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Method, system and device for predicting task load based on deep learning and medium

A technology of deep learning and task volume, applied in the field of deep learning, can solve problems such as complex temporal and spatial correlations, achieve the effect of preserving spatial correlations, facilitating feature learning and feature extraction, and preserving temporal correlations

Active Publication Date: 2021-06-15
SHANDONG YINGXIN COMP TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, due to the complexity of time and space correlation, the prediction of platform task volume has always been a challenging technology, and its difficulties are mainly reflected in: 1) Spatial correlation

Method used

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  • Method, system and device for predicting task load based on deep learning and medium
  • Method, system and device for predicting task load based on deep learning and medium
  • Method, system and device for predicting task load based on deep learning and medium

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Embodiment Construction

[0021] In order to make the object, technical solution and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0022] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are to distinguish two entities with the same name but different parameters or parameters that are not the same, see "first" and "second" It is only for the convenience of expression, and should not be construed as a limitation on the embodiments of the present invention, which will not be described one by one in the subsequent embodiments.

[0023] Based on the above purpose, the first aspect of the embodiments of the present invention proposes an embodiment of a method for predicting task volume based on deep learning. figure 1 What is shown is a schematic diagram of an embodiment of t...

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Abstract

The invention discloses a method, system and device for predicting task load based on deep learning and a storage medium; the method comprises the steps: constructing a directed adjacency matrix according to the temporal correlation and spatial correlation of historical task load of a platform, and constructing a short-time task load adjacency matrix according to the directed adjacency matrix; extracting a plurality of first features from the short-term task load adjacency matrix according to the time dimension and the space dimension, and constructing a plurality of short-term feature matrix sequences according to the first features; extracting a second feature from the short-term feature matrix sequence according to the periodic dimension; and fusing the first feature and the second feature, and based on the fused first feature and second feature, obtaining a predicted task amount in a future preset time period. According to the method, the task load of the platform can be predicted according to the time and space characteristics by constructing the digraph adjacency matrix of the task load of the platform node network.

Description

technical field [0001] The present invention relates to the field of deep learning, and more specifically, to a method, system, computer equipment and readable medium for predicting task quantities based on deep learning. Background technique [0002] The artificial intelligence platform encapsulates complex training environments and computing frameworks, and realizes efficient integration with big data and cloud computing, thereby more friendly meeting the needs of large-scale enterprise artificial intelligence applications, improving the development efficiency of artificial intelligence applications, and realizing resources intensive management. Therefore, the artificial intelligence platform needs to adopt more advanced and efficient management and operation methods for computing, storage, resource management and use, and has a strong ability to respond to changes in various resource requirements. An important indicator of the platform's responsiveness refers to the plat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/34G06N3/04G06N3/08
CPCG06F11/3452G06F11/3419G06N3/08G06N3/045
Inventor 陈利华
Owner SHANDONG YINGXIN COMP TECH CO LTD
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