A big data processing method for a segment-based two-grade deep learning model. The method includes: step (1), constructing and training a segment-based two-grade deep learning model, wherein the model is divided into two grades in a longitudinal level: a first grade and a second grade, each layer of the first grade is divided into M segments in a horizontal direction, and the weight between neuron nodes of adjacent layers in different segments of the first grade is zero; step (2), dividing big data to be processed into M sub-sets according to the type of the data and respectively inputting same into M segments of a first layer of the segment-based two-grade deep learning model for processing; and step (3), outputting a big data processing result. The method of the present invention can increase the big data processing speed and shorten the processing time.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A big data processing method for a segment-based two-grade deep learning model, the method comprising: step (1) constructing and training the segment-based two-grade deep learning model, wherein the segment-based two-grade deep learning model is divided into two grades in a longitudinal level: a first grade and a second grade; each layer of the first grade is divided into M segments in a horizontal direction; wherein, M is a modality number of a multimodality input, and a weight between neuron nodes of adjacent layers in different segments of the first grade is 0; step (2) dividing a big data to be processed into M sub-sets according to a type of the data, and respectively input into M segments of a first layer of the segment-based two-grade deep learning model for processing; and step (3) outputting a big data processing result. 2. The big data processing method for a segment-based two-grade deep learning model of claim 1, wherein, the step (1) further comprises: step (101) dividing the segment-based two-grade deep learning model with a depth of L layers into two grades in the longitudinal level: the first grade and the second grade; wherein, an input layer is a first layer, an output layer is an Lth layer, and an (L*)th layer is a division layer, 2≦L*≦L−1, then all the layers from the first layer to the (L*)th layer are referred to as the first grade, and all the layers from an (L*+1)th layer to the Lth layer are referred to as the second grade; step (102) dividing neuron nodes on each layer of the first grade into M segments in a horizontal direction: wherein an input width of the L-layer neural network is N, and each layer has N neuron nodes, the neuron nodes of the first grade are divided into M segments, and a width of each segment is Dm, 1≦m≦M and Σm=1MDm=N, and in a same segment, widths of any two layers are the same; step (103) dividing a training sample into M sub-sets, and respectively input into the M segments of the first layer of the deep learning model; step (104) respectively training sub-models of the M segments of the first grade: the weight between neuron nodes of adjacent layers in different segments of the first grade is 0, whereby a set of all the nodes of the mth segment is Sm, any node of the (l−1)th layer is si(m),l-1εSm, wherein 2≦l≦L*, while any node of the lth layer of the oth segment is sj(o),lεSo and m≠o, then a weight between node si(m),l-1 and node sj(o),l is 0, whereby wi(m),j(o),l=0; wherein, the sub-models of the M segments of the first grade are respectively trained via a deep neural network learning algorithm; step (105) training each layer of the second grade; and step (106) globally fine-tuning a network parameter of each layer via the deep neural network learning algorithm, till the network parameter of each layer reaches an optimal value. 3. The big data processing method for a segment-based two-grade deep learning model of claim 2, wherein, a value of L* is taken by determining an optimal value in a value taking interval of L* via a cross validation method.