VOC危险化学品泄漏光谱视频识别算法研究
Research on spectral video recognition algorithm for leakage of VOC hazardous chemicals
王雅杰 1孙秉才 1尤宝硕 2王建筑 3许斌4
作者信息
- 1. 中国石油安全环保技术研究院有限公司,北京 102206
- 2. 长庆油田分公司第十二采油厂,甘肃庆阳 745400
- 3. 质安科技检测集团有限公司,甘肃庆阳 745400
- 4. 长庆油田分公司数字和智能化事业部,陕西西安 710018
- 折叠
摘要
针对挥发性有机物(Volatile Organic Compounds,VOCs)气体特征微弱、视觉显著性差、形态多变等难题,本文基于时-空-频联合去噪、多模态视差匹配模型,提出了一种高精度气体泄漏光谱视频识别算法.通过挖掘时-空-谱高维数据的本征信息来实现VOC气体的高精度识别,并利用多模块级联联合优化将传统方法的可解释性与深度学习的强大表征能力有机结合.最后,通过与国际先进气体监测设备森西亚和锐百凌进行同等条件的对比,可知所提出的气体泄漏成像方法对于低浓度的甲烷气体识别准确率提升了 46.25％,误报降低至原来的1/3,验证了所提算法的有效性和可行性,为石化行业危险化学品泄漏监测提供了有力保障.
Abstract
In response to the challenges of weak features,poor visual saliency,and variable morphology of Volatile Organic Compounds(VOCs),a high-precision gas leakage spectral video recognition algorithm based on time-space-frequency joint denoising and multimodal disparity matching model is proposed in this paper.Firstly,the high-precision identification of VOCs is achieved by mining the intrinsic information of high-dimensional time-space-spectrum data,and then the interpret-ability of traditional methods is organically combined with the powerful representation ability of deep learning through multi-module cascading joint optimization.Finally,by comparing the proposed gas leakage imaging method with international ad-vanced gas monitoring equipment Sencia and Rebellion under the same conditions,it can be seen that the proposed gas leakage imaging method improves the accuracy of methane gas identification by 46.25％for low concentration,and reduces the false alarms to 1/3 of the original one,which verifies the validity and feasibility of the proposed algorithm,providing strong support for monitoring hazardous chemical leakage in the petrochemical industry.
关键词
泄漏监测/识别算法/深度学习/危害化学品/石油化工Key words
leakage monitoring/recognition algorithm/deep learning/hazardous chemicals/petrochemical industry引用本文复制引用
基金项目
国家重点研发油气勘探开发重大风险预防与控制研究项目(2021DJ6501)
出版年
2024
NETL
NSTL
万方数据