Intelligent early warning method for stability of surrounding rock of deep coal seam roadway

By constructing a combination of dynamic and static load superposition feature vectors and transfer learning, and combining LSTM networks and physical deformation feedback, the problems of dynamic and static load superposition quantification and model generalization in the early warning of surrounding rock stability in deep coal seam roadways are solved, achieving accurate early warning and system adaptation, and improving the scientificity and robustness of the early warning model.

CN121524813AActive Publication Date: 2026-02-13SHANDONG UNIV OF SCI & TECH
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Patent Information

Application Number
CN202610048694.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-15
Publication Date
2026-02-13
Estimated Expiration
2046-01-15

AI Technical Summary

Technical Problem

Existing technologies fail to effectively quantify the nonlinear disaster-causing mechanism of dynamic and static load superposition in early warning of surrounding rock stability in deep coal seam roadways. They also lack a closed-loop correction mechanism based on physical deformation feedback, and the models have poor generalization ability, making it difficult to adapt to changes in complex geological conditions.

Method used

By constructing feature vectors that reflect the superposition effect of dynamic and static loads, and combining transfer learning with LSTM networks, an early warning model based on long short-term memory networks is established. Physical deformation feedback is introduced for closed-loop correction, a laboratory sample library is constructed, and the model is fine-tuned using field data.

Benefits of technology

It enables accurate identification of the impact risk of surrounding rock in deep coal seam roadways, improves the scientificity and accuracy of the early warning model, enhances the robustness and adaptability of the system, and adapts to long-term stability under complex geological conditions.

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Abstract

The invention provides a deep coal seam roadway surrounding rock stability intelligent early warning method, and relates to the technical field of coal mine dynamic disaster prevention and control. According to the intelligent early warning method for the stability of the surrounding rock of the deep coal seam roadway, a basic sample library is constructed through a dynamic and static combined loading rock test; synchronously collecting field surrounding rock static stress and micro-seismic data and carrying out time sequence polymerization; constructing an equivalent dynamic and static combined stress index based on a dynamic and static load superposition principle, and forming a seismic-force coupling feature vector; establishing an LSTM early warning model, adopting a transfer learning strategy, pre-training by using laboratory data, and then performing fine tuning by using field data; inputting the real-time feature vector into a model and outputting an instability probability; and verifying and correcting the probability based on actual deformation data of the surrounding rock, and applying a missing report sample to online incremental updating of the model. According to the method, the dynamic and static load coupling effect is quantified, the difficulty of small sample training is overcome, and accurate and self-adaptive early warning of the deep roadway surrounding rock instability risk is realized through a physical feedback closed-loop correction mechanism.
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