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Information system resource capacity prediction method based on AI algorithm

A technology of information system and forecasting method, applied in the direction of calculation, calculation model, error detection/correction, etc., can solve problems such as strong dependence on experts, extensive management, long planning cycle, etc., to achieve short planning cycle, convenient management, and resources. Effective use of the effect

Pending Publication Date: 2021-07-23
STATE GRID INFORMATION & TELECOMM BRANCH
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AI Technical Summary

Problems solved by technology

Current pain points: (1) Strong dependence on experts; (2) Serious waste of resources; (3) Long planning cycle; (4) Extensive management

Method used

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  • Information system resource capacity prediction method based on AI algorithm
  • Information system resource capacity prediction method based on AI algorithm
  • Information system resource capacity prediction method based on AI algorithm

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Embodiment Construction

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0047] Refer to attached figure 1 to attach Figure 4 , an information system resource capacity prediction method based on an AI algorithm, comprising the following steps:

[0048] Step 1. Extract sample data according to the required scenarios and form a database;

[0049] Step 2. Provide AI algorithm foundation through computing frameworks such as tensorflow machine learning framework, tsfresh time series data feature library, and scikit-learn algorithm li...

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Abstract

The invention discloses an information system resource capacity prediction method based on an AI algorithm. The method comprises the following steps: step 1, extracting sample data according to a required scene and forming a database; step 2, providing an AI algorithm basis through calculation frameworks such as a tensorflow machine learning framework, a tsfreh time sequence data feature library, a score-learning algorithm library and the like; step 3, adopting a Holt-winters time sequence calculation model and a linear regression + Gaussian accounting method to carry out machine learning training to obtain a prediction result; and step 4, comparing a predicted value with an actual value and carrying out visual display, the system directly predicts a relatively accurate result which can be used as a reference, resources are effectively utilized, the planning period is short, the management is convenient, the effect is ideal, and the predicted result can effectively guide customers.

Description

technical field [0001] The invention relates to the field of capacity prediction, in particular to an information system resource capacity prediction method based on an AI algorithm. Background technique [0002] Capacity prediction is to predict the capacity trend in the future time period through the analysis of historical data and model training. The value of capacity prediction is to automatically predict the future trend of resources based on the historical status of resources to help customers plan in advance. It mainly provides the following values: Improve Operation and maintenance efficiency and prediction accuracy reduce labor costs and achieve the best return on investment; from passive expansion to active prediction, realize capacity demand trend perception, resource early warning and early procurement; greatly shorten the resource planning cycle and improve resource planning efficiency ; Refined and visualized resource capacity management, more accurate and intu...

Claims

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Application Information

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IPC IPC(8): G06F11/34G06N20/00
CPCG06F11/3409G06F11/3452G06N20/00
Inventor 何云瑞闫祎颖李扬陈亮党义杰
Owner STATE GRID INFORMATION & TELECOMM BRANCH
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