Water supply network acoustic leakage detection method based on enhanced semi-supervised model

By adopting enhanced semi-supervised model and data enhancement technology in the sound leakage detection of water supply pipeline networks, labelless data is used to improve detection accuracy and reliability, the problem of strong dependence on labeled data in the existing technology is solved, and more efficient and economical sound leakage detection is achieved.

CN119649848AActive Publication Date: 2025-03-18ZHEJIANG UNIV +1
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Patent Information

Application Number
CN202411509753.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-28
Publication Date
2025-03-18
Estimated Expiration
2044-10-28

AI Technical Summary

Technical Problem

The existing acoustic leakage detection methods of water supply pipelines rely on high-cost labeled data and fail to effectively utilize a large amount of label-free acoustic signal data, resulting in limited detection accuracy and reliability.

Method used

The detection method based on the enhanced semi-supervised model is adopted to enhance the data of the image samples through weak enhancement and strong enhancement operations, and a prediction network is built based on the semi-supervised network framework, and the model training is used to reduce the dependence on the marked data.

Benefits of technology

Under limited labeled data conditions, the accuracy and reliability of sound leakage detection are significantly improved, even exceeding the full supervision model, supporting the optimized operation and reliability management of the water supply pipeline network.

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Abstract

The invention discloses a water supply network sound leakage detection method based on an enhanced semi-supervised model, and the method comprises the steps: 1, obtaining a data set, and carrying out the weak enhancement operation and strong enhancement operation of data; 2, a prediction network is constructed based on a semi-supervised network framework, the prediction network comprises a marked learning branch and an unmarked learning branch which share weights, and image samples with labels in the weak enhancement sample set are input into the marked learning branch to output corresponding prediction distribution; and step 3, training the prediction network by using the weak enhancement sample set and the enhancement sample set to obtain a pipe network acoustic leakage monitoring model. According to the method provided by the invention, the identification precision and robustness of the acoustic leakage detection model can be ensured under the condition of limited labeled samples.
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