A data analysis management method and system based on digital intelligence engineering
By performing location and anomaly analysis on the digital engineering data set, determining missing and abnormal locations, and using similarity calculation to fill in the data, the problem of incomplete data is solved, and data call efficiency and analysis accuracy are improved.
CN120336306BActive Publication Date: 2025-10-03SICHUAN SHIZHENG NETWORK TECHNOLOGY CO LTD
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Patent Information
- Application Number
- CN202510483916.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-17
- Publication Date
- 2025-10-03
- Estimated Expiration
- 2045-04-17
AI Technical Summary
Technical Problem
Missing values and outliers in data in digital intelligence projects are not supplemented and modified, resulting in incomplete data, extending data retrieval time and increasing analysis errors.
Method used
The data set is analyzed and analyzed for location and anomaly through the analysis management terminal to determine missing and abnormal locations, and the data is filled in using similarity calculation and verification to generate a normal data set.
Benefits of technology
The integrity and correctness of the data are achieved, the data call time is shortened, and the accuracy of data analysis is improved.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention discloses a data analysis management method and system based on digital intelligence engineering, which relates to the field of data analysis management technology, including performing position analysis on a data set to be managed and determining the position of a first data to be filled. The present invention first determines the position of the first data to be filled, then determines the position of the abnormal data, and sets the position of the abnormal data to be the position of the second data to be filled. Finally, the data set to be filled is calculated by Euclidean calculation to obtain a first data similarity set and a second data similarity set, and the first data similarity set and the second data similarity set are selected and calculated to obtain first filling data and second filling data. In order to avoid the calculated first filling data and second filling data being abnormal data, the first filling data and the second filling data are verified to determine a normal data set. The above method not only realizes the filling and modification of missing data and abnormal data, but also shortens the data call time of digital intelligence engineering.
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