Technology Management Co., Ltd. of the Capital Airport Group. Participants included Chongqing University, Shandong University, Nanjing University, Southeast University, Harbin Institute of Technology, and the Institute of Intelligent Manufacturing of the Guangdong Academy of Sciences, among others. Shanghai Xijing Technology Co., Ltd. 、 Wuhan Wolong Technology Co., Ltd. , Shandong Inspur Science Research Institute Co., Ltd., Capital Airport Group Technology Management Co., Ltd., and a total of 10 entities The participating research institutions attended the seminar.
During the conference, the participating research institutions presented updates on project progress and outlined their plans for the next phase. Participants engaged in discussions on key technologies and collaborative mechanisms critical to advancing the project, while also conducting in-depth field investigations with frontline road‑maintenance personnel. These efforts provided valuable practical insights for refining and implementing technical solutions, thereby fully demonstrating the synergistic innovation advantages of deep integration among industry, academia, research, and application. Runway maintenance is a critical component in ensuring the safe and efficient operation of airports. This project focuses on four core objectives: intelligent detection of runway anomalies, multi-modal fusion for maintenance decision‑making, autonomous system learning and dynamic optimization, and the integration of an end‑to‑end closed-loop smart maintenance system. By addressing these key technological challenges, we will establish a cloud–edge–device collaborative smart maintenance architecture that enables precise identification of anomalies across the entire runway surface, autonomous decision‑making for maintenance tasks, and continuous evolution of system capabilities. This technological advancement will significantly enhance the efficiency and safety of runway pavement maintenance, reduce operational costs, and drive the transformation of runway management toward a “data‑driven and AI‑powered” paradigm. It will also spur the development and widespread adoption of advanced intelligent equipment, providing the industry with replicable, scalable standardized technical solutions and a robust model for intelligent operations.
It is worth noting that our company recently successfully completed the first on-site field test for this research project at Luoyang North Suburban Airport. Relying on the software platform and core algorithms developed by BoDong Technology, this test validated the accuracy and reliability of the intelligent detection algorithms and data‑analysis software, laying a solid foundation for subsequent technological iterations and engineering applications. At this conference, BoDong Technology reaffirmed its commitment to following the guidance and deployment outlined during the event, further strengthening collaborative partnerships with all participating research institutions, and striving to fully accomplish the project’s established R&D objectives and engineering validation targets—thereby contributing technical expertise to the independent innovation and demonstration application of China’s smart runway maintenance system.
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From July 27 to 28, 2026, Wave Technology attended the quarterly workshop of the National Key R&D Program project on the “Intelligent Maintenance and Management System for Airport Runways,” while simultaneously conducting field tests at Luoyang North Suburban Airport to validate the accuracy of its software algorithms, thereby providing data support for the project’s subsequent iterations.
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