Data recovery method, device and equipment based on machine learning in distributed environment
A distributed environment, machine learning technology, applied in the direction of machine learning, neural learning methods, multi-programming devices, etc., can solve problems such as insufficient parallelization, low reusability, and low data repair efficiency, so as to improve efficiency and accuracy sexual effect
Active Publication Date: 2022-03-25
湖南工商大学
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Problems solved by technology
[0005] In the process of realizing the present invention, the inventor realized that the prior art has at least the following technical problems: Based on the characteristics of large data volume and data storage distribution in the intelligent manufacturing industry, the existing data repair methods have low reusability and parallelization. Insufficient, difficult to transplant and other difficulties and problems, resulting in low efficiency of data repair
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[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the application; the terms used herein in the description of the application are only to describe specific embodiments The purpose is not to limit the present application; the terms "comprising" and "having" and any variations thereof in the specification and claims of the present application and the description of the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second" and the like in the description and claims of the present application or the above drawings are used to distinguish different objects, rather than to describe a specific order.
[0047] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The occurrenc...
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The invention discloses a data restoration method, device and equipment based on machine learning in a distributed environment and a medium, and the method comprises the steps: each node server carries out the missing data separation processing of a local data set, obtains a local complete data set and a local to-be-restored data set, obtains the data quantity and data dimension of the local complete data set, and stores the data quantity and data dimension of the local to-be-restored data set; and performing data missing simulation processing on the local complete data set based on the data quantity, the data dimension and the missing features of the local to-be-repaired data set to obtain a local simulated to-be-repaired data set, determining a simulated to-be-repaired set, and performing local data repair based on the simulated to-be-repaired set and the local data set of each node server. And performing model training on the secondary repair model by using the repair training set to obtain a trained secondary repair model, and repairing the to-be-repaired data set by using the trained secondary repair model. According to the invention, the data recovery efficiency and accuracy are improved.
Description
technical field [0001] The present invention relates to the field of data processing, in particular to a data repair method, device, computer equipment and medium based on machine learning in a distributed environment. Background technique [0002] As the construction of the industrial Internet platform becomes increasingly mature, intelligent manufacturing, as the core of the industrial Internet, has become an extremely critical existence for the development of advanced manufacturing. Intelligent manufacturing runs through all aspects of manufacturing activities such as design, production, management, and service, covering four levels: perception layer, network layer, execution layer, and application layer. Among them, the perception layer is composed of various industrial sensors, a large number of networked devices and RFID, etc., which provide substantial data support for the follow-up process of intelligent manufacturing production. However, in the actual environment, ...
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IPC IPC(8): G06F9/50G06K9/62G06N3/04G06N3/08G06N20/00
CPCG06F9/5072G06N3/08G06N20/00G06N3/045G06F18/23G06F18/10Y02P90/30
Inventor 陈晓红龚思远曹文治胡东滨胡春华徐雪松梁伟
Owner 湖南工商大学