A big data processing method based on a deep learning model satisfying K-degree sparse constraints comprises: step 1), constructing a deep learning model satisfying K-degree sparse constraints using an un-marked training sample via a gradient pruning method, wherein the K-degree sparse constraints comprise a node K-degree sparse constraint and a level K-degree sparse constraint; step 2), inputting an updated training sample into the deep learning model satisfying the K-degree sparse constraints, and optimizing a weight parameter of each layer of the model, so as to obtain an optimized deep learning model satisfying the K-degree sparse constraint; and step 3), inputting big data to be processed into the optimized deep learning model satisfying the K-degree sparse constraints for processing, and finally outputting a processing result. The method in the present invention can reduce the difficulty of big data processing and increase the speed of big data processing.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A big data processing method based on a deep learning model satisfying a K-degree sparse constraint, comprising: step 1) constructing a deep learning model satisfying the K-degree sparse constraint using an unmarked training sample via a gradient pruning method; wherein the K-degree sparse constraint comprises a node K-degree sparse constraint and a level K-degree sparse constraint; the node K-degree sparse constraint refers to that forward out-degrees of all nodes in the deep learning model do not exceed K; a value range of K is (1, N/H], wherein N is the number of all the nodes in the deep learning model, and H is the number of hidden layers of the model; and the level K-degree sparse constraint refers to that a sum of forward out-degrees of all nodes in an hth layer is less than a sum of forward out-degrees of all nodes in an (h−1)th layer; step 2) inputting an updated training sample into the deep learning model satisfying the K-degree sparse constraint; and optimizing a weight parameter of each layer of the deep learning model, so as to obtain an optimized deep learning model satisfying the K-degree sparse constraint; and step 3) inputting big data to be processed into the optimized deep learning model satisfying the K-degree sparse constraint for processing; and finally outputting a processed result. 2. The big data processing method based on the deep learning model satisfying the K-degree sparse constraint of claim 1, wherein a value of K is: K = [ d in  d out H ] ; wherein, din is a dimensionality of an input of the model, dout is a dimensionality of an output of the model, H is the number of hidden layers of the deep learning model, and [ ] is a rounding symbol. 3. The big data processing method based on the deep learning model satisfying the K-degree sparse constraint of claim 1, wherein, the step 1) further comprises: step 101): numbering each layer of the deep learning model according to an order from an input layer to an output layer; wherein h=−1; the deep learning model comprises an input layer, H hidden layers, and an output layer the deep learning model includes H+2 layers in total; and the input layer is numbered as 0, the first hidden layer is numbered as 1, . . . , the output layer is numbered as H+1; step 102) setting h=h+1, and initializing parameters of the hth layer and the (h+1)th layer; step 103) inputting an unmarked training sample set Y={xit} into the hth layer; and adjusting a connection weight between the hth layer and the (h+1)th layer and an offset weight of nodes in the (h+1)th layer during minimizing a cost function of the hth layer and the (h+1)th layer; step 104) determining whether to delete a connection via a probability function of a change of a reconstruction error when there is a connection weight less than a first threshold; wherein if the connection weight is attenuated to less than the first threshold, the change of the reconstruction ΔEr error is obtained by reconstructing samples with or without the current connection, and whether to delete the current connection is determined via the probability function of the change of the error min[1,exp(−ΔEr/Er)]; step 105) determining whether the forward out-degrees of all the nodes in the hth layer are less than K; if yes, proceeding to step 106); otherwise, returning to step 103); step 106) if h>0, determining whether the sum of forward out-degrees of all the nodes in the hth layer is less than the sum of forward out-degrees of all the nodes in the (h−1)th layer; if yes, proceeding to step 107); otherwise, returning to step 103); step 107) determining whether a change of the cost function is less than a second threshold; if yes, proceeding to step 108); otherwise, returning to step 103); and step 108) determining whether h>H is valid; if yes, ending the process of the step 1); otherwise, returning to step 102). 4. The big data processing method based on the deep learning model satisfying the K-degree sparse constraint of claim 3, wherein, the step 2) further comprises: inputting an updated training sample into the deep learning model satisfying the K-degree sparse constraint; when the input training sample is an unmarked sample set Y={xit}, obtaining an output from the input sample and then reversing it to an input again; performing a reverse reconstruction from the output layer to the input layer under the k-degree constraint; calculating a reconstruction error Er; and adjusting the weight of each layer in a mode of gradient descent or conjugated gradient descent till the error is less than a critical value. 5. The big data processing method based on the deep learning model satisfying the K-degree sparse constraint of claim 3, wherein the step 2) further comprises: inputting an updated training sample into the deep learning model satisfying the K-degree sparse constraint; when the input training sample is a labelled sample set X={xit,yit}, comparing the output with yit; calculating a training error Et; and adjusting the forward weight in a mode of gradient descent or conjugated gradient descent till the error is less than a critical value. 6. The big data processing method based on a deep learning model satisfying the K-degree sparse constraint of claim 1, wherein the deep learning model satisfying the K-degree sparse comprises: a node upper limit K-degree sparse network, a level upper limit K-degree sparse network, and an ideal upper limit K-degree sparse network; wherein the node upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K; the level upper limit K-degree sparse network refers to that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer; j refers to the layer number in the model; and the ideal upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K, and that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer. 7. The big data processing method based on a deep learning model satisfying the K-degree sparse constraint of claim 2, wherein the deep learning model satisfying the K-degree sparse comprises: a node upper limit K-degree sparse network, a level upper limit K-degree sparse network, and an ideal upper limit K-degree sparse network; wherein the node upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K; the level upper limit K-degree sparse network refers to that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer; j refers to the layer number in the model; and the ideal upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K, and that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer. 8. The big data processing method based on a deep learning model satisfying the K-degree sparse constraint of claim 3, wherein the deep learning model satisfying the K-degree sparse comprises: a node upper limit K-degree sparse network, a level upper limit K-degree sparse network, and an ideal upper limit K-degree sparse network; wherein the node upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K; the level upper limit K-degree sparse network refers to that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer; j refers to the layer number in the model; and the ideal upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K, and that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer. 9. The big data processing method based on a deep learning model satisfying the K-degree sparse constraint of claim 4, wherein the deep learning model satisfying the K-degree sparse comprises: a node upper limit K-degree sparse network, a level upper limit K-degree sparse network, and an ideal upper limit K-degree sparse network; wherein the node upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K; the level upper limit K-degree sparse network refers to that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer; j refers to the layer number in the model; and the ideal upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K, and that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer. 10. The big data processing method based on a deep learning model satisfying the K-degree sparse constraint of claim 5, wherein the deep learning model satisfying the K-degree sparse comprises: a node upper limit K-degree sparse network, a level upper limit K-degree sparse network, and an ideal upper limit K-degree sparse network; wherein the node upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K; the level upper limit K-degree sparse network refers to that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer; j refers to the layer number in the model; and the ideal upper limit K-degree sparse network refers to that forward out-degrees of all the nodes in the model equal to K, and that a sum of forward out-degrees of all the nodes in an jth layer equals to a sum of forward out-degrees of all the nodes in an (j+1)th layer.