Cross-domain knowledge assistance method and system based on neural network and deep learning

Abstract

The invention discloses a cross-domain knowledge assistance system based on a neural network and deep learning. The cross-domain knowledge assistance system comprises a plurality of devices (1) distributed in different knowledge fields, a deep learning coordination module (2) and a global data storage module (9), each device (1) comprises a data cleaning module (3), a data acquisition module (4) and a data reading module (5); the device (1) further comprises a single-machine storage module (7), and the single-machine storage module (7) is in data communication connection with the data cleaning module (3), the data acquisition module (4) and the data reading module (5). According to the federated machine learning-oriented full-process service migration method and system, before data record training is carried out, data cleaning is carried out on the data records, so that abnormal parts of the data records are eliminated, the accuracy of the data records is ensured, and the accuracy of a data model is ensured.

Description

technical field [0001] The invention relates to the field of intelligent manufacturing technology, specifically a method and system for cross-domain knowledge assistance based on neural network and deep learning. Background technique [0002] The 21st century has changed from the initial automation to the era of high automation, that is, the era of intelligence. The era of intelligence has brought real help to our life, work, industrial production and management, such as smart homes and home appliances based on artificial intelligence, industrial robots and industrial monitoring robots in industrial production, etc. These With the rapid development of science and technology, they appear more and more in people's lives. It can be said that the great improvement of production and life in human society depends on the progress of technology, but automated operation and intelligence The control of computerization usually requires computers or microcomputers to deal with numerous...

AI Technical Summary

Problems solved by technology

[0011] 1. In the existing technology, the reinforcement learning training model usually uses the data collected by itself for learning, optimization and control, and the processing of data is rarely cross-domain knowledge assistance, or assistance in data processing, due to the need to provide sufficient data Associated with relevant data to achieve intelligence, however, for data processing, there is no effective and fast processing method, so as to train the data training model as soon as possible

[0012] 2. In the existing technology, in order to synthesize and aggregate all kinds of data, although many institutions and scholars have proposed solutions to the dilemma of data islands and data privacy, there is no effective method for secure access and processing of multiple data

[0013] 3. In the prior art, when training data records, the size and quantity of data are not considered. When all data are directly trained in order to obtain a model, it is easy to cause the amount of data to be too large, so that on the one hand, the amount of data calculation Large data calculation is difficult; at the same time, a large amount of data may easily lead to inaccurate data training models

[0014] 4. In the existing technology, the abnormal data records that may exist in the data records have not been preliminarily cleaned up, and it is easy to generate abnormal data and cause the model obtained by data training to be abnormal.

But so far, there is no effective way to solve the above technical problems in the prior art

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