无人机在化工厂泄漏巡检中的应用研究 -安全工程专业

摘要

化工厂泄漏事故频发，对环境和人类健康构成严重威胁，传统巡检手段存在效率低、成本高及难以覆盖复杂区域等问题，因此亟需开发高效、精准的泄漏检测技术。本研究以无人机为载体，结合多传感器数据融合与人工智能算法，探索其在化工厂泄漏巡检中的应用潜力。研究旨在通过优化无人机路径规划、提升气体浓度检测精度以及实现泄漏源快速定位，构建一套智能化、自动化的泄漏巡检系统。具体方法包括基于激光雷达（LiDAR）的三维建模以辅助无人机自主导航，利用红外成像与气体传感器协同监测泄漏物质分布，并引入深度学习模型对采集数据进行实时分析与预警。实验结果表明，该系统能够显著提高泄漏检测的灵敏度和准确性，在复杂环境下仍保持稳定性能，且相较于传统方法可将巡检时间缩短约60%。本研究的主要创新点在于实现了多源异构数据的高效融合与智能解析，同时提出了适应化工场景的动态路径规划策略。研究成果为化工行业安全监管提供了新思路，具有重要的实际应用价值和推广前景。

关键词：无人机泄漏检测；多传感器数据融合；人工智能算法；路径规划；气体浓度监测

ABSTRACT

Chemical plant leakage accidents occur frequently, posing serious threats to the environment and human health. Traditional inspection methods suffer from low efficiency, high cost, and difficulty in covering complex areas, thus highlighting the urgent need for the development of efficient and precise leakage detection technologies. This study investigates the application potential of unmanned aerial vehicles (UAVs) as a carrier combined with multi-sensor data fusion and artificial intelligence algorithms for chemical plant leakage inspections. The research aims to construct an intelligent and automated leakage inspection system by optimizing UAV path planning, enhancing gas concentration detection accuracy, and enabling rapid localization of leakage sources. Specific methodologies include three-dimensional modeling based on Light Detection and Ranging (LiDAR) to assist autonomous UAV navigation, collaborative monitoring of leakage substance distribution using infrared imaging and gas sensors, and the introduction of deep learning models for real-time analysis and early warning of collected data. Experimental results demonstrate that the system significantly improves the sensitivity and accuracy of leakage detection, maintaining stable performance even in complex environments, while reducing inspection time by approximately 60% compared to traditional methods. The primary innovations of this study lie in the efficient fusion and intelligent interpretation of multi-source heterogeneous data, as well as the proposal of dynamic path planning strategies tailored to chemical plant scenarios. The findings provide new insights for safety supervision in the chemical industry and possess significant practical application value and prospects for promotion.

Keywords: Uav Leak Detection; Multi Sensor Data Fusion; Artificial Intelligence Algorithm; Path Planning; Gas Concentration Monitoring

摘要 I

ABSTRACT II

第1章绪论 1

1.1 化工厂泄漏巡检的背景与意义 1

1.2 无人机在泄漏巡检中的研究现状 1

1.3 本文研究方法与技术路线 2

第2章无人机泄漏检测关键技术分析 3

2.1 泄漏检测传感器技术研究 3

2.2 数据采集与传输系统设计 3

2.3 图像识别与异常分析算法 4

第3章无人机在化工厂环境中的适应性研究 6