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Intelligent-logistics data mining method based on mixed-cloud scheduling

A data mining and hybrid cloud technology, applied in logistics, data processing applications, instruments, etc., can solve problems such as implicit information analysis and insufficient processing capacity, achieve good application value, overcome in-depth mining, and improve analysis and processing capabilities. Effect

Inactive Publication Date: 2017-10-20
ZHEJIANG GONGSHANG UNIVERSITY
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Problems solved by technology

[0003] In order to overcome the problem of insufficient analysis and processing capabilities for the information implicit in the existing smart logistics data, the present invention provides an effective smart logistics data mining method based on hybrid cloud scheduling;

Method used

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  • Intelligent-logistics data mining method based on mixed-cloud scheduling
  • Intelligent-logistics data mining method based on mixed-cloud scheduling
  • Intelligent-logistics data mining method based on mixed-cloud scheduling

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Embodiment Construction

[0021] The present invention will be further described below in conjunction with accompanying drawing:

[0022] refer to figure 1 and figure 2 , a smart logistics data mining method based on hybrid cloud scheduling, including the following steps:

[0023] Step 1. Establish a hybrid cloud environment for logistics data: build a hybrid cloud scheduling environment including public cloud and private cloud, and divide the logistics task scheduling and allocation process into two cases: In the first case, the private cloud computing resources can meet the computing needs of users , there is no need to apply for computing resources in the public cloud at this time; in the second case, private cloud computing resources cannot meet user needs, and public cloud resources must be applied to assist in completing computing tasks. At this time, it is necessary to judge whether private cloud computing resources can meet the demand , when the private cloud can meet the demand, directly us...

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Abstract

The invention discloses an intelligent-logistics data mining method based on mixed-cloud scheduling. Methods of cloud computing, data mining and the like are introduced into an application scene of logistics data processing, a mixed-cloud environment of logistics data is established, and characteristics of mixed-cloud scheduling are analyzed; a logistics data processing model of the mixed-cloud environment is constructed, and includes a system model, a security model, a task allocation model and the like; and a task allocation strategy based on Pareto optimality is adopted to process a situation of uneven resource allocation due to the insufficient private-cloud computing capacity. Therefore, the logistics data analysis and processing capacity is improved, and the problem that the logistics data analysis and processing capacity of our country is not high is solved.

Description

technical field [0001] The invention relates to the field of cloud computing and data mining, in particular to a method for dispatching and allocating logistics tasks, which is especially suitable for problems in data processing of intelligent logistics. Background technique [0002] With the development of smart logistics, the data of logistics has also increased exponentially, so the research on cloud computing task scheduling applied to logistics big data is very necessary; at present, a large number of researches on cloud computing task scheduling have been started at home and abroad, and many of them involve logistics data. However, because the computing capacity of the private cloud sometimes cannot meet the actual needs; and it is uneconomical to expand the scale of the private cloud for these explosive resource requests, a relatively cheap way is to rent the computing power provided by the public cloud service provider on demand resources to assist in the completion ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q10/08
CPCG06Q10/06312G06Q10/083
Inventor 肖亮王璐雅陈庭贵
Owner ZHEJIANG GONGSHANG UNIVERSITY
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