Cross-domain knowledge assistance method and system based on neural network and deep learning
A deep learning and neural network technology, applied in the field of intelligent manufacturing, can solve problems such as large amount of data, large amount of data calculation, preliminary cleaning, etc., to achieve the effect of speeding up the generation method, the amount of calculation is suitable, and the accuracy is guaranteed
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specific Embodiment 1
[0051] A cross-domain knowledge assistance system based on neural network and deep learning, including multiple devices 1 distributed in different knowledge fields, a deep learning coordination module 2 and a global data storage module 9; each device 1 includes a data cleaning module 3, a data Obtaining module 4 and data reading module 5; Described equipment 1 also comprises stand-alone storage module 7, and described stand-alone storage module 7 communicates with described data cleaning module 3, described data obtaining module 4 and data reading module 5 respectively connect;
[0052] A data training fusion sub-module 6, the data training fusion sub-module 6 is arranged on part of the device 1; a local data storage module 8 is set on the device 1 provided with the data training fusion sub-module 6, and the The local area data storage module 8 is respectively connected with the data communication of the data training fusion sub-module 6 and the data reading module 5;
[0053...
specific Embodiment 2
[0067] A method for cross-domain knowledge assistance based on neural network and deep learning, including a cross-domain knowledge assistance system based on neural network and deep learning, comprising the following steps:
[0068] Step S1, initialize the cross-domain knowledge assistance system based on neural network and deep learning, the deep learning coordination module 2 pre-acquires the size of the data records of each of the devices 1, and when grouping all the devices 1 by using , group the data with a large amount of records in the same group, and group the data with a small amount of data records into one group, so as to prevent the data records with a large amount of data records from submerging the data with a small amount of records during data training, and the data with a large amount of data records The number of devices 1 in the group is small, and the number of data records is small, the number of devices 1 is large, so as to ensure that the number of data ...
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