Method and system for analyzing and mining business data

By employing automated feature extraction, integrated metric evaluation, and hyperparameter optimization methods for business data analysis and mining, we have solved the problem of low efficiency in traditional methods, achieving efficient and robust data analysis and supporting full-process visualization and intelligent recommendation.

CN122045692APending Publication Date: 2026-05-15姚远
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
姚远
Filing Date
2026-02-04
Publication Date
2026-05-15

AI Technical Summary

Technical Problem

Traditional data analysis and mining methods are limited by high technical barriers, low efficiency, and poor interpretability, making it difficult to adapt to the real-time and rapidly changing demands of the battlefield and unable to fully utilize the valuable information in massive heterogeneous data.

Method used

This paper provides a method for analyzing and mining business data, including automated feature extraction and optimization, construction of integrated indicator evaluation models, and automated hyperparameter search. Through feature engineering, automatic model selection and parameter tuning, it achieves efficient and robust intelligent data analysis.

Benefits of technology

It improves the efficiency and accuracy of data analysis and mining, reduces the difficulty of user operation, supports a fully visualized modeling process, and provides intelligent recommendations and interactive analysis.

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Abstract

The invention belongs to the technical field of analysis and mining, and discloses an analysis and mining method and system for business data, and the method comprises the steps: carrying out the analysis and preprocessing of the business data, obtaining the preprocessed business data, carrying out the automatic feature extraction and optimization of the preprocessed business data, and obtaining a feature engineering result; determining evaluation indexes and weight distribution on the basis of analysis mining task requirements and historical business data, constructing an integrated index evaluation model, evaluating the candidate analysis models through the integrated index evaluation model, and automatically screening the candidate analysis models according to evaluation results; and performing automatic search of an optimal hyper-parameter combination on the screened analysis model, determining the analysis model after parameter adjustment, and inputting a feature engineering result into the analysis model after parameter adjustment to complete analysis mining of business data. According to the invention, the user can easily understand and analyze the business data, so that the efficiency and accuracy of data analysis and mining are improved.
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