基于机器学习的矿山岗位人员行为数据融合处理方法及系统
By establishing dynamic association rules and machine learning fusion models in the processing of personnel behavior data in mines, the problems of data integration and model adaptability were solved, achieving efficient and accurate data fusion and behavioral logic mining, and improving the usability and consistency of the data.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YANKUANG ENERGY GRP CO LTD
- Filing Date
- 2025-09-30
- Publication Date
- 2026-07-17
AI Technical Summary
The processing of behavioral data of mine workers suffers from a lack of effective correlation rules among data from different sources, making it difficult to integrate into a complete behavioral data set. Furthermore, existing technologies cannot dynamically adjust the fusion model to adapt to different positions and work scenarios, resulting in reduced data accuracy and usability.
Establish dynamic association rules for the behavioral data of mine personnel, perform bidirectional adaptation processing through machine learning fusion models, dynamically generate feature processing sub-paths, perform cross-source feature fusion, and combine behavioral data association rules for verification and optimization to generate a fusion feature set that conforms to the behavioral logic of the job.
It enables efficient integration and in-depth mining of multi-source heterogeneous behavioral data in complex and ever-changing mining operation scenarios, improves the accuracy and consistency of data fusion, and uncovers the implicit behavioral logical connections of job positions.
Smart Images
Figure CN121327754B_ABST