A data checking method, device and system for a risk grading management system
CN122173473APending Publication Date: 2026-06-09DAQING OILFIELD CO LTD +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DAQING OILFIELD CO LTD
- Filing Date
- 2024-12-06
- Publication Date
- 2026-06-09
AI Technical Summary
Technical Problem
In the process of oil extraction, the low efficiency and accuracy of data verification lead to insufficient reliability of risk classification and control, which affects safe production.
Method used
By acquiring multidimensional risk classification data, analyzing historical changes and anomalies within the same area of responsibility, and combining the consistency of anomalies from different sources, data verification and correction are performed to ensure data accuracy and consistency.
Benefits of technology
This improves the accuracy of multidimensional risk classification data verification results, avoids the impact of missing or inaccurate data on risk classification management, and enhances the reliability of risk classification management.
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Smart Images
Figure CN122173473A_ABST
Abstract
This invention relates to the field of data risk classification and control technology, specifically to a data verification method, device, and system for a risk classification and control system. The method analyzes the historical changes of multidimensional risk classification data acquired from the same area of responsibility to obtain the anomaly rate of the multidimensional risk classification data. Then, it analyzes the consistency of the anomaly rates of multidimensional risk classification data acquired from different sources within the same area of responsibility and at the same time, verifying the multidimensional risk classification data. Finally, it adjusts and corrects the multidimensional risk classification data based on the verification results, and performs risk classification and control based on the adjusted multidimensional risk classification data. This method uses cross-validation based on the consistency of the anomaly rates of multidimensional risk classification data from different sources to avoid data anomalies caused by inconsistent attributes of multidimensional risk classification data from different sources for the same event, thus improving the accuracy of the verification results.
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