Data library construction method optimized by intelligent algorithm

A technology of intelligent algorithm and intelligent optimization algorithm, which is applied in electrical digital data processing, digital data information retrieval, special data processing applications, etc. Achieve the effect of lowering the threshold of artificial intelligence and shortening the cycle of algorithm optimization

Active Publication Date: 2021-12-28
武汉剑心科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, such data platforms are often used as enterprise big data strategic platforms, and their data is generally not open to the outside world and has a single application field.
Therefore, there are very few shared data platforms in the actual sense. On the one hand, scientific research institutions with algorithms lack suitable test data and need to obtain test data from scratch. The data reusability rate is low. Real-time tracking and adjustment of personnel, time-consuming and labor-intensive

Method used

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  • Data library construction method optimized by intelligent algorithm
  • Data library construction method optimized by intelligent algorithm
  • Data library construction method optimized by intelligent algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0082] The software and hardware configuration scheme based on Hadoop server cluster in Embodiment 1 is shown in Table 2.

[0083] Table 2

[0084]

[0085] (1) Split the Minist handwritten image data into 4 file blocks, upload them to the data library for users to choose and use, and perform backup processing according to the above security mechanism.

[0086] (2) The user selects the Mnist data set and a suitable machine learning framework (tensorflow is selected in this example), and uploads the algorithm to be optimized, including the construction of the neural network (relevant information can be found by yourself), the forward propagation output prediction value and its assumptions function h θ (x 1 , x 2 ,...,x n ) = θ 0 +θ 1 x 1 +…+θ n x n (The final optimization parameter θ 0 , θ 1 ,...θ n ), and the loss function setting of predicted value and real value:

[0087]

[0088] (3) Algorithm-related parameter initialization: mainly to initialize θ 0 , ...

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Abstract

The invention discloses a method for constructing a data library optimized by an intelligent algorithm, which comprises the following steps based on a big data processing framework: step S1, establishing a distributed storage system for data sets; step S2, building a deep learning framework; step S3, pre- The intelligent optimization algorithm is set to optimize the training algorithm model to be trained. The invention shortens the algorithm optimization cycle and lowers the artificial intelligence threshold.

Description

technical field [0001] The invention belongs to the field of big data and cloud computing. More specifically, the present invention relates to a data library construction method optimized by an intelligent algorithm. Background technique [0002] With the vigorous development of information technology, the mode of informatization construction has undergone fundamental changes, and a wave of artificial intelligence centered on technologies such as cloud computing, big data, and the Internet of Things is surging. The construction of data platforms is also developing rapidly, but at present, most data platforms are based on traditional relational databases, serving small and medium-sized enterprises with small data scales, mainly providing data storage and basic information interaction functions, and can only meet the requirements of relational data storage. Requirements, helpless for semi-structured and unstructured data. For example, see the Chinese patent: a logistics big ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/182G06F16/18G06F16/16G06N3/04
CPCG06F16/182G06F16/1847G06F16/16G06N3/045
Inventor 朱艺袁烨沈正月
Owner 武汉剑心科技有限公司
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