Data processing methods, apparatus, computer-readable media and electronic devices

By processing historical business data into binary classification data under different data dimensions, calculating distribution dissimilarity and category explanatory power, and generating decision trees, the problems of low efficiency and poor accuracy in existing technologies are solved, and efficient and accurate attribution analysis is achieved.

CN116756616BActive Publication Date: 2026-05-26BEIJING ZITIAO NETWORK TECH CO LTD
View PDF 1 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING ZITIAO NETWORK TECH CO LTD
Filing Date
2023-06-26
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

In existing technologies, manual decomposition and analysis methods are inefficient, time-consuming, and difficult to analyze the cross-influence of different dimensions. Furthermore, automatic attribution algorithms have low interpretability and cannot accurately handle the problem of multi-dimensional cross-influence.

Method used

By processing historical business data into binary data from different data dimensions, calculating distribution dissimilarity and category explanatory power, and generating decision trees to determine attribution analysis results, the efficiency and accuracy of automated analysis are improved.

Benefits of technology

It enables automated attribution analysis of historical business data, improves data attribution efficiency, accurately analyzes the cross-influence of different dimensions, and obtains attribution analysis results that conform to the actual situation.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN116756616B_ABST
    Figure CN116756616B_ABST
Patent Text Reader

Abstract

This disclosure relates to a data processing method, apparatus, computer-readable medium, and electronic device. The method includes: acquiring historical business data and determining the type of a target indicator based on the historical business data; processing the historical business data into binary data under different data dimensions; determining the distribution difference degree between each category of data in each binary category and the category explanatory power of each category of data based on the type of the target indicator; and determining the attribution analysis result of the historical business data based on the distribution difference degree and the category explanatory power. Through the above technical solution, automatic attribution analysis can be performed on historical business data from different data dimensions, thereby determining attribution analysis results that meet preset attribution indicators and improving data processing efficiency.
Need to check novelty before this filing date? Find Prior Art