面向海上无人系统的边缘模型协同与数据压缩算法

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中图分类号：TP301 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.05.34

Abstract：In sea environment，unmanned system relies on edge artificial inteligence（AI）model collaboration to implement data collection and edge processing tasks.However，facing with problems such as poor communication links，limited communication bandwidth，and sensitive to interferences，this paper firstly proposes a model collaborative training method，federated mutual distillation，to reduce thebandwidth requirements formodel training data，from the perspective of AI model collaboration among unmanned aerial vehicle.Secondly，from the perspectiveof data de-redundancy transmision，a data differential dynamic compression method is proposed to reduce the frequency of data transmisson.Simulation results show that the performance of the model，trained with the federated mutual distilation training method，is beter than thatof the benchmarks，and acostof communication bandwidth is reduced compared to the centralized training models. The proposed data diferential dynamic compression method can greatly reduce the sending length and frequency of communication messages，and adapt to the bandwidth bottleneck in the weak communication connection environment.

Keywords：weak communication link；edge model collaboration；federated mutual distillation；data compression

0 引言

近年来，随着海洋技术的发展，对于海上船舶信息监测的需求也在不断提高，然而在海上架设监测设备难度过大。（剩余19078字）