An intelligent early warning method for coal and gas outburst driven by experiment, theory and data
Through the experimental-theory-data hybrid-driven method, combined with physical simulation experiments and digital twin technology, an intelligent early warning system for coal and gas outbursts was built, which solved the problem of insufficient information utilization in the existing technology by human factors, and achieved intelligent early warning of high accuracy and applicability of coal and gas outbursts.
Patent Information
- Application Number
- CN202411234338.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-04
- Publication Date
- 2025-04-04
- Estimated Expiration
- 2044-09-04
AI Technical Summary
The existing coal and gas outburst early warning technology has great influence, insufficient mining and utilization of historical data and precursor information, low degree of fusion of multi-source information, relatively solidified indicators and models, and insufficient self-learning and self-optimization capabilities, resulting in the early warning effect not fully meeting the ideal needs.
Using the experimental-theory-data hybrid-driven method, a digital twin virtual model is built through combining physical simulation experiments and theoretical models, real-time simulation and feedback of downhole scene data, and input deep learning combination model to extract data characteristics and predict output, and combine multi-parameter comprehensive judgment criteria to achieve advanced intelligent early warning of coal and gas outburst.
It improves the accuracy and applicability of the digital twin model, enhances the input data quality of the deep learning model, realizes intelligent prediction and early warning level determination of coal and gas outbursts, and improves the accuracy and applicability of early warning.