Weighted analysis method for multi-attribute data in advanced geological forecast

By using cubic spline interpolation method to fill in missing data and normalize multi-attribute data in tunnel geological prediction, and calculate the weight coefficient for data update, the problems of redundancy, missing, anomalies and limited generalization capabilities are solved, and more accurate data analysis and revealing potential patterns are achieved.

CN119807613APending Publication Date: 2025-04-11NORTHWEST ENGINEERING CORPORATION LIMITED
0 Cites 0 Cited by

Patent Information

Application Number
CN202411849710.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-16
Publication Date
2025-04-11

AI Technical Summary

Technical Problem

The prior art has problems in processing multi-dimensional and multi-attribute data in tunnel geological prediction that redundant and mutually exclusive data are difficult to eliminate, missing and abnormal data processing effects, limited generalization capabilities of model and difficult to reveal potential patterns.

Method used

The cubic spline interpolation method is used to fill in missing or abnormal data in three-dimensional space, and the multi-attribute data is standardized, single-attribute weight coefficients and comprehensive attribute weight coefficients are calculated, and the attribute data is updated to realize the weighted analysis of the data.

Benefits of technology

It effectively eliminates redundant and mutually exclusive information, improves the accuracy of data analysis and the generalization ability of prediction models, reveals potential patterns in the data, and improves the accuracy of geological hidden danger identification.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a weighted analysis method for multi-attribute data in advanced geological forecast, belongs to the technical field of detection data optimization, and can solve the problems that redundant and mutually exclusive data are difficult to eliminate and missing and abnormal data processing effects are poor when the multi-attribute data are processed by an existing method. The method comprises the following steps: S1, normalizing various attribute data obtained by each grid point in a target area in advance geological forecast to obtain normalized data of each attribute in each grid point; s2, determining a single attribute weight coefficient of each attribute in each grid point according to the standardized data of each attribute in all the grid points, and determining a comprehensive attribute weight coefficient of each grid point according to the single attribute weight coefficient; and S3, determining update data of each attribute of each grid point by using the normalized data and the single attribute weight coefficient of each attribute in each grid point and the comprehensive attribute weight coefficient of each grid point. The method is used for multi-attribute data analysis.
Need to check novelty before this filing date? Find Prior Art