Sewage treatment industrial process anomaly monitoring method based on deep Gaussian mixture model

Through an abnormality monitoring method based on the deep Gaussian hybrid model, combined with the autoencoder and core principal component analysis and extracting features, and optimizing the model through the gradient descent backpropagation algorithm, the accuracy and efficiency of abnormality monitoring in the sewage treatment process are solved, dynamic features and model updates are achieved, and the safe and stable operation of the sewage treatment process is ensured.

CN119720055AActive Publication Date: 2025-03-28SHANDONG UNIV OF SCI & TECH
6 Cites 1 Cited by

Patent Information

Application Number
CN202510228665.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-28
Publication Date
2025-03-28
Estimated Expiration
2045-02-28

AI Technical Summary

Technical Problem

During the sewage treatment process, abnormal working conditions occur frequently due to factors such as fluctuations in the inlet water quality and equipment failure. It is difficult for the existing technology to effectively monitor and deal with these abnormalities, which affects the treatment efficiency and equipment safety.

Method used

Anomaly monitoring method based on the deep Gaussian hybrid model is adopted, features are extracted through autoencoder and core principal component analysis, and a deep Gaussian hybrid model is constructed, combined with gradient descent backpropagation algorithm, dynamic optimization adjustment of feature extraction and model determination is realized.

Benefits of technology

It improves the accuracy and efficiency of abnormal monitoring of sewage treatment industry processes, can dynamically update features and models, adapt to data distribution changes, and ensure the safe and stable operation of sewage treatment process.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a sewage treatment industrial process abnormity monitoring method based on a deep Gaussian mixture model, and belongs to the technical field of industrial process abnormity monitoring, and the method comprises the following steps: 1, collecting industrial process variable data generated in an urban sewage treatment process, and constructing an original data set; 2, designing a feature extraction strategy based on an auto-encoder and kernel principal component analysis, and obtaining low-dimensional feature representation; step 3, constructing a deep Gaussian mixture model to carry out abnormity monitoring in the sewage treatment industrial process; and 4, designing an anomaly monitoring strategy based on a threshold value, obtaining a threshold value evaluation index based on the constructed deep Gaussian mixture model, and further judging whether an anomaly phenomenon occurs at the current moment. According to the method, synchronous optimization and adjustment of feature extraction and model determination are realized, effective monitoring of abnormity in the actual sewage treatment process is realized, and stable operation of the sewage treatment industrial process can be ensured.
Need to check novelty before this filing date? Find Prior Art