Method and system for constructing open-pit copper mine group hole blasting vibration prediction model

Abstract

The present application relates to the technical field of mine engineering blasting, in particular to a method and system for constructing an open-pit copper mine group-hole blasting vibration prediction model, based on an open-pit copper mine designed group-hole test blasting scheme, obtaining open-pit copper mine initial blasting data according to the scheme, processing the blasting data to obtain open-pit copper mine blasting monitoring data; introducing a modulated filtered white noise model, and establishing a single-hole blasting vibration waveform simulation model according to the intensity envelope function and the blasting data; obtaining a blasting vibration waveform prediction function according to nonlinear prediction parameters, combining the detonation time expression, the blasting monitoring data and the blasting vibration waveform prediction function to construct an open-pit copper mine group-hole blasting vibration prediction model; and obtaining group-hole blasting vibration simulation waveforms through the open-pit copper mine group-hole blasting vibration prediction model. The prediction model provides technical support for open-pit copper mine blasting, simulates and predicts different blasting vibration conditions, and promotes the intelligent development of open-pit copper mine mining.

Description

Technical Field

[0001] This invention relates to the field of blasting technology in mining engineering, specifically to a method and system for constructing a vibration prediction model for multi-hole blasting in open-pit copper mines. Background Technology

[0002] During open-pit copper mining, blasting operations generate blasting vibrations while crushing ore and mining targets. If blasting vibrations are not properly controlled, they can affect the surrounding environment, mining facilities, and personnel safety. Excessive blasting vibrations can also cause slope instability and trigger geological disasters such as landslides. They may also damage production equipment in the mine, affecting production progress and increasing maintenance costs and economic losses.

[0003] Currently, the prediction and control of blasting vibration mainly rely on empirical formulas and simple models. However, empirical formulas are derived from experimental data under specific conditions and have a narrow scope of application. They are difficult to accurately predict blasting vibration in open-pit copper mines with different geological conditions, blasting parameters, and environmental factors. Simple models cannot fully consider the interaction between different factors during the blasting process.

[0004] With the continuous expansion of open-pit copper mining scale, the requirements for the accuracy of blasting vibration prediction are also increasing. Traditional prediction methods are insufficient to meet the demands of modern open-pit copper mining for safety, efficiency, and intelligent development. Therefore, a more accurate and comprehensive method for predicting blasting vibration in open-pit copper mines is needed to achieve effective control of blasting vibration, promote the intelligent development of the open-pit copper mining industry, and solve the problems existing in current technologies. Summary of the Invention

[0005] To address the shortcomings of existing methods and their limitations in practical application, this invention aims to accurately predict blasting vibrations by considering the interactions between different factors during the blasting process, ultimately achieving precise control of blasting vibrations and ensuring the safe, efficient, and smooth progress of open-pit copper mining operations. The first aspect of this invention provides a method for constructing a prediction model for open-pit copper mine multi-hole blasting vibrations, comprising the following steps: designing a multi-hole test blasting scheme based on the open-pit copper mine; obtaining initial blasting data for the open-pit copper mine based on the multi-hole test blasting scheme; transforming, extracting, and optimizing the initial blasting data to obtain open-pit copper mine blasting monitoring data; introducing a modulated white noise model; establishing a single-hole blasting vibration waveform simulation model based on the intensity envelope function, the open-pit copper mine blasting monitoring data, and the modulated white noise model; obtaining a blasting vibration waveform prediction function based on the single-hole blasting vibration waveform simulation model; constructing a multi-hole blasting vibration prediction model for the open-pit copper mine by combining the detonation time expression, the open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function; and obtaining the simulated waveform of multi-hole blasting vibrations through the open-pit copper mine multi-hole blasting vibration prediction model.

[0006] This invention takes into account the influence of detonation time on the vibration of multi-hole blasting, which can more accurately predict the vibration during multi-hole blasting, making the prediction model more complete and reliable, and helping to achieve accurate prediction and assessment of blasting vibration.

[0007] Optionally, the step of designing a multi-hole test blasting scheme based on an open-pit copper mine, obtaining initial blasting data of the open-pit copper mine based on the multi-hole test blasting scheme, and transforming, extracting, and optimizing the initial blasting data to obtain open-pit copper mine blasting monitoring data includes: setting blasting parameters and monitoring point locations in the multi-hole test blasting scheme based on the open-pit copper mine; monitoring the open-pit copper mine through the multi-hole test blasting scheme and obtaining initial blasting data of the open-pit copper mine; and transforming, extracting, and optimizing the initial blasting data using envelope detection, Hilbert method, and Gaussian smoothing method to obtain open-pit copper mine blasting monitoring data.

[0008] The envelope detection method of this invention can reduce the amount of data and the complexity of analysis; the Hilbert transform can convert real signals into analytic signals to better reveal the essential characteristics of the signals; the Gaussian smoothing method can effectively suppress these noises and make the signals smoother and more stable.

[0009] Optionally, the step of introducing a modulation-filtered white noise model and establishing a single-hole blasting vibration waveform simulation model based on the intensity envelope function, the open-pit copper mine blasting monitoring data, and the modulation-filtered white noise model includes: screening various single-peak envelope functions based on waveform adjustment capabilities to determine the intensity envelope function; obtaining the zero point and zero point difference of the measured waveform based on the open-pit copper mine monitoring data; setting the model parameters of the single-hole blasting vibration waveform simulation model based on the zero point and the zero point difference; and deriving the modulation-filtered white noise model by combining the intensity envelope function and the model parameters to obtain the single-hole blasting vibration waveform simulation model.

[0010] This invention sets model parameters based on actual measured zero points and zero point differences, making the model debugging and optimization process more targeted.

[0011] Optionally, the introduced modulation-filtered white noise model includes: The modulated filtered white noise model satisfies the following relationship: , in, To modulate the predicted waveform of the blasting vibration output by the white noise filtering model, A(t) is the intensity envelope function. This is the convolution operator. It is a frequency-nonstationary random process with zero mean and unit variance. This represents the standard deviation of the filter's non-stationary response to white noise. For time, Let be the filter's unit impulse response function. For Green's function variables, For time-varying parameters to be determined, It is white noise.

[0012] This invention utilizes a model to simulate blasting vibration waveforms under different blasting parameters, which can evaluate the impact of blasting parameters on vibration effects, thereby providing a reference and information basis for optimizing blasting parameters.

[0013] Optionally, the derivation of the modulation-filtered white noise model by combining the intensity envelope function and the model parameters to obtain the single-hole blasting vibration waveform simulation model includes: The single-hole blasting vibration waveform simulation model satisfies the following relationship: , in, The output of the single-hole blasting vibration waveform simulation model is the predicted blasting vibration waveform. For strength parameters, For time, Parameters for controlling the peak position of the intensity envelope, To control the parameters of the peak shape of the intensity envelope, For the filter damping ratio, The natural angular frequency of the filter. This is a Green's function variable.

[0014] This invention analyzes the characteristics of simulated waveforms under different parameter combinations using a model, which can accurately assess the impact of blasting parameters on vibration effects.

[0015] Optionally, the step of obtaining the blasting vibration waveform prediction function based on the single-hole blasting vibration waveform simulation model, and constructing an open-pit copper mine group-hole blasting vibration prediction model by combining the detonation time expression, the open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function includes: obtaining the nonlinear vibration relationship between the first and subsequent blasting holes under blasting load based on the open-pit copper mine monitoring data; setting and obtaining prediction nonlinear parameters based on the nonlinear vibration relationship and the single-hole blasting vibration waveform simulation model, wherein the nonlinear prediction parameters include the nonlinear vibration proportionality coefficient, the contribution amount of the blast hole to the blasting vibration, and the degree of influence of the first blasting hole on the subsequent blasting hole; The nonlinear vibration proportionality coefficient satisfies the following relationship: , in, This is the proportionality coefficient for nonlinear vibration. For the first The amount of explosive charge contributing to blasting vibration from each borehole. For the first The amount of explosive charge per blast hole This is the dosage index. Number the boreholes.

[0016] This invention analyzes nonlinear vibration relationships based on monitoring data and selects nonlinear prediction parameters accordingly, which can more accurately describe the interaction between the vibrations of each borehole during multi-hole blasting, avoiding the limitations of traditional linear models.

[0017] Optionally, obtaining the blasting vibration waveform prediction function based on the single-hole blasting vibration waveform simulation model includes: constructing the blasting vibration waveform prediction function based on the nonlinear vibration proportional coefficient, the contribution amount of the borehole to the blasting vibration, and the degree of influence of the first blasting hole on the subsequent blasting hole; The blasting vibration waveform prediction function satisfies the following relationship: , in, The output of the blasting vibration waveform prediction function is the predicted blasting vibration waveform. The total length of the waveform. This is the proportionality coefficient for nonlinear vibration. The site attenuation coefficient, Let be the distance from the i-th borehole to the measuring point. The distance decay exponent, For the first The amount of explosive charge per blast hole This is the dosage index. To test the waveform function of single-hole blasting vibration, For time, For the first Detonation delay of each blast hole.

[0018] The prediction function of this invention incorporates multiple key factors. By organically combining these factors, the formation mechanism of the vibration waveform of multi-hole blasting can be analyzed more comprehensively, thereby improving the scientific validity and feasibility of the model.

[0019] Optionally, the step of obtaining the blasting vibration waveform prediction function based on the single-hole blasting vibration waveform simulation model, and constructing the open-pit copper mine multi-hole blasting vibration prediction model by combining the initiation time expression, the open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function includes: establishing an initiation time expression based on the open-pit copper mine monitoring data; The detonation time expression satisfies the following relationship: , in, For the first Ranked The detonation time of each blast hole. This is the number for the row of boreholes. For row delay, Number the boreholes in the same row. For the time delay between holes.

[0020] The detonation time expression of this invention is an important component of the blasting vibration prediction model. It can more realistically reflect the detonation sequence and time interval of each blast hole during the blasting process, and can more accurately simulate the propagation and superposition process of blasting vibration.

[0021] Optionally, the step of obtaining the blasting vibration waveform prediction function based on the single-hole blasting vibration waveform simulation model, and constructing an open-pit copper mine multi-hole blasting vibration prediction model by combining the detonation time expression, the open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function includes: deriving the blasting vibration waveform prediction function based on the randomness of blasting vibration to obtain a blasting vibration randomness-multi-hole blasting vibration prediction model; and substituting the detonation time expression into the blasting vibration randomness-multi-hole blasting vibration prediction model for further derivation to obtain an open-pit copper mine multi-hole blasting vibration prediction model. The blasting vibration randomness-group blasting vibration prediction model satisfies the following relationship: , in, This is a prediction waveform for blasting vibration based on the randomness of blasting vibration. The total length of the waveform. This is the proportionality coefficient for nonlinear vibration. For the first Simulated waveform of single-hole blasting vibration of each blast hole For time, For the first Detonation delay of each blast hole; The open-pit copper mine group blasting vibration prediction model satisfies the following relationship: , in, Predicted waveforms for multi-hole blasting vibration in open-pit copper mines. The number of rows of boreholes. For the first Number of blast holes in a row For the first Ranked Vibration proportionality coefficient of each borehole The first simulation obtained based on the modulation-filtered white noise model Ranked The single-hole blasting vibration velocity waveform of each blast hole. For time, For the first Ranked The detonation time of each blast hole.

[0022] This invention constructs a prediction model for the randomness of blasting vibration in multi-hole blasting, and uses a modulated filtered white noise model to simulate the vibration waveform of a single-hole blasting, thus realistically reproducing the non-stationary random characteristics of the blasting vibration signal. Simultaneously, by introducing a nonlinear vibration proportionality coefficient and initiation time, the superposition and interference effects of vibration waves from each single hole in multi-hole blasting can be accurately calculated in the time domain. This results in the final output predicted waveform closely approximating the actual monitoring data in terms of amplitude, frequency, and duration, significantly reducing prediction errors.

[0023] Beneficial effects: This invention overcomes the shortcomings of traditional linear models that cannot reflect the randomness of vibration and waveform interference effects by constructing a multi-hole blasting vibration prediction model that integrates modulation and filtering white noise with nonlinear vibration relationship. It achieves high-fidelity simulation and accurate prediction of blasting vibration waveforms, providing a scientific and reliable basis for the optimized design of blasting parameters and vibration safety control in open-pit copper mines.

[0024] Secondly, this invention also provides a system for constructing a prediction model for open-pit copper mine group-hole blasting vibration. This system can efficiently execute the method for constructing such a model. The system includes an input device, a processor, an output device, and a memory, all interconnected. The memory includes a computer-readable storage medium as described in the first aspect of this invention. The memory stores a computer program, which includes program instructions. The processor is configured to call these program instructions. The open-pit copper mine group-hole blasting vibration prediction model construction system provided by this invention has a compact structure, strong applicability, and significantly improved operating efficiency. Attached Figure Description

[0025] Figure 1 This is a flowchart of the method for constructing a prediction model for blasting vibration in open-pit copper mines according to the present invention; Figure 2 This is a schematic diagram of the open-pit copper mine blasting scheme and monitoring point layout according to the present invention; Figure 3 This is a schematic diagram of the envelope detection method of the present invention; Figure 4 This is a schematic diagram of the explosion vibration waveform obtained after separation according to the present invention, wherein... Figure 4 (a) is the single-hole blasting vibration waveform of the first row; Figure 4 (b) shows the single-hole blasting vibration waveform of the second row; Figure 4 (c) shows the single-hole blasting vibration waveform of the third row; Figure 4 (d) Vibration waveform of multi-hole blasting; Figure 4 (e) shows the single-hole blasting vibration waveform of the fourth row; Figure 4(f) shows the single-hole blasting vibration waveform of the fifth row; Figure 5 This is a schematic diagram comparing the waveform adjustment capabilities of various single-peak envelope functions of the present invention; Figure 6 This is a flowchart illustrating the traversal search algorithm of the present invention; Figure 7 This is a flowchart illustrating the limited search algorithm of the present invention; Figure 8 This is a schematic diagram comparing the waveforms of the multiple holes in this invention; Figure 9 This is a schematic diagram of the structure of the open-pit copper mine group blasting vibration prediction model construction system of the present invention. Detailed Implementation

[0026] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0027] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0028] In open-pit copper mining operations, predicting blasting vibrations is crucial for ensuring safe, efficient, and smooth operations. To analyze the interactions between various factors during blasting, accurately predict blasting vibrations, meet actual production needs related to blasting vibrations, and provide technical support for open-pit copper mining, please refer to [link to relevant documentation]. Figure 1 The method for constructing a prediction model for blasting vibration in open-pit copper mines proposed in this invention includes the following steps: S1. The embodiment designed a multi-hole test blasting scheme based on an open-pit copper mine. Initial blasting data for the open-pit copper mine was obtained based on the multi-hole test blasting scheme. This initial blasting data was then transformed, extracted, and optimized to obtain open-pit copper mine blasting monitoring data. The implementation details are as follows: First, the blasting parameters and monitoring point locations in the test blasting scheme for open-pit copper mines were determined.

[0029] In this embodiment, the actual conditions of the open-pit copper mine need to be analyzed to determine appropriate blasting parameters. The ore in this open-pit copper mine is mainly marble. Based on the characteristics of the ore, the scale of mining, and equipment conditions, the bench height is set at 15m, and the borehole diameter at 250mm. Considering both blasting effect and economy, the borehole spacing range is determined to be 7-7.5m, and the row spacing range is 6-6.5m. To control blasting vibration and fragmentation effect, the inter-hole delay is set at 42ms, and the inter-row delay at 100ms. Simultaneously, to ensure blasting safety and effectiveness, the packing height is set at 7-8m, and continuous charging is used. The explosive used is emulsion explosive. See the borehole layout diagram of the open-pit copper mine site. Figure 2 .

[0030] To obtain the superimposed wavelet in the group-hole blasting more accurately, five experimental single-hole blasts (26#, 27#, 28#, 29#, and 30#) were added after rows 1-5 of the mine's blast holes, without affecting normal mine production operations. The blasting parameters of these experimental single-hole blasts were consistent with those of the blast holes in the same row of the group-hole blasting. For specific information on the blasting parameters, please refer to Table 1. By setting up experimental single-hole blasts, the blasting vibration characteristics of each blast hole can be studied independently, providing basic data for analyzing the contribution and interaction of each blast hole in the vibration of the group-hole blasting.

[0031] Table 1 Blasting parameters of single-hole blasting vibration test

[0032] Meanwhile, based on factors such as the extent of the blasting area, topography, and monitoring point areas, the location of monitoring points should be reasonably determined. These monitoring points should be able to comprehensively reflect the propagation characteristics of blasting vibrations in different directions and distances. At the same time, they should facilitate the installation of sensors and data acquisition. Multiple monitoring points can be set up at different distances and directions around the blasting area to obtain rich blasting vibration data.

[0033] Then, based on the multi-hole test blasting scheme, the open-pit copper mine was monitored and the initial blasting data of the open-pit copper mine was obtained.

[0034] The open-pit copper mine was blasted according to the above-mentioned multi-hole test blasting scheme, and sensors were installed at the pre-determined monitoring points for monitoring. The sensors have the characteristics of high precision, high sensitivity and wide bandwidth, and can accurately record various parameters of blasting vibration waves. During the blasting process, the sensors will collect blasting vibration signals in real time and transmit the signals to the data acquisition system for storage. The collected data is the initial blasting data of the open-pit copper mine.

[0035] During the propagation of blasting vibration waves, the characteristics of the blast source of the columnar concentrated charge will gradually become obvious, and the vertical vibration component in the blast source is relatively strong. In order to facilitate subsequent theoretical research and analysis, this embodiment takes the vertical blasting vibration waveform monitored by the sensor as the research basis; the single-hole blasting vibration waveform is usually located in the middle and later part or the tail of the vibration acquisition signal.

[0036] Finally, envelope detection, Hilbert method, and Gaussian smoothing method were used to transform, extract, and optimize the initial blasting data to obtain open-pit copper mine blasting monitoring data.

[0037] Since the arrival time of the blasting vibration waveform in the initial blasting data is unclear, this embodiment uses the envelope detection method to identify the arrival time of the blasting vibration waveform. The envelope detection method extracts the envelope of the signal to highlight the amplitude variation trend of the signal, thereby determining the arrival time of the signal. The specific process of the envelope detection method can be found in [link to documentation]. Figure 3 .

[0038] The Hilbert transform is used to process the initial blasting data and extract the signal envelope. The Hilbert transform can convert a real signal into an analytic signal, and the magnitude of the analytic signal refers to the envelope of the original signal. This allows for accurate acquisition of the envelope information of the blasting vibration signal, providing a basis for subsequent determination of the arrival time.

[0039] To further improve the accuracy and smoothness of the signal envelope, Gaussian smoothing is used to optimize the extracted signal envelope. Weighted averaging of the signal envelope can effectively remove noise interference and make the amplitude trend smoother. The signal envelope after Gaussian smoothing can more clearly reflect the amplitude variation characteristics of the blasting vibration signal.

[0040] The arrival time of the blasting vibration signal is accurately determined by finding the point where the derivative first exceeds zero. When the blasting vibration signal arrives at the monitoring point, the amplitude of the signal increases rapidly, and the derivative of its envelope changes accordingly.

[0041] The vibration waveform after the arrival time is determined is separated from the initial blasting data, and the time starting point of the above waveform is assigned as the initial time. The separated blasting vibration waveforms can completely display the group-hole and single-hole blasting vibration waveforms in the measured signal, and the curves have no redundant or missing parts in the initial stage, which facilitates further analysis of the waveforms. Please refer to the separated blasting vibration waveforms for details. Figure 4 ,in Figure 4 (a) shows the vibration waveform of multi-hole blasting. Figure 4 (b)-(f) refer to the single-hole blasting vibration waveform diagrams of 26#-30#, respectively.

[0042] The above steps can effectively obtain initial blasting data, and after transformation, extraction and optimization, the final open-pit copper mine blasting monitoring data can be used for subsequent blasting vibration analysis and prediction.

[0043] S2. Introduce a modulation-filtered white noise model, and establish a single-hole blasting vibration waveform simulation model based on the intensity envelope function, open-pit copper mine blasting monitoring data, and the modulation-filtered white noise model. The specific implementation details are as follows: I. Modulation-Filtered White Noise Model The modulated filtered white noise model is the foundation for constructing a simulation model of single-hole blasting vibration waveforms, and it mainly consists of non-stationary random sequences. and intensity envelope function Composed of two parts, and combined with convolution operations to generate the predicted waveform of blasting vibration, the modulation-filtered white noise model in this embodiment satisfies the following relationship: , in, To modulate the predicted waveform of the blasting vibration output by the white noise filtering model, A(t) is the intensity envelope function. This is the convolution operator. It is a frequency-nonstationary random process with zero mean and unit variance. This represents the standard deviation of the filter's non-stationary response to white noise. For time, Let be the filter's unit impulse response function. For Green's function variables, For time-varying parameters to be determined, It is white noise.

[0044] The intensity envelope function in the model can describe the variation of the amplitude of the blasting vibration signal over time; the non-stationary random process of frequency reflects the random characteristics of the blasting vibration signal; the standard deviation of the filter's non-stationary response to white noise normalizes the non-stationary response of white noise after passing through the filter; the time-varying parameters determine the change of the filter's frequency characteristics over time; white noise is a random signal with a uniform power spectral density.

[0045] The operating mechanism of the above modulation-filtered white noise model is as follows: First, it uses a linear filter whose parameters change over time. The filter modulates and filters white noise, causing its frequency response to change over time; then, the filter output is normalized to obtain a non-stationary random sequence. Finally, the non-stationary random sequence Multiply by the intensity envelope function This allows us to obtain blasting vibration prediction waveforms with practical physical significance. .

[0046] The standard deviation of the filter's non-stationary response to white noise is calculated using the following formula: , in, This represents the standard deviation of the filter's non-stationary response to white noise. Pi For time, Let be the filter's unit impulse response function. For time-varying parameters to be determined, The natural angular frequency of the filter. For Green's function variables, denoted as the power spectral density of white noise.

[0047] Furthermore, the natural angular frequency of the filter satisfies the following relationship; , in, The natural angular frequency of the filter. The starting natural (angular) frequency of the filter, The frequency decrease index, This is a Green's function variable.

[0048] II. Establishing a Simulation Model for Single-Hole Blasting Vibration Waveforms First, based on waveform adjustment capabilities, various single-peak envelope functions are screened to determine the intensity envelope function.

[0049] The intensity envelope function plays a crucial role in accurately simulating the amplitude variation of single-hole blasting vibration waveforms. In this embodiment, various single-peak envelope functions are screened based on waveform adjustment capabilities. These single-peak envelope functions include the decaying sine function, the Hu Yuxian-Zhou Xiyuan double exponential function, and the Saragoni-Hart Gamma function. A comparative analysis of these three single-peak envelope functions is conducted based on the curve characteristics of the single-hole blasting vibration waveform. For details, please refer to [link to relevant documentation]. Figure 5 .

[0050] Using waveform adjustment capability as a screening criterion, the Gamma function can better fit the amplitude variation characteristics of a single-hole blasting vibration waveform. Therefore, in this embodiment, the Gamma function was selected as the intensity envelope function, and its expression is as follows: , in, Let be the intensity envelope function. For strength parameters, For time, Parameters for controlling the peak position of the intensity envelope, These parameters control the shape of the peak value of the intensity envelope. The aforementioned intensity parameters determine the overall amplitude of the waveform; they can be adjusted... The value changes the timing of the waveform peak; the magnitude of the parameter controlling the shape of the intensity envelope peak affects the steepness of the waveform peak.

[0051] Three intensity parameters in the model's Gamma function Two frequency parameters in a non-stationary sequence control the amplitude of the analog waveform. The frequency of the simulated waveform is controlled. In order to ensure that the simulated waveform matches the non-stationary characteristics of the measured blasting vibration velocity waveform, the values ​​of the above-mentioned different parameters need to be determined based on the relevant characteristics of the measured blasting vibration velocity waveform.

[0052] In this embodiment, the zero point and zero point difference of the measured waveform are obtained based on the monitoring data of the open-pit copper mine.

[0053] It uses a trial-and-error method to conduct preliminary tests on the model parameters to determine the approximate range of each parameter. The range values ​​set in the example are as follows: , , , , , Then, a limited traversal search algorithm is used to determine the optimal combination of model parameters. The specific steps are as follows: Based on monitoring data from the open-pit copper mine, the zero point and zero point difference of the measured waveform on site were obtained. The zero point is the intersection of the waveform on the time axis and the horizontal axis, and the zero point difference is the time interval between adjacent zero points.

[0054] The absolute values ​​of all zero-point differences between the simulated waveform and the measured waveform are subtracted using the following formula: , in, Subtract the absolute values ​​of all zero-point differences. For the zero-point difference sequence of different measured waveforms, This represents a sequence of zero-point difference values ​​for different simulated waveforms. In this example, the parameter combination corresponding to the zero-point difference with the smallest absolute value difference is selected as the optimal model frequency parameter for this waveform. , For the flowchart of the traversal search algorithm, please refer to [link / reference]. Figure 6 The vibration prediction model is evaluated by iteratively traversing all parameter combinations.

[0055] Next, the model parameters of the single-hole blasting vibration waveform simulation model are set according to the zero point and the zero point difference.

[0056] The extreme points of the measured waveforms were obtained based on the monitoring data of the open-pit copper mine. These extreme points mainly include the peak moment and the peak velocity. The peak moment is the time when the waveform reaches its maximum amplitude, and the peak velocity is the vibration speed at that moment.

[0057] Based on the relationship between peak velocity and peak time, the parameters are determined using the following formula. Value and value: , , in, To control the parameters of the peak shape of the intensity envelope, Parameters for controlling the peak position of the intensity envelope, At peak time, For strength parameters, This represents the peak vibration velocity.

[0058] In this embodiment, all combined simulated waveforms are output, and the intensity parameter corresponding to the simulated waveform with the highest residual similarity is selected. For the execution flow of the limiting search algorithm, please refer to [link / reference needed]. Figure 7 .

[0059] Finally, the modulation-filtered white noise model is derived by combining the intensity envelope function and model parameters to obtain the simulation model of single-hole blasting vibration waveform.

[0060] By combining the determined intensity envelope function and model parameters, the modulation-filtered white noise model can be derived, resulting in the simulation model of the single-hole blasting vibration waveform. The simulation model of the single-hole blasting vibration waveform satisfies the following relationship: , in, The output of the single-hole blasting vibration waveform simulation model is the predicted blasting vibration waveform. For strength parameters, For time, Parameters for controlling the peak position of the intensity envelope, To control the parameters of the peak shape of the intensity envelope, For the filter damping ratio, It is white noise. This is a Green's function variable.

[0061] Furthermore, the model also satisfies the basic relationships of the modulation-filtered white noise model: ; ; The above steps establish a simulation model of single-hole blasting vibration waveform, providing technical support and analytical tools for further research on the blasting vibration characteristics of open-pit copper mines.

[0062] S3. Based on the single-hole blasting vibration waveform simulation model, the blasting vibration waveform prediction function is obtained. Combining the detonation time expression, open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function, the open-pit copper mine multi-hole blasting vibration prediction model of this embodiment is constructed. The specific implementation details are as follows: The first step involves analyzing the nonlinear vibration relationship between pre-blast and post-blast holes under blasting loads based on open-pit copper mine monitoring data. In open-pit copper mine blasting operations, accurate prediction of blasting vibrations requires setting nonlinear prediction parameters. This first involves analyzing the nonlinear vibration relationship between pre-blast and post-blast holes under blasting loads based on open-pit copper mine monitoring data. The on-site monitoring data of the open-pit copper mine is organized and analyzed to obtain the intrinsic connection between the blasting vibrations of pre-blast and post-blast holes, laying the foundation for setting nonlinear prediction parameters.

[0063] In this embodiment, the nonlinear vibration relationship between the first and second blast holes under blasting load is analyzed based on monitoring data of open-pit copper mines. This provides a data foundation and theoretical basis for subsequent model construction and helps to more accurately grasp the essential characteristics of blasting vibration.

[0064] The second embodiment is based on a single-hole blasting vibration waveform simulation model, and at the same time, a nonlinear superposition prediction model is used to set nonlinear prediction parameters. The above parameters mainly include the nonlinear vibration proportional coefficient, the contribution amount of the borehole to the blasting vibration, and the degree of influence of the first blasting hole on the subsequent blasting hole.

[0065] Introducing a nonlinear vibration proportionality coefficient This can demonstrate the effect of rock impact damage on vibration attenuation. The analytical expression is as follows: , in, This is the proportionality coefficient for nonlinear vibration. For the first The amount of explosive charge contributing to blasting vibration from each borehole. For the first The amount of explosive charge per blast hole This is the dosage index. Number the boreholes.

[0066] Contribution of blast hole to blasting vibration by explosive charge The following relationship must be satisfied: , in, For the first The amount of explosive charge contributing to blasting vibration from each borehole. For the first The amount of explosive charge per blast hole For the first blast hole to the second blast hole (the first) The degree of influence of (each borehole) It is a positive integer. For site coefficient, Number the boreholes. for Natural numbers between.

[0067] The degree of influence of the first borehole blasting on the subsequent borehole blasting The relationship is as follows: , in, For the first blast hole to the second blast hole (the first) The degree of influence of (each borehole) For the first The distance from the detonation hole to the nearest detonating hole. It is a constant, and in the example, it is taken as... , The average hole spacing, This represents the average charge per borehole. For the first The amount of explosive charge per blast hole.

[0068] The different parameters in the embodiments can reflect the influence of various factors on vibration during the blasting process from different perspectives. At the same time, the calculation relationship of different parameters is given, which can more accurately simulate the actual blasting vibration.

[0069] Then, a prediction function for blasting vibration waveforms is established.

[0070] In this embodiment, a blasting vibration waveform prediction function is constructed based on the nonlinear vibration proportional coefficient, the contribution of the borehole to the blasting vibration, and the degree of influence of the first blast hole on the subsequent blast hole. The above parameters together reflect the influence of various factors on the vibration during the blasting process. The values ​​of the above parameters can be determined based on the open-pit copper mine blasting monitoring data and the on-site conditions of the open-pit copper mine.

[0071] The prediction function for blasting vibration waveforms satisfies the following relationship: , in, The output of the blasting vibration waveform prediction function is the predicted blasting vibration waveform. The total length of the waveform. This is the proportionality coefficient for nonlinear vibration. This is the multiplication operator. The site attenuation coefficient, Let be the distance from the i-th borehole to the measuring point. The distance decay exponent, For the first The amount of explosive charge per blast hole This is the dosage index. To test the waveform function of single-hole blasting vibration, For time, For the first Detonation delay of each blast hole.

[0072] The above-mentioned blasting vibration waveform prediction function comprehensively considers the influence of various key factors on the vibration waveform during the blasting process, enabling the prediction function to predict the blasting vibration waveform more scientifically and accurately. In an optional embodiment, the values ​​of each parameter in the function can be determined based on open-pit copper mine blasting monitoring data and open-pit copper mine site conditions, enhancing the adaptability and practicality of the model, and enabling accurate prediction of open-pit copper mine blasting under different site conditions.

[0073] Next, the detonation time expression is introduced.

[0074] Establishing an initiation time expression based on open-pit copper mine monitoring data can more accurately describe the initiation time of each borehole, which is of great importance for analyzing the temporal characteristics of blasting vibration. The initiation time expression satisfies the following relationship: , in, For the first Ranked The detonation time of each blast hole. This is the number for the row of boreholes. For row delay, Number the boreholes in the same row. For the time delay between holes.

[0075] The initiation time expression can accurately describe the initiation time of each borehole, providing technical support for analyzing the temporal characteristics of blasting vibration. By clarifying the relationship between inter-row delay and inter-hole delay and initiation time, it helps to gain a deeper understanding of the variation law of vibration over time during blasting.

[0076] Finally, a vibration prediction model for group blasting in open-pit copper mines was constructed.

[0077] The first step is to derive the prediction function for blasting vibration waveform based on the randomness of blasting vibration, thereby obtaining the prediction model for blasting vibration randomness-group blasting vibration.

[0078] When multiple boreholes are detonated in sequence, the vibration waveform generated by the blasting boreholes is random. Since the nonlinear superposition model cannot reflect this factor, the simulation results based on the modulation filtered white noise model are used as the single-hole blasting vibration waveforms participating in the superposition in this embodiment to take into account the randomness of the blasting vibration.

[0079] Therefore, the blasting vibration randomness-group blasting vibration prediction model satisfies the following relationship: , in, This is a prediction waveform for blasting vibration based on the randomness of blasting vibration. The total length of the waveform. This is the proportionality coefficient for nonlinear vibration. For the first Simulated waveform of single-hole blasting vibration of each blast hole For time, For the first Detonation delay of each blast hole.

[0080] The second step involves substituting the detonation time expression into the blasting vibration randomness-group blasting vibration prediction model for derivation, thus obtaining the open-pit copper mine group blasting vibration prediction model.

[0081] To investigate the impact of different inter-hole delays and inter-row delays on the vibration caused by multi-hole blasting, the implementation example decomposes the delay time in multi-row multi-hole blasting into different inter-hole delays and inter-row delays. Modulated filtered white noise is used to simulate the simulated waveform sequence of each blast hole under multi-hole blasting, and the blasting vibration randomness-multi-hole blasting vibration prediction model is deformed and optimized.

[0082] An example of a vibration prediction model for group blasting in an open-pit copper mine satisfies the following relationship: , in, Predicted waveforms for multi-hole blasting vibration in open-pit copper mines. The number of rows of boreholes. For the first Number of blast holes in a row For the first Ranked Vibration proportionality coefficient of each borehole The first simulation obtained based on the modulation-filtered white noise model Ranked The single-hole blasting vibration velocity waveform of each blast hole. For time, For the first Ranked The detonation time of each blast hole.

[0083] In this embodiment, the delay time in multi-row multi-hole blasting is decomposed into inter-hole delay and inter-row delay. Modulated filtered white noise is used to simulate the waveform sequence of each blast hole under multi-hole blasting. The blasting vibration randomness-multi-hole blasting vibration prediction model is deformed and optimized. This allows for convenient investigation of the impact of multi-hole blasting on vibration caused by different inter-hole delays and inter-row delays, providing a theoretical basis and technical support for optimizing blasting parameters and controlling blasting vibration.

[0084] S4. After completing the construction of the open-pit copper mine group-hole blasting vibration prediction model, the simulated waveform of group-hole blasting vibration can be obtained through the open-pit copper mine group-hole blasting vibration prediction model. The specific implementation content is as follows: Vibration prediction model for open-pit copper mine blasting Before generating the simulated waveform, it is necessary to determine the optimal model parameters for each row. The determination of parameters must be closely combined with the actual situation of the open-pit copper mine, including but not limited to rock properties, explosive properties, and borehole layout.

[0085] The nonlinear vibration proportionality coefficient should be calculated according to the calculation method in the embodiment, combined with the field monitoring data and rock impact damage characteristics. Considering that the rock conditions of different rows and different holes may be different, the vibration proportionality coefficient value of each row and each hole needs to be calculated and optimized separately to ensure that it can accurately reflect the effect of rock impact damage on vibration attenuation at that location.

[0086] The simulation waveform for single-hole blasting vibration needs to be based on a modulated filtered white noise model. When applying the model, it is also necessary to reasonably set various parameters in the model, such as noise spectrum characteristics and filtering conditions, according to the site conditions of the open-pit copper mine, in order to generate a simulation waveform for single-hole blasting vibration that conforms to the actual situation. Furthermore, to improve the accuracy of the simulation results, multiple simulation experiments can be conducted, and the model parameters can be continuously adjusted and optimized based on the comparison between the simulation results and actual monitoring data, ultimately determining the optimal simulation waveform for each hole in each row of holes.

[0087] In this embodiment, based on the determined optimal model parameters for each row, 25 simulated single-hole blasting vibration waveforms are generated using numerical simulation software or a calculation program. During the generation process, it is essential to ensure that the simulated waveforms accurately reflect the vibration variation over time during single-hole blasting, including but not limited to the initial, peak, and decay stages of the vibration.

[0088] To ensure the quality of the simulated waveform, the generated waveform needs to be checked. The check includes the waveform's integrity, continuity, and whether it conforms to the basic characteristics of blasting vibration. For waveforms that do not meet the requirements, the reasons should be analyzed and the model parameters or simulation method should be adjusted in a timely manner, and the waveform should be regenerated until it meets the requirements.

[0089] After obtaining 25 simulated single-hole blasting vibration waveforms, the inter-hole delay was set to 42ms and the inter-row delay was set to 100ms. These parameters were then substituted into the open-pit copper mine group blasting vibration prediction model. The calculations are performed in the middle.

[0090] Meanwhile, the vibration contribution of each row and each hole is calculated one by one during the calculation process, and the vibration contributions of all holes are superimposed to obtain the simulated waveform of the blasting vibration of the group holes. To further improve the calculation efficiency and accuracy, automated calculation can be achieved by programming, and the calculation process can be recorded in detail for subsequent inspection and verification.

[0091] The embodiment also compares the calculated simulated waveform of multi-hole blasting vibration with the measured waveform of multi-hole blasting vibration, and the comparison is illustrated in the diagram. Figure 8 The comparison between the two waveforms is clearly shown, including waveform shape, peak position, and dominant frequency distribution, combined with... Figure 8 The predictive accuracy of the model can be evaluated intuitively.

[0092] according to Figure 8 The information indicates that the peak velocity of the measured waveform of the multi-hole group is The peak velocity of the simulated waveform of the group of holes is The error between the two is only The dominant frequency of the measured waveform of the group of holes is The dominant frequency of the simulated group of holes waveform is The error between the two is Furthermore, the prediction accuracy of the open-pit copper mine group blasting vibration prediction model was verified. The open-pit copper mine group blasting vibration prediction model of this invention can accurately simulate the blasting vibration of open-pit copper mine group blasting.

[0093] Furthermore, the simulated waveforms of multi-hole blasting vibration provide important data for analyzing the impact of delay time on vibration. Based on the simulated waveforms, by changing the parameter values ​​of inter-hole delay and inter-row delay, the variation law of multi-hole blasting vibration under different delay time combinations can be further analyzed, thereby providing scientific guidance for optimizing blasting parameters and reducing the impact of blasting vibration on the surrounding environment.

[0094] Furthermore, the beneficial effects of this invention include: by introducing a modulated white noise model and a Gamma function envelope, a high-precision single-hole blasting vibration simulation model is constructed; and by combining the principle of nonlinear superposition, accurate prediction of blasting vibration in open-pit copper mines with multiple boreholes is achieved. Simultaneously, it effectively solves the problem that traditional models struggle to reflect the randomness and nonlinear decay of blasting vibrations; the model's predicted peak velocity and dominant frequency errors are extremely low, accurately simulating actual blasting conditions. In addition, this invention supports quantitative analysis of the vibration impact under different combinations of inter-hole and inter-row delays, providing a scientific basis and technical support for optimizing blasting parameters and reducing vibration hazards.

[0095] Please see Figure 9 In an optional embodiment, the present invention also provides a system for constructing a prediction model for blasting vibration in open-pit copper mines. This system includes a processor, an input device, an output device, and a memory, all interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions and execute the specific steps of the method and related embodiments for constructing a prediction model for blasting vibration in open-pit copper mines provided by the present invention. The system for constructing a prediction model for blasting vibration in open-pit copper mines of the present invention is structurally complete and objectively stable.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for constructing a vibration prediction model for open-pit copper mine group blasting, characterized in that, Includes the following steps: Based on the design of a multi-hole test blasting scheme for open-pit copper mines, the initial blasting data of the open-pit copper mines is obtained according to the multi-hole test blasting scheme. The initial blasting data is then transformed, extracted, and optimized to obtain open-pit copper mine blasting monitoring data. A modulation-filtered white noise model is introduced, and a single-hole blasting vibration waveform simulation model is established based on the intensity envelope function, the open-pit copper mine blasting monitoring data, and the modulation-filtered white noise model. Based on the single-hole blasting vibration waveform simulation model, the blasting vibration waveform prediction function is obtained. Combined with the detonation time expression, the open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function, an open-pit copper mine multi-hole blasting vibration prediction model is constructed. The simulated waveform of the blasting vibration of the open-pit copper mine was obtained by using the blasting vibration prediction model of the open-pit copper mine.

2. The method for constructing a prediction model for open-pit copper mine group blasting vibration according to claim 1, characterized in that, The open-pit copper mine design group-hole test blasting scheme, based on the group-hole test blasting scheme, obtains initial blasting data for the open-pit copper mine. The initial blasting data is then transformed, extracted, and optimized to obtain open-pit copper mine blasting monitoring data, including: Based on the blasting parameters and monitoring point locations in the aforementioned open-pit copper mine multi-hole test blasting scheme; The open-pit copper mine was monitored using the aforementioned multi-hole test blasting scheme, and initial blasting data for the open-pit copper mine was obtained. The initial blasting data was transformed, extracted, and optimized using envelope detection, Hilbert method, and Gaussian smoothing method to obtain open-pit copper mine blasting monitoring data.

3. The method for constructing a prediction model for blasting vibration in open-pit copper mines according to claim 1, characterized in that, The introduction of a modulation-filtered white noise model, and the establishment of a single-hole blasting vibration waveform simulation model based on the intensity envelope function, the open-pit copper mine blasting monitoring data, and the modulation-filtered white noise model, includes: The intensity envelope function is determined by screening various single-peak envelope functions based on waveform adjustment capability; The zero point and zero point difference of the measured waveform were obtained based on the monitoring data of the open-pit copper mine. The model parameters of the single-hole blasting vibration waveform simulation model are set according to the zero point and the difference between the zero points; The modulation-filtered white noise model is derived by combining the intensity envelope function and the model parameters to obtain a simulation model of single-hole blasting vibration waveform.

4. The method for constructing a prediction model for blasting vibration in open-pit copper mines according to claim 1, characterized in that, The introduced modulation-filtered white noise model includes: The modulated filtered white noise model satisfies the following relationship: ， in, To modulate the predicted waveform of the blasting vibration output by the white noise filtering model, A(t) is the intensity envelope function. This is the convolution operator. It is a frequency-nonstationary random process with zero mean and unit variance. This represents the standard deviation of the filter's non-stationary response to white noise. For time, Let be the filter's unit impulse response function. For Green's function variables, For time-varying parameters to be determined, It is white noise.

5. The method for constructing a prediction model for blasting vibration in open-pit copper mines according to claim 3, characterized in that, The derivation of the modulation-filtered white noise model by combining the intensity envelope function and the model parameters to obtain the single-hole blasting vibration waveform simulation model includes: The single-hole blasting vibration waveform simulation model satisfies the following relationship: ， in, The output of the single-hole blasting vibration waveform simulation model is the predicted blasting vibration waveform. For strength parameters, For time, Parameters for controlling the peak position of the intensity envelope, To control the parameters of the peak shape of the intensity envelope, For the filter damping ratio, The natural angular frequency of the filter. This is a Green's function variable.

6. The method for constructing a prediction model for blasting vibration in open-pit copper mines according to claim 1, characterized in that, The step of obtaining the blasting vibration waveform prediction function based on the single-hole blasting vibration waveform simulation model, and constructing the open-pit copper mine multi-hole blasting vibration prediction model by combining the detonation time expression, the open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function includes: Based on the monitoring data of the open-pit copper mine, the nonlinear vibration relationship between the pre-blast holes and the post-blast holes under blasting load was obtained; Based on the nonlinear vibration relationship and the simulation model of the single-hole blasting vibration waveform, the nonlinear prediction parameters are set and obtained. The nonlinear prediction parameters include the nonlinear vibration proportional coefficient, the contribution of the borehole to the blasting vibration, and the degree of influence of the first blasting hole on the subsequent blasting hole. The nonlinear vibration proportionality coefficient satisfies the following relationship: ， in, It is the proportionality coefficient for nonlinear vibration. For the first The amount of explosive charge contributing to blasting vibration from each borehole. For the first The amount of explosive charge per blast hole This is the dosage index. Number the boreholes.

7. The method for constructing a prediction model for blasting vibration in open-pit copper mines according to claim 6, characterized in that, The blasting vibration waveform prediction function obtained based on the single-hole blasting vibration waveform simulation model includes: A blasting vibration waveform prediction function is constructed based on the nonlinear vibration proportional coefficient, the contribution amount of the borehole to the blasting vibration, and the degree of influence of the first blasting hole on the subsequent blasting hole. The blasting vibration waveform prediction function satisfies the following relationship: ， in, The output of the blasting vibration waveform prediction function is the predicted blasting vibration waveform. The total length of the waveform. It is the proportionality coefficient for nonlinear vibration. The site attenuation coefficient, Let be the distance from the i-th borehole to the measuring point. The distance decay exponent, For the first The amount of explosive charge per blast hole This is the dosage index. To test the waveform function of single-hole blasting vibration, For time, For the first Detonation delay of each blast hole.

8. The method for constructing a prediction model for blasting vibration in open-pit copper mines according to claim 6, characterized in that, The step of obtaining the blasting vibration waveform prediction function based on the single-hole blasting vibration waveform simulation model, and constructing the open-pit copper mine multi-hole blasting vibration prediction model by combining the detonation time expression, the open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function includes: An expression for the detonation time was established based on the monitoring data of the open-pit copper mine. The detonation time expression satisfies the following relationship: ， in, For the first Ranked The detonation time of each blast hole. This is the number for the row of boreholes. For row delay, Number the boreholes in the same row. For the time delay between holes.

9. The method for constructing a prediction model for blasting vibration in open-pit copper mines according to claim 8, characterized in that, The step of obtaining the blasting vibration waveform prediction function based on the single-hole blasting vibration waveform simulation model, and constructing the open-pit copper mine multi-hole blasting vibration prediction model by combining the detonation time expression, the open-pit copper mine blasting monitoring data, and the blasting vibration waveform prediction function includes: The blasting vibration waveform prediction function is derived based on the randomness of blasting vibration to obtain the blasting vibration randomness-group hole blasting vibration prediction model. The initiation time expression is substituted into the blasting vibration randomness-group blasting vibration prediction model for derivation to obtain the open-pit copper mine group blasting vibration prediction model. The blasting vibration randomness-group blasting vibration prediction model satisfies the following relationship: ， in, This is a prediction waveform for blasting vibration based on the randomness of blasting vibration. The total length of the waveform. It is the proportionality coefficient for nonlinear vibration. For the first Simulated waveform of single-hole blasting vibration of each blast hole For time, For the first Detonation delay of each blast hole; The open-pit copper mine group blasting vibration prediction model satisfies the following relationship: ， in, Predicted waveforms for multi-hole blasting vibration in open-pit copper mines. The number of rows of boreholes. For the first Number of blast holes in a row For the first Ranked Vibration proportionality coefficient of each borehole The first simulation obtained based on the modulation-filtered white noise model Ranked The single-hole blasting vibration velocity waveform of each blast hole. For time, For the first Ranked The detonation time of each blast hole.

10. A system for constructing a prediction model for blasting vibration in open-pit copper mines, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the method for constructing a vibration prediction model for blasting in open-pit copper mines as described in any one of claims 1-9.

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