Blasting Signal Detection Method and Device Based on Modal Decomposition

Abstract

The present invention relates to the technical field of tunnel blasting, and particularly relates to a blasting signal detection method and device based on modal decomposition. The method includes obtaining a blasting signal of tunnel blasting through a data acquisition instrument and using the blasting signal as an original signal; performing decomposition processing on the original signal by using an optimized modal decomposition model and classifying the obtained signal components into a signal component set; and performing signal reconstruction according to the signal component set to obtain a processed blasting signal. The device includes a data acquisition instrument, a signal processing module, and a signal reconstruction module; the signal processing module is respectively connected to the data acquisition instrument and the signal reconstruction module, and the signal processing module is internally provided with an optimized modal decomposition model and a signal component set, and is used to implement the foregoing blasting signal detection method based on modal decomposition. By using the optimized modal decomposition model, the present invention avoids problems such as endpoint effect, modal aliasing, and false components, making the blasting signal modal clear and improving the quality of the blasting signal.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel blasting, and particularly relates to a blasting signal detection method and device based on modal decomposition. Background Art

[0002] Blasting vibration testing is an important part of blasting construction. On the one hand, the blasting method and parameters affect the vibration intensity of blasting earthquakes. By monitoring, it can guide blasting construction and improve blasting construction efficiency. On the other hand, it can ensure the safety of the protected objects (personnel and buildings around the work area), avoid disputes, and bring benefits to the enterprise. Blasting vibration testing equipment mainly monitors the three component values of the particle vibration velocity, the main vibration frequency, and the attenuation change curve of the vibration velocity with time.

[0003] At present, there are huge drawbacks in the processing of vibration test signals for blasting construction, such as problems like endpoint effects, modal aliasing, and spurious components. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a blasting signal detection method based on modal decomposition, including:

[0005] S100: Obtain the blasting signal of tunnel blasting through a data acquisition instrument, and use the blasting signal as the original signal;

[0006] S200: Use an optimized modal decomposition model to decompose the original signal, and classify the decomposed signal components into a signal component set;

[0007] S300: Perform signal reconstruction according to the signal component set to obtain the processed blasting signal.

[0008] Optionally, in step S200, the optimized modal decomposition model adopts the complementary ensemble empirical mode decomposition algorithm, and the decomposition processing method for the original signal is as follows:

[0009] S210: Add different white noises and the difference signals between the original signal and the corresponding white noises to the original signal respectively to obtain multiple types of aliased signals of two types;

[0010] S220: Perform modal decomposition on the first type of aliased signal obtained by adding white noise, and classify the obtained signal components into the first signal set;

[0011] S230: Perform modal decomposition on the second type of aliased signal obtained by adding the difference signal, and classify the obtained signal components into the second signal set;

[0012] S240: Divide the signal components in the first signal set and the second signal set that are related to the same white noise into a group, calculate the average value of each group of signal components, and the obtained signal components are put into the signal component set.

[0013] Optionally, in step S300, the signal reconstruction is performed as follows:

[0014] S310: Use the signal components in the signal component set as dimension vectors, establish a signal component matrix, initialize it to obtain an atom matrix and a residual, and calculate the norm of the residual;

[0015] S320: Determine the number of vectors according to the norm of the residual, and select column vectors in the signal component matrix;

[0016] S330: Use the union of the column vectors and the atom matrix as a fusion matrix, and calculate the least squares value of the fusion matrix and the dimension vector;

[0017] S340: Correct the residual according to the dimension vector, the fusion matrix and the least squares value to obtain a corrected residual and a corrected norm;

[0018] S350: If the corrected norm is not lower than the preset norm threshold, execute S320; otherwise, execute S360;

[0019] S360: Use the least squares value as the reconstructed signal, and reconstruct the dimension vector at the corresponding scale to form a new blasting signal.

[0020] Optionally, initialize the generator for generating white noise. The initialization method is as follows:

[0021] Start the generator, collect the white noise generated by the generator, and extract the waveform parameters of the white noise;

[0022] Compare the extracted waveform parameters with the noise waveform setting parameters. If the deviation exceeds the set range, calculate the ratio of the difference between each parameter and the corresponding waveform parameter, and adjust the state of the white noise generator according to the ratio until the deviation is within the set range.

[0023] The present invention also provides a blasting signal detection device based on modal decomposition, including a data acquisition instrument, a signal processing module and a signal reconstruction module;

[0024] The data acquisition instrument is used to obtain the blasting signal of tunnel blasting and use the blasting signal as the original signal;

[0025] The signal processing module is respectively connected to the data acquisition instrument and the signal reconstruction module. The signal processing module is built-in with an optimized modal decomposition model and a signal component set. The modal decomposition model is used to decompose the original signal, and the decomposed signal components are classified into the signal component set;

[0026] The signal reconstruction module is used to perform signal reconstruction according to the signal component set to obtain the processed blasting signal.

[0027] Optionally, the data acquisition instrument includes multiple acquisition channels, and the maximum sampling rate of each acquisition channel is not less than 50 KSps.

[0028] Optionally, the data acquisition instrument is built-in with an adaptive module, and the adaptive module is used to automatically adapt to the signal intensity under the condition of setting the automatic trigger frequency and sampling time.

[0029] Optionally, the signal processing module is connected with a camera and a display screen; the camera is used to take blasting images; the signal processing module is built-in with an analog sub-module, and the analog sub-module is used to construct an analog waveform according to the blasting signal; the display screen is used to display the blasting images and the analog waveform of the blasting signal.

[0030] Optionally, the signal processing module is connected with a correction module and a barometric temperature sensitive element. The barometric temperature sensitive element is used to detect the ambient air pressure and ambient temperature. The correction module corrects the original signal according to the ambient air pressure and ambient temperature, and transmits the corrected original signal to the modal decomposition model.

[0031] Optionally, the detection device further includes a rechargeable lithium battery and a power management module. The power management module includes a first power input terminal, a second power input terminal and a power output terminal. The first power input terminal is connected to the rechargeable lithium battery, the second power input terminal is used to connect to an external power source, and the power output terminal is used to supply power to the data acquisition instrument, the signal processing module and the signal reconstruction module;

[0032] The power management module is built-in with a power regulation circuit. The power regulation circuit includes a regulation module, a field effect transistor Q1, a voltage regulation module, a capacitor C1, an amplifier U1, an error amplifier U2, a variable resistor R1, a variable resistor R2 and a variable resistor R3.

[0033] For the blasting signal detection method and device based on modal decomposition of the present invention, the collected blasting signal is used as the original signal, which is decomposed by an optimized modal decomposition model to obtain signal components, and a signal component set is constructed, and signal reconstruction is performed with the signal component set to obtain the processed blasting signal; this solution avoids problems such as endpoint effect, modal aliasing and spurious components by using an optimized modal decomposition model, making the blasting signal modal clear and improving the quality of the blasting signal.

[0034] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification, claims, and drawings.

[0035] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention. In the accompanying drawings:

[0037] Figure 1 is a flowchart of a blasting signal detection method based on modal decomposition in an embodiment of the present invention;

[0038] Figure 2 is a decomposition processing flowchart adopted in the embodiment of the blasting signal detection method based on modal decomposition of the present invention;

[0039] Figure 3 is a signal reconstruction flowchart adopted in the embodiment of the blasting signal detection method based on modal decomposition of the present invention;

[0040] Figure 4 is a schematic diagram of a blasting signal detection device based on modal decomposition in an embodiment of the present invention;

[0041] Figure 5 is a schematic diagram of a power regulation circuit built in a power management module adopted in the embodiment of the blasting signal detection device based on modal decomposition of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for explaining and understanding the present invention, and are not used to limit the present invention.

[0043] As Figure 1 shown, the embodiment of the present invention provides a blasting signal detection method based on modal decomposition, including:

[0044] S100: Obtain the blasting signal of tunnel blasting through a data acquisition instrument, and use the blasting signal as the original signal;

[0045] S200: Use an optimized modal decomposition model to decompose the original signal, and classify the decomposed signal components into a signal component set;

[0046] S300: Reconstruct the signal according to the signal component set to obtain the processed blasting signal.

[0047] The working principle and beneficial effects of the above technical solution are as follows: In this solution, the collected blasting signal is used as the original signal, which is decomposed by an optimized modal decomposition model to obtain signal components. These signal components are constructed into a signal component set, and signal reconstruction is performed using the signal component set to obtain the processed blasting signal. By adopting the optimized modal decomposition model, this solution avoids problems such as endpoint effects, modal aliasing, and spurious components, making the blasting signal modality clear and improving the quality of the blasting signal.

[0048] In one embodiment, as Figure 2 shown, in step S200, the optimized modal decomposition model uses the complementary ensemble empirical mode decomposition algorithm to decompose the original signal in the following manner:

[0049] S210: Different white noises and the difference signals between the original signal and the corresponding white noises are respectively added to the original signal to obtain multiple types of aliased signals of two kinds;

[0050] S220: The first type of aliased signal obtained by adding white noise is subjected to modal decomposition, and the obtained signal components are classified into the first signal set;

[0051] S230: The second type of aliased signal obtained by adding the difference signal is subjected to modal decomposition, and the obtained signal components are classified into the second signal set;

[0052] S240: The signal components in the first signal set and the second signal set that are related to the same white noise are grouped together, and the average value of each group of signal components is calculated. The obtained signal components are put into the signal component set.

[0053] The working principle and beneficial effects of the above technical solution are as follows: The white noise in this solution refers to the noise whose power spectral density is constant throughout the frequency domain, that is, the random noise with the same energy density at all frequencies is called white noise. By adding different white noises to the original signal in this solution, for example, adding n times, that is, the ensemble average number is n; modal decomposition is respectively performed on the signals added with white noise and the difference signal, and the average value is used to obtain the final signal component set. The ensemble average number of this solution will be reduced, the noise of the reconstructed signal is significantly reduced, the data processing amount is reduced, the data processing speed and efficiency are improved, and it meets the needs of blasting signal detection.

[0054] In one embodiment, as Figure 3 shown, in step S300, the signal reconstruction method is as follows:

[0055] S310: The signal components in the signal component set are used as dimension vectors to establish a signal component matrix, which is initialized to obtain an atom matrix and a residual, and the norm of the residual is calculated;

[0056] S320: Determine the number of vectors according to the norm of the residual, and select column vectors from the signal component matrix;

[0057] S330: Use the union of the column vectors and the atom matrix as the fusion matrix, and calculate the least squares value of the fusion matrix and the dimension vector;

[0058] S340: Correct the residual according to the dimension vector, the fusion matrix and the least squares value to obtain the corrected residual and the corrected norm;

[0059] S350: If the corrected norm is not lower than the preset norm threshold, execute S320; otherwise, execute S360;

[0060] S360: Use the least squares value as the reconstructed signal, and reconstruct the dimension vector at the corresponding scale to form a new burst signal.

[0061] The working principle and beneficial effects of the above technical solution are as follows: By vectorizing the signal components and constructing a vector matrix, this solution realizes the digital processing of signals, improving the accuracy of signal processing; introducing the concepts of residual and norm, using data fusion technology to perform least squares value correction, and using the norm to evaluate the digital processing effect. After meeting the requirements, the signal is reconstructed through the corrected and optimized least squares value; through the digital processing of signal components, this solution can reduce the reconstruction error and further improve the quality of the reconstructed signal.

[0062] In one embodiment, initialize the generator for generating white noise. The initialization method is as follows:

[0063] Start the generator, collect the white noise generated by the generator, and extract the waveform parameters of the white noise;

[0064] Compare the extracted waveform parameters with the noise waveform setting parameters. If the deviation exceeds the set range, calculate the ratio of the difference of each parameter to the corresponding waveform parameter, and adjust the state of the white noise generator according to the ratio until the deviation is within the set range.

[0065] The working principle and beneficial effects of the above technical solution are as follows: By extracting the waveform parameters of the white noise and comparing them with the noise waveform setting parameters, this solution determines whether the deviation of the white noise exceeds the set range. If it exceeds, calculate the ratio of the difference of each parameter to the corresponding waveform parameter, and adjust the state of the white noise generator according to the ratio, so that the deviation of the white noise is within the set range; then use this white noise generator for the decomposition processing of the original signal; by initializing the generator for generating white noise in advance, this solution makes the white noise used within the set noise waveform accuracy range, thus ensuring the accuracy of the decomposition processing of the original signal.

[0066] Such as Figure 4As shown in the figure, an embodiment of the present invention provides a blasting signal detection device based on modal decomposition, including a data acquisition instrument 10, a signal processing module 20, and a signal reconstruction module 30;

[0067] The data acquisition instrument 10 is used to obtain the blasting signal of tunnel blasting and take the blasting signal as the original signal;

[0068] The signal processing module 20 is respectively connected to the data acquisition instrument 10 and the signal reconstruction module 30. The signal processing module 20 is built-in with an optimized modal decomposition model and a signal component set. The modal decomposition model is used to decompose the original signal, and the decomposed signal components are classified into the signal component set;

[0069] The signal reconstruction module 30 is used to perform signal reconstruction according to the signal component set to obtain the processed blasting signal.

[0070] The working principle and beneficial effects of the above technical solution are as follows: In this solution, the blasting signal collected by the data acquisition instrument is used as the original signal, which is decomposed by the optimized modal decomposition model built in the signal processing module to obtain signal components, forming a signal component set. The signal reconstruction module performs signal reconstruction with the signal component set to obtain the processed blasting signal; By adopting the optimized modal decomposition model, this solution avoids problems such as endpoint effects, modal aliasing, and spurious components, making the blasting signal mode clear and improving the quality of the blasting signal.

[0071] In one embodiment, the data acquisition instrument includes multiple acquisition channels, and the maximum sampling rate of each acquisition channel is not less than 50 KSps.

[0072] The working principle and beneficial effects of the above technical solution are as follows: In this solution, by using a data acquisition instrument with multiple acquisition channels, for example, the data acquisition instrument can have 3 acquisition channels for synchronous sampling, the coordinated control of synchronous sampling can be simplified, so that subsequent signal processing does not need to consider the problem of signal asynchrony, reducing the complexity of signal processing; By limiting the maximum sampling rate of each acquisition channel, the frequency of data sampling can be ensured to be suitable for blasting detection requirements, preventing signal distortion caused by too long sampling period; Among them, the sampling rate unit ksps (kilo Samples per Second) means sampling thousands of times per second.

[0073] In one embodiment, the data acquisition instrument is built-in with an adaptive module, and the adaptive module is used to automatically adapt to the signal intensity under the conditions of setting the automatic trigger frequency and sampling time.

[0074] The working principle and beneficial effects of the above technical solution are as follows: In this solution, an adaptive module is built into the data acquisition instrument. Under preset conditions, it automatically triggers the frequency and sampling, and automatically adapts to the intensity of the sampling signal, eliminating the need for manual range setting; this solution improves the automation level of the detection device and the intelligence of control, can eliminate the influence of human factors, and avoid human deviation in detection.

[0075] In one embodiment, the signal processing module is connected to a camera and a display screen; the camera is used to capture blasting images; the signal processing module incorporates an analog sub-module, and the analog sub-module is used to construct an analog waveform based on the blasting signal; the display screen is used to display the blasting images and the analog waveform of the blasting signal.

[0076] The working principle and beneficial effects of the above technical solution are as follows: In this solution, by setting a camera to capture blasting images, setting an analog sub-module to construct an analog waveform based on the blasting signal, and using a display screen to display the blasting images and the analog waveform, the visualization of blasting and detection is achieved, enabling technicians to directly observe the blasting images and the blasting signals formed by the generated vibrations; the signal processing module can also use image processing and recognition technologies to process and recognize the blasting images, and analyze the blasting signals and blasting effects in combination.

[0077] In one embodiment, the signal processing module is connected to a correction module and a barometric temperature-sensitive element. The barometric temperature-sensitive element is used to detect the ambient air pressure and ambient temperature. The correction module corrects the original signal according to the ambient air pressure and ambient temperature, and transmits the corrected original signal to the modal decomposition model.

[0078] The working principle and beneficial effects of the above technical solution are as follows: In this solution, by setting a correction module and a barometric temperature-sensitive element, using the barometric temperature-sensitive element to detect the ambient air pressure and ambient temperature, and correcting the original signal by detecting the ambient data; this solution takes into account the influence of different on-site air states reflected by the ambient air pressure and ambient temperature on the transmission of blasting signals, reflects the on-site air state through the ambient air pressure and ambient temperature, and sets the correction processing of the original signal according to the influence of different air states on the transmission of blasting signals, so as to ensure that the blasting signals will not deviate due to environmental factor differences in different on-site conditions (such as different air states caused by altitude or weather factors), improving the adaptability of the detection site, ensuring the consistency of the same blasting detection data in different environmental factors, eliminating environmental interference, and further improving the accuracy and reliability of the detected blasting signals.

[0079] In one embodiment, it further includes a rechargeable lithium battery and a power management module. The power management module includes a first power input terminal, a second power input terminal, and a power output terminal. The first power input terminal is connected to the rechargeable lithium battery. The second power input terminal is used to connect to an external power source. The power output terminal is used to supply power to the data acquisition instrument, the signal processing module, and the signal reconstruction module;

[0080] The power management module is built with a power regulation circuit. As Figure 5 shown, the power regulation circuit includes an adjustment module, a field effect transistor Q1, a voltage regulation module, a capacitor C1, an amplifier U1, an error amplifier U2, a variable resistor R1, a variable resistor R2, and a variable resistor R3;

[0081] The input terminals of the adjustment module are respectively connected to the first power input terminal and the second power input terminal. The output terminal of the adjustment module is connected to the source electrode of the field effect transistor Q1. The input pin 2 of the error amplifier U2 is connected to the drain electrode of the field effect transistor Q1 as the output terminal;

[0082] The control terminal of the adjustment module is connected to the pin 4 of the error amplifier U2. The input pin 1 of the error amplifier U2 is connected to the output pin 4 of the amplifier U1 through the variable resistor R1. The output pin 5 of the error amplifier U2 is connected to the gate electrode of the field effect transistor Q1. The pin 3 of the error amplifier U2 is respectively connected to the output pin 3 of the voltage regulation module, the input pin 1 of the amplifier U1, and the pin 3 of the amplifier U1;

[0083] The input pin 2 of the amplifier U1 is respectively connected to one end of the variable resistor R2 and one end of the variable resistor R3. The other end of the variable resistor R2 is connected to the input pin 1 of the error amplifier U2. The other end of the variable resistor R3 is grounded. The input pins 1 and 2 of the voltage regulation module are respectively connected to the anode and cathode of the capacitor C1. The anode of the capacitor C1 is connected to the positive pole of the DC power supply.

[0084] The working principle and beneficial effects of the above technical solution are as follows: The power management module of this solution is provided with a first power input terminal and a second power input terminal, enabling the device to be applicable to various different power sources. The charging lithium battery can be adapted to field operations; by setting a power adjustment circuit in the power management module, stable power supply can be achieved through adjustment when applicable to different power sources. The voltage stabilization module provides a reference voltage by configuring capacitor C1 as a reference. After the reference voltage is processed by amplifier U1, multiple variable resistors, and error amplifier U2, it acts on the adjustment module and field effect transistor Q1 to perform voltage stabilization control on the output voltage of the power supply and precisely control the output current, ensuring the magnetization quality of the magnet and the consistency of magnetization quality, and improving the yield rate; the adjustment module is used to limit the current supplied to the source of field effect transistor Q1, making the supplied output voltage stable, the current control precise, the risk controllable, and improving the service life of the magnetization device; the power adjustment circuit adopted in this solution has a fast response speed and excellent transient response characteristics, and can achieve stable operation by matching capacitors under ultra-low voltage differences.

[0085] In one embodiment, the data acquisition instrument is built with a filtering module, and the filtering module uses the following algorithm to filter the original signal:

[0086] y(n) = W T (n)X(n)

[0087] e(n) = d(n) - y(n)

[0088]

[0089] W(n + 1) = W(n) + 2μ(n)e(n)X(n)

[0090] Among them, y(n) represents the detected signal output at the nth moment, α, β are adjustment parameters of μ(n), μ(n) represents the step factor of the filtering module, d(n) represents the expected detected signal at the nth moment, e(n) represents the difference between the expected detected signal and the output detected signal, X(n) represents the input vector of the filtering module at the nth moment, W(n) represents the tap weight vector of the filtering module at the nth moment, W(n + 1) represents the tap weight vector of the filtering module at the (n + 1)th moment, and W T (n) represents the transposed tap weight vector of the filtering module at the nth moment.

[0091] The working principle and beneficial effects of the above technical solution are as follows: The filtering algorithm of the filtering module adopted in the present invention improves the convergence speed of the filtering algorithm, enhances the response ability to filter the original signal, and also improves the filtering accuracy, further ensuring the accuracy of the collected signal and ensuring the accuracy of the detected signal transmitted to the signal processing module; the filtering module of the present invention also takes into account the steady-state error and improves the performance of the filtering algorithm.

[0092] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations therein.

Claims

1. A blasting signal detection method based on modal decomposition, characterized in that Including: S100: Obtain the blasting signal of tunnel blasting through a data acquisition instrument, and use the blasting signal as the original signal; S200: Use an optimized modal decomposition model to decompose the original signal, and classify the signal components obtained after decomposition into a signal component set; S3 S300: Perform signal reconstruction according to the signal component set to obtain the processed blasting signal; In step S200, the optimized modal decomposition model adopts the complementary ensemble empirical mode decomposition algorithm, and the decomposition processing method for the original signal is as follows: S210: Add different white noises and the difference signals between the original signal and the corresponding white noises to the original signal respectively to obtain multiple aliased signals of two types; S220: Perform modal decomposition on the first type of aliased signal obtained by adding white noise, and classify the obtained signal components into the first signal set; S230: Perform modal decomposition on the second type of aliased signal obtained by adding the difference signal, and classify the obtained signal components into the second signal set; S240: Divide the signal components related to the same white noise in the first signal set and the second signal set into a group, calculate the average value of each group of signal components, and the obtained signal components are put into the signal component set.

2. The blasting signal detection method based on modal decomposition according to claim 1, wherein In step S300, the signal reconstruction method is as follows: S310: Use the signal components in the signal component set as dimension vectors, establish a signal component matrix, initialize it, obtain an atom matrix and a residual, and calculate the norm of the residual; S320: Determine the number of vectors according to the norm of the residual, and select column vectors in the signal component matrix; S330: Use the union of the column vectors and the atom matrix as a fusion matrix, and calculate the least squares value of the fusion matrix and the dimension vector; S340: Correct the residual according to the dimension vector, the fusion matrix and the least squares value to obtain a corrected residual and a corrected norm; S350: If the corrected norm is not lower than the preset norm threshold, execute S320, otherwise execute S360; S360: Use the least squares value as the reconstructed signal, and reconstruct the dimension vector at the corresponding scale to form a new blasting signal.

3. The blasting signal detection method based on modal decomposition according to claim 1, wherein Perform initialization processing on the generator for generating white noise, and the initialization processing method is: Start the generator, collect the white noise generated by the generator, and extract the waveform parameters of the white noise; Compare the extracted waveform parameters with the noise waveform setting parameters. If the deviation exceeds the set range, calculate the ratio of the difference between each parameter and the corresponding waveform parameter, and adjust the state of the white noise generator according to the ratio until the deviation is within the set range.

4. A blasting signal detection device based on modal decomposition, characterized in that, Including a data acquisition instrument, a signal processing module and a signal reconstruction module; The data acquisition instrument is used to obtain the blasting signal of tunnel blasting and use the blasting signal as the original signal; The signal processing module is respectively connected to the data acquisition instrument and the signal reconstruction module. The signal processing module is built-in with an optimized modal decomposition model and a signal component set. The modal decomposition model is used to decompose the original signal, and the signal components obtained after decomposition are classified into the signal component set; The signal reconstruction module is used to perform signal reconstruction according to the signal component set to obtain the processed blasting signal; Among them, the optimized modal decomposition model adopts the complementary ensemble empirical mode decomposition algorithm, and the decomposition processing method for the original signal is as follows: Add different white noises and the difference signals between the original signal and the corresponding white noises to the original signal respectively to obtain multiple aliased signals of two types; Perform modal decomposition on the first type of aliased signal obtained by adding white noise, and classify the obtained signal components into the first signal set; Perform modal decomposition on the second type of aliased signal obtained by adding the difference signal, and classify the obtained signal components into the second signal set; Divide the signal components in the first signal set and the second signal set that are related to the same white noise into a group, calculate the average value of each group of signal components, and the obtained signal components are put into the signal component set.

5. The blasting signal detection device based on modal decomposition according to claim 4, characterized in that The data acquisition instrument includes multiple acquisition channels, and the maximum sampling rate of each acquisition channel is not less than 50 KSps.

6. The blasting signal detection device based on modal decomposition according to claim 4, characterized in that The data acquisition instrument is built-in with an adaptive module, and the adaptive module is used to automatically adapt to the signal intensity under the condition of setting the automatic trigger frequency and sampling time.

7. The blasting signal detection device based on modal decomposition according to claim 4, characterized in that The signal processing module is connected with a camera and a display screen; the camera is used to take blasting images; the signal processing module is built-in with an analog sub-module, and the analog sub-module is used to construct an analog waveform according to the blasting signal; the display screen is used to display the blasting images and the analog waveforms of the blasting signals.

8. The blasting signal detection device based on modal decomposition according to claim 4, characterized in that The signal processing module is connected with a correction module and a barometric pressure and temperature sensitive element. The barometric pressure and temperature sensitive element is used to detect the ambient barometric pressure and ambient temperature. The correction module corrects the original signal according to the ambient barometric pressure and ambient temperature, and transmits the corrected original signal to the modal decomposition model.

9. The blasting signal detection device based on modal decomposition according to any one of claims 4-8, characterized in that, It also includes a rechargeable lithium battery and a power management module. The power management module includes a first power input terminal, a second power input terminal and a power output terminal. The first power input terminal is connected to the rechargeable lithium battery, the second power input terminal is used to connect to an external power source, and the power output terminal is used to supply power to the data acquisition instrument, the signal processing module and the signal reconstruction module; The power management module is built-in with a power adjustment circuit. The power adjustment circuit includes an adjustment module, a field effect transistor Q1, a voltage stabilization module, a capacitor C1, an amplifier U1, an error amplifier U2, a variable resistor R1, a variable resistor R2 and a variable resistor R3.

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