Advanced geological prediction method and system based on perception while drilling

1 . An advanced geological prediction method based on perception while drilling, comprising:

acquiring drilling parameters during an advanced drilling;

obtaining physical and mechanical parameters of tunnel surrounding rocks by carrying out an inversion of the drilling parameters through a trained prediction model;

acquiring rock slag or rock powder from a flushing fluid collected during drilling;

acquiring geochemical characteristic parameters of the rock slag or the rock powder;

inputting the physical and mechanical parameters of tunnel surrounding rocks and the geochemical characteristic parameters into a pre-trained deep learning model, to analyze engineering geological conditions ahead of a tunnel face of the tunnel; and

carrying out a directional intervention on unfavorable geological conditions recognized and predicted ahead of the tunnel face of the tunnel or adjusting tunnel engineering construction parameters according to analysis results output by the pre-trained deep learning model, to avoid geological disasters in the tunnel;

wherein, using the pre-trained deep learning model to process the physical and mechanical parameters of tunnel surrounding rocks and the geochemical characteristic parameters, and outputting the analysis results, comprises:

performing a characteristic extraction on the input physical and mechanical parameters of tunnel surrounding rocks and the geochemical characteristic parameters respectively by using a first-layer structure of the pre-trained deep learning model comprising more than one fully connected layer, then concatenating the characteristics after the extraction;

performing continuously the characteristic extraction on the characteristics after the concatenation by using a second-layer structure of the pre-trained deep learning model comprising more than one fully connected layer, to obtain a characteristic extraction vector X;

concatenating the characteristic extraction vector X and a transfer parameter H t-1 , then being multiplied with different weight matrices, and then converted by an activation function, to obtain matrices Y1, Y2, Y3, and Y4 between −1 and 1; wherein, the transfer parameter H t-1 is a parameter obtained by self-learning of the pre-trained deep learning model;

performing characteristic extraction the matrices Y1, Y2, Y3, and Y4 respectively by using a plurality of residual fully connected blocks, to obtain characteristic vectors Z1, Z2, Z3, and Z4;

performing point multiplication of the characteristic vector Z1 and a parameter C t-1 introduced in a previous analysis of the pre-trained deep learning model, to obtain an attention matrix A1;

performing point multiplication of the characteristic vectors Z2 and Z3, to obtain an attention matrix A2;

obtaining a transfer parameter H t for a next analysis of the pre-trained deep learning model by adding the attention matrices A1 and A2 and activating them through a first activation function;

obtaining an attention matrix A3 by adding the attention matrices A1 and A2 and activating them through a second activation function, and then point multiplication with the characteristic vector Z4; and obtaining a parameter C t for the next analysis of the pre-trained deep learning model by activating a result of the point multiplication with the characteristic vector Z4 through the first activation function; and

outputting the analysis results obtained by calculating the attention matrix A3 by using a cross entropy function.

2 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein the geochemical characteristic parameters comprise types and content of elements in rock mass, types and content of minerals, and types and content of anions and cations in an aqueous solution.

3 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein the physical and mechanical parameters of tunnel surrounding rocks comprise compressive strength, cohesion, internal friction angle, abradability, and integrity of rock mass.

4 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein training of the deep learning model specifically comprises:

constructing a training set for adverse geology recognition based on an existing data set, and training the deep learning model by using the training set to obtain a trained adverse geology recognition model; and constructing a training set for surrounding rock classification based on the existing data set, and training the deep learning model by using the training set to obtain a trained surrounding rock classification model.

5 . The advanced geological prediction method based on perception while drilling according to claim 4 , wherein a process of mining the existing data set comprises: collecting physical and mechanical parameters of compressive strength, cohesion, internal friction angle, abradability, and integrity of rock mass in various adverse geologies and influence areas thereof on a tunneling route, as well as types and content of elements, types and content of minerals, and types and content of anions and cations in an aqueous solution, and mining, based on a data mining mode, physical and mechanical parameters capable of reflecting geology precursor characteristic information and geochemical characteristic gradual evolution information in the rock mass on the tunneling route.

6 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein the corresponding deep learning model is continuously updated and optimized according to the physical and mechanical parameters of tunnel surrounding rocks, the geochemical characteristic parameters and the adverse geology recognition result as a drilling process progresses; and the corresponding deep learning model is continuously updated and optimized according to the physical and mechanical parameters of tunnel surrounding rocks, the geochemical characteristic parameters and the surrounding rock classification result.

7 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein the obtaining physical and mechanical parameters of tunnel surrounding rocks by inversion based on the drilling parameters specifically comprises: constructing a mapping relation between the drilling parameters and the physical and mechanical parameters of tunnel surrounding rocks based on historical data; and determining the physical and mechanical parameters of tunnel surrounding rocks based on the mapping relation and the acquired drilling parameters.

8 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein the first activation function is Sigmoid.

9 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein the second activation function is TanH.

10 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein the cross entropy function is Softmax.

11 . The advanced geological prediction method based on perception while drilling according to claim 1 , wherein the different weight matrices are obtained by the self-learning of the pre-trained deep learning model.