Prediction method and application of slope blasting fragmentation based on Bubble Sort-GWO-ELM model

Abstract

The present invention belongs to the field of slope engineering construction. To address the problem of the inability to accurately predict slope blasting fragmentation during slope construction, which in turn affects construction quality and cost control, a slope blasting fragmentation prediction method based on a Bubble Sort-GWO-ELM model is proposed. The method comprises monitoring blasting control parameters; performing data preprocessing; constructing a blasting control parameter data set; screening the blasting control parameters using a bubble sort combined with a square error algorithm to select the characteristic parameters that have the greatest impact on the prediction results; training an extreme learning machine (ELM) model and optimizing and verifying the model parameters using an improved gray wolf optimization algorithm; determining the optimal parameters of the extreme learning machine (ELM) model, establishing a blasting effect prediction model; and performing prediction using the blasting effect prediction model. The present invention can improve the accuracy of blasting construction and enhance the economy and safety of the entire project by scientifically predicting and rationally controlling blasting parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of slope engineering construction, and in particular to a slope blasting fragmentation prediction method based on a Bubble Sort-GWO-ELM model and an application thereof. Background Art

[0002] The Drilling and Blasting Method (DBL) is a rock excavation technique commonly used in mining, tunneling, and large-scale civil engineering projects. This method achieves excavation by drilling, loading explosives, filling with inert materials, and detonating to break up the rock. The main steps include drilling, charging, filling, blasting, and clearing and evaluation. This method is suitable for various hard rocks, offers high flexibility, and is low-cost, making it widely used in mining, tunneling, and foundation engineering. With the development of smooth blasting technology and blasting vibration control technology, DBL is increasingly being used as the primary construction method for rock cuts and slopes.

[0003] During rocky roadcut slope construction, the quality of the blasting design plays a crucial role in both construction quality and cost control. This impact is not only reflected in the blasting results themselves, but also in the effectiveness of vibration reduction measures. However, there is currently a lack of methods for predicting slope blasting fragmentation, which hinders the design of blasting plans. To optimize construction quality and control costs, an efficient slope blasting fragmentation prediction model is urgently needed. Summary of the Invention

[0004] The present invention aims to provide a slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model. This prediction model accurately determines optimal blasting parameters, proactively mitigating the negative impacts of blasting on existing structures. It also effectively controls excessive or insufficient excavation, avoiding cumulative damage to the surrounding rock. Furthermore, it significantly reduces the consumption of slope support materials and lowers the overall cost of drilling and blasting operations.

[0005] To achieve the above object, the present invention provides a slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model, comprising the following steps:

[0006] Step 1: Monitor blasting control parameter data at the construction site;

[0007] Step 2: Preprocessing the monitored blasting control parameter data;

[0008] Step 3: Use bubble sort combined with square difference algorithm to filter the preprocessed blasting control parameter data, construct a blasting control parameter data set based on the filtered blasting control parameter data, and divide the blasting control parameter data set into a training set and a test set;

[0009] Step 4: Normalize the obtained training set and test set;

[0010] Step 5: Based on the normalized training set, the parameters of the extreme learning machine (ELM) model are optimized using the improved gray wolf optimization algorithm to determine the optimal parameters of the optimized extreme learning machine (ELM) model.

[0011] Step 6: Optimize the extreme learning machine (ELM) model with the optimal parameters, test the optimized ELM model on the test set, calculate the output of the predictions for the test set samples, evaluate the optimal parameters, and establish a blasting effect prediction structure model based on the optimal parameters.

[0012] Step 7: Use the blasting effect prediction model to predict the slope blasting fragmentation and output the prediction results.

[0013] Furthermore, the specific steps of step 3 include:

[0014] Step 3.1: Define the monitored n blasting control parameter data as an array A containing n elements. Each element in array A corresponds to a data to be screened and sorted. Each element X in array A is i It includes six types of blasting control parameters, namely tensile strength, blasting resistance line, step fracture angle, explosive consumption, hole spacing and row spacing;

[0015] Step 3.2: Sort n elements X in array A by bubble sort algorithm i Sorting is performed, and based on the position of the elements in each round of sorting, different combinations of six types of blasting control parameters are achieved;

[0016] Step 3.3: Calculate the squared difference of each of the different combinations according to the following formula:

[0017]

[0018] Where N is the number of data points in each combination, X i is the actual value monitored, is the mean of the data;

[0019] Step 3.4: Repeat steps 3.2 and 3.3 until the elements in array A are sorted in ascending order, and then obtain the square differences of various combinations in each round of bubble sort;

[0020] Step 3.5: By comparing the square differences of various combinations, the blasting control parameter data corresponding to the combination with the smallest square difference is selected;

[0021] Step 3.6: Based on the blasting control parameter data screened in step 3.5, a blasting control parameter dataset is established, and the blasting control parameter dataset is divided into a training set and a test set.

[0022] Furthermore, the specific steps of step 5 include:

[0023] Step 5.1: Obtain the normalized training set data;

[0024] Step 5.2: Initialize the number of hidden layer neurons L and activation function of the extreme learning machine ELM model;

[0025] Step 5.3: Randomly initialize the input weight matrix W and hidden layer bias b, and optimize the parameters W and b using the improved gray wolf optimization algorithm based on the data of the training set;

[0026] Step 5.4: Based on the optimized parameters W and b, calculate the hidden layer output matrix H and determine the number of hidden layer neurons L;

[0027] Step 5.5: Output the optimal parameters of the current extreme learning machine (ELM) model, including the number of hidden layer neurons L, W, and b.

[0028] Furthermore, the improved gray wolf optimization algorithm in step 5.3 includes the following steps:

[0029] Step 5.3.1: Initialize the coefficient vectors A and C by the following formula:

[0030]

[0031] Where a is the convergence factor, which decreases linearly from 2 to 0 with the number of iterations t, and r1 and r2 are random vectors between [0, 1];

[0032] Step 5.3.2: Calculate the fitness of each wolf in the pack. Label the three alpha wolves with the highest fitness as α, β, and δ, respectively. Label the remaining wolves as ω. Calculate the position of the wolves to surround the prey using the following formula:

[0033] X(t+1)=Ω·(X p (t)-A·D)

[0034] D=|C·X p (t)-X(t)|

[0035] Among them, t is the current iteration number, X p is the position vector of the current prey, X(t) is the position vector of the t-th generation leader, and Ω is the adaptive weight factor;

[0036] and

[0037] Step 5.3.3: Update the positions of the three alpha wolves in the wolf pack using the following formula:

[0038]

[0039] in, and Represent the distances between α wolf, β wolf and δ wolf and other individuals respectively; and Represent the current positions of α wolf, β wolf and δ wolf respectively; and is a random number, is the current location of the individual gray wolf;

[0040] According to the positions of α, β and δ, the positions of all gray wolf individuals are updated by the following gray wolf individual position update formula:

[0041]

[0042] in, They represent the adjusted position of ω wolf affected by α wolf, β wolf and δ wolf respectively;

[0043]

[0044] in, Indicates the latest updated location of the gray wolf;

[0045] Step 5.3.4: Determine whether the maximum number of iterations has been reached. If so, output the optimal solution. If not, return to step 5.3.2.

[0046] Furthermore, the specific steps of step 5.4 include:

[0047] Step 5.4.1: For each input sample x i , the output h of the i-th hidden layer node is obtained according to the following formula i :

[0048] h i =g(w i ,b i ,x)=g(w i x+b i ),w i ∈R D ,b i ∈R

[0049] Among them, w i represents the input weight vector; b i represents the bias of the i-th neuron; x represents the input sample; R D represents D-dimensional real space; g(wi ,b i ,x) represents the activation function;

[0050] Step 5.4.2: Based on the hidden layer output vectors of all samples, form the hidden layer output matrix H:

[0051] H=[h1(x),...,h L (x)]

[0052] Where L represents the number of neurons in the hidden layer;

[0053] Step 5.4.3: Obtain the output of the single hidden layer feedforward neural network ELM according to the following formula:

[0054]

[0055] Among them, β is the output weight between the hidden layer and the output layer;

[0056] Step 5.4.4: Establish the optimization objective function of the output weight β based on the output weight β:

[0057] min||Hβ-T|| 2 ,β∈R L×m

[0058] Where H represents the output matrix of the hidden layer, T represents the target matrix, which contains the true output label or value of each training sample; m represents the number of training set samples;

[0059] Step 5.4.5: Solve the above equation by minimizing the approximate square error method to obtain the optimal solution β * ;

[0060] Step 5.4.6: According to the optimal solution β * Calculate the output matrix of the hidden layer.

[0061] A slope blasting fragmentation prediction system is implemented based on the above-mentioned slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model, which includes a data acquisition module, a data preprocessing module, a parameter screening module, a blasting effect prediction model establishment module, and a slope blasting fragmentation prediction module;

[0062] Data acquisition module, used to monitor blasting control parameter data at the construction site;

[0063] A data preprocessing module, used for preprocessing the data collected by the data acquisition module;

[0064] A blasting control parameter data set construction module is used to filter the preprocessed blasting control parameter data using bubble sort combined with square difference algorithm, construct a blasting control parameter data set based on the filtered blasting control parameter data, and divide the blasting control parameter data set into a training set and a test set;

[0065] A normalization processing module is used to normalize the obtained training set and test set;

[0066] The model optimization module is used to optimize the parameters of the extreme learning machine (ELM) model based on the normalized training set using the improved gray wolf optimization algorithm, and determine the optimal parameters of the optimized extreme learning machine (ELM) model;

[0067] The blasting effect prediction model establishment module is used to optimize the extreme learning machine (ELM) model with optimal parameters, test the optimized ELM model on the test set, calculate the output of the prediction of the test set samples, evaluate the optimal parameters, and establish the blasting effect prediction model based on the optimal parameters.

[0068] The slope blasting fragmentation prediction module is used to predict the slope blasting fragmentation using the blasting effect prediction model and output the prediction results.

[0069] Preferably, the model optimization module includes:

[0070] A normalized data acquisition unit, used to acquire the normalized training set data;

[0071] Model initialization unit, used to initialize the number L of hidden layer neurons and activation function of the extreme learning machine ELM model;

[0072] Parameter optimization unit, used to randomly initialize the input weight matrix W and hidden layer bias b, and optimize the parameters W and b using the improved gray wolf optimization algorithm based on the data of the training set;

[0073] The hidden layer neuron determination unit is used to calculate the hidden layer output matrix H and determine the number of hidden layer neurons L based on the optimized parameters W and b;

[0074] The optimal parameter output unit is used to output the optimal parameters of the current extreme learning machine ELM model, including the number of hidden layer neurons L, W and b.

[0075] An electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model.

[0076] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model.

[0077] Compared with the prior art, the present invention adopts the above-mentioned slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model, which has the following beneficial effects:

[0078] First, the present invention obtains a blasting parameter data set through a bubble sorting method. The blasting parameter data set includes tensile strength, blasting resistance line, step fracture angle, explosive consumption, hole spacing, and row spacing. These six characteristics are used as evaluation indicators, so that the scheme evaluation results can better reflect the comprehensive effect of the blasting design. At the same time, by designing a prediction model based on an improved Grey Wolf Optimization (GWO) algorithm, the characteristics of the Grey Wolf Optimization algorithm, such as its simple structure, few parameters to be adjusted, and easy implementation, are fully utilized. The algorithm can effectively complete the prediction of slope blasting effects when used, and has the advantages of fast convergence speed, high output calculation results, and high calculation accuracy.

[0079] Second, the prediction model proposed in this paper uses GWO as the framework and undergoes parameter screening through Bubble Sort, which has the characteristics of strong generalization ability and high operating efficiency. Compared with other models, the evaluation results of the GWO model are more accurate and efficient.

[0080] In summary, predictive models not only improve the accuracy of blasting operations but also enhance the economic efficiency and safety of the entire project. By scientifically predicting and rationally controlling blasting parameters, we can ensure a smoother construction process, reduce unnecessary resource waste and potential safety hazards, and thus provide a strong guarantee for the successful completion of the project.

[0081] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a flow chart of the present invention using the Grey Wolf Algorithm to improve the ELM model.

[0083] Figure 2This is a histogram of the six characteristic evaluation indicators of the prediction model proposed in the present invention;

[0084] Figure 3 Schematic diagram of the ELM (extreme learning machine) network structure of the prediction model proposed in this invention.

[0085] Figure 4 The flowchart of the establishment method proposed in the present invention. DETAILED DESCRIPTION

[0086] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art will make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the application.

[0087] according to Figures 1-4 As shown, the present invention provides a slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model, which includes the following steps:

[0088] 1. Establish blasting control parameter data set

[0089] By monitoring the construction site, n blasting control parameters are obtained. These n blasting control parameters are screened using the Bubble Sort algorithm combined with the square difference algorithm. An initial blasting control parameter dataset is constructed based on the screened m blasting control parameters, and the blasting control parameter dataset is divided into a training set and a test set.

[0090] Specifically, the specific steps of the Bubble Sort combined with the square difference algorithm to perform parameter screening are as follows:

[0091] (1) For the n monitored blasting control parameter data, define it as an array A containing n elements, A = {X i}, i=0,1,...,nEach element corresponds to a data to be sorted, and each element X in A i (X i ={A j ,B j ,C j ,D j ,E j ,F j}) contains six types of blasting control parameters, namely tensile strength, blasting resistance line, step fracture angle, explosive consumption, hole spacing and row spacing;

[0092] For example, five blasting control parameter data are currently monitored and defined as an array A = {X2, X4, X5, X3, X1}, where each element Xi, i = 1, 2, 3, 4, 5, Xi = {A, B, C, D, E, F}, i.e., each element Xi contains six types of blasting control parameters, A, B, C, D, E, F represent tensile strength, blasting resistance line, step fracture angle, explosive consumption, hole spacing, and row spacing data respectively;

[0093] (2) Sort n elements X in array A by Bubble Sort i Sorting is performed. Since the bubble sort swaps the elements once in each round, different combinations of the six types of blast control parameters can be achieved, and the square difference (Sum of Squares, SS) of each combination is calculated by the following formula:

[0094]

[0095] Where N is the number of data points, X i is the actual value monitored, is the mean of the data.

[0096] For example, bubble sort is performed on the array A = {X2, X4, X5, X3, X1} containing 5 elements. During the first round of sorting, the elements are swapped to obtain the array A1 {X2, X4, X3, X1, X5}, and the square difference of the current array A1 is calculated. Then, the second round of sorting is performed, and the elements are swapped again to obtain the array A2 {X2, X3, X1, X4, X5}. At this time, the square difference of the current array A2 is calculated. Similarly, the square difference of the array A3 obtained by the third round of sorting and the square difference of the array A4 obtained by the fourth round of sorting are calculated respectively.

[0097] (3) By comparing the square differences of various combinations, the blasting control parameter data corresponding to the combination with the smallest square difference is selected.

[0098] For example, in the sorting process of the array A={X2, X4, X5, X3, X1} containing 5 elements, the square differences of the arrays obtained in each round of sorting are compared respectively, and the parameter data corresponding to the combination with the smallest square difference is screened out.

[0099] The time complexity of the above calculation for bubble sorting of n elements is:

[0100]

[0101] The worst-case and average time complexities of bubble sort are both O(n 2In the optimal case (that is, when the array is already sorted), the time complexity is O(n), because there are no swaps after the first traversal and the algorithm will terminate immediately.

[0102] In the above step (2), n blasting control parameters X i The specific process of bubble sorting is as follows:

[0103] S2.1: Get an array A of length n to be sorted;

[0104] S2.2: Initialize variable k. For k = 0, 1, ..., n-2, set the flag swapped = False.

[0105] S2.3: For i = 1 to nk-1, if A[i] > A[i+1], swap A[i] and A[i+1] and set swapped = True;

[0106] S2.4: Determine whether swapped is equal to False. If so, exit the loop; otherwise, return to step S2.3.

[0107] S2.5: Finally, output a sorted array A' of length n in ascending order.

[0108] In summary, the present invention uses the bubble sort method to calculate the square differences of different combinations in each sorting process. By comparing the calculated square differences, the combination with the smallest square difference is screened out. Subsequently, the blasting control parameters corresponding to the combination with the smallest square difference can be used as input variables of the adaptive model and the improved gray wolf optimization algorithm.

[0109] This method enables systematic evaluation and screening of various blasting control parameters during the data processing phase, ultimately selecting the feature combination that optimizes the model's performance. Using squared error as the evaluation criterion effectively selects feature combinations with low volatility and high stability, thereby improving the accuracy and reliability of subsequent models.

[0110] 2. Normalize the blasting control parameter data set

[0111] The present invention normalizes the obtained training set and test set, thereby unifying the data range, improving the training speed and stability, and avoiding numerical problems.

[0112] The specific steps of normalization are:

[0113] (1) Unify the input features x j The dimension of the sample data x in the training set is calculated by the following formula: x = [x1, x2, ... x j ,…,x n ] TPerform normalization:

[0114]

[0115] Among them, α=0.95, β=0.05; x max 、x min are the maximum and minimum values ​​in the input eigenvector respectively;

[0116] (2) The predicted sample data in the test set is calculated by the following formula Perform normalization:

[0117]

[0118] in, Output normalized value for ELM network; b last,1 is the threshold of the first neuron in the hidden layer; W last,1 is the weight; V last is the inter-layer weight; R is the activation function R(x)=max(0,x); U1 is the input of the hidden layer neuron;

[0119] (3) For the normalized learning sample x1 and prediction sample x Ⅱ , the following denormalization formula can be used to denormalize the data:

[0120]

[0121] Among them, Y Ⅰ,max =t max ; Y Ⅰ,min =t min ;β is the momentum coefficient, α=0.95.

[0122] 3. Improve the ELM adaptive model and obtain the optimal parameters

[0123] The present invention uses the improved gray wolf optimization algorithm to improve the above-mentioned adaptive model, optimize the optimization effect of the training set, and obtain the optimal parameter results;

[0124] The specific steps include:

[0125] (1) Data preparation and parameter setting: The data has been normalized in the previous step. In addition, the number of hidden layer neurons L and the activation function of the ELM model need to be determined. The activation function of the present invention is sigmoid. According to the rule of thumb, the number of hidden layer neurons L is determined through multiple experiments and cross-validation. L is usually a value between the number of input layer neurons and the number of output layer neurons.

[0126] (2) Randomly Initializing the Input Weight Matrix W and Hidden Layer Bias b: The unique feature of ELM is the random initialization of its hidden layer parameters. The input weight matrix W and hidden layer bias b are both randomly generated and typically initialized within a small random range. The present invention uses the Gray Wolf Algorithm to optimize W and b.

[0127] (3) Calculate the hidden layer output matrix H:

[0128] For each input sample x i :

[0129] The output of the hidden layer is the sum of the input multiplied by the corresponding weight plus the bias, and then passed through a nonlinear function. H is the ELM nonlinear mapping (hidden layer output matrix), h i is the output of the ith hidden layer node. The output function of the hidden layer node is not unique, and different output functions can be used for different hidden layer neurons. Usually, in practical applications, h i Expressed as:

[0130] h i =g(w i ,b i ,x)=g(w i x+b i ),w i ∈R D ,b i ∈R (6)

[0131] Among them, w i represents the input weight vector, which is used to connect the input layer and the i-th hidden layer neuron; b i is the bias of the i-th neuron; x represents the input sample; R D represents D-dimensional real space; g(w i ,b i ,x) is the activation function;

[0132] g(w i ,b i , x) is a nonlinear piecewise continuous function that satisfies the ELM universal approximation ability theorem. The present invention uses the sigmoid function, so the g function in the formula is:

[0133]

[0134] The hidden layer output vectors of all samples are combined to form the hidden layer output matrix H. The calculation formula of the hidden layer output matrix H is as follows:

[0135] H=[h1(x),...,h L (x)] (8)

[0136] After passing through the hidden layer and entering the output layer, according to formula (8), the output of the "generalized" single hidden layer feedforward neural network ELM is:

[0137]

[0138] Among them, β is the output weight between the hidden layer and the output layer. Since W and b have completed the initialization input, it is now necessary to solve the weight β of the output layer.

[0139] In order to obtain β with good results on the training set, it is necessary to ensure that its training error is minimized. The training error is evaluated by minimizing the squared error using Hβ and the sample label T. The solution with the minimum objective function shown in formula (10) is the optimal solution. That is, the weight β connecting the hidden layer and the output layer is solved by minimizing the approximate squared error. The objective function is as follows:

[0140] min||Hβ-T|| 2 ,β∈R L×m (10)

[0141] Where H represents the output matrix of the hidden layer, T represents the target matrix, which contains the true output label or value of each training sample; m represents the number of training set samples, and L represents the number of neurons in the hidden layer.

[0142] The optimal solution to formula (10) is:

[0143] β * =H * T (11)

[0144] Among them, H * is the Moore–Penrose generalized inverse matrix of the matrix H.

[0145] When H * When H is a non-singular matrix, use the orthogonal projection method to calculate H using formula (12) * :

[0146] H * =(H T H) -1 H T (12);

[0147] Get H * Then, substitute it into formula (11) to get the optimal weight β of the output layer.

[0148] (4) Improved Gray Wolf Optimization Algorithm

[0149] The present invention proposes an improved gray wolf optimization algorithm, which specifically includes:

[0150] S1. Construct a social hierarchy model for gray wolves;

[0151] Specifically, the fitness of each individual in the population is calculated, and the three wolves with the best fitness are labeled α, β, and δ, respectively, while the remaining wolves are labeled ω. The optimization process of GWO is mainly guided by the best three solutions (i.e., α, β, and δ) in each generation of the population;

[0152] S2. The gray wolf pack surrounded and approached the prey;

[0153] First, initialize the coefficient vectors A and C as follows:

[0154]

[0155] in:

[0156]

[0157] Where t is the current iteration number, t max is the maximum number of iterations; a is the convergence factor, as the number of iterations decreases linearly from 2 to 0, r1 and r2 obey a uniform distribution between [0, 1];

[0158] Secondly, in traditional GWO, the gray wolf uses the following position update formula to surround the prey during hunting:

[0159]

[0160] Among them, X p is the position vector of the current prey, and X(t) is the position vector of the t-th generation leader.

[0161] The present invention improves the position update step by introducing a dynamic adjustment mechanism during the position update process. By introducing an adaptive weight factor Ω to improve the above position update formula, the algorithm has stronger global search capabilities in the early stage and stronger local search capabilities in the later stage, thereby improving the convergence speed and accuracy of the algorithm.

[0162] The improved position update formula is:

[0163] X(t+1)=Ω·(X p (t)-A·D)

[0164] D=|C·X p (t)-X(t)|

[0165] in:

[0166]

[0167] Where t is the current iteration number, Ω is the adaptive weight factor;

[0168] S3. Attack the prey and update the gray wolf's position;

[0169] In nature, although the hunting process is usually led by the alpha wolf, and the wolves of other levels cooperate to surround, hunt and attack the prey, in the process of evolutionary calculation, the position of the prey (optimal solution) is unknown, so in GWO we consider the optimal gray wolf to be α, the second optimal gray wolf to be β, the third optimal gray wolf to be δ, and the remaining gray wolves to be ω. We establish a model based on the characteristic that α (potential optimal solution), β, and δ have more knowledge about the location of the prey. In the iterative process, α, β, and δ are used to guide the movement of ω, thereby achieving global optimization.

[0170] Using the positions of α, β, and δ, update the positions of the three wolves using the following equations:

[0171]

[0172] in, and Represent the distances between α wolf, β wolf and δ wolf and other individuals respectively; and Represent the current positions of α wolf, β wolf and δ wolf respectively; and is a random number, is the current location of the individual gray wolf.

[0173] According to the positions of wolf α, wolf β, and wolf δ, the positions of all gray wolves are updated. The update formula for the position of each gray wolf is:

[0174]

[0175] in, They represent the position of ω wolf after being affected by α wolf, β wolf and δ wolf respectively. Here we take the average value, that is:

[0176]

[0177] When the convergence factor a decreases from 2 to 0, the corresponding value of A also changes in the interval [-a, a]: a larger value of a will cause the gray wolves to move away from the prey, hoping to find a more suitable prey, thus prompting the wolf pack to conduct a global search (|A|>1); if the value of a is smaller, the gray wolves will move closer to the prey, prompting the wolf pack to conduct a local search (|A|<1);

[0178] S4. Search for prey and determine whether the maximum number of iterations has been reached.

[0179] 4. Calculate and evaluate the optimal parameters

[0180] After obtaining the optimal parameters W and b, the ELM model uses the optimal parameters to make predictions on the test set. By statistically analyzing the output of the predictions on the test set samples at this time, the optimal parameter results of the model are evaluated. The evaluation indicators include: MAPE (mean absolute percentage error), MAE (mean absolute error), and MSE (mean square error).

[0181] The calculation formula for each evaluation index is:

[0182]

[0183] Among them, m represents the number of samples in the test set, y i is the actual value of the blasting effect evaluation of the i-th sample, is the calculated blasting effect evaluation prediction value of the i-th sample.

[0184] 5. Build a blasting effect prediction model and predict

[0185] Based on the verified optimal parameter results, a blasting effect prediction model is established, and the slope blasting fragmentation is predicted using the constructed blasting effect prediction model, which includes the following steps:

[0186] Step 5.1: Use the obtained optimal parameters to retrain the ELM model and establish the final blasting effect prediction model;

[0187] Step 5.2: Use the trained ELM model to predict the slope blasting fragmentation;

[0188] Among them, step 5.1 specifically includes:

[0189] Step 5.1.1: Retrain the model: Use all training data and optimal parameters to retrain the ELM model to ensure that the model fully learns the patterns in the data;

[0190] Step 5.1.2: Model establishment: Use the trained model as the established blasting effect prediction model;

[0191] Step 5.2 specifically includes:

[0192] Step 5.2.1: Input the latest blasting control parameters monitored in real time on site into the trained ELM model;

[0193] Step 5.2.2: Normalize the new input data to make it consistent with the training data;

[0194] Step 5.2.3: Use the trained ELM model to predict the slope blasting fragmentation;

[0195] Step 5.2.4: Use formula (5) to denormalize the prediction results and convert them back to the original data scale to make the prediction results meaningful.

[0196] Example

[0197] To validate the effectiveness of the proposed prediction model, the proposed slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model was compared with a traditional slope blasting fragmentation prediction method (MLR). Multiple linear regression (MLR) is a traditional method for predicting slope blasting fragmentation. The MLR model assumes a linear relationship between input parameters and output results and performs predictions by fitting regression coefficients. The two methods were compared in the following aspects:

[0198] 1. Data preprocessing and feature selection

[0199] (1) Traditional method (MLR)

[0200] Its feature selection relies on expert experience or simple statistical analysis methods, which may lead to the omission of key features or the addition of redundant features. In addition, the data processing process of MLR is relatively simple and cannot fully utilize the potential information of the data.

[0201] (2) Bubble Sort-GWO-ELM method proposed in the present invention

[0202] It uses a Bubble Sort algorithm combined with squared difference (SS) for feature selection, effectively screening out the features that have the greatest impact on prediction results. Combining analysis using six blasting control parameters (tensile strength, blasting resistance line, step fracture angle, explosive consumption, hole spacing, and row spacing) ensures more scientific and comprehensive data processing, improving the accuracy and reliability of the model.

[0203] In addition, before building the prediction model, the present invention performs complete data preprocessing on the collected raw data, including data cleaning, normalization, and feature selection, thereby improving the model performance.

[0204] 2. Model construction

[0205] (1) Traditional method (MLR)

[0206] It uses a simple linear relationship and cannot capture the nonlinear relationship between input parameters and output results. For solving model parameters, the least squares method is used, and the calling process is relatively simple.

[0207] (2) Bubble Sort-GWO-ELM method proposed in the present invention

[0208] The model, built on the Extreme Learning Machine (ELM), can capture nonlinear relationships, boasts faster training speeds and improved generalization capabilities, making it suitable for complex blasting prediction problems. It also uses the Grey Wolf Optimization (GWO) algorithm to automatically optimize model parameters, reducing manual intervention and improving the efficiency and accuracy of model parameter tuning.

[0209] 3. Model Validation

[0210] (1) Traditional method (MLR)

[0211] It usually uses simple cross-validation or leave-one-out method to validate the model, which results in a relatively simple validation process. The interpretation of the validation results is more dependent on human experience and may be subjective.

[0212] (2) Bubble Sort-GWO-ELM method proposed in the present invention

[0213] It uses cross-validation combined with the GWO algorithm to conduct multiple validations to ensure the accuracy and reliability of the validation results. And through a systematic validation process, it comprehensively evaluates the model performance.

[0214] 4. Example comparison

[0215] The present invention constructs a slope blasting dataset based on data acquired from actual blasting projects. This dataset includes data from multiple blasting operations conducted over different time periods. During the dataset construction process, blasting data covering different geological conditions, blasting design schemes, and operating environments were selected as samples to ensure that the data is sufficiently representative to guarantee the accuracy and reliability of the experimental results. After preprocessing and screening, the slope blasting dataset was used to validate the multivariate linear regression (MLR) model and the Bubble Sort-GWO-ELM model proposed in this invention. Table 1 shows the performance comparison results of the traditional MLR method and the Bubble Sort-GWO-ELM model in predicting slope blasting fragmentation.

[0216] Table 1

[0217]

[0218] It can be seen from Table 1 that, first, the prediction accuracy of this method (R 2 ) is significantly higher than the traditional MLR method, indicating that the proposed method can more accurately capture the changing trend of blasting fragmentation; second, the mean absolute error (MAE) of the proposed method is significantly lower than that of the MLR method, indicating that the deviation between the predicted value and the actual value of the proposed method is smaller; third, the root mean square error (RMSE) of the proposed method is much lower than that of the MLR method, indicating that the volatility and error margin of the prediction results of the proposed method are smaller.

[0219] In summary, compared to traditional multivariate linear regression methods, the Bubble Sort-GWO-ELM model-based approach proposed in this paper offers significant advantages in feature selection, model construction, model validation, and practical application. Through scientific feature selection and parameter optimization, it not only improves prediction accuracy and efficiency but also effectively addresses complex nonlinear relationships, providing more accurate and reliable predictions of slope blasting fragmentation.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. The slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model is characterized by: The following steps are involved: Step 1: Monitor blasting control parameter data at the construction site; Step 2: Preprocessing the monitored blasting control parameter data; Step 3: Screen the preprocessed blasting control parameter data, construct a blasting control parameter data set based on the screened blasting control parameter data, and divide the blasting control parameter data set into a training set and a test set; The specific steps of step 3 include: Step 3.1: Monitor n The blasting control parameter data is defined as a n An array of elements A , array A Each element in the array corresponds to a data to be filtered and sorted. A Each element in It includes six types of blasting control parameters, namely tensile strength, blasting resistance line, step fracture angle, explosive consumption, hole spacing and row spacing; Step 3.2: Sort the array using the bubble sort algorithm A in n Elements Sorting is performed, and based on the position of the elements in each round of sorting, different combinations of six types of blasting control parameters are achieved; Step 3.3: Calculate the squared difference of each of the different combinations according to the following formula: in, is the number of data points in each combination, is the actual value monitored, is the mean of the data; Step 3.4: Repeat steps 3.2 and 3.3 until the elements in array A are sorted in ascending order, and then obtain the square differences of various combinations in each round of bubble sort; Step 3.5: By comparing the square differences of various combinations, the blasting control parameter data corresponding to the combination with the smallest square difference is selected; Step 3.6: Establish a blasting control parameter data set based on the blasting control parameter data screened in step 3.5, and divide the blasting control parameter data set into a training set and a test set; Step 4: Normalize the obtained training set and test set; Step 5: Based on the normalized training set, the parameters of the extreme learning machine (ELM) model are optimized using the improved gray wolf optimization algorithm to determine the optimal parameters of the optimized extreme learning machine (ELM) model. Step 6: Optimize the extreme learning machine (ELM) model with the optimal parameters, test the optimized ELM model on the test set, calculate the output of the predictions for the test set samples, evaluate the optimal parameters, and establish a blasting effect prediction structure model based on the optimal parameters. Step 7: Use the blasting effect prediction model to predict the slope blasting fragmentation and output the prediction results.

2. The slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model according to claim 1 is characterized in that: The specific steps of step 5 include: Step 5.1: Obtain the normalized training set data; Step 5.2: Initialize the number of hidden layer neurons of the extreme learning machine ELM model and activation functions; Step 5.3: Randomly initialize the input weight matrix and hidden layer bias , and based on the data of the training set, the parameters are optimized using the improved gray wolf optimization algorithm. and Optimize Step 5.4: Based on the optimized parameters and , calculate the hidden layer output matrix , determine the number of hidden layer neurons L ; Step 5.5: Output the optimal parameters of the current extreme learning machine ELM model, including the number of hidden layer neurons 、 and .

3. The slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model according to claim 2 is characterized in that: The improved gray wolf optimization algorithm in step 5.3 includes the following steps: Step 5.3.1: Initialize the coefficient vector by and : in, is the convergence factor, which increases with the number of iterations t Decreases linearly from 2 to 0, and yes A random vector between ; Step 5.3.2: Calculate the fitness value of each gray wolf in the wolf pack, and mark the three wolf leaders with the largest fitness values ​​as , the remaining gray wolves are marked as , calculate the position of the gray wolf by the following formula and surround the prey: in, is the current iteration number, is the current prey position vector, is the position vector of the t-th generation alpha wolf, is the adaptive weight factor; and ; Step 5.3.3: Update the positions of the three alpha wolves in the wolf pack using the following formula: in, 、 and Respectively Wolf, Wolf and the distance between the wolf and other individuals; 、 and Respectively represent Wolf, Wolf and The wolf's current location; 、 and is a random number, is the current location of the individual gray wolf; according to 、 and The positions of all gray wolf individuals are updated by the following gray wolf individual position update formula: in, Respectively express Wolf, Wolf and Wolf influence, The wolf's adjusted position; in, Indicates the latest updated location of the gray wolf; Step 5.3.4: Determine whether the maximum number of iterations has been reached. If so, output the optimal solution. If not, return to step 5.3.

2.

4. The slope blasting fragmentation prediction method based on the Bubble Sort-GWO-ELM model according to claim 3 is characterized in that: The specific steps of step 5.4 include: Step 5.4.1: For each input sample , according to the following formula we can get i The output of hidden layer nodes : in, represents the input weight vector; Indicates the i The bias of each neuron; represents the input sample; R D represents D-dimensional real space; represents the activation function; Step 5.4.2: Form the hidden layer output matrix based on the hidden layer output vectors of all samples : ； in, L represents the number of neurons in the hidden layer; Step 5.4.3: Obtain the output of the single hidden layer feedforward neural network ELM according to the following formula: in, is the output weight between the hidden layer and the output layer; Step 5.4.4: Based on output weights Establishing output weights The optimization objective function is: in, represents the output matrix of the hidden layer, Represents the target matrix, which contains the true output label or value of each training sample; m Indicates the number of training set samples; Step 5.4.5: Solve the above equation by minimizing the approximate square error method to obtain the optimal solution ; Step 5.4.6: According to the optimal solution Calculate the output matrix of the hidden layer.

5. A slope blasting fragmentation prediction system, characterized in that: The method according to any one of claims 1 to 4 is implemented, comprising a data acquisition module, a data preprocessing module, a parameter screening module, a blasting effect prediction model establishment module, and a slope blasting fragmentation prediction module; Data acquisition module, used to monitor blasting control parameter data at the construction site; A data preprocessing module, used for preprocessing the data collected by the data acquisition module; A blasting control parameter data set construction module is used to filter the preprocessed blasting control parameter data using bubble sort combined with square difference algorithm, construct a blasting control parameter data set based on the filtered blasting control parameter data, and divide the blasting control parameter data set into a training set and a test set; A normalization processing module is used to normalize the obtained training set and test set; The model optimization module is used to optimize the parameters of the extreme learning machine (ELM) model based on the normalized training set using the improved gray wolf optimization algorithm, and determine the optimal parameters of the optimized extreme learning machine (ELM) model; The blasting effect prediction model establishment module is used to optimize the extreme learning machine (ELM) model with optimal parameters, test the optimized ELM model on the test set, calculate the output of the prediction of the test set samples, evaluate the optimal parameters, and establish the blasting effect prediction model based on the optimal parameters. The slope blasting fragmentation prediction module is used to predict the slope blasting fragmentation using the blasting effect prediction model and output the prediction results.

6. A slope blasting fragmentation prediction system according to claim 5, characterized in that: The model optimization module includes: A normalized data acquisition unit, used to acquire the normalized training set data; Model initialization unit, used to initialize the number of hidden layer neurons of the extreme learning machine ELM model and activation functions; Parameter optimization unit, used to randomly initialize the input weight matrix and hidden layer bias , and based on the data of the training set, the parameters are optimized using the improved gray wolf optimization algorithm. and Optimize Hidden layer neuron determination unit, used based on the optimized parameters and , calculate the hidden layer output matrix , determine the number of hidden layer neurons L ; The optimal parameter output unit is used to output the optimal parameters of the current extreme learning machine ELM model, including the number of hidden layer neurons 、 and .

7. An electronic device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1 to 4.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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