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Intelligent push processing method and system based on block chain and big data mining

A processing method and big data technology, applied in the field of cloud business, can solve the problems of poor feature learning effect, affecting the accuracy of interest mining, affecting the accuracy of smart virtual business user groups, etc., to improve the accuracy and improve the effect of feature learning.

Active Publication Date: 2022-05-31
山邮数字科技(山东)有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In related technologies, it is usually necessary to combine big data analysis with cloud business linkage behavior databases for interest mining. In the process of deep learning optimization for interest decision-making, the current optimization scheme does not take into account the allocation of deep learning optimization stages, resulting in feature learning. If the effect is not good, it will not only affect the accuracy of interest mining, but also affect the accuracy of subsequent information push to relevant target smart virtual business user groups

Method used

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  • Intelligent push processing method and system based on block chain and big data mining
  • Intelligent push processing method and system based on block chain and big data mining

Examples

Experimental program
Comparison scheme
Effect test

Embodiment A

[0038]Determine the global event concern variable from the cloud service linkage event log group, determine the variable loss value of other event concern variables in the cloud service linkage event log group and the global event concern variable, and obtain the cloud service linkage event log group and all variables in the cloud service linkage event log group. The event attention variable with the largest variable loss value among the global event attention variables, and the variable loss value between the largest event attention variable and the global event attention variable is determined as the cloud service linkage event log group feature learning cost The learning cost value of , wherein the variable loss value is positively correlated with the feature learning cost.

Embodiment B

[0040] Corresponding to each cloud service linkage event log in each cloud service linkage event log group, determine the cycle traversal quantity value where the first interest mining of the cloud service linkage event log is confirmed based on the interest mining model of the cloud service scenario, based on the The preset reference coefficient corresponding to the cyclic traversal quantity value is used to obtain the target reference coefficient of the cloud service linkage event log, and the cyclic traversal quantity value and the target reference coefficient are in a negative correlation. Corresponding to each cloud service linkage event log group, determine the target interest real value corresponding to the cloud service linkage event log group according to the target reference coefficient corresponding to each cloud service linkage event log group in the cloud service linkage event log group. Based on the real value of interest corresponding to each cloud service linkag...

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Abstract

The embodiment of the invention provides an intelligent push processing method and system based on a block chain and big data mining, and the method comprises the steps: carrying out the deep learning optimization of an interest decision of a target interest mining model based on a cloud business linkage event; deep learning optimization stage allocation is performed on the cloud service linkage event log in the cloud service linkage event according to the feature learning cost, so that the feature learning effect can be improved and the interest mining precision can be improved when deep learning optimization of interest decision is performed on the target interest mining model; and thus, the precision of subsequently pushing the information to the related target intelligent virtual service user group is improved.

Description

technical field [0001] The present invention relates to the field of cloud business technology, in particular, to an intelligent push processing method and system based on blockchain and big data mining. Background technique [0002] The integration of cloud business technology and artificial intelligence ultimately pursues the formation of an intelligent ecosystem. In this system, the interoperability between different intelligent terminal devices, different system platforms, and different application scenarios is realized. Everything blends. [0003] In related technologies, it is usually necessary to combine big data analysis with cloud business linkage behavior databases for interest mining. In the process of deep learning optimization for interest decision-making, the current optimization scheme does not take into account the allocation of deep learning optimization stages, resulting in feature learning. If the effect is not good, it will not only affect the accuracy o...

Claims

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Application Information

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IPC IPC(8): G06F16/2458G06F16/9535G06F21/64G06N3/08
CPCG06F16/9535G06F21/64G06F16/2465G06N3/08Y02D10/00
Inventor 肖伟霞赵书民
Owner 山邮数字科技(山东)有限公司
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