管路堵塞检测方法及其装置

This pipeline blockage detection method, which links multi-source sensing features with underlying fluid physics mechanisms, solves the problem of environmental interference under complex working conditions and achieves high-precision and high-efficiency pipeline blockage detection.

CN122409175APending Publication Date: 2026-07-17GD MIDEA AIR CONDITIONING EQUIP CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GD MIDEA AIR CONDITIONING EQUIP CO LTD
Filing Date
2026-06-17
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies cannot effectively isolate the impact of environmental temperature and pressure fluctuations on pipeline blockage detection under complex operating conditions, resulting in high false alarm and false negative rates, and failing to achieve efficient and accurate pipeline blockage detection.

Method used

By employing a method that deeply integrates multi-source sensing features with underlying fluid physics mechanisms, fluid correction data is obtained through a physical information recognition model. Environmental interference is removed, and by combining deep learning with physical mechanism constraints, an adaptive diagnostic closed loop is constructed to improve detection accuracy and anti-interference capability.

Benefits of technology

It significantly improves the accuracy and anti-interference ability of pipeline blockage detection, reduces the false alarm rate and false negative rate, and achieves efficient automated detection.

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Abstract

本发明提供一种管路堵塞检测方法及其装置,属于智能检测技术领域,包括:获取目标管路部件包含温度、环境气压及流体检测数据的多源检测数据;将其输入至物理信息识别模型获取流体校正数据;基于流体校正数据评估异常状态,确定堵塞诊断结果;该物理信息识别模型配置有表征温度、气压、截面积与流体状态物理耦合关系的物理机制约束,并在该物理机制约束下结合历史样本训练得到。本发明通过在神经网络中内嵌物理机制约束,深度融合了热膨胀与流体动力学底层机理,精准剔除了环境温压波动带来的基准漂移干扰,彻底打破了传统纯数据驱动算法的黑盒局限,显著提升了管路堵塞检测的准确率、抗干扰鲁棒性与自动化排障效率。
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