Abnormity recognition method and device based on time sequence features and storage medium

By obtaining interlayer stability parameters from the three-dimensional geological information of the mine, generating a window data matrix, and performing time series rearrangement and weighting, the problem of misjudgment in the spatial anomaly identification of mine roadways was solved, and the accuracy of anomaly identification was improved.

CN122087494APending Publication Date: 2026-05-26HUNAN WEIAN TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511948323.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-23
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing methods for identifying anomalies in mine roadway spaces often result in misjudgments because they fail to effectively integrate geological information about the mine roadway space, and the correlation between mine characteristic indicators may not occur at the same time, leading to misjudgments at abnormal moments.

Method used

By acquiring rock mass structure and environmental data from the three-dimensional geological information of the mine, interlayer stability parameters are generated, a sliding window is set and a window data matrix is ​​generated, and time series rearrangement and weighting are performed in combination with the interlayer fracture disturbance index and anomaly weight to determine the time of anomaly occurrence.

Benefits of technology

It reduces misjudgments of abnormal moments in mine roadway space, improves the accuracy of anomaly identification, and, combined with geological information of mine roadway space, can better locate abnormal moments.

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Abstract

The invention discloses an anomaly recognition method and device based on time sequence features and a storage medium, which are used for reducing misjudgment of an abnormal moment in a mine roadway space. Rock mass structure data and rock mass environment data of the mine roadway space are obtained; generating interlayer stability parameters according to the rock mass structure data and the rock mass environment data; generating a window data matrix through the collected data set of the mine characteristic indexes; generating an interlayer crack disturbance index set according to the acquired data corresponding to the interlayer stability parameters and the vibration type features, and replacing the interlayer crack disturbance index set into the window data matrix; performing rearrangement processing on elements of the structural stability features and the mine environment features in the window data matrix according to the interlayer fracture disturbance index set; generating an anomaly weight for each type of mine characteristic indexes of the window data matrix; and generating an anomaly weighting matrix according to the anomaly weight and the window data matrix, and determining an anomaly occurrence moment of the mine roadway space according to the anomaly weighting matrix.
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