Gas consumption behavior analysis method based on UMAP and DBSCAN

Feature extraction and dimensionality reduction were performed through the UMAP algorithm, and cluster analysis was performed in combination with the DBSCAN model, which solved the problem of indetailed gas use information and poor clustering effect in traditional gas management, and achieved efficient gas use behavior analysis and refined gas use planning.

CN119693023APending Publication Date: 2025-03-25WUHAN SHUCHUN INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202411698809.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-26
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

It is difficult to provide detailed gas usage information in traditional gas management, and the existing clustering algorithms are not effective when facing complex data distribution, making it difficult to achieve refined gas usage management and accurate prediction.

Method used

UMAP algorithm is used for feature extraction and dimensionality reduction, the local structure and topological characteristics of the data are retained, and density clustering is combined with the DBSCAN model. Parameters are optimized through the grid parametric method to evaluate the clustering effect using coverage and accuracy indicators.

Benefits of technology

Effective dimensional reduction and cluster analysis of high-dimensional data is realized, and the accuracy and interpretability of gas usage behavior analysis is improved, so that business departments can make reasonable gas usage planning according to the gas usage patterns of different users.

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Abstract

The invention relates to the technical field of gas consumption behavior analysis, in particular to a gas consumption behavior analysis method based on UMAP and DBSCAN, and the method comprises the following steps: original data acquisition, data preprocessing, parameter initialization, DBSCAN density clustering and user marking. UMAP is adopted, feature extraction is achieved, the time-consuming and labor-consuming feature engineering link is omitted, when high-dimensional data is mapped to a low-dimensional space, the local structure and topological features of the data are reserved, visualization is facilitated, data characteristics are fully observed, a more suitable model is selected for clustering, and the clustering efficiency is improved. And secondly, through a whole set of user grouping scheme which is high in interpretability, aims at the gas industry and is used for subdividing full-quantity users, a business department can carry out reasonable gas use planning according to gas use modes of different users.
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