基于云边协同的多模态洪涝灾害预测架构

陈淑萍; 方禾; 石宇歆; 林舟; 张弛张

doi:10.11959/j.issn.2096-3750.2026.00516

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基于云边协同的多模态洪涝灾害预测架构

理论与技术 | 更新时间：2026-04-16

- 基于云边协同的多模态洪涝灾害预测架构
- A multimodal flood prediction architecture based on cloud-edge collaboration
- 物联网学报 2026年10卷第1期页码：172-180
- 作者机构：
  
  福建师范大学计算机与网络空间安全学院，福建福州 350117
- 作者简介：
  
  [ "陈淑萍（2004‒ ），女，福建师范大学计算机与网络空间安全学院在读，主要研究方向为云边协同、神经网络。" ]
  [ "方禾（1991‒ ），女，博士，福建师范大学计算机与网络空间安全学院教授、博士生导师，主要研究方向为无线网络安全、智能认证、信任管理、异常检测与智能监测、分布式机器学习与多任务优化方法。" ]
  [ "石宇歆（2004‒ ），男，福建师范大学计算机与网络空间安全学院在读，主要研究方向为神经网络。" ]
  [ "林舟（2004‒ ），男，福建师范大学计算机与网络空间安全学院在读，主要研究方向为边缘计算。" ]
  [ "张弛张（2004‒ ），男，福建师范大学计算机与网络空间安全学院在读，主要研究方向为神经网络、多模态融合。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62401145);国家级大学生创新创业训练计划项目(202510394023);福建省科技重大专项专题项目(2024YZ040025);江苏省自然科学基金资助项目(BK20230479)
- DOI：10.11959/j.issn.2096-3750.2026.00516
  中图分类号： TP393.09
- 收稿：2025-03-29，
  
  修回：2025-07-28，
  
  录用：2025-09-01，
  
  纸质出版：2026-03-30
- 稿件说明：
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陈淑萍,方禾,石宇歆等.基于云边协同的多模态洪涝灾害预测架构[J].物联网学报,2026,10(01):172-180.

Chen Shuping,Fang He,Shi Yuxin,et al.A multimodal flood prediction architecture based on cloud-edge collaboration[J].Chinese Journal on Internet of Things,2026,10(01):172-180.
陈淑萍,方禾,石宇歆等.基于云边协同的多模态洪涝灾害预测架构[J].物联网学报,2026,10(01):172-180. DOI： 10.11959/j.issn.2096-3750.2026.00516.

Chen Shuping,Fang He,Shi Yuxin,et al.A multimodal flood prediction architecture based on cloud-edge collaboration[J].Chinese Journal on Internet of Things,2026,10(01):172-180. DOI： 10.11959/j.issn.2096-3750.2026.00516.

摘要

洪涝灾害频发严重威胁社会经济稳定与居民财产安全，提升洪涝灾害预测的准确性与实时性成为亟须解决的问题。为此，提出了基于云边协同的多模态洪涝灾害预测架构，突破传统云计算在传输时延、计算负载与实时性方面的瓶颈。该架构利用物联网设备采集原始数据，在边缘层构建基于长短期记忆网络（LSTM）的局部实时预测模型生成局部预测结果；在云端构建基于Transformer的全局融合预测模型提取长距离依赖关系，形成全局预测结果，并设计权重自适应调整算法以优化局部与全局结果的协同。实验结果显示，该架构在洪涝灾害预测的准确性、数据传输时延、实际带宽速率及边缘计算资源利用率均优于传统集中式云计算架构。研究表明，云边协同与多模态融合能够有效提升洪涝灾害预测的准确性与实时性，为防灾减灾和科学决策提供了新思路。

Abstract

The frequent occurrence of flood disasters is found to pose a serious threat to socio-economic stability and residents' property security

and the improvement of prediction accuracy and timeliness is identified as an urgent issue. To address this problem

a multimodal flood disaster prediction architecture based on cloud-edge collaboration was proposed

which overcame the bottlenecks of traditional cloud computing in terms of transmission latency

computational load

and real-time performance. In this architecture

raw data was collected by IoT devices

a local real-time prediction model based on LSTM networks was constructed at the edge layer to generate local prediction results

and a global fusion model based on Transformer networks was built in the cloud to capture long-range dependencies and produce global results. Moreover

an adaptive weight adjustment algorithm was designed to optimize the coordination between local and global outputs. Experimental results show that the proposed architecture outperforms traditional centralized cloud computing in prediction accuracy

data transmission latency

actual bandwidth rate

and edge computing resource utilization. It is concluded that cloud-edge collaboration and multimodal fusion effectively enhance the accuracy and timeliness of flood disaster prediction

providing new insights for disaster prevention

mitigation

and scientific decision-making.

关键词

Keywords

references

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