A method and system for predicting peak vibration during open-pit bench blasting

Abstract

This application provides a method and system for predicting peak vibration of open-pit bench blasting, relating to the field of engineering blasting safety technology. The method, after acquiring monitoring data, constructs a multi-parameter formula for predicting the maximum peak velocity of blasting vibration through dimensional analysis. Then, based on the velocity prediction formula, Bayesian theory is used to obtain the optimal prediction model for peak blasting vibration, which is then used to predict the peak blasting vibration. This method introduces Bayesian theory into the field of blasting vibration prediction, achieving a correction of the blasting vibration prediction model that balances data fitting and anti-interference capabilities from both physical and statistical perspectives. This method improves the accuracy of peak vibration prediction, provides a reliable basis for controlling blasting vibration, and solves the problem of low accuracy in peak vibration prediction.

Description

Technical Field

[0001] This application relates to the field of engineering blasting safety technology, and in particular to a method and system for predicting peak vibration during open-pit bench blasting. Background Technology

[0002] Blasting, as a highly efficient and low-consumption engineering construction method, can be widely used in large-scale engineering projects such as mining, water conservancy and hydropower projects, and transportation infrastructure construction. However, during blasting, some energy from the explosives leaks out, breaking the target rock and generating negative impacts such as ground vibration, shock waves, and flyrock, which can damage surrounding structures and even cause casualties and economic losses. Among these, vibration poses the greatest threat to the surrounding environment and is considered the primary hazard during blasting. Therefore, predicting and controlling blasting vibration is of significant practical importance for engineering blasting safety.

[0003] When performing vibration prediction, empirical models for blasting vibration prediction that are suitable for the specific conditions of the construction site can be summarized based on a large amount of field test data. These empirical models can quickly and accurately predict vibration velocities using empirical formulas, leading to their widespread application in the field of blasting vibration velocity prediction. Furthermore, the empirical models constructed vary depending on the construction area.

[0004] However, empirical models based on empirical formulas cannot meet the requirements of all projects. Therefore, a modified empirical formula method can also be used to predict blasting vibration velocities. This modified method can incorporate field parameters such as elevation, borehole diameter, and borehole spacing based on the unique environment of the blasting site. Using dimensional analysis principles, a new prediction formula can be established on the basis of the empirical formula. However, because the selection of field influencing factors relies on expert experience, a rapid and reliable method for selecting influencing factors has not been developed. Furthermore, the manual selection method makes it difficult to find all influencing factors, thus failing to obtain a modified formula with the best fit, reducing the accuracy of vibration peak value prediction. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method and system for predicting peak vibration of open-air bench blasting, in order to solve the problem of low accuracy in predicting peak vibration.

[0006] According to a first aspect of this application, a method for predicting peak vibration during open-pit bench blasting is provided, the method comprising: Acquire monitoring data, including blasting vibration data and blasting parameter data; Based on the monitoring data, a multi-parameter vibration velocity prediction formula is constructed through dimensional analysis; the vibration velocity prediction formula is used to predict the maximum peak vibration velocity of blasting vibration; the vibration velocity prediction formula includes prediction formulas corresponding to all collected parameters between the target area and the blasting site. Based on the vibration velocity prediction formula, the optimal prediction model is obtained using Bayesian theory. The optimal prediction model is used to predict the optimal peak blasting vibration. The optimal prediction model is a combination of prediction formulas determined by dividing the prior distribution of the model corresponding to the vibration velocity prediction formula into multiple model variables according to the distribution coefficient, and then using the evaluation parameters of the combination of the model variables. The optimal prediction model is used to predict the peak value of blasting vibration.

[0007] In some embodiments, acquiring monitoring data includes: Acquire environmental information, including on-site condition parameters and geological condition parameters of the open-pit blasting area; the on-site condition parameters include the blasting site and the target area. A data acquisition request is generated based on the environmental information; The data acquisition request is sent to the monitoring target, which includes a preset number of blasting vibration monitoring instruments arranged between the blasting area and the target area in the open-pit blasting area; Receive the blasting vibration data fed back from the monitored target.

[0008] In some embodiments, acquiring monitoring data further includes: After receiving the blasting vibration data, record the blasting parameter data corresponding to the current round of blasting test. The blasting parameter data includes one or more of the following: maximum charge per hole, blast center distance, resistance line length, row spacing, step height, filling length, over-depth, borehole diameter, seismic wave propagation velocity, and rock mass density. Establish the correlation between the blasting vibration data and the blasting parameter data; Based on the aforementioned correlation, the blasting vibration data and the blasting parameter data are stored as the monitoring data.

[0009] In some embodiments, based on the monitoring data, a multi-parameter vibration velocity prediction formula is constructed through dimensional analysis, including: The independent parameters in the monitoring data are traversed, and the independent parameters include multiple blasting parameters corresponding to the blasting parameter data; Select independent dimensions from the independent parameters; Using dimensional analysis, based on the independent dimensions and the independent parameters, a functional relationship between the independent parameters and the dimensionless combination π term is established. The vibration velocity prediction formula is constructed based on the stated functional relationship.

[0010] In some embodiments, constructing the vibration velocity prediction formula based on the functional relationship includes: Based on empirical formulas, the functional relationships are combined to obtain the first relational expression; The second relation is determined based on the first relation. The second relation is obtained by multiplying the parameters on both sides of the equality in the first relation and then taking the exponent with base e. Take the logarithm of the parameters on both sides of the second relation to obtain the vibration velocity prediction formula.

[0011] In some embodiments, the vibration velocity prediction formula is: ; in, V This represents the peak velocity of the particle. Q This represents the maximum amount of drug per orifice. R The distance between the centers of the explosion; B For the length of the resistance line; S This refers to the row spacing; H The distance between the centers of the explosion; D The diameter of the borehole; T The length of the blockage; U For excessively deep boreholes; a For on-site conditions; b 1. b 2. b 3. b 4. b 5. b 6 represents the decay index.

[0012] In some embodiments, the optimal prediction model is obtained using Bayesian theory based on the vibration velocity prediction formula, including: Obtain the distribution coefficients of the prior distribution of the model, which are determined based on the optimal solution vector; According to the vibration velocity prediction formula, the model prior distribution is divided into multiple model variables in the order of the distribution coefficients, and the multiple model variables include a first model variable and a second model variable; The first model variable is fixed, and the second model variable is arranged and combined to obtain multiple model combinations; The monitoring data is substituted into the multiple model combinations to obtain the evaluation parameters for each model combination. The evaluation parameters include one or more combinations of variance, maximum likelihood estimation, Occam's factor, and confidence. The optimal peak blast vibration prediction model is determined based on the evaluation parameters.

[0013] In some embodiments, the optimal prediction model is obtained using Bayesian theory based on the vibration velocity prediction formula, including: The monitoring data is divided into a training set and a validation set; Obtain the evaluation function, which includes the maximum likelihood function and the Occam's factor formula; The monitoring data in the training set are respectively substituted into the multiple model combinations, and the evaluation parameters are calculated using the evaluation function; The candidate models are determined according to the evaluation parameters. The monitoring data in the validation set is input into the candidate model to output predicted vibration based on the candidate model; The optimal peak blasting vibration prediction model is determined by comparing the predicted vibration with the measured vibration.

[0014] In some embodiments, using the optimal prediction model to predict the peak value of blasting vibration includes: Obtain pre-blasting data for the area to be blasted; The pre-blasting data is input into the optimal prediction model to obtain the peak value of the blasting vibration output by the optimal prediction model; Blasting strategy information is generated based on the peak value of the blasting vibration.

[0015] According to a second aspect of this application, an open-pit bench blasting vibration peak prediction system is provided, the system comprising: The data acquisition module is used to acquire monitoring data, including blasting vibration data and blasting parameter data. The dimensional analysis module is used to construct a multi-parameter vibration velocity prediction formula based on the monitoring data through dimensional analysis; the vibration velocity prediction formula is used to predict the maximum peak vibration velocity of the blasting vibration; the vibration velocity prediction formula includes prediction formulas corresponding to all collected parameters between the target area and the blasting site. The optimal model determination module is used to obtain the optimal prediction model based on the vibration velocity prediction formula using Bayesian theory. The optimal prediction model is used to predict the optimal peak blasting vibration. The optimal prediction model is a combination of prediction formulas determined by dividing the prior distribution of the model corresponding to the vibration velocity prediction formula into multiple model variables according to the distribution coefficient, and then using the evaluation parameters of the combination of the model variables. The peak prediction module is used to predict the peak value of blasting vibration using the optimal prediction model.

[0016] According to a third aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described method for predicting peak vibration of open-pit bench blasting.

[0017] According to a fourth aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described method for predicting peak vibrations during open-pit bench blasting.

[0018] By employing the above technical solutions, embodiments of this application provide a method and system for predicting peak blasting vibration in open-pit bench blasting. The method, after acquiring monitoring data, constructs a multi-parameter formula for predicting the maximum peak velocity of blasting vibration through dimensional analysis. Then, based on the velocity prediction formula, Bayesian theory is used to obtain the optimal prediction model for peak blasting vibration, thereby using the optimal prediction model to predict the peak blasting vibration. This method introduces Bayesian theory into the field of blasting vibration prediction, achieving a correction of the blasting vibration prediction model that balances data fitting and anti-interference capabilities from both physical and statistical perspectives. This method can improve the accuracy of peak vibration prediction, providing a reliable basis for the control of blasting vibration and solving the problem of low accuracy in peak vibration prediction.

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the method for predicting peak vibration of open-air bench blasting provided in an embodiment of this application; Figure 2 This is a schematic diagram of the process for acquiring monitoring data provided in an embodiment of this application; Figure 3 A comparison chart of predicted vibration velocity and measured vibration velocity provided by the Sachs formula in the embodiments of this application; Figure 4 A comparison chart of predicted and measured vibration velocities for alternative model "1 2 4" provided in this application embodiment; Figure 5 This is a schematic diagram of the process for predicting peak blasting vibration provided in an embodiment of this application; Figure 6 A schematic diagram of the structure of the open-air bench blasting vibration peak prediction system provided in the embodiments of this application. Detailed Implementation

[0021] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0022] In this embodiment, the method for predicting peak vibration of open-pit bench blasting can be applied to engineering construction such as mining, water conservancy and hydropower projects, and transportation infrastructure. By predicting the peak vibration of open-pit bench blasting, a comprehensive assessment of blasting operations can be achieved, thereby accurately controlling construction parameters and improving construction safety.

[0023] During blasting operations, some energy from the explosives leaks out, breaking the target rock and generating negative effects such as ground vibration, shock waves, and flying debris. These effects can damage surrounding structures and even cause casualties and economic losses. Among these, vibration poses the greatest threat to the surrounding environment and is considered the primary hazard during blasting. Therefore, predicting and controlling blasting vibration is of significant practical importance for engineering blasting safety.

[0024] In some embodiments, when performing vibration prediction, an empirical model for predicting blasting vibration that conforms to the conditions of the construction site can be summarized based on a large amount of field test data. The empirical model can quickly and accurately predict vibration velocity based on empirical formulas.

[0025] For example, empirical formulas may include the Sadovsky formula, which estimates the vibration velocity of blasting vibrations by considering factors such as explosive charge, distance, terrain, and blasting method. The Sadovsky formula, as an empirical formula, is expressed as follows:

[0026] in, v It is the peak blast vibration velocity (cm / s). Q It is the maximum amount of explosives (kg) for a single detonation. R It is the source distance (m). K and η It is the on-site coefficient.

[0027] The above empirical formulas can be used to construct different empirical models for different construction areas. However, empirical models based on empirical formulas cannot meet the requirements of all projects. Therefore, blasting vibration velocities can also be predicted based on empirical formula correction methods.

[0028] In some embodiments, empirical formula correction can be used to introduce field parameters such as elevation, borehole diameter, and borehole spacing based on the unique environment of the blasting site, and to establish a new prediction formula based on the empirical formula using the principle of dimensional analysis. However, since the selection of field influencing factors relies on expert experience, a fast and reliable method for selecting influencing factors has not been formed, and the manual selection method is difficult to find all influencing factors, thus failing to obtain a correction formula with the best fit and reducing the accuracy of vibration peak prediction.

[0029] To address the issue of low accuracy in peak vibration prediction, some embodiments of this application provide a method for predicting peak vibration during open-pit bench blasting. This method can be applied to electronic devices with data processing capabilities. These electronic devices include, but are not limited to, computers, servers, mobile terminals, smart wearable devices, and industrial control computers. For ease of description, this application uses an electronic device as the execution subject of the method. It should be understood that the method can also be applied to other types of execution subjects, which are not illustrated in all embodiments of this application. Figure 1 As shown, the method includes: S101. Obtain monitoring data.

[0030] When predicting the peak vibration of open-pit bench blasting, monitoring data must first be acquired. This monitoring data includes blasting vibration data and blasting parameter data. Blasting vibration data refers to the monitoring data obtained from real-time detected vibration signals. Vibration signals can be obtained using vibration monitoring instruments after the blasting operation is initiated. Blasting parameter data refers to a combination of one or more parameters related to the blasting operation recorded during the blasting process.

[0031] In order to obtain blasting vibration data from the monitoring data, before performing vibration peak prediction for step blasting, multiple vibration monitoring instruments can be set up in the construction area according to the site conditions, and the vibration signals in the construction area can be obtained by measuring the vibration signals through the vibration monitoring instruments to obtain blasting vibration data.

[0032] Therefore, as Figure 2 As shown, in some embodiments, the electronic device may first acquire environmental information when acquiring monitoring data. This environmental information includes on-site condition parameters and geological condition parameters of the open-pit blasting area. The on-site condition parameters include the blasting site and the target area.

[0033] After acquiring environmental information, a data acquisition request is generated based on this information and sent to the monitoring target, thereby receiving blasting vibration data fed back from the monitoring target. The monitoring target includes a predetermined number of blasting vibration monitoring instruments deployed between the blasting area and the target area within the open-pit blasting zone. To obtain more accurate prediction results, the number of vibration monitoring instruments, i.e., the predetermined number, should not be less than the predetermined requirement. Furthermore, multiple vibration monitoring instruments need to maintain a specific monitoring distance when monitoring vibration signals.

[0034] For example, when collecting multi-parameter bench blasting data, at least seven blasting vibration monitoring instruments can be arranged along the line connecting the blasting area and the target location, based on the site conditions and geological conditions of the open-pit mine. The distance from the blast center of the nearest vibration monitoring instrument to the blasting area should be no less than 50 meters. That is, the required number is seven, and the monitoring distance is no less than 50 meters. Furthermore, each vibration monitoring instrument should be located on the centerline of the blasting area.

[0035] To obtain blasting parameter data from the monitoring data, multiple blasting vibration tests can be conducted within the blasting zone, and the relevant blasting parameters for each test can be recorded. Therefore, in some embodiments, when acquiring monitoring data, the blasting parameter data corresponding to the current round of blasting tests can be recorded after receiving the blasting vibration data. The blasting parameter data includes one or more of the following: maximum charge per hole, detonation distance, resistance line length, row spacing, bench height, filling length, over-depth, borehole diameter, seismic wave propagation velocity, and rock mass density.

[0036] For example, blasting parameter data can include 10 types of on-site blasting parameters such as maximum charge per hole, distance from the blast center, length of the resistance line, spacing between rows, step height, filling length, over-depth, borehole diameter, seismic wave propagation velocity, and rock density.

[0037] After recording the blasting parameter data corresponding to the current round of blasting tests, multiple blasting vibration tests can be repeated, and the blasting parameter data corresponding to each round of blasting tests can be recorded. For example, no fewer than 10 open-pit bench blasting tests need to be conducted, and 10 field blasting parameters corresponding to each vibration measuring instrument during each test should be recorded, including the maximum charge per hole, detonation distance, resistance line length, row spacing, bench height, filling length, over-depth, borehole diameter, seismic wave propagation velocity, and rock density, thereby obtaining blasting parameter data.

[0038] Based on the monitored blasting vibration data and recorded blasting parameter data, a correlation between the blasting vibration data and the blasting parameter data can be established, and the blasting vibration data and blasting parameter data can be stored as monitoring data according to the correlation.

[0039] S102. Based on monitoring data, construct a multi-parameter vibration velocity prediction formula through dimensional analysis.

[0040] After acquiring monitoring data, a multi-parameter vibration velocity prediction formula can be constructed based on this data through dimensional analysis. This vibration velocity prediction formula is used to predict the maximum peak velocity of blasting vibrations, and it includes prediction formulas for all acquired parameters between the target area and the blasting zone. Therefore, electronic equipment can construct a multi-parameter maximum peak velocity prediction formula for blasting vibrations based on the acquired blasting vibration data and blasting parameter data using dimensional analysis.

[0041] To construct a vibration velocity prediction formula, in some embodiments, when the electronic device constructs a multi-parameter vibration velocity prediction formula based on monitoring data and through dimensional analysis, it can first iterate through the independent parameters in the monitoring data. These independent parameters include multiple blasting parameters corresponding to the blasting parameter data. For example, blasting parameters may include seismic wave propagation velocity. c Rock mass density ρ Maximum single-hole dosage Q , explosion center distance R、 Resistance line length B Row spacing S , explosion center distance H Hole diameter D Length of blockage T and the extremely deep borehole U Accordingly, the independent parameters are shown in Table 1: Table 1. Independent Parameter Table;

[0042] Then, select independent dimensions from the independent parameters, and use dimensional analysis to establish the functional relationship between the independent parameters and the dimensionless combination π term, based on the independent dimensions and independent parameters. Then, construct the vibration velocity prediction formula based on the functional relationship.

[0043] For example, using dimensional analysis π Theorem, taking the speed of seismic wave propagation c , explosion center distance R Maximum single-hole dosage Q As independent units, seven units about are formed using the 10 mutually independent parameters in Table 1. π i The function between, that is:

[0044] in, π , π 1. π 2. π 3. π 4. π 5. π 6 represents dimensionless π item; v Peak velocity; c This refers to the speed of seismic wave propagation. ρ Density of the rock mass; Q This represents the maximum amount of drug per orifice. R The distance between the centers of the explosion; S This refers to the row spacing; B For the length of the resistance line; H The distance between the centers of the explosion; D The diameter of the borehole;T The length of the blockage; U The boreholes are extremely deep.

[0045] To construct a vibration velocity prediction formula, in some embodiments, when constructing the vibration velocity prediction formula based on functional relationships, it is also possible to simultaneously solve functional relationships based on empirical formulas to obtain a first relational expression. For example, based on the types of collected parameters and combined with dimensional analysis methods, when obtaining a prediction formula containing all collected parameters between the target area and the blasting zone, it can be assumed that the seismic wave propagation velocity and rock mass density are constants within the blasting area. Then, based on empirical formulas, simultaneously solving functional relationships yields the following first relational expression:

[0046] Then, the second relation is determined based on the first relation. The second relation is obtained by multiplying the parameters on both sides of the equality in the first relation and then taking the exponents with the natural constant e as the base. For example, multiplying the parameters on both sides of the equality in the first relation and then taking the exponents with the natural constant e as the base yields the following second relation: ; Then, take the logarithm of the parameters on both sides of the second equation to obtain the vibration velocity prediction formula. For example, by taking the logarithm of both sides of the second equation, the prediction formula for the maximum peak velocity of blasting vibration can be obtained, as follows:

[0047] in, V This represents the peak velocity of the particle. Q This represents the maximum amount of drug per orifice. R The distance between the centers of the explosion; B For the length of the resistance line; S This refers to the row spacing; H The distance between the centers of the explosion; D The diameter of the borehole; T The length of the blockage; U For excessively deep boreholes; a For on-site conditions; b 1. b 2. b 3. b 4. b 5. b 6 represents the decay index.

[0048] S103. Based on the vibration velocity prediction formula, the optimal prediction model is obtained using Bayesian theory.

[0049] After constructing a multi-parameter vibration velocity prediction formula through dimensional analysis, the optimal prediction model can be obtained based on the obtained formula using Bayesian theory. This optimal prediction model is used to predict the optimal peak blasting vibration.

[0050] The optimal prediction model can be determined by dividing the prior distribution of the vibration velocity prediction formula into multiple model variables according to the distribution coefficients, and then using the evaluation parameters based on the permutation and combination results of the model variables. Therefore, in some embodiments, when obtaining the optimal prediction model using Bayesian theory based on the vibration velocity prediction formula, the distribution coefficients of the prior distribution of the model can be obtained first, wherein the distribution coefficients... θ It can be determined based on the optimal solution vector.

[0051] Based on the vibration velocity prediction formula, the model prior distribution is divided into multiple model variables in order of distribution coefficients. These multiple model variables include a first model variable and a second model variable. The first model variable contains the field condition coefficient 'a', which is equal to the Sachs formula, i.e., it contains a constant. The second model variables represent the influence of different field parameters on the model.

[0052] After dividing the model variables into multiple types, the first model variable can be fixed, and the second model variable can be permuted and combined to obtain multiple model combinations. For example, after fixing the necessary variable 1 of the vibration model, the other variables can be permuted and combined, resulting in a total of 32 model combinations.

[0053] By substituting monitoring data into multiple model combinations, evaluation parameters for each model combination are obtained. These evaluation parameters include one or more combinations of variance, maximum likelihood estimation, Occam's factor, and confidence level. For example, by substituting multiple sets of field test data into the monitoring parameters, the corresponding values ​​for variance, maximum likelihood estimation, Occam's factor, and confidence level can be calculated.

[0054] Then, the optimal peak blast vibration prediction model is determined based on the evaluation parameters. For example, to obtain the optimal peak blast vibration prediction formula, the prediction formula for the maximum peak blast vibration obtained in the above embodiments can be used to adjust the model's prior distribution according to the distribution coefficient. θ The variables are divided into six categories. Variable 1 includes the constant 'a', which is equal to the Sachs formula, while the other variables represent the influence of different field parameters on the model.

[0055] After fixing the first necessary variable in the vibration model, the other variables were arranged and combined, resulting in 32 model combinations. Multiple sets of field test data from the monitoring data were then input to calculate the values ​​of evaluation parameters such as variance, maximum likelihood estimation, Occam's factor, and reliability. Thus, the optimal blasting vibration prediction model was established through maximum likelihood estimation and the Occam's factor.

[0056] When the evaluation parameters include maximum likelihood estimation and Occam's factor, parameter derivation can be based on Bayesian theory. First, the derivation is based on the law of total probability, which is:

[0057] In the formula, A 1 and A 2 represents two independent events.

[0058] By assuming that parameter E is a set of monitoring data with N discrete points, and parameter C is the set of all possible model forms, the parameter... μ This represents the initial morphological assessment of the model before computation. Then, using the above parameters and the law of total probability, we can obtain E and E under the given conditions. μ Below, each model The probability of:

[0059] in, P ( E | μ It can be decomposed by the law of total probability as follows:

[0060] For ease of calculation, the prior distribution will be... P ( C j | μ After normalization, we can obtain:

[0061] Due to the initial judgment of the model μ With prior distribution P ( C j | μ There is no direct relationship between them, so they can be ignored. Therefore, the simplified version... P ( E | μ The Laplace expansion of ) is approximately:

[0062] In the formula, C j Indicates the first j One model, j =1, 2, 3 , Nc ; For the Hyssian matrix, Let represent the optimal solution vector. Therefore, the maximum likelihood estimate can be expressed as:

[0063] Occam's factor O j It can then be expressed as:

[0064] in, C j Indicates the first j One model, j =1, 2, 3 , Nc ; This represents the optimal solution vector under given conditions C. The prior probability; π Represents pi; N j Indicates the first j The number of parameters in a model represents the number of unknown and uncertain parameters; For the Hyssian matrix, This represents the optimal solution vector.

[0065] Therefore, in some embodiments, when obtaining the optimal prediction model using Bayesian theory based on the vibration velocity prediction formula, the monitoring data can be divided into a training set and a validation set, and an evaluation function can be obtained. This evaluation function includes the maximum likelihood function and the Occam's factor formula. Then, the monitoring data in the training set are substituted into multiple model combinations, and the evaluation parameters are calculated using the evaluation function.

[0066] For example, based on the application of Bayesian theory, the maximum likelihood function of the model can be obtained as follows:

[0067] The Occam's factor formula is:

[0068] In the formula, O j Indicates the first j Occam's factor for each model; Indicates under given conditions C Optimal solution vector The prior probability; π Represents pi; N j Indicates the first j The number of parameters in a model represents the number of unknown and uncertain parameters; N Indicates the number of observation data; For the Hyssian matrix, Represents the optimal solution vector; This represents the standard deviation of the error term.

[0069] Then, candidate models are determined according to the evaluation parameters, and the monitoring data from the validation set are input into the candidate models to output predicted vibrations. Finally, by comparing the predicted vibrations and the measured vibrations, the optimal peak blast vibration prediction model is determined.

[0070] For example, to accurately demonstrate the advantages of the Bayesian theory-modified prediction model, an open-pit quarry composed of sandstone, shale, crushed sandstone, and mudstone interlayers was selected as the engineering background when determining the optimal peak blast vibration prediction formula. This project used digital electronic detonators for initiation, with 2-4 rows of boreholes per blast, and each row containing 10-30 boreholes depending on site conditions. The maximum charge per borehole ranged from 55 kg to 227 kg.

[0071] While measuring the blasting vibration velocity, all available on-site blasting parameters were recorded. Eighty-eight sets of blasting vibration data were selected from all data as the total sample, and 70 sets were randomly selected from the total sample as the training sample (i.e., the training set). The remaining 18 sets were used as the validation sample (i.e., the validation set).

[0072] Based on the types of parameters collected, and using dimensional analysis, a prediction formula containing all collected parameters was obtained between the target area and the blasting zone. Since the field data includes 10 field blasting parameters—maximum charge per hole, detonation distance, resistance line length, row spacing, bench height, filling length, over-depth, borehole diameter, seismic wave propagation velocity, and rock density—conventional dimensional analysis would result in lengthy formulas due to the large number of dependent variables, and the establishment of the model's posterior distribution would lead to unequal dimensions between the left and right equations. Therefore, the resistance line, row spacing, bench height, filling length, over-depth, and borehole diameter were processed to eliminate their unit dimensions, participating in the model establishment process only as dimensionless parameters, as shown in Table 1.

[0073] Dimensional analysis π Theorem yields the formula for predicting the maximum peak velocity of blasting vibrations:

[0074] Then, based on the vibration velocity prediction formula, the optimal peak blast vibration prediction formula was obtained. That is, based on 70 sets of field measured data, the parameters of the Bayes formula were valued according to 32 model forms, and then the corresponding variance, maximum likelihood estimate, Occam's factor, and confidence level were calculated. The results are shown in Table 2. Table 2 Evaluation Parameter Table;

[0075] Considering both the model's anti-interference capabilities and its fitting ability, model "1 2 4" is selected as the optimal blasting vibration prediction model, i.e.:

[0076] By substituting 18 randomly selected sets of validation sample data into Sadovsky's empirical formula and model "1+24", a comparison graph of the vibration predicted by the two models and the measured vibration was plotted, as shown below. Figure 3 and Figure 4 As shown in the figure. Through comparison, it was found that the prediction accuracy of the Bayesian theory-based model "1 2 4" is better than that of the Sadovsky empirical formula.

[0077] S104. Predict the peak value of blasting vibration using the optimal prediction model.

[0078] Once the optimal prediction model is determined, electronic equipment can use it to predict the peak value of blasting vibration. Therefore, by using the optimal prediction model to predict the peak value of blasting vibration, it is possible to predict the peak value of blasting vibration in open-pit benches. During the prediction process, Bayesian theory can be introduced into the field of blasting vibration prediction. From the perspectives of physics and statistics, and taking into account both data fitting and anti-interference capabilities, the blasting vibration prediction model can be modified, thereby alleviating the problem of selecting influencing factors on site and providing a reliable basis for the control of blasting vibration.

[0079] By applying the technical solutions of the above embodiments, the open-pit bench blasting vibration peak prediction method described in the above embodiments can predict the open-pit bench blasting vibration peak in the field of engineering blasting vibration safety technology. The method first conducts multiple sets of blasting vibration tests in the target blasting area and collects blasting vibration data and blasting parameter data. Then, based on the monitoring data and Sadovsky's empirical formula, a multi-parameter blasting vibration maximum peak velocity prediction formula is constructed. Then, based on the multi-parameter prediction formula, Bayesian theory is used to obtain the optimal blasting peak vibration prediction model, thereby using the optimal prediction model to predict the blasting vibration peak. This method can improve the accuracy of vibration peak prediction and solve the problem of low accuracy in vibration peak prediction.

[0080] In some embodiments, as a refinement and extension of the specific implementation of the above embodiments, and in order to fully illustrate the specific implementation process of this embodiment, some embodiments of this application also provide a method for predicting the peak vibration of open-pit bench blasting, such as... Figure 5 As shown, the method differs from the above embodiment in that, in the step of predicting the peak value of blasting vibration using the optimal prediction model, it further includes: S201. Obtain pre-blasting data for the area to be predicted; S202. Input the pre-blasting data into the optimal prediction model to obtain the peak value of the blasting vibration output by the optimal prediction model. S203. Generate blasting strategy information based on the peak value of blasting vibration.

[0081] To accurately predict the peak vibration of open-pit bench blasting, electronic equipment can first acquire pre-blasting data of the area to be blasted. The pre-blasting data can include blasting parameter data of the area to be blasted, such as maximum charge per hole, distance between blast centers, length of resistance line, row spacing, bench height, filling length, over-depth, borehole diameter, seismic wave propagation velocity, rock density, etc.

[0082] The acquired pre-blasting data is then input into the optimal prediction model, allowing the model to perform predictive calculations based on this data, thus obtaining the peak blasting vibration output by the optimal prediction model. Blasting strategy information is then generated based on this peak blasting vibration. This strategy information can be used to determine whether the current pre-blasting data is set appropriately. For example, if the peak blasting vibration exceeds the preset maximum vibration threshold, it indicates that the vibration generated by the current blasting operation is excessive. In this case, the blasting parameters can be adjusted to reduce the vibration generated during the blasting operation.

[0083] By applying the technical solutions of the above embodiments, the open-pit bench blasting vibration peak prediction method described in the above embodiments can first obtain pre-blasting data of the blasting area to be predicted when using the optimal prediction model to predict the blasting vibration peak. Then, the pre-blasting data is input into the optimal prediction model to obtain the blasting vibration peak output by the optimal prediction model. Finally, blasting strategy information is generated based on the blasting vibration peak. This method can determine whether the pre-blasting data is reasonable before blasting construction is carried out, based on the judgment result, whether blasting construction can be carried out. Therefore, this method can predict the blasting vibration peak in advance, reduce trial and error costs, and help improve the safety of blasting construction.

[0084] In some embodiments, as a specific implementation of the open-pit bench blasting vibration peak prediction method in the above embodiments, some embodiments of this application also provide an open-pit bench blasting vibration peak prediction system, such as... Figure 6 As shown, the system includes: The data acquisition module is used to acquire monitoring data, including blasting vibration data and blasting parameter data. The dimensional analysis module is used to construct a multi-parameter vibration velocity prediction formula based on the monitoring data through dimensional analysis; the vibration velocity prediction formula is used to predict the maximum peak vibration velocity of the blasting vibration; the vibration velocity prediction formula includes prediction formulas corresponding to all collected parameters between the target area and the blasting site. The optimal model determination module is used to obtain the optimal prediction model based on the vibration velocity prediction formula using Bayesian theory. The optimal prediction model is used to predict the optimal peak blasting vibration. The optimal prediction model is a combination of prediction formulas determined by dividing the prior distribution of the model corresponding to the vibration velocity prediction formula into multiple model variables according to the distribution coefficient, and then using the evaluation parameters of the combination of the model variables. The peak prediction module is used to predict the peak value of blasting vibration using the optimal prediction model.

[0085] By applying the technical solutions of the above embodiments, the open-pit bench blasting vibration peak prediction system described in the above embodiments can acquire monitoring data through a data acquisition module, and construct a multi-parameter blasting vibration maximum peak velocity prediction formula through dimensional analysis by a dimensional analysis module. The optimal model determination module then uses Bayesian theory to obtain the optimal prediction model for blasting peak vibration based on the velocity prediction formula, thereby enabling the peak prediction module to predict the blasting vibration peak using the optimal prediction model. This system can introduce Bayesian theory into the field of blasting vibration prediction, achieving a correction of the blasting vibration prediction model that balances data fitting and anti-interference capabilities from the perspectives of physics and statistics. This method can improve the accuracy of vibration peak prediction, provide a reliable basis for blasting vibration control, and solve the problem of low accuracy in vibration peak prediction.

[0086] It should be noted that other corresponding descriptions of the functional units involved in the open-pit bench blasting vibration peak prediction system provided in this application embodiment can be found in the corresponding descriptions in the open-pit bench blasting vibration peak prediction method provided in the above embodiment, and will not be repeated here.

[0087] This application also provides a computer device, specifically a personal computer, server, network device, etc. The computer device includes a bus, processor, memory, and communication interface, and may also include input / output interfaces and a display device. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores location information. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0088] Those skilled in the art will understand that the structure of the computer device described above is only a partial structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific computer device may include more or fewer components, or combine certain components, or have different component arrangements.

[0089] In one embodiment, a computer-readable storage medium is also provided, which may be non-volatile or volatile, and a computer program is stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0090] In one embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0091] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0092] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0093] Any references to memory, database, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc.

[0094] Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take many forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0095] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0096] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method of predicting peak vibration of open bench blasting, characterized by, The method comprises: acquiring monitoring data, the monitoring data comprising blasting vibration data and blasting parameter data; based on the monitoring data, constructing a multi-parameter vibration velocity prediction formula through dimensional analysis; the vibration velocity prediction formula is used to predict the maximum peak vibration velocity of blasting vibration; the vibration velocity prediction formula comprises a prediction formula corresponding to all collected parameters between a target area and a blast site; according to the vibration velocity prediction formula, obtaining an optimal prediction model using Bayesian theory, the optimal prediction model being used to predict an optimal blasting peak vibration; the optimal prediction model is determined by dividing the model prior distribution corresponding to the vibration velocity prediction formula into multiple model variables according to a distribution coefficient, and then determining the prediction formula combination according to the evaluation parameters of the arrangement combination results of the model variables; using the optimal prediction model to predict the peak value of blasting vibration.

2. The method of claim 1, wherein, Acquiring monitoring data comprises: acquiring environmental information, the environmental information comprising site condition parameters and geological condition parameters of an open blasting area; the site condition parameters comprise a blast site and a target area; generating a data acquisition request according to the environmental information; sending the data acquisition request to a monitoring target, the monitoring target comprising a preset number of blasting vibration monitoring instruments arranged between the blast site and the target area in the open blasting area; receiving the blasting vibration data fed back by the monitoring target.

3. The method of claim 2, wherein, Acquiring monitoring data further comprises: after receiving the blasting vibration data, recording the blasting parameter data corresponding to the current round of blasting test, the blasting parameter data comprising one or more combinations of single-hole maximum charge, blast center distance, resistance line length, row spacing, step height, filling length, overburden, borehole diameter, seismic wave propagation velocity, and rock density; establishing an association between the blasting vibration data and the blasting parameter data; according to the association, storing the blasting vibration data and the blasting parameter data as the monitoring data.

4. The method of claim 1, wherein, Based on the monitoring data, a multi-parameter vibration velocity prediction formula is constructed through dimensional analysis, comprising: traversing independent parameters in the monitoring data, the independent parameters comprising a plurality of blasting parameters corresponding to the blasting parameter data; selecting independent dimensions from the independent parameters; using dimensional analysis, establishing a functional relationship of the independent parameters with respect to the dimensionless combination π item according to the independent dimensions and the independent parameters; constructing the vibration velocity prediction formula according to the functional relationship.

5. The method of claim 4, wherein, Constructing the vibration velocity prediction formula according to the functional relationship comprises: based on the functional relationship, obtaining a first relationship by combining the functional relationship; determining a second relationship according to the first relationship, the second relationship being obtained by multiplying the parameters on both sides of the equal sign in the first relationship and then taking the base e exponent at the same time; taking the logarithm of the parameters on both sides of the equal sign of the second relationship to obtain the vibration velocity prediction formula.

6. The method of claim 5, wherein, The vibration velocity prediction formula is: wherein, V is the peak particle velocity; Q is the maximum single-hole charge; R is the distance from the blast center; B is the length of the resistance line; S is the row spacing; H is the distance from the blast center; D is the borehole diameter; T is the length of the plug; U is the borehole overbreak; a is the on-site condition coefficient; b 1, b 2, b 3, b 4, b 5, b 6 is the attenuation index.

7. The method of claim 1, wherein, According to the vibration velocity prediction formula, an optimal prediction model is obtained using Bayesian theory, comprising: obtaining a distribution coefficient of the model prior distribution, the distribution coefficient being determined according to an optimal solution vector; According to the vibration velocity prediction formula, the model prior distribution is divided into multiple model variables in the order of the distribution coefficients, and the multiple model variables include a first model variable and a second model variable; The first model variable is fixed, and the second model variable is arranged and combined to obtain multiple model combinations; The monitoring data is substituted into the multiple model combinations to obtain the evaluation parameters for each model combination. The evaluation parameters include one or more combinations of variance, maximum likelihood estimation, Occam's factor, and confidence. The optimal peak blast vibration prediction model is determined based on the evaluation parameters.

8. The method of claim 1, wherein, Based on the aforementioned vibration velocity prediction formula, the optimal prediction model is obtained using Bayesian theory, including: The monitoring data is divided into a training set and a validation set; Obtain the evaluation function, which includes the maximum likelihood function and the Occam's factor formula; The monitoring data in the training set are respectively substituted into the multiple model combinations, and the evaluation parameters are calculated using the evaluation function; The candidate models are determined according to the evaluation parameters. The monitoring data in the validation set is input into the candidate model to output predicted vibration based on the candidate model; The optimal peak blasting vibration prediction model is determined by comparing the predicted vibration with the measured vibration.

9. The method of claim 1, wherein, Predicting the peak value of blasting vibration using the optimal prediction model includes: Obtain pre-blasting data for the area to be blasted; The pre-blasting data is input into the optimal prediction model to obtain the peak value of the blasting vibration output by the optimal prediction model; Blasting strategy information is generated based on the peak value of the blasting vibration.

10. An open bench blasting vibration peak prediction system, characterized by, The system includes: The data acquisition module is used to acquire monitoring data, including blasting vibration data and blasting parameter data. The dimensional analysis module is used to construct a multi-parameter vibration velocity prediction formula based on the monitoring data through dimensional analysis; the vibration velocity prediction formula is used to predict the maximum peak vibration velocity of the blasting vibration; the vibration velocity prediction formula includes prediction formulas corresponding to all collected parameters between the target area and the blasting site. The optimal model determination module is used to obtain the optimal prediction model based on the vibration velocity prediction formula using Bayesian theory. The optimal prediction model is used to predict the optimal peak blasting vibration. The optimal prediction model is a combination of prediction formulas determined by dividing the prior distribution of the model corresponding to the vibration velocity prediction formula into multiple model variables according to the distribution coefficient, and then using the evaluation parameters of the combination of the model variables. The peak prediction module is used to predict the peak value of blasting vibration using the optimal prediction model.

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