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A fast retrieval method for multilevel redundant data in grid resource business middle office based on improved
1State Grid Anhui Electric Power Research Institute, Hefei, 230601, China. qimei85274@163.com.
Scientific Reports
|August 2, 2025
Summary
This study introduces an improved decision tree algorithm to efficiently retrieve redundant data in power grid operations. The new method enhances data retrieval accuracy and speed for complex grid resource business platforms.
Area of Science:
- Computer Science
- Data Science
- Electrical Engineering
Background:
- Power grid businesses manage vast data, facing challenges with redundant data retrieval.
- Traditional decision tree methods are inadequate for accurate data acquisition amidst redundant interference.
Purpose of the Study:
- To propose a multi-level redundant data retrieval method for grid resource business platforms.
- To enhance data acquisition accuracy and efficiency in power grid operations.
Main Methods:
- Developed a multi-level data decision tree using grid resource business middle-platform data.
- Applied Akaike information criterion-based pruning and ant colony algorithm for parameter optimization.
- Implemented a novel redundant data retrieval method with designed processing flows and discrimination mechanisms.
Main Results:
- The improved decision tree algorithm increased multi-level redundant data retrieval accuracy by 14%.
- Achieved a maximum F1-score of 0.99 with a retrieval time of 4.5 seconds.
- Demonstrated effective retrieval of hierarchical redundant data, including image and text categories.
Conclusions:
- The proposed method significantly improves redundant data retrieval in power grid systems.
- The optimized decision tree model provides a comprehensive representation of grid resource data hierarchies.
- The approach offers excellent practical performance for complex data management in the power industry.
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