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A hyper-converged full-stack cloud data center system and method

A cloud data center, hyper-converged technology, applied in the field of big data, can solve problems such as misjudgment and decision-making loss of fighters, and achieve the effects of diverse functions, improved security, and wide application.

Active Publication Date: 2022-03-18
浙江联云智鼎信息科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Otherwise, no matter how clever military leaders and commanders are, they will be overwhelmed by the vast amount of data, which may lead to misjudgment, delay in decision-making and loss of combat opportunities, resulting in disastrous consequences

Method used

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  • A hyper-converged full-stack cloud data center system and method
  • A hyper-converged full-stack cloud data center system and method
  • A hyper-converged full-stack cloud data center system and method

Examples

Experimental program
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Effect test

Embodiment 1

[0025] A hyper-converged full-stack cloud data center system, the system includes: a business layer, a backbone network, a service layer, a fusion layer, and a base layer; the backbone network obtains different scales, different depths, and different types of Information, providing data transmission for each layer; the fusion layer completes data fusion through multiple resource data pools set up and multiple fusion branch networks stacked in sequence; the business layer provides services for users to access; The service layer implements data security encryption, load balancing, data backup, and cloud host services in the cloud; the base layer provides underlying hardware support for the system; To add supervision information to the fusion branch network, use the binary classification cross entropy loss function to perform segmentation prediction.

[0026] Specifically, one of the advantages of cloud development is economies of scale. Using the infrastructure provided by clou...

Embodiment 2

[0031] On the basis of the previous embodiment, the forward propagation of the backbone network is composed of stacked continuous convolutional layers and downsampling layers; the multiple sequentially stacked reverse fusion branches fuse different scales, different depths, and different types The segmentation network will add supervision information to the fusion branch network with multiple reverse fusion branches stacked in sequence, and use the binary classification cross entropy loss function to predict the segmentation; the backbone network also includes one: deep supervision weighted fusion network; the deep supervision weighted fusion network weights and fuses the multi-level outputs of a plurality of sequentially stacked reverse fusion branches.

[0032] Specifically, the forward propagation of the backbone network of the deep supervised parallel fusion network obtains multi-scale features of the input information for segmentation: deep-level features that encode rich ...

Embodiment 3

[0043] On the basis of the previous embodiment, the method for performing data security encryption at the service layer performs the following steps: setting a password with a length of S bits as an encryption object, and S is a positive integer; splitting the set S-bit password into A-bit short password P and B-bit strong key Q, said A and B are positive integers; embed the split strong key Q into a two-dimensional sequence to obtain a sequence strong key; securely encrypt the obtained sequence The key H performs discrete chaotic mapping, and sets the control parameters to obtain the scrambled sequence G, and arrange the scrambled sequence G from top to bottom and from left to right to obtain the scrambled sequence J; choose the chaotic neural network , and set the initial value and control parameters, and iteratively solve the chaotic neural network to obtain the chaotic sequence K; use the chaotic sequence K to diffuse the obtained scrambling sequence S to realize the equali...

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Abstract

The invention belongs to the technical field of big data, and in particular relates to a hyper-integrated full-stack cloud data center system. The system and method include: a business layer, a backbone network, a service layer, a fusion layer, and a base layer; the backbone network obtains information of different scales, depths, and types of each layer, and provides data transmission for each layer; The fusion layer completes data fusion by setting multiple resource data pools and multiple sequentially stacked fusion branch networks; the business layer provides users with business access; the service layer realizes data security in the cloud Encryption, load balancing, data backup and cloud hosting services; the base layer provides underlying hardware support for the system; it has the advantages of low data redundancy, high operating efficiency and high data security.

Description

technical field [0001] The invention belongs to the technical field of big data, and in particular relates to a hyper-converged full-stack cloud data center system and method. Background technique [0002] Data fusion technology includes the collection, transmission, synthesis, filtering, correlation and synthesis of useful information given by various information sources, so as to assist people in situation / environmental judgment, planning, detection, verification, and diagnosis. This is extremely important for timely and accurate acquisition of various useful information on the battlefield, timely and complete evaluation of battlefield conditions and threats and their importance, and implementation of tactical and strategic auxiliary decision-making and command and control of combat troops. The future battlefield is changing rapidly, and there are more and more complex factors affecting decision-making. Commanders are required to make the most accurate judgments on the bat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06F21/60
CPCG06F21/602G06N3/0418G06N3/084G06N3/045G06F18/214G06F18/253
Inventor 张海涛林受皿薛孝足
Owner 浙江联云智鼎信息科技有限公司
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