基于气压特征融合的包裹碰撞与挤压识别

段晨昊; 朱磊; 张媛; 杜艳平

doi:10.19554/j.cnki.1001-3563.2026.17.023

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包装工程（技术栏目） ›› 2026, Vol. 47 ›› Issue (17) : 223-233. DOI: 10.19554/j.cnki.1001-3563.2026.17.023

自动化与智能化技术

基于气压特征融合的包裹碰撞与挤压识别

段晨昊^1a,b, 朱磊^1a,b*, 张媛^1a,b, 杜艳平^1b

作者信息 +

Recognition of Parcel Collision and Squeeze Based on Air Pressure Feature Fusion

DUAN Chenhao^1a,b, ZHU Lei^1a,b*, ZHANG Yuan^1a,b, DU Yanping^1b

Author information +

文章历史 +

摘要

目的为解决非可视物流运输中包裹破损追溯困难、内部缓冲结构失效评估缺乏客观依据的问题,提供一种低成本、抗干扰的包裹受力状态与受损方位精准监测手段。方法搭建模拟真实物流包裹的准密闭腔体实验平台,在其内部对称部署高频动态与低频静态气压传感器,采集单侧撞击、双侧挤压等典型工况下的气压信号。首先,利用基于物理能量分布先验的自适应算法对原始高噪声信号进行清洗。随后,将对齐后的多模态气压序列输入所构建的压力上下文卷积循环神经网络（PC-CRNN）中,通过残差卷积提取空间特征并结合双向长短期记忆网络解析时序演变规律,最终完成受力状态的识别。结果该系统有效克服了传统方法难以辨识“双侧连续挤压”与“单侧强撞击”的混淆局限。在包含多种受力状态的复杂工况下,系统综合识别准确率达到88.95%。尤其是针对破坏性极强的“双侧挤压”工况,其识别准确率较无时序特征提取的基线模型大幅提升了23.8%。结论双尺度气压特征融合与时空深度学习架构成功实现了包裹定向碰撞与挤压的高精度辨识,为物流安全监测提供了一种低成本、高鲁棒性且支持多点受力分析的技术方案。

Abstract

The work aims to propose a low-cost and anti-interference precise monitoring method for the force state and damage location of packages to deal with the problems of difficult traceability of package damage and the lack of an objective basis for assessing the failure of internal buffer structures in non-visual logistics transportation. A quasi-sealed chamber experimental platform that simulated real logistics packages was built, and high-frequency dynamic and low-frequency static barometric pressure sensors were symmetrically deployed inside it to collect pressure signals under typical working conditions such as single-side impact and double-side compression. Firstly, the original high-noise signal was cleaned through an adaptive algorithm based on the prior physical energy distribution. Then, the aligned multimodal pressure sequence was input into the constructed pressure-context convolutional recurrent neural network (PC-CRNN). This network utilized residual convolutions to extract spatial energy fingerprints and employed a bidirectional long short-term memory (Bi-LSTM) network to parse the temporal pulse features of multi-point impacts. Experimental results demonstrated that this system effectively solved the confusion of traditional methods in distinguishing between "bilateral continuous squeezes" and "unilateral strong impacts". The comprehensive recognition rate under complex working conditions reached 88.95%. Notably, the recognition accuracy for bilateral squeezes increased by 23.8% compared with a baseline model lacking temporal feature extraction. The dual-scale pneumatic feature fusion and spatio-temporal deep learning architecture have successfully achieved high-precision identification of package directional collision and compression, providing a low-cost, highly robust technical solution for logistics safety monitoring and multi-point force analysis.

导出引用

段晨昊, 朱磊, 张媛, 杜艳平. 基于气压特征融合的包裹碰撞与挤压识别[J]. 包装工程. 2026, 47(17): 223-233 https://doi.org/10.19554/j.cnki.1001-3563.2026.17.023

DUAN Chenhao, ZHU Lei, ZHANG Yuan, DU Yanping. Recognition of Parcel Collision and Squeeze Based on Air Pressure Feature Fusion[J]. Packaging Engineering. 2026, 47(17): 223-233 https://doi.org/10.19554/j.cnki.1001-3563.2026.17.023

中图分类号： TB48

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