Cloud computing resource expansion demand prediction method, system, terminal device and product

By analyzing cloud computing resource usage data using deep neural network models, the problem of inaccurate prediction of cloud computing resource expansion intentions in existing technologies has been solved, enabling accurate prediction and efficient marketing under explosive data growth.

CN115511041BActive Publication Date: 2026-06-19CHINA MOBILE GROUP ZHEJIANG +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA MOBILE GROUP ZHEJIANG
Filing Date
2021-06-04
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing shallow machine learning algorithms based on decision trees and random forests cannot accurately predict the willingness to expand cloud computing resources, especially when data is growing explosively and customer needs are diverse and difficult to determine.

Method used

A deep neural network model is used, and model weights are obtained through offline training. Combined with data from cloud computing resources, calculations are performed to predict users' expansion needs.

Benefits of technology

It significantly improves the accuracy of predicting cloud computing resource expansion needs, enabling refined forecasting and efficient marketing in the event of explosive data growth.

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Abstract

This invention discloses a method, system, terminal device, and computer program product for predicting cloud computing resource expansion needs. The method involves acquiring cloud computing resource usage data of the user to be predicted; acquiring the model weights of a preset deep neural network model, wherein the deep neural network model is obtained through offline training based on training samples; and calculating the cloud computing resource usage data based on the model weights to obtain a prediction result of the user's cloud computing resource expansion needs. Compared to traditional methods that predict users' willingness to expand cloud computing resources using shallow machine learning algorithms such as decision trees and random forests, this invention significantly improves the accuracy of predicting users' cloud computing resource expansion needs by combining reasonable model weights obtained through continuous training of a deep neural network to analyze the user's cloud computing resource usage data.
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