Ecological restoration method for gangue slope of mountain road

By using real-time monitoring and adaptive optimization, the classification criteria for coal gangue slopes along mountain roads are dynamically adjusted, solving the problem that existing repair schemes cannot adapt to slope changes and achieving more efficient repair results and stability.

CN121599503APending Publication Date: 2026-03-03GUIZHOU POLYTECHNIC COLLEGE OF COMM
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Patent Information

Application Number
CN202511507901.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-21
Publication Date
2026-03-03

AI Technical Summary

Technical Problem

Existing methods for repairing coal gangue slopes along mountain roads lack adaptive mechanisms and cannot adjust classification criteria in real time according to the dynamic changes of the slope, resulting in unsatisfactory repair effects.

Method used

By using a sensor network to monitor the weathering degree and compaction density of the slope in real time, cluster analysis and regression analysis are used to dynamically update the classification criteria, generate optimized repair schemes, and verify and implement the optimized schemes through simulation modules to achieve adaptive optimization of slope repair.

Benefits of technology

It improves the success rate and long-term stability of slope restoration, reduces resource waste, and ensures that the restoration effect continuously matches the actual slope characteristics.

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Abstract

The invention provides a mountain road coal gangue slope ecological restoration method, which comprises the following steps: acquiring real-time monitoring data from a coal gangue slope through a sensor network, including weathering degree and compaction density indexes, and obtaining an initial feature set; according to the initial feature set, carrying out preliminary grouping on the side slopes by adopting a clustering analysis method, and determining a classification standard threshold value; feedback data in the restoration implementation process are obtained and compared with the preliminary groups, and the effect deviation degree is judged; if the effect deviation exceeds a preset threshold value, a classification standard threshold value is updated through a regression analysis model, and an adjusted classification system is obtained; for the adjusted classification system, extracting a corresponding repair scheme template from a database, and generating an optimization scheme version; performing virtual testing on the optimization scheme version through an analog simulation module to obtain a prediction effect index; and collecting subsequent monitoring data according to the final execution parameter, and performing iterative comparison with the prediction effect index to obtain a further adjustment signal.
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