A method for controlling the range of blasting cracks in surrounding rock of open-pit mines

Abstract

The present invention discloses a method for controlling the blasting gap range of surrounding rock in an open-pit mine, comprising: determining a target blasting gap range according to the geological conditions and mining requirements of the target open-pit mine; arranging a plurality of preset blasting holes in the surrounding rock, wherein the depth and spacing of the blasting holes are optimized according to the target blasting gap range; loading a preset amount of explosives in each blasting hole, and providing a plurality of segmented detonating devices, wherein the detonation time of the segmented detonating devices is controlled according to the target blasting gap range; sequentially detonating the explosives in the blasting holes by the segmented detonating devices to form a preset blasting gap range; utilizing monitoring equipment to monitor the crack expansion during the blasting process in real time, and dynamically adjusting subsequent blasting parameters according to the monitoring results; the present invention accurately predicts the blasting gap range through a neural network, and then accurately controls the blasting parameters, which has important practical significance for improving blasting efficiency, ensuring surrounding rock stability, and reducing mining costs.

Description

Technical Field

[0001] The present invention relates to the technical field of open-pit mine surrounding rock blasting gap range control, and in particular to a method for open-pit mine surrounding rock blasting gap range control. Background Art

[0002] In open-pit mining, rock blasting is a key technology for achieving efficient mining and ensuring slope stability. However, traditional blasting methods have limitations, such as difficulty in precisely controlling the extent of the blasting crack, which can lead to damage to the integrity of the surrounding rock, stress concentration, and difficulties in subsequent support.

[0003] In recent years, with in-depth research and technological innovation in blasting technology, several new methods and techniques have been proposed to address these issues. For example, pre-splitting blasting and smooth blasting effectively reduce blasting damage to the slope rock mass by creating pre-cracks in the surrounding rock. Furthermore, by optimizing blasting parameters (such as hole spacing, row spacing, and explosive consumption per unit), blasting effectiveness can be further improved and damage to the surrounding rock can be reduced.

[0004] However, while these technologies have improved blasting efficiency and surrounding rock stability to a certain extent, some challenges remain in their practical application. For example, how to precisely control the blasting range under complex geological conditions (such as surrounding rocks with well-developed joints and fissures) remains an urgent issue. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for controlling the range of blasting cracks in surrounding rock of an open-pit mine, so as to solve the above-mentioned problems existing in the prior art.

[0006] The specific application is as follows:

[0007] A method for controlling the blasting crack range of surrounding rock in an open-pit mine comprises the following steps:

[0008] S1. Determine the target blasting fracture range based on the geological conditions and mining requirements of the target open-pit mine;

[0009] S2. Arranging a plurality of preset blasting holes in the surrounding rock, wherein the depth and spacing of the blasting holes are optimized according to the target blasting gap range;

[0010] S3. A preset amount of explosives is loaded into each blast hole, and a plurality of segmented detonating devices are provided, wherein the detonation time of the segmented detonating devices is controlled according to the target blasting gap range;

[0011] S4, sequentially detonating the explosives in the blast holes by means of a staged detonating device to form a preset blasting gap range, wherein the blasting gap range is within a preset error range;

[0012] S5. Use monitoring equipment to monitor the crack expansion during the blasting process in real time, and dynamically adjust subsequent blasting parameters based on the monitoring results.

[0013] Furthermore, determining the target blasting gap range in S1 includes:

[0014] Determine the target blasting fracture range based on the geological conditions of the target open-pit mine through numerical simulation and comprehensive analysis of data from blasting operations, wherein the geological conditions include at least rock type, degree of fracture development, and joint distribution;

[0015] The specific implementation of the target blasting gap range includes:

[0016] S11. Geological conditions analysis: Based on the geological conditions of the target open-pit mine, generate ore body structure characteristics and rock characteristics through geological exploration;

[0017] S12, numerical simulation, performing N blasting numerical simulations using a numerical simulation method, wherein the input of the numerical simulation method is the geological conditions and blasting parameters of the target open-pit mine, and the output of the numerical simulation method is a simulated target blasting gap range;

[0018] S13, neural network algorithm analysis, using a trained RBF neural network to analyze the target blasting gap range of the target open-pit mine, wherein the input of the RBF neural network is the drilling parameters, rock properties, and blasting parameters of the target open-pit mine, and the output of the RBF neural network is the RBF neural network target blasting gap range;

[0019] S14. Obtaining the target blasting gap range, calculating the first similarity between the target blasting gap range of the RBF neural network and the simulated target blasting gap range generated by N blasting numerical simulations by the cosine similarity algorithm, and presetting a similarity threshold. If the first similarity between the simulated target blasting gap range output by the nth blasting numerical simulation and the target blasting gap range of the RBF neural network meets the preset similarity threshold, then finding the simulated target blasting gap range output by the corresponding nth blasting numerical simulation is the final target blasting gap range, and at the same time finding the input blasting parameters of the corresponding nth blasting numerical simulation as the initial blasting parameters.

[0020] The specific process of obtaining the degree of crack development and joint distribution is as follows:

[0021] S15, performing image grayscale conversion and image binary statistics on the crack development degree image and joint distribution image obtained by the high-definition camera, selecting a wavelet transform series, performing a two-level Daubechies wavelet transform on the image statistical binary image; and performing processing using a one-dimensional Daubechies wavelet transform;

[0022] S16, selecting a Gaussian filter and a filter radius to smooth the transformed image data, performing peak impulse detection on the smoothed image data to obtain a peak distribution array, selecting model parameters, and using a peak impulse response model to calculate an impulse response of the peak distribution array;

[0023] S17. Using the peak impulse response result, determine the position where the peak distribution array response first drops to 0. Based on the response position, determine the most recent maximum value, use it as a threshold, perform an inverse Daubechies wavelet transform, restore it to the position of the original binary image, and use this position as the segmentation threshold of the original grayscale image to segment the image, thereby obtaining data information corresponding to the degree of crack development and joint distribution, respectively.

[0024] S18. Processing the data information corresponding to the degree of crack development and the distribution of joints by image morphology to obtain the features corresponding to the degree of crack development and the distribution of joints, respectively. The features include the number of crack endpoints, the number of intersections, the surface crack ratio, and the fractal dimension value.

[0025] Furthermore, the selection of neuron parameters of the input layer, output layer and hidden layer of the RBF neural network in S13 is as follows:

[0026] Input layer: drilling speed, rotation speed, drilling impact pressure, rotation pressure, drill bit acceleration, drill bit torque, drill rod thrust, drill bit vibration state, drilling angle, drilling depth, rock type, blasting parameters, blasting effect, so the input layer has a total of 13 parameters and 13 neurons;

[0027] Output layer: predicts the target burst gap range, so the output layer has 1 parameter and 1 neuron;

[0028] Hidden layer: The number of hidden layer nodes is adjusted according to the network training results.

[0029] Furthermore, in S5, the crack expansion during the blasting process is monitored in real time using monitoring equipment, including:

[0030] S54, using a distributed optical fiber sensor to monitor temperature changes inside the surrounding rock, inverting the expansion of the cracks through the temperature changes, and generating a first temperature change feature;

[0031] S55. Use optical fiber sensors and strain gauges to monitor strain changes on the surrounding rock surface, obtain dynamic information of crack expansion in real time, and generate a first dynamic information feature.

[0032] Furthermore, in S5, the real-time monitoring of the crack expansion during the blasting process using monitoring equipment also includes:

[0033] S56. Using an acoustic emission monitoring sensor to monitor the acoustic emission signal of the surrounding rock during the blasting process in real time, determining the expansion position and rate of the crack through signal analysis, and generating a first signal feature;

[0034] S57. Use a multi-physics field coupling monitoring sensor to simultaneously monitor the stress, strain, temperature, and acoustic emission signals of the surrounding rock to generate a first coupling feature.

[0035] Furthermore, in S5, the subsequent blasting parameters are dynamically adjusted according to the monitoring results, including:

[0036] S61. Adjust the explosive charge, detonation time, and hole spacing of subsequent blast holes based on the real-time monitored crack expansion to ensure that the crack range meets the target requirements;

[0037] S62. Establishing a mathematical model between blasting parameters and crack range, analyzing the first temperature change characteristic, the first dynamic information characteristic, the first signal characteristic, and the first coupling characteristic using a first blasting parameter analysis algorithm to predict optimal blasting parameters.

[0038] S63. After each blasting, the blasting effect is evaluated and the parameters of subsequent blasting are adjusted according to the evaluation results to form a closed-loop control.

[0039] Furthermore, in S63, after each blasting, the blasting effect is evaluated and the parameters of subsequent blasting are adjusted according to the evaluation results, forming a closed-loop control including:

[0040] S64. Using a high-resolution camera mounted on a drone, quickly scan the surrounding rock surface after blasting to obtain three-dimensional distribution information of cracks and generate a first three-dimensional image feature.

[0041] S65. Analyze the first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature in real time using a first deep learning algorithm to identify key features of crack extension and predict a final range of the crack. The first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature are divided into a training set and a test set, which are used for training the first deep learning algorithm.

[0042] S66. Automatically adjust subsequent blasting parameters based on the prediction results.

[0043] Furthermore, the first deep learning algorithm includes:

[0044] The first deep learning algorithm uses a convolutional neural network (CNN) combined with a long short-term memory (LSTM) network; the training set is analyzed using the CNN-LSTM model to obtain a final model after the CNN-LSTM model is trained. The convolutional neural network (CNN) uses a DenseNet neural network framework; the long short-term memory (LSTM) network is used to capture the dependency between the accumulation of the first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature and time, and then output a final range value of the crack;

[0045] The specific implementation process of the CNN-LSTM model is as follows:

[0046] The CNN-LSTM model architecture and processing process are as follows: the CNN model and the LSTM model are connected in series, the first three-dimensional image feature is input into the CNN model, the CNN model performs feature processing, and a first CNN data set is output, and the first temperature change feature, the first dynamic information feature, the first signal feature, and the first coupling feature are input into the LSTM model;

[0047] The processing process of the CNN model is:

[0048] Applying kernel K-means clustering analysis with adaptive weight allocation to the first three-dimensional image feature Y, feature enhancement and dimensionality reduction are performed, and the expression of the enhanced feature vector G' is obtained as follows:

[0049]

[0050] in, is the kernel function, W' is the dimensionality reduction weight matrix;

[0051] The enhanced feature vector G' is input into a multi-layer fusion and convolution network to generate the fused feature vector H, which is expressed as:

[0052] where d i is the weight of the i-th mode, M and N represent the first temperature change feature and the first dynamic information feature respectively, represents a nonlinear function;

[0053] Input the fused feature vector H into the three-dimensional convolution layer and calculate the output value of the three-dimensional convolution layer. The expression is:

[0054] D = tanh(W*H+b), where tanh is the rectified linear activation function, W is the weight matrix of the three-dimensional convolutional layer, and b is the bias term of the three-dimensional convolutional layer.

[0055] Furthermore, the LSTM model processing process is:

[0056] S68. The LSTM model is divided into three layers, marked as the first layer, the second layer, and the third layer. The first CNN data set is used to input the first layer, the first signal feature is used to input the second layer, and the first coupling feature is used to input the third layer.

[0057] S69. The final range value calculation formula of the output prediction crack of the LSTM model is:

[0058] Q=σ(W1.y LSTM1 +W2.y LSTM2 +W3.y LSTM3 +b e ),

[0059] Among them, W1, W2, and W3 represent the weight coefficients of the fully connected layers corresponding to the first, second, and third layers respectively; b e represents the bias term of the fully connected layer; y LSTM1 、y LSTM2 、y LSTM3 They represent the output features of the first, second, and third layers respectively; σ represents the activation function, and Q represents the final range value of the output crack of the LSTM model.

[0060] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0061] The embodiment of the present invention provides a method for determining a target blasting gap range according to the geological conditions and mining requirements of a target open-pit mine; arranging a plurality of preset blasting holes in the surrounding rock, and optimizing the depth and spacing of the blasting holes according to the target blasting gap range; loading a preset amount of explosives in each blasting hole, and providing a plurality of segmented detonating devices, and controlling the detonation time of the segmented detonating devices according to the target blasting gap range; sequentially detonating the explosives in the blasting holes by the segmented detonating devices to form a preset blasting gap range; utilizing monitoring equipment to monitor the crack expansion during the blasting process in real time, and dynamically adjusting subsequent blasting parameters according to the monitoring results; the present invention accurately predicts the blasting gap range through a neural network, and then accurately controls the blasting parameters, which has important practical significance for improving blasting efficiency, ensuring surrounding rock stability, and reducing mining costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention provides a flow chart of a method for controlling the blasting crack range of surrounding rock in an open-pit mine. DETAILED DESCRIPTION

[0063] The present invention will be described in detail below with reference to the accompanying drawings.

[0064] Example 1

[0065] The embodiment of the present invention provides a method for controlling the blasting crack range of surrounding rock in an open-pit mine. Figure 1 , including the following steps:

[0066] S1. Determine the target blasting fracture range based on the geological conditions and mining requirements of the target open-pit mine;

[0067] S2. Arranging a plurality of preset blasting holes in the surrounding rock, wherein the depth and spacing of the blasting holes are optimized according to the target blasting gap range;

[0068] S3. Loading a preset amount of explosives into each blast hole and setting up multiple segmented detonating devices, wherein the detonation time of the segmented detonating devices is controlled according to the target blasting gap range;

[0069] S4, sequentially detonating the explosives in the blast holes by means of a staged detonating device to form a preset blasting gap range, wherein the blasting gap range is within a preset error range;

[0070] S5. Use monitoring equipment to monitor the crack expansion during the blasting process in real time, and dynamically adjust subsequent blasting parameters based on the monitoring results.

[0071] Specifically, the target blasting gap range is determined according to the geological conditions and mining requirements of the target open-pit mine; a plurality of preset blasting holes are arranged in the surrounding rock, and the depth and spacing of the blasting holes are optimized according to the target blasting gap range; a preset amount of explosives is loaded in each blasting hole, and a plurality of segmented detonating devices are set, and the detonation time of the segmented detonating devices is controlled according to the target blasting gap range; the explosives in the blasting holes are detonated in sequence by the segmented detonating devices to form a preset blasting gap range; the crack expansion during the blasting process is monitored in real time by monitoring equipment, and subsequent blasting parameters are dynamically adjusted according to the monitoring results; the present invention accurately predicts the blasting gap range through a neural network, and then accurately controls the blasting parameters, which has important practical significance for improving blasting efficiency, ensuring surrounding rock stability and reducing mining costs.

[0072] In the above embodiment, specifically, determining the target burst gap range in S1 includes:

[0073] Determine the target blasting fracture range based on the geological conditions of the target open-pit mine through numerical simulation and comprehensive analysis of data from blasting operations, wherein the geological conditions include at least rock type, degree of fracture development, and joint distribution;

[0074] The specific implementation of the target blasting gap range includes:

[0075] S11. Geological conditions analysis: Based on the geological conditions of the target open-pit mine, generate ore body structure characteristics and rock characteristics through geological exploration;

[0076] S12, numerical simulation, performing N blasting numerical simulations using a numerical simulation method, wherein the input of the numerical simulation method is the geological conditions and blasting parameters of the target open-pit mine, and the output of the numerical simulation method is a simulated target blasting gap range;

[0077] S13, neural network algorithm analysis, using a trained RBF neural network to analyze the target blasting gap range of the target open-pit mine, wherein the input of the RBF neural network is the drilling parameters, rock properties, and blasting parameters of the target open-pit mine, and the output of the RBF neural network is the RBF neural network target blasting gap range;

[0078] S14. Obtaining the target blasting gap range, calculating the first similarity between the target blasting gap range of the RBF neural network and the simulated target blasting gap range generated by N blasting numerical simulations by the cosine similarity algorithm, and presetting a similarity threshold. If the first similarity between the simulated target blasting gap range output by the nth blasting numerical simulation and the target blasting gap range of the RBF neural network meets the preset similarity threshold, then finding the simulated target blasting gap range output by the corresponding nth blasting numerical simulation is the final target blasting gap range, and at the same time finding the input blasting parameters of the corresponding nth blasting numerical simulation as the initial blasting parameters.

[0079] It should be noted that the implementation process of the cosine similarity algorithm is:

[0080]

[0081] The cosine similarity algorithm can be used to calculate the similarity of two burst gap ranges, where x i The set of feature vectors representing the target burst gap range of the RBF neural network, y i The set of characteristic vectors representing the range of the simulated target blasting gap output by the numerical simulation of the blasting sequence.

[0082] It should be noted that the specific implementation process of the numerical simulation method for N blasting is as follows:

[0083] D1. Establishing a numerical model:

[0084] Choose an appropriate numerical method:

[0085] Finite element method (FEM) is suitable for dealing with mechanical problems of continuous media and can simulate the stress and strain distribution of rocks under blasting loads.

[0086] The discrete element method (DEM) is suitable for simulating the behavior of granular materials and can simulate rock crushing and crack propagation.

[0087] Fluid dynamics methods (CFD), suitable for simulating the interaction between gas and rock during blasting;

[0088] This embodiment uses the finite element method to establish a numerical model;

[0089] Build the geometry model:

[0090] constructing a geometric model of the rock and explosives, including the initial state of the rock and the arrangement of the explosives, using a numerical simulation method, wherein the input of the numerical simulation method is the geological conditions of the target open-pit mine and blasting parameters;

[0091] For complex fractured rock masses, the model can be analyzed and reorganized using tools such as MATLAB to construct a three-dimensional model that conforms to the actual fracture distribution law.

[0092] Define the material properties:

[0093] Define appropriate constitutive models and parameters for materials such as rock, explosives, and air. For example, the HJC model can be used for rock, and the JWL equation of state can be used for explosives.

[0094] D2. Apply loads and boundary conditions:

[0095] Apply the load:

[0096] Apply blasting loads, including blast stress waves and gas pressure, in numerical simulation methods;

[0097] The effect of high-pressure gas generated by explosive explosion on rock can be simulated by fluid-solid coupling method;

[0098] Define the boundary conditions:

[0099] Define the boundary conditions of the numerical simulation method according to the actual working conditions, such as free surface, fixed boundary, etc.;

[0100] D3. Numerical Calculation and Simulation

[0101] Choose an appropriate numerical algorithm:

[0102] For large deformation problems, the Arbitrary Lagrangian-Eulerian (ALE) algorithm can be used, which combines the advantages of the Lagrange algorithm and the Euler algorithm to avoid calculation problems caused by mesh distortion.

[0103] Perform numerical calculations:

[0104] Use finite element software (such as LS-DYNA) to perform numerical calculations to simulate stress, strain, and crack propagation during the blasting process;

[0105] By changing the blasting parameters (such as charge amount, hole spacing, etc.), N blasting numerical simulations are performed;

[0106] Through numerical simulation methods, the scope and shape of blasting cracks in the surrounding rock of open-pit mines can be accurately predicted, providing theoretical support for optimizing blasting parameters and designing reasonable blasting plans. Numerical simulation methods include finite element method (FEM), discrete element method (DEM) and fluid dynamics method (CFD). Combined with fluid-solid coupling technology and appropriate numerical algorithms, they can effectively deal with complex mechanical problems in the blasting process.

[0107] The specific process of obtaining the degree of crack development and joint distribution is as follows:

[0108] S15, performing image grayscale conversion and image binary statistics on the crack development degree image and joint distribution image obtained by the high-definition camera, selecting a wavelet transform series, performing a two-level Daubechies wavelet transform on the image statistical binary image; and performing processing using a one-dimensional Daubechies wavelet transform;

[0109] S16, selecting a Gaussian filter and a filter radius to smooth the transformed image data, performing peak impulse detection on the smoothed image data to obtain a peak distribution array, selecting model parameters, and using a peak impulse response model to calculate an impulse response of the peak distribution array;

[0110] S17. Using the peak impulse response result, determine the position where the peak distribution array response first drops to 0. Based on the response position, determine the most recent maximum value, use it as a threshold, perform an inverse Daubechies wavelet transform, restore it to the position of the original binary image, and use this position as the segmentation threshold of the original grayscale image to segment the image, thereby obtaining data information corresponding to the degree of crack development and joint distribution, respectively.

[0111] S18. Processing the data information corresponding to the degree of crack development and the distribution of joints by image morphology to obtain the features corresponding to the degree of crack development and the distribution of joints, respectively. The features include the number of crack endpoints, the number of intersections, the surface crack ratio, and the fractal dimension value.

[0112] It should be noted that this embodiment uses a skeleton extraction algorithm in image morphology to process the data information corresponding to the degree of crack development and the distribution of joints. By performing N erosion and opening operations on the image X obtained based on the hit-and-miss transformation, the morphological skeleton of the image corresponding to the data information of the degree of crack development and the distribution of joints is finally obtained.

[0113] The specific implementation process of step S18 is:

[0114] S181, Number of crack endpoints:

[0115] S1811. Create a matrix P to store all coordinates of the fracture skeleton points. The size of the matrix P is M×2, where M represents the number of skeleton points. Create an empty matrix E to record the coordinates of all skeleton endpoints.

[0116] S1812. Traverse each skeleton coordinate point. If there is only one skeleton point within the eight-neighborhood range of the point, record the coordinates of the point in matrix E. If there is more than one skeleton point within the eight-neighborhood range of the point, check whether there are other skeleton points in the opposite direction of each adjacent point. If not, save the coordinates of the point in matrix E.

[0117] S1813, repeat step S1812 until all skeleton coordinate points are traversed, and then the matrix E stores all endpoint coordinates of the skeleton, and the number of all endpoint coordinates is represented as the number of crack endpoints;

[0118] S182, number of intersection points:

[0119] Traverse each skeleton pixel point and check the number of skeleton points within its eight-neighborhood range. If there are at least three adjacent points within the eight-neighborhood range, the point satisfies the multi-connectivity feature and is a skeleton intersection point. The point is then identified as a skeleton intersection point, and the total number of skeleton intersection points is expressed as the number of intersection points.

[0120] S183, Surface crack rate:

[0121] Surface crack rate K t It is defined as the total length of cracks contained in the rock mass per unit area, and the formula is:

[0122]

[0123] Where A is the total area of ​​the selected rock mass image, It is expressed as the total length of the cracks in the rock mass image. For binary rock mass crack images, the surface crack ratio is expressed as the ratio of crack pixels to the pixels of the entire image. The rock mass crack ratio can reflect the degree of development of the rock mass structural surface. The larger the surface crack ratio, the more developed the rock mass structural surface.

[0124] S184, fractal dimension value:

[0125] This embodiment uses the box-counting fractal dimension method for calculation. Small rectangles of equal side length r are used to obtain the crack coverage in the image corresponding to the data information of the crack development degree and joint distribution. The number of cracks passing through the small rectangles is obtained, N(r). The corresponding N(r) is obtained by continuously changing the size of the rectangle. The corresponding relationship between N(r) and r is as follows:

[0126] N(r)=ar -D, where a is a constant, r ranges from 1 / h to 2 / h, r represents the height of the image, and D represents the fractal dimension. Finally, the corresponding relationship formula is described in the lnr-lnN coordinate system. The slope of the corresponding relationship formula is calculated using the least squares method. The absolute value of the slope represents the fractal dimension. For the rock mass structural surface, the fractal dimension value D ranges from 1.0 to 2.0. The closer the fractal dimension value D is to 1, the better the rock mass quality is. The higher the fractal dimension value D is, the worse the rock mass quality is.

[0127] In the above embodiment, specifically, the selection of the neuron parameters of the input layer, output layer and hidden layer of the RBF neural network in S13 is as follows:

[0128] Input layer: drilling speed, rotation speed, drilling impact pressure, rotation pressure, drill bit acceleration, drill bit torque, drill rod thrust, drill bit vibration state, drilling angle, drilling depth, rock type, blasting parameters, blasting effect, so the input layer has a total of 13 parameters and 13 neurons;

[0129] Output layer: predicts the target burst gap range, so the output layer has 1 parameter and 1 neuron;

[0130] Hidden layer: The number of hidden layer nodes is adjusted according to the network training results.

[0131] It should be noted that the surrounding rock blasting gap range model based on RBF neural network analyzes and studies the collected data parameter information such as drilling speed, rotation speed, drilling impact pressure, rotation pressure, drill bit acceleration, drill bit torque, drill rod thrust, drill bit vibration state, drilling angle, drilling depth, rock type, blasting parameters, blasting effect, etc., establishes a neural network model, and outputs the surrounding rock blasting gap range information.

[0132] Due to the high complexity and nonlinearity of the rock blasting process, neural networks can play a significant role in modeling. Since neural networks can directly build models based on object input / output data, they don't require object knowledge or complex mathematical formulas. Using appropriate training algorithms, network learning accuracy can be achieved. Therefore, using neural network models to implement system modeling is highly effective and relatively easy. Neural network models were constructed for drilling velocity, rotational speed, drilling impact pressure, rotational pressure, drill bit acceleration, drill bit torque, drill rod thrust, drill bit vibration, drilling angle, drilling depth, rock type, blasting parameters, blasting effect, and the range of blasting fractures in the surrounding rock.

[0133] A three-layer neural network is used to construct a rockburst fracture range prediction design model (input layer, hidden layer and output layer). The process is to first train the network through a large number of engineering example training samples, select the appropriate number of hidden layer nodes, training accuracy, and number of iterations, so as to obtain the optimal transfer coefficient of the network node function, and then perform inference calculation based on the input parameters to obtain the optimal output.

[0134] A hyperbolic tangent function is used between the input layer and the hidden layer, a linear rectification function ReLU is used between the hidden layer and the output layer, and a gradient descent function Adam is used as a training function.

[0135] The range of the number of neurons in the hidden layer is calculated as follows:

[0136]

[0137] Where H is the number of neurons in the hidden layer, a is the number of neurons in the input layer, b is the number of neurons in the output layer, and c is a positive integer between 1 and 10.

[0138] A neural network model was established, using a three-layer neural network (input layer, hidden layer, and output layer) to construct the design model. Its input parameters were the relevant parameters obtained during drilling and those before and after blasting. The output was information about the range of blasting fractures in the surrounding rock. This neural network model, composed of an RBF neural network structure, trained the network by collecting a large amount of actual engineering data to obtain the optimal transfer coefficient of the network node function. Then, based on the input parameters (the relevant parameters obtained during drilling and those before and after blasting, a total of 13 parameters, or n = 13), it performed inference calculations to obtain the optimal output.

[0139] In the above embodiment, specifically, using monitoring equipment to monitor the crack expansion during the blasting process in real time in S5 includes:

[0140] S54, using a distributed optical fiber sensor to monitor temperature changes inside the surrounding rock, inverting the expansion of the cracks through the temperature changes, and generating a first temperature change feature;

[0141] S55. Use optical fiber sensors and strain gauges to monitor strain changes on the surrounding rock surface, obtain dynamic information of crack expansion in real time, and generate a first dynamic information feature.

[0142] In the above embodiment, specifically, the step of using monitoring equipment to monitor the crack expansion during the blasting process in real time in S5 further includes:

[0143] S56. Using an acoustic emission monitoring sensor to monitor the acoustic emission signal of the surrounding rock during the blasting process in real time, determining the expansion position and rate of the crack through signal analysis, and generating a first signal feature;

[0144] S57. Use a multi-physics field coupling monitoring sensor to simultaneously monitor the stress, strain, temperature, and acoustic emission signals of the surrounding rock to generate a first coupling feature.

[0145] It should be noted that the first temperature change feature, the first dynamic information feature, the first signal feature, and the first coupling feature are all extracted in the following manner:

[0146] L1. Preprocess and label the first temperature change feature, the first dynamic information feature, the first signal feature, and the first coupling feature before feature extraction. Use statistical methods to detect and eliminate outliers. Normalize the data from different sensors. Synchronize all data by timestamp and label them as first temperature change data, first dynamic information data, first signal data, and first coupling data, respectively.

[0147] L2. Drawing waveform signal graphs for the first temperature change data, the first dynamic information data, the first signal data, and the first coupling data, respectively; calculating signal frequency fluctuation information of the first temperature change data, the first dynamic information data, the first signal data, and the first coupling data, respectively, based on the waveform signal graphs; determining abnormal signal locations based on the signal frequency fluctuation information; performing waveform extraction on the abnormal signal locations in the waveform signal graphs to determine noise types present in the first temperature change data, the first dynamic information data, the first signal data, and the first coupling data;

[0148] L3. Determine a signal noise removal method for the first temperature change data, the first dynamic information data, the first signal data, and the first coupling data based on the existing noise type, and perform exception processing on the first temperature change data, the first dynamic information data, the first signal data, and the first coupling data according to the signal noise removal method;

[0149] L4. Divide the first temperature change data, the first dynamic information data, the first signal data, and the first coupling data after the abnormality processing into N equal waveform signal data blocks, convert N into a binary representation, and determine the binary bit length of N;

[0150] L5. Perform CWT continuous wavelet transform operation on the waveform signal data block based on wavelet transform and wavelet function, and then perform scaling and translation to obtain frequency domain data of first temperature change data, first dynamic information data, first signal data, and first coupling data respectively;

[0151] L6. Plotting the frequency domain data of the first temperature change data, the first dynamic information data, the first signal data, and the first coupling data into a waveform signal spectrum diagram;

[0152] L7. Calculate the peak frequency, frequency bandwidth, center frequency, and spectrum energy of the waveform signal spectrum graph to obtain spectrum characteristics of the waveform signal spectrum graph, where the spectrum characteristics of the waveform signal spectrum graph are respectively represented as a first temperature change characteristic, a first dynamic information characteristic, a first signal characteristic, and a first coupling characteristic.

[0153] Specifically, the extracted first temperature change feature, first dynamic information feature, first signal feature, and first coupling feature are divided into a training set and a test set. The training set data is used to train the first deep learning algorithm. After the training is completed, the feature importance evaluation method provided by the first deep learning algorithm is used to identify the features that have the greatest impact on the final range value of the crack. The test set is input into the trained first deep learning algorithm to obtain the prediction results of each sample, predict the final range value of the crack, form an output list of the final range value of the crack, and use accuracy, recall rate, and F1-score to evaluate the performance of the model. In this embodiment, the extracted features are divided into a training set (70%) and a test set (30%).

[0154] In the above embodiment, specifically, dynamically adjusting subsequent blasting parameters according to the monitoring results in S5 includes:

[0155] S61. Adjust the explosive charge, detonation time, and hole spacing of subsequent blast holes based on the real-time monitored crack expansion to ensure that the crack range meets the target requirements;

[0156] S62. Establishing a mathematical model between blasting parameters and crack range, analyzing the first temperature change characteristic, the first dynamic information characteristic, the first signal characteristic, and the first coupling characteristic using a first blasting parameter analysis algorithm to predict optimal blasting parameters.

[0157] S63. After each blasting, the blasting effect is evaluated and the parameters of subsequent blasting are adjusted according to the evaluation results to form a closed-loop control.

[0158] Specifically, the specific implementation process of the first blasting parameter analysis algorithm is:

[0159] In the process of predicting the optimal blasting parameter combination, the blasting parameters and the corresponding first temperature change characteristics, first dynamic information characteristics, first signal characteristics and first coupling characteristics are integrated into a data set to ensure that each set of blasting parameters corresponds to the subsequent crack range; the Pearson correlation coefficient is used to calculate the correlation between each blasting parameter and the open-pit mine surrounding rock blasting monitoring parameters, generate a correlation matrix, and identify positive and negative correlations. In this embodiment, the correlation coefficient between the unit consumption of explosives and the stress of the open-pit mine surrounding rock wall is 0.86 (strong positive correlation), and the correlation coefficient between the detonation interval and the vibration acceleration is -0.78 (negative correlation); the association rule learning algorithm Apriori is applied to explore the relationship between the blasting parameters and the open-pit mine surrounding rock blasting monitoring parameters and identify potential association rules; through association rule learning, it is found that when the unit consumption of explosives is greater than 0.7 kg / m and the peripheral hole spacing is less than 0.5 m, the risk of exceeding the limit of the open-pit mine surrounding rock wall stress increases significantly (confidence level is 80%);

[0160] Bayesian linear regression was selected for modeling. The open-pit mine surrounding rock blasting monitoring parameters were used as dependent variables and the blasting parameters as independent variables. The data was divided into a training set and a test set. The Bayesian linear regression model was trained using the training set and validated on the test set to evaluate the model's fit and predictive ability. The coefficients of the Bayesian linear regression model were used to quantitatively evaluate the influence of each blasting parameter on the open-pit mine surrounding rock blasting monitoring parameters. The larger the absolute value of the coefficient, the greater the influence of the blasting parameter on the open-pit mine surrounding rock blasting monitoring parameters.

[0161] The decision tree model performed well and was able to accurately predict the blasting monitoring parameters of open-pit mine surrounding rock after blasting. By combining the output of the decision tree model with expert advice, the optimal blasting parameter schemes under different parameter combinations were identified. Linear regression analysis results showed that explosive consumption per unit, detonation interval, and peripheral hole spacing significantly affected the monitoring parameters of open-pit mine surrounding rock blasting. Explosive consumption per unit had the greatest positive impact on the stress of the open-pit mine surrounding rock wall, while detonation interval had a significant negative impact on vibration acceleration. Association rule mining results provided multiple potential parameter combination rules. For example, a combination of higher explosive consumption per unit and smaller peripheral hole spacing may increase the risk of excessive stress in the open-pit mine surrounding rock wall.

[0162] In the above embodiment, specifically, in S63, after each blasting, the blasting effect is evaluated, and the parameters of the subsequent blasting are adjusted according to the evaluation results to form a closed-loop control, which includes:

[0163] S64. Using a high-resolution camera mounted on a drone, quickly scan the surrounding rock surface after blasting to obtain three-dimensional distribution information of cracks and generate a first three-dimensional image feature;

[0164] S65. Analyze the first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature in real time using a first deep learning algorithm to identify key features of crack extension and predict a final range of the crack. The first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature are divided into a training set and a test set, which are used for training the first deep learning algorithm.

[0165] S66. Automatically adjust subsequent blasting parameters based on the prediction results.

[0166] It should be noted that, based on the prediction results, the parameters of subsequent blasting are automatically adjusted, and the system automatically sends optimization suggestions to the on-site staff, who adjust the blasting parameters according to the suggestions.

[0167] It should be noted that, in this embodiment, some blasting parameters are as follows: charge: 0.5-1.5 kg / m, peripheral hole spacing: 0.3-0.8 m, number of blast holes: 2-6, blast hole depth: 2-3.5 m, explosive consumption per unit: 0.4-1.0 kg / m 3 , detonation interval: 0.001~0.005s. Initial safety index (SI): open-pit mine surrounding rock wall stress: 11.2MPa, open-pit mine surrounding rock displacement: 2.8mm, vibration acceleration: 30m / s2, fracture zone range: 14.41~16.73 times the blasthole radius.

[0168] Parameters before optimization: Charge: 1.2kg / m, Peripheral hole spacing: 0.5m, Number of blastholes: 4, Blasthole depth: 3.0m, Explosive consumption per unit: 0.85kg / m 3 , detonation interval time: 0.003s, crack zone range: 15.83 times the blast hole radius.

[0169] The monitoring results are as follows: open-pit mine surrounding rock wall stress: 11.2MPa, open-pit mine surrounding rock displacement: 2.8mm, vibration acceleration: 30m / s2.

[0170] Parameters after the first deep learning algorithm optimization: Charge: 1.0kg / m, Peripheral hole spacing: 0.45m, Number of blastholes: 2, Explosive consumption per unit: 0.78kg / m 3 , detonation interval time: 0.0025s, crack zone range: 14.56 times the blast hole radius.

[0171] The monitoring results are as follows: open-pit mine surrounding rock wall stress: 9.8MPa (reduced by 12.5%), open-pit mine surrounding rock displacement: 2.1mm (reduced by 25%), vibration acceleration: 25m / s2 (reduced by 16.7%), fracture zone range: 14.63 times the blast hole radius (reduced by 11.7%).

[0172] In the above embodiment, specifically, the first deep learning algorithm includes:

[0173] The first deep learning algorithm uses a convolutional neural network (CNN) combined with a long short-term memory (LSTM) network; the training set is analyzed using the CNN-LSTM model to obtain a final model after the CNN-LSTM model is trained. The convolutional neural network (CNN) uses a DenseNet neural network framework; the long short-term memory (LSTM) network is used to capture the dependency between the accumulation of the first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature and time, and then output a final range value of the crack;

[0174] The specific implementation process of the CNN-LSTM model is as follows:

[0175] The CNN-LSTM model architecture and processing process are as follows: the CNN model and the LSTM model are connected in series, the first three-dimensional image feature is input into the CNN model, the CNN model performs feature processing, and a first CNN data set is output, and the first temperature change feature, the first dynamic information feature, the first signal feature, and the first coupling feature are input into the LSTM model;

[0176] The processing process of the CNN model is:

[0177] Applying kernel K-means clustering analysis with adaptive weight allocation to the first three-dimensional image feature Y, feature enhancement and dimensionality reduction are performed, and the expression of the enhanced feature vector G' is obtained as follows:

[0178]

[0179] in, is the kernel function, W' is the dimensionality reduction weight matrix;

[0180] The enhanced feature vector G' is input into a multi-layer fusion and convolution network to generate the fused feature vector H, which is expressed as:

[0181] where d i is the weight of the i-th mode, M and N represent the first temperature change feature and the first dynamic information feature respectively, represents a nonlinear function;

[0182] Input the fused feature vector H into the three-dimensional convolution layer and calculate the output value of the three-dimensional convolution layer. The expression is:

[0183] D = tanh(W*H+b), where tanh is the rectified linear activation function, W is the weight matrix of the three-dimensional convolutional layer, and b is the bias term of the three-dimensional convolutional layer.

[0184] In the above embodiment, specifically, the LSTM model processing process is:

[0185] S68. The LSTM model is divided into three layers, marked as the first layer, the second layer, and the third layer. The first CNN data set is used to input the first layer, the first signal feature is used to input the second layer, and the first coupling feature is used to input the third layer.

[0186] S69. The final range value calculation formula of the output prediction crack of the LSTM model is:

[0187] Q=σ(W1.y LSTM1 +W2.y LSTM2 +W3.y LSTM3 +b e ),

[0188] Among them, W1, W2, and W3 represent the weight coefficients of the fully connected layers corresponding to the first, second, and third layers respectively; b e represents the bias term of the fully connected layer; y LSTM1 、y LSTM2 、y LSTM3 They represent the output features of the first, second, and third layers respectively; σ represents the activation function, and Q represents the final range value of the output crack of the LSTM model.

[0189] It should be noted that adaptive weights are introduced for W1, W2, and W3 to satisfy the following formula:

[0190]

[0191] in, represents the adaptive weight of the i-th layer at the t-th iteration, T represents the maximum number of iterations, W max Represents the maximum weight coefficient, W min represents the minimum weight coefficient, represents the maximum adaptive inertia value at the tth iteration, Represents the minimum adaptive inertia value at the t-th iteration.

[0192] It should be understood that the above embodiments are one or more embodiments of the present invention, and there are many other embodiments and variations thereof based on the present invention; the variations and modifications made by ordinary technicians in this industry through the present invention without making groundbreaking innovations all fall within the scope of protection of the present invention.

Claims

1. A method for controlling the blasting crack range of surrounding rock in an open-pit mine, characterized in that: The following steps are involved: S1. Determine the target blasting gap range and initial blasting parameters based on the geological conditions and mining requirements of the target open-pit mine; S2. Arranging a plurality of preset blasting holes in the surrounding rock, wherein the depth and spacing of the blasting holes are designed according to the initial blasting parameters; S3. Filling each blast hole with a preset amount of explosives and setting a plurality of segmented detonating devices, wherein the detonation time of the segmented detonating devices is controlled according to the initial blasting parameters; S4, sequentially detonating the explosives in the blast holes by means of a staged detonating device to form a preset blasting gap range, wherein the blasting gap range is within a preset error range; S5. Use monitoring equipment to monitor the crack expansion during the blasting process in real time, and dynamically adjust subsequent blasting parameters based on the monitoring results; Determining the target blasting gap range in S1 includes: Determine the target blasting fracture range based on the geological conditions of the target open-pit mine through numerical simulation and comprehensive analysis of data from blasting operations, wherein the geological conditions include at least rock type, degree of fracture development, and joint distribution; The specific implementation of the target blasting gap range includes: S11. Geological conditions analysis: Based on the geological conditions of the target open-pit mine, generate ore body structure characteristics and rock characteristics through geological exploration; S12, numerical simulation, performing N blasting numerical simulations using a numerical simulation method, wherein the input of the numerical simulation method is the geological conditions and blasting parameters of the target open-pit mine, and the output of the numerical simulation method is a simulated target blasting gap range; S13, neural network algorithm analysis, using a trained RBF neural network to analyze the target blasting gap range of the target open-pit mine, wherein the input of the RBF neural network is the drilling parameters, rock properties, and blasting parameters of the target open-pit mine, and the output of the RBF neural network is the RBF neural network target blasting gap range; S14, obtaining the target blasting gap range, calculating the first similarity between the target blasting gap range of the RBF neural network and the simulated target blasting gap range generated by N blasting numerical simulations by using the cosine similarity algorithm, and presetting a similarity threshold. If the first similarity between the simulated target blasting gap range output by the nth blasting numerical simulation and the target blasting gap range of the RBF neural network meets the preset similarity threshold, then finding the simulated target blasting gap range output by the corresponding nth blasting numerical simulation as the final target blasting gap range, and at the same time finding the input blasting parameters of the corresponding nth blasting numerical simulation as the initial blasting parameters; In S5, the real-time monitoring of crack expansion during the blasting process using monitoring equipment includes: S54, using a distributed optical fiber sensor to monitor temperature changes inside the surrounding rock, inverting the expansion of the cracks through the temperature changes, and generating a first temperature change feature; S55. Use optical fiber sensors and strain gauges to monitor strain changes on the surrounding rock surface, obtain dynamic information of crack expansion in real time, and generate a first dynamic information feature.

2. A method for controlling the blasting crack range of surrounding rock in an open-pit mine according to claim 1, characterized in that: The specific process of obtaining the degree of crack development and joint distribution is as follows: S15, performing image grayscale conversion and image binary statistics on the crack development degree image and joint distribution image obtained by the high-definition camera, selecting a wavelet transform series, performing a two-level Daubechies wavelet transform on the image statistical binary image; and performing processing using a one-dimensional Daubechies wavelet transform; S16, selecting a Gaussian filter and a filter radius to smooth the transformed image data, performing peak impulse detection on the smoothed image data to obtain a peak distribution array, selecting model parameters, and using a peak impulse response model to calculate an impulse response of the peak distribution array; S17. Using the peak impulse response result, determine the position where the peak distribution array response first drops to 0. Based on the response position, determine the most recent maximum value, use it as a threshold, perform an inverse Daubechies wavelet transform, restore it to the position of the original binary image, and use this position as the segmentation threshold of the original grayscale image to segment the image, thereby obtaining data information corresponding to the degree of crack development and joint distribution, respectively. S18. Processing the data information corresponding to the degree of crack development and the distribution of joints by image morphology to obtain the features corresponding to the degree of crack development and the distribution of joints, respectively. The features include the number of crack endpoints, the number of intersections, the surface crack ratio, and the fractal dimension value.

3. The method for controlling the blasting crack range of surrounding rock in an open-pit mine according to claim 1, characterized in that: The selection of neuron parameters for the input layer, output layer, and hidden layer of the RBF neural network in S13 is as follows: Input layer: drilling speed, rotation speed, drilling impact pressure, rotation pressure, drill bit acceleration, drill bit torque, drill rod thrust, drill bit vibration state, drilling angle, drilling depth, rock type, blasting parameters, blasting effect, so the input layer has a total of 13 parameters and 13 neurons; Output layer: predicts the target burst gap range, so the output layer has 1 parameter and 1 neuron; Hidden layer: The number of hidden layer nodes is adjusted according to the network training results.

4. The method for controlling the blasting crack range of surrounding rock in an open-pit mine according to claim 1, characterized in that: In S5, the use of monitoring equipment to monitor the crack expansion during the blasting process in real time also includes: S56. Using an acoustic emission monitoring sensor to monitor the acoustic emission signal of the surrounding rock during the blasting process in real time, determining the expansion position and rate of the crack through signal analysis, and generating a first signal feature; S57. Use a multi-physics field coupling monitoring sensor to simultaneously monitor the stress, strain, temperature, and acoustic emission signals of the surrounding rock to generate a first coupling feature.

5. The method for controlling the blasting crack range of surrounding rock in an open-pit mine according to claim 1, characterized in that: In S5, the subsequent blasting parameters are dynamically adjusted according to the monitoring results, including: S61. Adjust the explosive charge, detonation time, and hole spacing of subsequent blast holes based on the real-time monitored crack expansion to ensure that the crack range meets the target requirements; S62. Establishing a mathematical model between blasting parameters and crack range, analyzing the first temperature change characteristic, the first dynamic information characteristic, the first signal characteristic, and the first coupling characteristic using a first blasting parameter analysis algorithm to predict optimal blasting parameters. S63. After each blasting, the blasting effect is evaluated and the parameters of subsequent blasting are adjusted according to the evaluation results to form a closed-loop control.

6. A method for controlling the blasting crack range of surrounding rock in an open-pit mine according to claim 5, characterized in that: In S63, after each blast, the blasting effect is evaluated and the parameters of subsequent blasting are adjusted according to the evaluation results, forming a closed-loop control including: S64. Using a high-resolution camera mounted on a drone, quickly scan the surrounding rock surface after blasting to obtain three-dimensional distribution information of cracks and generate a first three-dimensional image feature. S65. Analyze the first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature in real time using a first deep learning algorithm to identify key features of crack extension and predict a final range of the crack. The first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature are divided into a training set and a test set, which are used for training the first deep learning algorithm. S66. Automatically adjust subsequent blasting parameters based on the prediction results.

7. A method for controlling the blasting crack range of surrounding rock in an open-pit mine according to claim 6, characterized in that: The first deep learning algorithm includes: The first deep learning algorithm uses a convolutional neural network (CNN) combined with a long short-term memory (LSTM) network; the training set is analyzed using the CNN-LSTM model to obtain a final model after the CNN-LSTM model is trained. The convolutional neural network (CNN) uses a DenseNet neural network framework; the long short-term memory (LSTM) network is used to capture the dependency between the accumulation of the first temperature change feature, the first dynamic information feature, the first signal feature, the first coupling feature, and the first three-dimensional image feature and time, and then output a final range value of the crack; The specific implementation process of the CNN-LSTM model is as follows: The CNN-LSTM model architecture and processing process are as follows: the CNN model and the LSTM model are connected in series, the first three-dimensional image feature is input into the CNN model, the CNN model performs feature processing, and a first CNN data set is output, and the first temperature change feature, the first dynamic information feature, the first signal feature, and the first coupling feature are input into the LSTM model; The processing process of the CNN model is: Applying kernel K-means clustering analysis with adaptive weight allocation to the first three-dimensional image feature Y, feature enhancement and dimensionality reduction are performed, and the expression of the enhanced feature vector G' is obtained as follows: ； in, is the kernel function, W' is the dimensionality reduction weight matrix; The enhanced feature vector G' is input into a multi-layer fusion and convolution network to generate the fused feature vector H, which is expressed as: , where d i is the weight of the i-th mode, M and N represent the first temperature change feature and the first dynamic information feature respectively, represents a nonlinear function; Input the fused feature vector H into the three-dimensional convolution layer and calculate the output value of the three-dimensional convolution layer. The expression is: D = tanh(W*H+b), where tanh is the rectified linear activation function, W is the weight matrix of the three-dimensional convolutional layer, and b is the bias term of the three-dimensional convolutional layer.

8. A method for controlling the blasting crack range of surrounding rock in an open-pit mine according to claim 7, characterized in that: The LSTM model processing process is as follows: S68. The LSTM model is divided into three layers, marked as the first layer, the second layer, and the third layer. The first CNN data set is used to input the first layer, the first signal feature is used to input the second layer, and the first coupling feature is used to input the third layer. S69. The final range value calculation formula of the output prediction crack of the LSTM model is: Among them, W1, W2, and W3 represent the weight coefficients of the fully connected layers corresponding to the first, second, and third layers respectively; b e represents the bias term of the fully connected layer; y LSTM1 、y LSTM2 、y LSTM3 Represent the output features corresponding to the first layer, second layer, and third layer respectively; represents the activation function, and Q represents the final range value of the LSTM model output crack.

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