Big data intelligent type selection design method for rock burst roadway anti-impact hydraulic support

Abstract

The invention provides a big data intelligent type selection design method for a rock burst roadway anti-impact hydraulic support, and relates to the technical field of big data.The method comprises the steps that firstly, data about geological conditions, rock mechanical properties, mine working face layout, use performance and maintenance records of previous hydraulic supports and the like are collected from different mining areas and historical records; and then key factors influencing the performance of the hydraulic support are identified from the integrated data set by using a machine learning and statistical analysis method, so that an intelligent hydraulic support type selection system is provided, the accuracy and efficiency of hydraulic support type selection can be improved, and the safety is guaranteed. According to the method, through deep analysis of a large amount of geological data, mine working face conditions and historical hydraulic support use conditions, most suitable hydraulic support selection suggestions are provided for roadways.

Description

Technical Field

[0001] The present invention relates to the field of mining big data technology, and in particular to a big data intelligent selection and design method for a rock burst tunnel anti-impact hydraulic support. Background Art

[0002] Rock burst in coal mines is a dynamic phenomenon in which high-stress coal and rock mass releases energy instantly, which often produces significant impact and damage to the surrounding rock. More than 90% of rock burst in my country occurs in tunnels. Tunnel anti-rock burst hydraulic support equipment is the last line of defense against rock burst. The selection and design of the support has a significant impact on the effectiveness of resisting coal and rock impact damage.

[0003] At present, the main methods for selecting and designing hydraulic supports for mining tunnels in rock burst working faces include analytical theoretical analysis, experimental simulation, numerical calculation analysis, empirical formulas, etc., but the following problems still exist:

[0004] (1) When performing numerical calculations, complex geological conditions usually need to be simplified for ease of calculation. This simplification may ignore some key factors, such as the heterogeneity of rock formations and the influence of joints and fissures, resulting in deviations between simulation results and actual conditions;

[0005] (2) Indoor experimental test prototypes are expensive and difficult to simulate comprehensive and accurate geological information and its geomechanical processes.

[0006] (3) Empirical formulas are generally summarized from field experience, but they lack universality for projects under extreme conditions. It is difficult to propose empirical formulas and update them regularly.

[0007] With the vigorous promotion of intelligent mine construction in my country, in 2023, "How to achieve intelligent, safe and efficient mining of rock burst coal seams?" became the only issue in the field of energy and mining engineering to be included in the list of the top ten industrial technology issues released by the China Association for Science and Technology in 2023. Therefore, how to establish a big data intelligent selection and design method is of great significance to the research and development of intelligent technology and equipment for rock burst prevention and control in coal mines. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides a big data intelligent selection and design method for anti-impact hydraulic supports in rock burst tunnels; based on an in-depth analysis of the historical usage of existing tunnel hydraulic supports, by giving corresponding geological conditions and parameter conditions in the mining process, a reasonable and safe hydraulic support selection method is given using a big data system, providing a scientific basis for the selection and design of hydraulic supports in rock burst tunnels.

[0009] A big data intelligent selection and design method for rock burst tunnel anti-rock burst hydraulic support, comprising the following steps:

[0010] Step 1: Data collection;

[0011] Step 1.1: Establish a table of geomechanical characterization parameters for rock burst working face mining tunnels;

[0012] Specifically, it includes geological factor data and mining technology factor data, among which the geological factor data include the following data: uniaxial compressive strength of coal rock σ c , coal rock impact tendency index K, coal rock elastic modulus E, internal friction angle Mean ground stress P 0 , mining depth h 0 , the history of rock burst in the same horizontal coal seam n; mining technical factor data include the following data: the degree of pressure relief of the protective layer P 1 , the horizontal distance h from the coal pillar left over from the upper protective layer mining 1 , working surface length L 0 , the width of the coal pillar in the section B, the thickness of the bottom coal T, the distance h between the tunnel excavated into the goaf, that is, the stop position and the goaf 2 , the working face advancing towards the goaf, that is, the distance h between the stop mining line and the goaf 3 ;

[0013] Step 1.2: Establish a table of key parameters for selecting energy-absorbing and anti-impact hydraulic supports for rock burst tunnels;

[0014] Specifically including the following data: initial support force F C , working resistance R w , support strength S;

[0015] Step 1.3: Collect data;

[0016] Based on the literature collection and field investigation analysis on the Internet, the geomechanical characterization parameter information of the mining tunnel of the rock burst working face of group M and the key parameter information of the performance of the energy-absorbing hydraulic support of the rock burst tunnel were collected and analyzed.

[0017] Step 2: Based on the neural network model, establish training samples for intelligent selection of stents;

[0018] Step 2.1: Select a neural network model suitable for hydraulic support selection; the neural network model selects an MLP neural network model;

[0019] Step 2.2: Define the basic structure of the neural network model;

[0020] Step 2.2.1: Determine the input layer and its size;

[0021] The geomechanical characterization parameters of the mining tunnel in the rock burst working face are used as the input layer of the neural network model for calculation, which is expressed as follows:

[0022]

[0023] In the formula, represents the input layer of the mth training sample of the neural network; X m Represents the feature vector of the mth training sample;

[0024] Step 2.2.2: Determine the number of intermediate layers;

[0025] Assuming that the neural network model has a total of L layers, the middle layer has L-1 layers;

[0026] Step 2.2.3: Determine the number of neurons in each layer;

[0027]

[0028] In the formula is the number of neurons in the lth layer; N o is the number of neurons in the output layer; N i is the number of neurons in the input layer; M is the number of samples; α is an arbitrary value variable, l≤L-1;

[0029] Step 2.2.4: Systematically construct the middle layer model;

[0030] Starting with the geomechanical characterization parameters of the mining tunnel in the rock burst working face, after linear transformation, the new data of the next layer is obtained by activation function processing, and in this way, it is passed layer by layer, and finally reflects the key parameters for the selection of energy-absorbing and anti-impact hydraulic supports in rock burst tunnels, as shown in the following formula:

[0031]

[0032] Where W l represents the weight matrix of the lth layer of the neural network; b l Represents the bias vector of the lth layer of the neural network; Represents the output result of the mth training sample after the l-1th layer of the neural network; It represents the result obtained after the m-th training sample is linearly transformed in the l-1th layer of the neural network; for Middle Quantity; express The result obtained after the activation function transformation;

[0033] Step 2.2.5: Set the output layer configuration of the neural network model:

[0034] The key parameters for selecting energy-absorbing and anti-impact hydraulic supports in rock burst tunnels are used as the output layer to predict the initial support force, working resistance and support strength of the hydraulic supports. The calculation of the output layer is expressed by the following formula:

[0035]

[0036] The ReLU activation function is used to obtain the predicted values ​​of the key parameters for the selection of energy-absorbing and anti-impact hydraulic supports in rock burst tunnels. The specific formula is as follows:

[0037]

[0038] Where F cm , R wm , S m They respectively represent the key parameter values ​​for selecting the energy-absorbing and anti-impact hydraulic support for rock burst tunnels, namely, the initial support force, working resistance and support strength of the hydraulic support;

[0039] Step 3: Optimize the parameter configuration of the neural network model;

[0040] Step 3.1: Calculate the loss function value;

[0041] Select mean square error as the loss function, and the calculation formula is:

[0042]

[0043] Z m =[F C ,R w ,S]

[0044] Where Loss is the loss function, Z m is the actual value matrix;

[0045] Step 3.2: Calculate the gradient;

[0046] Calculate the gradient of the loss function with respect to the weight matrix and bias vector;

[0047]

[0048] in, Represents the loss function Loss for the weight matrix W of the l-th layer neural network l The gradient of Represents the loss function Loss for the l-th layer neural network bias vector b l Gradient

[0049] Step 3.3: Iteratively optimize the neural network model parameters;

[0050] Update the weight matrix and bias vector, the calculation formula is:

[0051]

[0052] Among them, t represents the number of iterations, β represents the correction coefficient, which is used to control the update of the weight matrix W of the l-th layer neural network. l and the bias vector b of the lth layer neural network l the step size in the process;

[0053] Repeatedly update the weight matrix and bias vector, each update t = t + 1, until the condition for stopping iteration is:

[0054]

[0055] In the formula, express The infinite norm of ; express The infinite norm of 1 , ε 2 Indicates the set threshold;

[0056] Step 4: Using the known geomechanical characterization parameters of the mining tunnel of the rock burst working face and the mapping relationship obtained by training in steps 2 and 3, intelligent selection of hydraulic supports is realized;

[0057] Step 4.1: Real-time data collection;

[0058] With the help of the dynamic data monitoring system, the geomechanical characterization parameters of the rock burst working face mining tunnel in Table 1.1 are collected in real time and expressed as:

[0059] In the formula The superscript 0 in the table indicates the geomechanical characterization parameters of the mining tunnel corresponding to the rock burst working face collected in real time;

[0060] Step 4.2: Use the neural network model to predict the performance of the hydraulic support;

[0061] Input the data collected in step 4.1 into the neural network model trained in step 3. The trained neural network model outputs the predicted key parameters for selecting the energy-absorbing and anti-impact hydraulic support for rock burst tunnels. Output value Used to guide the intelligent selection of hydraulic supports.

[0062] The beneficial effects of adopting the above technical solution are:

[0063] The present invention provides a big data intelligent selection and design method for rock burst tunnel anti-impact hydraulic supports. The present invention provides the most suitable hydraulic support selection suggestions for tunnels through in-depth analysis of a large amount of geological data, mine working face conditions and historical hydraulic support usage. First, data on geological conditions, rock mechanical properties, mine working face layout, and previous hydraulic support usage performance and maintenance records are collected from different mining areas and historical records. Then, machine learning and statistical analysis methods are used to identify the key factors affecting the performance of hydraulic supports from the integrated data set, and then an intelligent hydraulic support selection system is provided. The invention can improve the accuracy and efficiency of hydraulic support selection and ensure safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is an overall flow chart of the intelligent selection and design method of the rock burst tunnel anti-rock burst hydraulic support in the embodiment of the present invention;

[0065] Figure 2 Schematic diagram of a neural network model in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0067] This example is aimed at the 1206 working face of a certain mine, and its coal rock uniaxial compressive strength σ c =3.09MPa, coal rock impact tendency index K = 2.196, coal rock elastic modulus E = 15.7GPa, internal friction angle Mean ground stress P 0 =27.01MPa, mining depth h 0 =752.5m, the number of rock bursts in the same horizontal coal seam is n=0, the pressure relief degree of the protective layer is average, (the pressure relief degree of the protective layer is divided into "good, medium, average, and very poor", let P in these four cases 1 The values ​​are 0, 1, 2, and 3 respectively. 1 =2, the horizontal distance h from the coal pillar left over from the upper protective layer mining 1 =60m, working surface length L 0 =150m, section coal pillar width B = 25m, bottom coal thickness T = 800mm, tunnel excavated into the goaf, distance h between stop excavation position and goaf 2 =50m, the distance between the working face advancing towards the goaf and the stop mining line and the goaf h 3 =40m

[0068] A big data intelligent selection and design method for anti-rock pressure hydraulic support in rock burst tunnels, such as Figure 1As shown, the following steps are included:

[0069] Step 1: Data collection;

[0070] Step 1.1: Establish a table of geomechanical characterization parameters for rock burst working face mining tunnels;

[0071]

[0072]

[0073] Step 1.2: Establish a table of key parameters for selecting energy-absorbing and anti-impact hydraulic supports for rock burst tunnels;

[0074] Serial number Parameter name symbol Parameter specific meaning 1 Initial support force <![CDATA[F C (10 6 N)]]> The force exerted by the hydraulic support when it first contacts the roof 2 Working resistance <![CDATA[R w (10 6 N)]]> The maximum support force of the hydraulic support when it is under pressure from the top plate 3 Support strength S(MPa) The support capacity that hydraulic supports can provide per unit area

[0075] Step 1.3: Collect data;

[0076] Based on the literature collection and field investigation analysis on the Internet, the geomechanical characterization parameter information of the mining tunnel of the rock burst working face of group M and the key parameter information of the performance of the energy-absorbing hydraulic support of the rock burst tunnel were collected and analyzed.

[0077] In this embodiment, 200 sets of data σ for establishing geomechanical characterization parameters of mining tunnels in rock burst working faces were collected through field investigation and literature search. c ,K,E, P 0 ,h 0 ,n,P 1 ,h 1 ,L 0 ,B,T,h 2 ,h 3 And the corresponding key parameter data for establishing the selection of energy-absorbing and anti-impact hydraulic supports for rock burst tunnels F C ,R w ,S and construct the sample matrix. The samples are divided into three parts: training set, validation set and test set. The number of samples is 160 groups, 40 groups and 40 groups respectively, and the training set and validation set are normalized.

[0078] Step 2: Based on the neural network model, such as Figure 2 As shown, a training sample for intelligent selection of stents is established;

[0079] Step 2.1: Select a neural network model suitable for hydraulic support selection; the neural network model selects an MLP neural network model;

[0080] Step 2.2: Define the basic structure of the neural network model;

[0081] Step 2.2.1: Determine the input layer and its size;

[0082] The geomechanical characterization parameters of the mining tunnel in the rock burst working face are used as the input layer of the neural network model for calculation, which is expressed as follows:

[0083]

[0084] In the formula, represents the input layer of the mth training sample of the neural network; X m Represents the feature vector of the mth training sample;

[0085] Step 2.2.2: Determine the number of intermediate layers;

[0086] It is assumed that the neural network model has a total of L layers, and the middle layer has L-1 layers; in this embodiment, according to factors such as the number of data types, the neural network is assumed to have a total of 4 layers, and the middle layer has 3 layers;

[0087] Step 2.2.3: Determine the number of neurons in each layer;

[0088] According to the "empirical formula" proposed by stackoverflow;

[0089]

[0090] In the formula is the number of neurons in the lth layer; N o is the number of neurons in the output layer; N i is the number of neurons in the input layer; M is the number of samples; α is a variable that can take any value, usually 2-10, l≤L-1; in this embodiment, the value of α is 4, and the number of neurons in each layer is

[0091] Step 2.2.4: Systematically construct the middle layer model;

[0092] In a neural network, the core of the forward propagation process of information lies in the continuous transformation between levels. According to this technical solution, the geomechanical characterization parameters of the mining tunnel of the rock burst working face are used as the starting point. After linear transformation, the new data of the next layer is obtained by the activation function processing. In this way, the key parameters of the selection of the energy-absorbing and anti-impact hydraulic support for the rock burst tunnel are finally reflected, as shown in the following formula:

[0093]

[0094] Where W l represents the weight matrix of the lth layer of the neural network; b l Represents the bias vector of the lth layer of the neural network; Represents the output result of the mth training sample after the l-1th layer of the neural network; It represents the result obtained after the m-th training sample is linearly transformed in the l-1th layer of the neural network; for Middle Quantity; express The result obtained after the activation function transformation;

[0095] Step 2.2.5: Set the output layer configuration of the neural network model:

[0096] The key parameters for selecting energy-absorbing and anti-impact hydraulic supports in rock burst tunnels are used as the output layer to predict the initial support force, working resistance and support strength of the hydraulic supports. The calculation of the output layer is expressed by the following formula:

[0097]

[0098] The ReLU activation function is used to obtain the predicted values ​​of the key parameters for the selection of energy-absorbing and anti-impact hydraulic supports in rock burst tunnels. The specific formula is as follows:

[0099]

[0100] Where F cm , R wm , S m They respectively represent the key parameter values ​​for selecting the energy-absorbing and anti-impact hydraulic support for rock burst tunnels, namely, the initial support force, working resistance and support strength of the hydraulic support;

[0101] Step 3: Optimize the parameter configuration of the neural network model;

[0102] Step 3.1: Calculate the loss function value;

[0103] Select mean square error as the loss function, and the calculation formula is:

[0104]

[0105] Z m =[F C ,R w ,S]

[0106] Where Loss is the loss function, Z m is the actual value matrix;

[0107] Step 3.2: Calculate the gradient;

[0108] Calculate the gradient of the loss function with respect to the weight matrix and bias vector;

[0109]

[0110]

[0111] in, Represents the loss function Loss for the weight matrix W of the l-th layer neural network l The gradient of Represents the loss function Loss for the l-th layer neural network bias vector b l Gradient

[0112] Step 3.3: Iteratively optimize the neural network model parameters;

[0113] Update the weight matrix and bias vector, the calculation formula is:

[0114]

[0115] Among them, t represents the number of iterations, β represents the correction coefficient, which is used to control the update of the weight matrix W of the l-th layer neural network. l and the bias vector b of the lth layer neural network l The step size in the process; in this embodiment, the number of iterations t=100;

[0116] Repeatedly update the weight matrix and bias vector, each update t = t + 1, until the condition for stopping iteration is:

[0117]

[0118] In the formula, express The infinite norm of ; express The infinite norm of 1 , ε 2 Indicates the set threshold;

[0119] Step 4: Using the known geomechanical characterization parameters of the mining tunnel of the rock burst working face and the mapping relationship obtained by training in steps 2 and 3, intelligent selection of hydraulic supports is realized;

[0120] Step 4.1: Real-time data collection;

[0121] With the help of the dynamic data monitoring system, the geomechanical characterization parameters of the rock burst working face mining tunnel in Table 1.1 are collected in real time and expressed as:

[0122] In the formula The superscript 0 in the table indicates the geomechanical characterization parameters of the mining tunnel corresponding to the rock burst working face collected in real time;

[0123] In this embodiment, the uniaxial compressive strength of coal rock, coal rock impact tendency index, coal rock elastic modulus, and internal friction angle are obtained by testing indoors with a testing machine; the degree of pressure relief of the protective layer is obtained by an existing stress meter; the mean ground stress is obtained by a stress contact method ground stress test method, the mining depth is recorded by drawings, and the historical number of rock bursts in the same horizontal coal seam, that is, the ledger information of rock bursts; the degree of pressure relief of the protective layer, the horizontal distance from the coal pillar left over from the mining of the upper protective layer, the length of the working face, the width of the section coal pillar, the thickness of the bottom coal, the tunnel excavated into the goaf, the distance between the stop excavation position and the goaf, the working face advancing into the goaf, and the distance between the stop mining line and the goaf can be obtained by measuring the plan of the mining project;

[0124] Step 4.2: Use the neural network model to predict the performance of the hydraulic support;

[0125] Input the data collected in step 4.1 into the neural network model trained in step 3. The trained neural network model outputs the predicted key parameters for selecting the energy-absorbing and anti-impact hydraulic support for rock burst tunnels. Output value Used to guide the intelligent selection of hydraulic supports.

[0126] Using the data from the examples, write out the data in the data set in a table format and give the specific values ​​involved in the above process;

[0127]

[0128] The specific values ​​involved in the above process are;

[0129] Number of neurons per layer Number of layers L = 4; number of iterations t = 100; threshold ε 1 =ε 2 =1*10 -3

[0130] The weights of each layer:

[0131]

[0132] Bias vectors for each layer:

[0133] b 1 =[-0.1649 -0.2819 -0.2472]

[0134] b 2 =[-0.2326 0.0803 0.3128]

[0135] b 3 =[0.2840 0.3948 -0.4565]

[0136] b 4 =[0.7036 0.6204 0.2743]

[0137] Real-time data with sequence number 1 K 1 =2.196, E 1 =15.7, n 1 =0, B 1 =25, T 1 =

[0138] 800, For example, the prediction result is:

[0139] Real-time data with sequence number 2 K 2 =2.25, E 2 =16.0, n 2 =1, B 2 =30, T 2 =850, For example, the prediction result is:

[0140] Real-time data with sequence number 3 K 3 =2.05, E 3 =15.5, n 3 =0, B 3 =20, T 3 =780, For example, the prediction result is:

[0141] Then, the key parameters for selecting hydraulic supports for energy absorption and anti-impact hydraulic supports in rock burst tunnels are predicted to be:

[0142] No. 1 Real-time data prediction: initial support force Working resistance Support strength

[0143] No. 2 Real-time data prediction: initial support force Working resistance Support strength

[0144] No. 3 Real-time data prediction: initial support force Working resistance Support strength

[0145] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) to form a technical solution.

Claims

1. A big data intelligent selection and design method for anti-rock pressure hydraulic support in rock burst tunnels, characterized in that: The following steps are involved: Step 1: Data collection; Step 2: Based on the neural network model, establish training samples for intelligent selection of stents; Step 3: Optimize the parameter configuration of the neural network model; Step 4: The known geomechanical characterization parameters of the mining tunnel of the rock burst working face are used to realize the intelligent selection of hydraulic supports through the mapping relationship obtained through training in steps 2 and 3.

2. According to claim 1, a big data intelligent selection and design method for rock burst tunnel anti-rock burst hydraulic support is characterized in that: The step 1 specifically comprises the following steps: Step 1.1: Establish a table of geomechanical characterization parameters for rock burst working face mining tunnels; Specifically, it includes geological factor data and mining technology factor data, among which the geological factor data include the following data: uniaxial compressive strength of coal rock σ c , coal rock impact tendency index K, coal rock elastic modulus E, internal friction angle The average value of ground stress P0, mining depth h0, and the history of rock burst in the same horizontal coal seam n; the mining technical factor data include the following data: the degree of pressure relief of the protective layer P1, the horizontal distance from the coal pillar left by the upper protective layer mining h1, the length of the working face L0, the width of the section coal pillar B, the thickness of the bottom coal T, the tunnel excavated into the goaf, that is, the distance between the stop excavation position and the goaf h2, and the working face advanced into the goaf, that is, the distance between the stop mining line and the goaf h3; Step 1.2: Establish a table of key parameters for selecting energy-absorbing and anti-impact hydraulic supports for rock burst tunnels; Specifically including the following data: initial support force F C , working resistance R w , support strength S; Step 1.3: Collect data; Based on the literature collection and field investigation analysis on the Internet, the geomechanical characterization parameter information of the mining tunnel of the rock burst working face of group M and the key parameter information of the performance of the energy-absorbing hydraulic support of the rock burst tunnel were collected and analyzed.

3. The big data intelligent selection and design method for rock burst tunnel anti-rock burst hydraulic support according to claim 1 is characterized in that: The step 2 specifically includes the following steps: Step 2.1: Select a neural network model suitable for hydraulic support selection; the neural network model selects an MLP neural network model; Step 2.2: Define the basic structure of the neural network model.

4. The big data intelligent selection and design method for rock burst tunnel anti-rock burst hydraulic support according to claim 3 is characterized in that: The step 2.2 specifically includes the following steps: Step 2.2.1: Determine the input layer and its size; The geomechanical characterization parameters of the mining tunnel in the rock burst working face are used as the input layer of the neural network model for calculation, which is expressed as follows: In the formula, represents the input layer of the mth training sample of the neural network; X m Represents the feature vector of the mth training sample; Step 2.2.2: Determine the number of intermediate layers; Assuming that the neural network model has a total of L layers, the middle layer has L-1 layers; Step 2.2.3: Determine the number of neurons in each layer; In the formula is the number of neurons in the lth layer; N o is the number of neurons in the output layer; N i is the number of neurons in the input layer; M is the number of samples; α is an arbitrary value variable, l≤L-1; Step 2.2.4: Systematically construct the middle layer model; Starting with the geomechanical characterization parameters of the mining tunnel in the rock burst working face, after linear transformation, the new data of the next layer is obtained by activation function processing, and in this way, it is passed layer by layer, and finally reflects the key parameters for the selection of energy-absorbing and anti-impact hydraulic supports in rock burst tunnels, as shown in the following formula: Where W l represents the weight matrix of the lth layer of the neural network; b l Represents the bias vector of the lth layer of the neural network; Represents the output result of the mth training sample after the l-1th layer of the neural network; It represents the result obtained after the m-th training sample is linearly transformed in the l-1th layer of the neural network; for Middle Quantity; express The result obtained after the activation function transformation; Step 2.2.5: Set the output layer configuration of the neural network model: The key parameters for selecting energy-absorbing and anti-impact hydraulic supports in rock burst tunnels are used as the output layer to predict the initial support force, working resistance and support strength of the hydraulic supports. The calculation of the output layer is expressed by the following formula: The ReLU activation function is used to obtain the predicted values ​​of the key parameters for the selection of energy-absorbing and anti-impact hydraulic supports in rock burst tunnels. The specific formula is as follows: Where F cm , R wm , S m They respectively represent the key parameter values ​​for the selection of the predicted energy-absorbing and anti-impact hydraulic support for rock burst tunnels, namely the initial support force, working resistance and support strength of the hydraulic support.

5. The big data intelligent selection and design method for rock burst tunnel anti-rock burst hydraulic support according to claim 1 is characterized in that: The step 3 specifically comprises the following steps: Step 3.1: Calculate the loss function value; Select mean square error as the loss function, and the calculation formula is: Z m =[F c ,R w ,S] Where Loss is the loss function, and Zm is the actual value matrix; Step 3.2: Calculate the gradient; Calculate the gradient of the loss function with respect to the weight matrix and bias vector; in, Represents the loss function Loss for the weight matrix W of the l-th layer neural network l The gradient of Represents the loss function Loss for the l-th layer neural network bias vector b l Gradient Step 3.3: Iteratively optimize the neural network model parameters; Update the weight matrix and bias vector, the calculation formula is: Among them, t represents the number of iterations, β represents the correction coefficient, which is used to control the update of the weight matrix W of the l-th layer neural network. l and the bias vector b of the lth layer neural network l the step size in the process; Repeatedly update the weight matrix and bias vector, each update t = t + 1, until the condition for stopping iteration is: In the formula, express The infinite norm of ; express The infinite norm of ; ε1 and ε2 represent the set thresholds.

6. The big data intelligent selection and design method for rock burst tunnel anti-rock burst hydraulic support according to claim 1 is characterized in that: The step 4 specifically comprises the following steps: Step 4.1: Real-time data collection; With the help of the dynamic data monitoring system, the geomechanical characterization parameters of the rock burst working face mining tunnel in Table 1.1 are collected in real time and expressed as: In the formula The superscript 0 in the column indicates the geomechanical characterization parameters of the mining tunnel corresponding to the rock burst working face collected in real time; Step 4.2: Use the neural network model to predict the performance of the hydraulic support; Input the data collected in step 4.1 into the neural network model trained in step 3. The trained neural network model outputs the predicted key parameters for selecting the energy-absorbing and anti-impact hydraulic support for rock burst tunnels. Output value Used to guide the intelligent selection of hydraulic supports.

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