Slope deformation real-time early warning algorithm and system based on deep reinforcement learning

Through the real-time slope deformation warning algorithm based on deep reinforcement learning, the problems of insufficient multi-source data fusion and insufficient real-time performance of traditional algorithms in the existing technology are solved, and the coordinated optimization of dynamic perception and adaptive decision-making is realized, and the system's real-time warning capabilities and environmental adaptability are improved.

CN120032500AActive Publication Date: 2025-05-23CHENGDU UNIVERSITY OF TECHNOLOGY +1
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Patent Information

Application Number
CN202510513118.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-23
Publication Date
2025-05-23
Estimated Expiration
2045-04-23

AI Technical Summary

Technical Problem

The prior art has problems such as insufficient multi-source data fusion, poor synergy between mechanical structures and electronic systems, difficulty in matching spatial and temporal resolution, and insufficient real-time performance of traditional algorithms in slope deformation monitoring, making it difficult to achieve real-time early warning.

Method used

The real-time early warning algorithm for slope deformation based on deep reinforcement learning is adopted. By obtaining multi-source monitoring data, standardized state characterization vectors are generated, state quality evaluation indicators are established, dual-mode strategy architecture is built, preliminary decisions and confidence evaluations are generated, final early warning instructions and policy update suggestions are formed, and weak links in strategy performance evaluation reports are identified, the changing trends of state feature distribution are continuously tracked, and the strategy degradation early warning system is built.

Benefits of technology

The coordinated optimization of dynamic perception and adaptive decision-making is realized, the system's dynamic environmental adaptability is improved, the balanced control of real-time response and long-term evolution is ensured, and the risk of false alarms and omissions caused by monitoring data noise, environmental changes or model aging is reduced.

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Abstract

The invention relates to the technical field of deep learning, and particularly provides a slope deformation real-time early warning algorithm and system based on deep reinforcement learning, and the method comprises the steps: generating a standardized state representation vector, and building a state quality evaluation index; receiving the state representation vector as input, constructing a dual-mode strategy architecture, generating a preliminary decision and confidence evaluation thereof, and forming a quantitative index of decision reliability; forming a final early warning instruction and a strategy updating suggestion, and generating a strategy performance evaluation report; continuously tracking the change trend of state feature distribution, constructing a strategy degradation early warning system, and triggering a model reconstruction mechanism; the updated policy network parameters are fed back to the dual-mode policy architecture, and the optimized feature extraction suggestions are fed back to the establishment of the state quality evaluation indexes. The system comprises an index establishment module, a report generation module and a reconstruction mechanism module. According to the slope deformation real-time early warning algorithm, a monitoring-decision-optimization end-to-end closed-loop control system is realized through a deep reinforcement learning framework.
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