一种多巡飞器软件协同升级方法、装置、设备及介质
By constructing a Markov decision process model and using deep reinforcement learning algorithms to optimize the software upgrade sequence of multiple patrol aircraft, the problems of low upgrade efficiency, poor network stability, and unreasonable scheduling were solved, achieving efficient and stable collaborative software upgrades and improving upgrade success rate and consistency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- AVIC (CHENGDU) UAS CO LTD
- Filing Date
- 2026-03-31
- Publication Date
- 2026-07-17
AI Technical Summary
The software upgrade process for multi-loitering aircraft suffers from problems such as low upgrade efficiency, poor network connection stability, and lack of intelligent scheduling mechanisms, resulting in low task execution efficiency, frequent upgrade errors, and poor consistency.
By constructing a Markov decision process model and using deep reinforcement learning algorithms to train scheduling strategies, the software upgrade sequence of multi-roaming aircraft is optimized. Combined with multi-threaded distribution technology and real-time monitoring mechanisms, efficient and stable collaborative software upgrades are achieved.
It significantly improves the efficiency and stability of multi-loitering vehicle software upgrades, ensures consistency after upgrades, adapts to different numbers of loitering vehicle scenarios, reduces manual intervention, and improves upgrade success rate and network connection reliability.
Smart Images
Figure CN121934865B_ABST