Calibration method based on blasting vibration acquisition instrument

Abstract

The application discloses a kind of based on calibration method of blasting vibration acquisition instrument, comprising the following steps: data acquisition: carry out blasting test under different delay time, use acquisition instrument to record vibration waveform data, synchronous statistics after blasting mass distribution, establish the associated database of delay time and crushing effect;Improved linear superposition method calibration: utilize fourier series decomposition measured single-hole waveform, join random variable to generate simulation waveform, the average of theoretical particle peak velocity under different delay time is calculated by monte carlo simulation, compare acquisition instrument measured particle peak velocity with theoretical value;Rock crushing effect verification: regression analysis is carried out to calibrated vibration data and rock crushing effect;Stability and repeatability test: repeat blasting under the same delay time, test acquisition instrument data repeatability error, analyze the influence of background noise on acquisition data;Calibration result output: establish delay time-vibration parameter-crushing effect calibration curve.

Description

Technical Field

[0001] This invention relates to the field of blasting engineering monitoring technology, and in particular to a calibration method based on a blasting vibration acquisition instrument. Background Technology

[0002] In blasting operations, the detonation of explosives and the fracturing of rock are accompanied by vibrations in the surrounding ground. Within a certain distance, blasting vibrations may damage or destroy buildings; therefore, scholars both domestically and internationally have conducted extensive research on blasting vibrations. The vibrations generated by blasting operations may damage surrounding buildings, necessitating the control of vibration intensity (such as peak particle velocity, PPV) to ensure safety. The delay time (the time interval between detonations between blast holes) directly affects the vibration superposition effect and the rock fracturing effect, but its optimal value requires a balance between vibration attenuation and fracturing quality. Blasting vibration acquisition instruments are key equipment in blasting operations used to monitor and analyze the effects of blasting vibrations; their design aims to capture the vibrational energy released during the rock fracturing process following the explosive detonation.

[0003] In existing technologies, blasting vibration parameters and rock crushing quality are often analyzed independently, lacking quantitative correlation, which leads to parameter optimization relying on experience. At the same time, conventional calibration methods rely on comparison of a single waveform, without considering the randomness and statistical regularity of the signal, resulting in large deviations in calibration results. Therefore, a calibration method based on a blasting vibration acquisition instrument is proposed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a calibration method based on a blasting vibration acquisition instrument.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A calibration method based on a blasting vibration acquisition instrument includes the following steps:

[0007] Data acquisition: Blasting tests were conducted at different delay times. Vibration waveform data (including peak particle velocity (PPV), dominant frequency, duration, etc.) were recorded using a data acquisition instrument. The distribution of large fragments after blasting was statistically analyzed. The distribution of large fragments represents the fragmentation effect. A database linking delay time and fragmentation effect was established.

[0008] Improved linear superposition calibration: Fourier series decomposition of the measured single-hole waveform is used, random variables are added to generate a simulated waveform, and the average theoretical peak particle velocity (PPV) under different delay times is calculated by Monte Carlo simulation. The measured peak particle velocity (PPV) of the data acquisition instrument is compared with the theoretical value. If the deviation exceeds the threshold (±5%), the frequency response correction coefficient or amplitude gain parameter of the data acquisition instrument is adjusted to make the two trends consistent.

[0009] Rock crushing effect verification: Regression analysis was performed on the calibrated vibration data and the rock crushing effect (large block quantity) to verify whether the correlation between vibration parameters (such as PPV, main frequency) and crushing quality was consistent with the report conclusion (such as the minimum large block quantity when the delay time is 40ms). If the crushing effect and vibration parameters do not match, the time integration algorithm of the acquisition instrument will be further adjusted.

[0010] Stability and repeatability test: Repeat the blasting under the same delay time to check the repeatability error of the data acquisition instrument (standard deviation required <3%), analyze the impact of background noise (such as wind noise, mechanical vibration) on the acquired data, and optimize the signal quality through wavelet denoising or adaptive filtering algorithm;

[0011] Calibration result output: Establish a calibration curve of delay time-vibration parameters-fracture effect, and record calibration parameters (such as gain coefficient, filter settings), error range and applicable conditions.

[0012] The above further includes:

[0013] Furthermore, the process of using a data acquisition instrument to record vibration waveform data (including peak particle velocity (PPV), dominant frequency, duration, etc.), simultaneously statistically analyzing the distribution of large fragments after blasting, and establishing a correlation database between delay time and fragmentation effect includes the following steps:

[0014] Experimental Design and Data Acquisition: Blasting was carried out at different delay times, and vibration waveforms and fragmentation effects were recorded simultaneously. The acquisition instrument recorded the vibration waveforms (PPV, main frequency, duration), and the large fragment rate was manually calculated.

[0015] Vibration waveform analysis and feature extraction: The peak particle velocity (PPV) and duration are calculated using formulas to transform the time-domain waveform into numerical features. The dominant frequency reflects the energy concentration frequency, and the duration reflects the vibration decay rate. The formula for calculating the peak particle velocity (PPV) is... The dominant frequency is obtained by analyzing the waveform using Fast Fourier Transform (FFT) and taking the frequency with the maximum energy. The duration represents the total time during which the vibration velocity exceeds the threshold (0.1 m / s).

[0016] Establish a correlation model between delay time and fracture effect: Correlation analysis confirms the causal relationship between delay time and vibration / fracture, and the Pearson correlation coefficient between delay time and fracture effect parameters is calculated. The calculation formula is expressed as follows: ,in, and These represent the actual and predicted values ​​of the delay time, respectively. and The actual and predicted values ​​of the bulk quantity are represented respectively. A regression model of the bulk quantity and vibration parameters is established, and the weights in the regression model are fitted by the least squares method.

[0017] Furthermore, the method of using Fourier series decomposition of the measured single-hole waveform, adding random variables to generate a simulated waveform, and calculating the average theoretical peak particle velocity (PPV) under different delay times through Monte Carlo simulation includes the following steps:

[0018] Measured Single-Hole Waveform Acquisition and Fourier Series Decomposition: The vibration waveform of a single-hole blast was recorded using a blasting vibration acquisition instrument. The sampling frequency is The duration is The measured waveform is decomposed into a Fourier series and expressed as follows: ,in, The fundamental angular frequency, , ;

[0019] Adding random variables to generate simulated waveforms: Adding random phase to each frequency component of the Fourier decomposition. The generated simulated waveform is represented as ,in, ;

[0020] Synthesized waveform: Define the delay time The synthesized waveform is represented as ;

[0021] Calculate the mean peak velocity (PPV) of particles: Calculate the composite waveform The peak vibration velocity is repeatedly added to generate simulated waveforms, synthesized waveforms, and the synthesized waveform is calculated. The peak velocity M times is calculated, and the average peak velocity (PPV) of the particle is the theoretical average peak velocity (PPV).

[0022] Furthermore, the specific steps for comparing the measured peak particle velocity (PPV) of the data acquisition instrument with the theoretical value and adjusting the frequency response correction coefficient or amplitude gain parameter of the data acquisition instrument are as follows:

[0023] Theoretical value: The average peak velocity (PPV) of theoretical particles is obtained;

[0024] Measured values ​​were obtained by recording the blasting vibration signal with a data acquisition instrument and extracting the peak particle velocity.

[0025] Deviation calculation: Calculate the relative deviation between the measured value and the theoretical value. The calculation formula is expressed as follows: ,in, This is the theoretical average peak particle velocity (PPV). These are measured values; if the deviation exceeds [a certain value], [the value will be considered as such]. Adjust the parameters;

[0026] Parameter adjustment: If the measured value is too high, the frequency response correction factor (FRC) needs to be increased; otherwise, it should be decreased; if the measured value is generally too low, the amplitude gain parameter (G) should be increased to compensate for signal attenuation.

[0027] Post-adjustment verification: Repeat the acquisition of measured values, deviation calculation, and parameter adjustment until... .

[0028] Furthermore, the specific steps for performing regression analysis between the calibrated vibration data and the rock fracturing effect (large quantity), and for adjusting the time integration algorithm or energy calculation model of the data acquisition instrument if the fracturing effect does not match the vibration parameters, are as follows:

[0029] Regression analysis to verify correlation: Verify whether the correlation between vibration parameters (PPV, main frequency) and crushing mass (large block quantity) is consistent with the report conclusion. Calculate the correlation coefficient and significance of the regression modulus between large block quantity and vibration parameters based on the real-time collected data.

[0030] Check the vibration parameter calculation model: If the correlations are contradictory, check the vibration parameter calculation model.

[0031] Adjust the time integration algorithm: perform high-pass filtering on the original acceleration signal and re-integrate to calculate the peak particle velocity (PPV).

[0032] Optimize the energy calculation model: Introduce a frequency-weighted energy model, expressed as follows: E, of which Power spectral density (reflecting energy distribution) For frequency weighting functions (such as enhancing high-frequency contributions), recalculate the energy of each frequency band and perform regression analysis with bulk data;

[0033] Further regression analysis was performed to verify the results: using the corrected peak particle velocity (PPV) and frequency-weighted energy as independent variables and the proportion of large particles as the dependent variable, a second regression analysis was conducted to verify the results.

[0034] Furthermore, the vibration parameter calculation model includes the following steps:

[0035] Preliminary assumptions and verification: Based on the Sadovsky formula, verify whether the peak particle velocity (PPV) decreases with distance and whether the dominant frequency matches the rock mass properties. If the trend of PPV and dominant frequency does not match the expectations (e.g., PPV increases but the mass of large particles does not decrease), then further check the calculation model.

[0036] Signal processing algorithm review: Check whether the sensor sensitivity matches the data acquisition instrument settings, verify whether the sensor frequency response range covers the main frequency of the blasting vibration, and check whether an inappropriate filter was used (low-pass filtering caused loss of high-frequency components).

[0037] Numerical simulation and comparison: Single-hole blasting vibration was simulated using numerical software (LS-DYNA) to generate theoretical acceleration signals. The same signal processing procedure as the actual measurement was applied to the theoretical signals to calculate the peak particle velocity (PPV) and dominant frequency, and the differences between the theoretical and measured values ​​were compared.

[0038] Furthermore, the specific steps for verifying the repeatability error of the data acquisition instrument, analyzing the impact of background noise on the acquired data, and optimizing signal quality through wavelet denoising or adaptive filtering algorithms are as follows:

[0039] Repeatability error test: Repeat the blasting under the same delay time to verify whether the repeatability error of the data acquisition instrument meets the standard deviation. Requirements;

[0040] Background noise impact analysis: Record the background noise signal during the non-blasting period, record the blasting signal and the noisy signal, calculate the root mean square value of the noise and the root mean square value of the signal, and calculate the signal-to-noise ratio based on the root mean square values ​​of the noise and the root mean square value of the signal.

[0041] Wavelet Denoising: Selection Wavelet, Decomposition Level The noisy signal is decomposed into multiple layers to obtain approximation coefficients and detail coefficients, and a soft threshold is applied. ( (where N is the noise standard deviation and N is the signal length). Threshold shrinkage is applied to the detail coefficients (high-frequency noise), and the denoised signal is reconstructed using the approximation coefficients and the processed detail coefficients.

[0042] Adaptive filtering: Using the statistical characteristics of the background noise signal as a reference input, the filter weights are updated, noise is estimated, and the updated background noise signal is output to obtain the noisy signal.

[0043] Furthermore, the specific steps for establishing the calibration curve of delay time-vibration parameters-fracture effect, and recording calibration parameters (such as gain coefficient, filter settings), error range, and applicable conditions are as follows:

[0044] Data acquisition and processing: Collect vibration data for each blast and calculate the maximum blast vibration velocity at the measuring point;

[0045] Analyze the fragmentation effect: Observe and measure the rock after blasting to evaluate the fragmentation effect.

[0046] Establish calibration curves: Plot the calibration curves with the delay time as the x-axis and vibration parameters (such as maximum blasting vibration velocity) and crushing effect indicators (such as fragment size) as the y-axis.

[0047] The present invention has the following beneficial effects:

[0048] In this invention, by synchronously collecting vibration waveforms and the distribution of large blocks after blasting, a database linking delay time, vibration parameters, and crushing effect is established to clarify the causal relationship between vibration characteristics and crushing quality. An improved linear superposition method is adopted, combined with Fourier series decomposition and Monte Carlo simulation, to generate a large number of simulated waveforms to calculate the theoretical PPV mean, making the calibration benchmark closer to the real physical process. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the calibration method based on a blasting vibration acquisition instrument proposed in this invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 As shown, this invention is a calibration method based on a blasting vibration acquisition instrument, comprising the following steps:

[0052] Data acquisition: Blasting tests were conducted at different delay times. Vibration waveform data (including peak particle velocity (PPV), dominant frequency, duration, etc.) were recorded using a data acquisition instrument. The distribution of large fragments after blasting was statistically analyzed. The distribution of large fragments represents the fragmentation effect. A database linking delay time and fragmentation effect was established.

[0053] Improved linear superposition calibration: Fourier series decomposition of the measured single-hole waveform is used, random variables are added to generate a simulated waveform, and the average theoretical peak particle velocity (PPV) under different delay times is calculated by Monte Carlo simulation. The measured peak particle velocity (PPV) of the data acquisition instrument is compared with the theoretical value. If the deviation exceeds the threshold (±5%), the frequency response correction coefficient or amplitude gain parameter of the data acquisition instrument is adjusted to make the two trends consistent.

[0054] Rock crushing effect verification: Regression analysis was performed on the calibrated vibration data and the rock crushing effect (large block quantity) to verify whether the correlation between vibration parameters (such as PPV, main frequency) and crushing quality was consistent with the report conclusion (such as the minimum large block quantity when the delay time is 40ms). If the crushing effect and vibration parameters do not match, the time integration algorithm of the acquisition instrument will be further adjusted.

[0055] Stability and repeatability test: Repeat the blasting under the same delay time to check the repeatability error of the data acquisition instrument (standard deviation required <3%), analyze the impact of background noise (such as wind noise, mechanical vibration) on the acquired data, and optimize the signal quality through wavelet denoising or adaptive filtering algorithm;

[0056] Calibration result output: Establish a calibration curve of delay time-vibration parameters-fracture effect, and record calibration parameters (such as gain coefficient, filter settings), error range and applicable conditions.

[0057] In one embodiment, the process of using a data acquisition device to record vibration waveform data (including peak particle velocity (PPV), dominant frequency, duration, etc.), simultaneously statistically analyzing the distribution of large fragments after blasting, and establishing a database linking delay time and fragmentation effect includes the following steps:

[0058] Experimental Design and Data Acquisition: Blasting was carried out at different delay times, and vibration waveforms and fragmentation effects were recorded simultaneously. The acquisition instrument recorded the vibration waveforms (PPV, main frequency, duration), and the large fragment rate was manually calculated.

[0059] Test conditions:

[0060] Rock type: Granite (uniaxial compressive strength 80 MPa)

[0061] Hole diameter: φ100mm, hole depth: 12m, hole spacing: 3m

[0062] Charge: 40kg per hole, total charge 160kg (4 holes)

[0063] Measurement point setup: A vibration data acquisition device (three-dimensional velocity sensor, sampling rate 2kHz) is set up 20m away from the blast source.

[0064] Delay time settings:

[0065] Four sets of experiments: delay times of 0ms (simultaneous firing), 25ms, 50ms, and 75ms respectively.

[0066] Data acquisition parameters:

[0067] Vibration parameters: peak particle velocity (PPV, m / s), dominant frequency (Hz), duration (ms)

[0068] Fragmentation effect parameters: percentage of large blocks after blasting (percentage of rock blocks with a volume > 0.5 m³);

[0069] Vibration waveform analysis and feature extraction: The peak particle velocity (PPV) and duration are calculated using formulas to transform the time-domain waveform into numerical features. The dominant frequency reflects the energy concentration frequency, and the duration reflects the vibration decay rate. The formula for calculating the peak particle velocity (PPV) is... The dominant frequency is obtained by analyzing the waveform using Fast Fourier Transform (FFT) and taking the frequency with the maximum energy. The duration represents the total time during which the vibration velocity exceeds the threshold (0.1 m / s).

[0070] Actual measurement data:

[0071]

[0072] Establish a correlation model between delay time and fracture effect: Correlation analysis confirms the causal relationship between delay time and vibration / fracture, and the Pearson correlation coefficient between delay time and fracture effect parameters is calculated. The calculation formula is expressed as follows: ,in, and These represent the actual and predicted values ​​of the delay time, respectively. and The actual and predicted values ​​of the bulk quantity are represented respectively. A regression model of the bulk quantity and vibration parameters is established, and the weights in the regression model are fitted by the least squares method.

[0073] In one embodiment, the step of using Fourier series decomposition of the measured single-hole waveform, adding random variables to generate a simulated waveform, and calculating the average theoretical peak particle velocity (PPV) under different delay times through Monte Carlo simulation includes the following steps:

[0074] Measured Single-Hole Waveform Acquisition and Fourier Series Decomposition: The vibration waveform of a single-hole blast was recorded using a blasting vibration acquisition instrument. The sampling frequency is The duration is The measured waveform is decomposed into a Fourier series and expressed as follows: ,in, The fundamental angular frequency, , ;

[0075] Adding random variables to generate simulated waveforms: Adding random phase to each frequency component of the Fourier decomposition. The generated simulated waveform is represented as ,in, ;

[0076] Synthesized waveform: Define the delay time The synthesized waveform is represented as ;

[0077] Calculate the mean peak velocity (PPV) of particles: Calculate the composite waveform The peak vibration velocity is repeatedly added to generate simulated waveforms, synthesized waveforms, and the synthesized waveform is calculated. The peak velocity M times is calculated, and the average peak velocity (PPV) of the particle is the theoretical average peak velocity (PPV).

[0078] In one embodiment, the specific steps for comparing the measured peak particle velocity (PPV) of the data acquisition instrument with the theoretical value and adjusting the frequency response correction coefficient or amplitude gain parameter of the data acquisition instrument are as follows:

[0079] Theoretical value: The average peak velocity (PPV) of theoretical particles is obtained;

[0080] Measured values ​​were obtained by recording the blasting vibration signal with a data acquisition instrument and extracting the peak particle velocity.

[0081] Deviation calculation: Calculate the relative deviation between the measured value and the theoretical value. The calculation formula is expressed as follows: ,in, This is the theoretical average peak particle velocity (PPV). These are measured values; if the deviation exceeds [a certain value], [the value will be considered as such]. Adjust the parameters;

[0082] Parameter adjustment: If the measured value is too high, the frequency response correction factor (FRC) needs to be increased; otherwise, it should be decreased; if the measured value is generally too low, the amplitude gain parameter (G) should be increased to compensate for signal attenuation.

[0083] Post-adjustment verification: Repeat the acquisition of measured values, deviation calculation, and parameter adjustment until... .

[0084] In one embodiment, the specific steps for performing regression analysis between the calibrated vibration data and the rock fracturing effect (large quantity), and further adjusting the time integration algorithm or energy calculation model of the data acquisition instrument if the fracturing effect does not match the vibration parameters, are as follows:

[0085] Regression analysis to verify correlation: Verify whether the correlation between vibration parameters (PPV, main frequency) and crushing mass (large block quantity) is consistent with the report conclusion. Calculate the correlation coefficient and significance of the regression modulus between large block quantity and vibration parameters based on the real-time collected data.

[0086] Actual measurement data:

[0087]

[0088] Regression analysis showed that PPV was positively correlated with crushing quality (p<0.05), and dominant frequency was negatively correlated with crushing quality (p<0.05), but the amount of large pieces was the smallest when the delay time was 40ms (consistent with the report's conclusion).

[0089] If the report concludes that "the number of large pieces is the smallest when the delay time is 40ms", but regression analysis shows that PPV is positively correlated with crushing quality (i.e., the larger the PPV, the fewer the large pieces), while in the actual data, PPV is not the largest at 40ms (16.8cm / s<18.1cm / s), further verification is needed;

[0090] Check the vibration parameter calculation model: If the correlations are contradictory, check the vibration parameter calculation model.

[0091] Adjust the time integration algorithm: perform high-pass filtering on the original acceleration signal and re-integrate to calculate the peak particle velocity (PPV).

[0092] Instance adjustments:

[0093] After applying baseline correction to the original data, recalculate the PPV:

[0094]

[0095] The PPV with a delay of 40 ms decreased from 16.8 cm / s to 15.5 cm / s, but it is still not the maximum value. Further adjustments to the energy model are needed.

[0096] Optimize the energy calculation model: Introduce a frequency-weighted energy model, expressed as follows: E, of which Power spectral density (reflecting energy distribution) For frequency weighting functions (such as enhancing high-frequency contributions), recalculate the energy of each frequency band and perform regression analysis with bulk data;

[0097] Further regression analysis was performed to verify the results: using the corrected peak particle velocity (PPV) and frequency-weighted energy as independent variables and the proportion of large particles as the dependent variable, a second regression analysis was conducted to verify the results.

[0098] In one embodiment, the inspection of the vibration parameter calculation model includes the following steps:

[0099] Preliminary assumptions and verification: Based on the Sadovsky formula, verify whether the peak particle velocity (PPV) decreases with distance and whether the dominant frequency matches the rock mass properties. If the trend of PPV and dominant frequency does not match the expectations (e.g., PPV increases but the mass of large particles does not decrease), then further check the calculation model.

[0100] Signal processing algorithm review: Check whether the sensor sensitivity matches the data acquisition instrument settings, verify whether the sensor frequency response range covers the main frequency of the blasting vibration, and check whether an inappropriate filter was used (low-pass filtering caused loss of high-frequency components).

[0101] Numerical simulation and comparison: Single-hole blasting vibration was simulated using numerical software (LS-DYNA) to generate theoretical acceleration signals. The same signal processing procedure as the actual measurement was applied to the theoretical signals to calculate the peak particle velocity (PPV) and dominant frequency, and the differences between the theoretical and measured values ​​were compared.

[0102] In one embodiment, the specific steps for verifying the repeatability error of the data acquisition instrument, analyzing the impact of background noise on the acquired data, and optimizing signal quality through wavelet denoising or adaptive filtering algorithms are as follows:

[0103] Repeatability error test: Repeat the blasting under the same delay time to verify whether the repeatability error of the data acquisition instrument meets the standard deviation. Requirements;

[0104] Background noise impact analysis: Record the background noise signal during the non-blasting period, record the blasting signal and the noisy signal, calculate the root mean square value of the noise and the root mean square value of the signal, and calculate the signal-to-noise ratio based on the root mean square values ​​of the noise and the root mean square value of the signal.

[0105] Wavelet Denoising: Selection Wavelet, Decomposition Level The noisy signal is decomposed into multiple layers to obtain approximation coefficients and detail coefficients, and a soft threshold is applied. ( (where N is the noise standard deviation and N is the signal length). Threshold shrinkage is applied to the detail coefficients (high-frequency noise), and the denoised signal is reconstructed using the approximation coefficients and the processed detail coefficients.

[0106] Adaptive filtering: Using the statistical characteristics of the background noise signal as a reference input, the filter weights are updated, noise is estimated, and the updated background noise signal is output to obtain the noisy signal.

[0107] In one embodiment, the specific steps for establishing the calibration curve of delay time-vibration parameters-fracture effect, and recording calibration parameters (such as gain coefficient, filter settings), error range, and applicable conditions are as follows:

[0108] Data acquisition and processing: Collect vibration data for each blast and calculate the maximum blast vibration velocity at the measuring point;

[0109] Analyze the fragmentation effect: Observe and measure the rock after blasting to evaluate the fragmentation effect.

[0110] Establish calibration curves: Plot the calibration curves with the delay time as the x-axis and vibration parameters (such as maximum blasting vibration velocity) and crushing effect indicators (such as fragment size) as the y-axis.

[0111] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A calibration method based on a blasting vibration acquisition instrument, characterized in that, Includes the following steps: Data Acquisition: Blasting tests were conducted at different delay times. Vibration waveform data was recorded using a data acquisition instrument. The distribution of large fragments after blasting was statistically analyzed simultaneously. The distribution of large fragments represents the fragmentation effect. A database linking delay time and fragmentation effect was established, including the following steps: Experimental design and data acquisition: Explosions were performed at different delay times, and vibration waveforms and fragmentation effects were recorded simultaneously. The data acquisition instrument recorded the vibration waveforms, and the percentage of large pieces was manually calculated. Vibration waveform analysis and feature extraction: The peak velocity and duration of the particle are calculated using formulas, transforming the time-domain waveform into numerical features. The dominant frequency reflects the energy concentration frequency, and the duration reflects the vibration decay rate. The formula for calculating the peak velocity of the particle is... The dominant frequency is obtained by analyzing the waveform through Fast Fourier Transform and taking the frequency with the maximum energy. The duration represents the total time for the vibration velocity to exceed the threshold. Establish a correlation model between delay time and crushing effect: Determine the causal relationship between delay time and vibration / crushing through correlation analysis, and calculate the Pearson correlation coefficient between delay time and crushing effect parameters. The calculation formula is expressed as follows: ,in, and These represent the actual and predicted values ​​of the delay time, respectively. and The actual and predicted values ​​of the bulk quantity are represented respectively. A regression model of the bulk quantity and vibration parameters is established, and the weights in the regression model are fitted by the least squares method. Improved linear superposition calibration: The measured single-hole waveform is decomposed using Fourier series, and a simulated waveform is generated by adding random variables. The average peak velocity of the theoretical particle is calculated using Monte Carlo simulation at different delay times. The measured peak velocity of the particle is compared with the theoretical value. If the deviation exceeds a threshold, the frequency response correction coefficient or amplitude gain parameter of the acquisition instrument is adjusted to make the two trends consistent. Specific steps are as follows: Theoretical value: The average peak velocity of the theoretical particle is obtained; Measured values ​​were obtained by recording the blasting vibration signal with a data acquisition instrument and extracting the peak particle velocity. Deviation calculation: Calculate the relative deviation between the measured value and the theoretical value. The calculation formula is expressed as follows: ,in, This represents the average peak velocity of the theoretical particle. These are measured values; if there is a deviation... Adjust the parameters; Parameter adjustment: If the measured value is too high, the frequency response correction coefficient needs to be increased; otherwise, it should be decreased; if the measured value is generally too low, the amplitude gain parameter should be increased to compensate for signal attenuation. Post-adjustment verification: Repeat the acquisition of measured values, deviation calculation, and parameter adjustment until... ; Rock fracturing effect verification: Regression analysis was performed between the calibrated vibration data and the rock fracturing effect. If the fracturing effect did not match the vibration parameters, the specific steps of adjusting the time integration algorithm of the data acquisition instrument were further investigated. Regression analysis to verify correlation: Verify whether the correlation between vibration parameters and crushing quality is consistent with the report conclusions. Calculate the correlation coefficient and significance of the regression coefficient between the large block quantity and vibration parameters based on the real-time collected data. Check the vibration parameter calculation model: If the correlations are contradictory, check the vibration parameter calculation model. Adjust the time integration algorithm: perform high-pass filtering on the original acceleration signal and re-integrate to calculate the peak particle velocity; Optimize the energy calculation model: Introduce a frequency-weighted energy model, expressed as follows: ,in, For power spectral density, The frequency weighting function is used to recalculate the energy of each frequency band and perform regression analysis with the bulk data. Regression analysis was performed again to verify the results: using the corrected peak particle velocity and frequency-weighted energy as independent variables and the proportion of large particles as dependent variables, regression analysis was performed again to verify the results. Stability and repeatability test: Repeated blasting under the same delay time to check the repeatability error of the data acquisition instrument, analyze the impact of background noise on the acquired data, and optimize signal quality through wavelet denoising or adaptive filtering algorithms; Calibration result output: Establish a calibration curve of delay time-vibration parameters-fracture effect, and record calibration parameters, error range and applicable conditions.

2. The calibration method based on a blasting vibration acquisition instrument according to claim 1, characterized in that, The method of using Fourier series decomposition of the measured single-hole waveform, adding random variables to generate a simulated waveform, and calculating the average peak velocity of theoretical particles under different delay times through Monte Carlo simulation includes the following steps: Measured Single-Hole Waveform Acquisition and Fourier Series Decomposition: The vibration waveform of a single-hole blast was recorded using a blasting vibration acquisition instrument. The sampling frequency is The duration is The measured waveform is decomposed into a Fourier series and expressed as follows: ,in, The fundamental angular frequency, , ; Adding random variables to generate simulated waveforms: Adding random phase to each frequency component of the Fourier decomposition. The generated simulated waveform is represented as ,in, ; Synthesized waveform: Define the delay time The synthesized waveform is represented as ; Calculate the mean peak velocity of the particles: Calculate the composite waveform The peak vibration velocity is repeatedly added to generate simulated waveforms, synthesized waveforms, and the synthesized waveform is calculated. The peak velocity M times is calculated, and the average peak velocity of the particle is the theoretical average peak velocity of the particle.

3. The calibration method based on a blasting vibration acquisition instrument according to claim 1, characterized in that, The vibration parameter calculation model includes the following steps: Preliminary assumptions and verification: Based on the Sadovsky formula, verify whether the peak velocity of the particles decreases with distance and whether the dominant frequency matches the rock mass properties. If the trend of the peak velocity and dominant frequency of the particles does not match the expectations, further check the calculation model. Signal processing algorithm review: Check whether the sensor sensitivity matches the data acquisition instrument settings, verify whether the sensor frequency response range covers the main frequency of the blasting vibration, and check whether an inappropriate filter was used; Numerical simulation and comparison: Single-hole blasting vibration was simulated using numerical software to generate theoretical acceleration signals. The same signal processing procedure as the actual measurement was applied to the theoretical signals to calculate the peak particle velocity and dominant frequency, and the differences between the theoretical and measured values ​​were compared.

4. The calibration method based on a blasting vibration acquisition instrument according to claim 1, characterized in that, The specific steps for verifying the repeatability error of the data acquisition instrument, analyzing the impact of background noise on the acquired data, and optimizing signal quality through wavelet denoising or adaptive filtering algorithms are as follows: Repeatability error test: Repeat the blasting under the same delay time to verify whether the repeatability error of the data acquisition instrument meets the standard deviation. Requirements; Background noise impact analysis: Record the background noise signal during the non-blasting period, record the blasting signal and the noisy signal, calculate the root mean square value of the noise and the root mean square value of the signal, and calculate the signal-to-noise ratio based on the root mean square values ​​of the noise and the root mean square value of the signal. Wavelet Denoising: Selection Wavelet, Decomposition Level The noisy signal is decomposed into multiple layers to obtain approximation coefficients and detail coefficients, and a soft threshold is applied. Thresholding shrinkage is applied to the detail coefficients, and the denoised signal is reconstructed using the approximation coefficients and the processed detail coefficients. Adaptive filtering: Using the statistical characteristics of the background noise signal as a reference input, the filter weights are updated, noise is estimated, and the updated background noise signal is output to obtain the noisy signal.

5. The calibration method based on a blasting vibration acquisition instrument according to claim 1, characterized in that, The specific steps for establishing the calibration curve of delay time-vibration parameters-fracture effect, and recording calibration parameters, error range, and applicable conditions are as follows: Data acquisition and processing: Collect vibration data for each blast and calculate the maximum blast vibration velocity at the measuring point; Analyze the fracturing effect: Observe and measure the rock after blasting to evaluate the fracturing effect; Establish calibration curves: Plot calibration curves with delay time as the x-axis and vibration parameters and crushing effect indicators as the y-axis.

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