Initiating explosive device danger mechanical excitation and response monitoring method and system

Abstract

The invention relates to an initiating explosive device dangerous mechanical excitation and response monitoring method and system, which utilizes the acoustic emission principle to obtain dangerous mechanical excitation and response conditions in real time by monitoring initiating explosive device acoustic signals, so as to evaluate the safety danger level of initiating explosive device dangerous mechanical excitation and response and improve the production, transportation and use safety of initiating explosive devices. Comprising a mechanical excitation generation part used for generating or simulating initiating explosive device external danger excitation; the acoustic emission monitoring part is used for arranging a plurality of acoustic sensors on the initiating explosive device, monitoring acoustic characteristics of the initiating explosive device in real time and outputting acoustic emission signals; the signal processing part is used for receiving the acoustic emission signal of the acoustic sensor output by the acoustic emission monitoring part and processing, recording and analyzing the signal to obtain parameters such as dangerous mechanical excitation energy and position of the initiating explosive device and to obtain the response intensity of the initiating explosive device after being excited by the dangerous mechanical excitation; therefore, the initiating explosive device danger mechanical excitation and response safety danger level is evaluated.

Description

Technical Field

[0001] The invention belongs to the technical field of instruments and meters, and in particular relates to a method and system for monitoring the excitation and response of dangerous machinery containing explosives. Background Art

[0002] With the development of technology, the energy density of explosives, propellants and other pyrotechnics is constantly increasing, and safety issues are coming along with it. During the entire life cycle of pyrotechnics production, storage, and use, they are susceptible to dangerous mechanical excitations such as collisions and falls, which can cause shell deformation and fracture, and even cause safety accidents such as combustion, explosion, and detonation. Therefore, the monitoring of dangerous mechanical excitation and response of pyrotechnics is one of its important safety issues.

[0003] At present, the main way to monitor the excitation and response of dangerous machinery of pyrotechnics is to install conventional sensors such as temperature and strain on the shell. Due to the limited temporal and spatial resolution of these sensors, it is still impossible to achieve comprehensive safety monitoring of pyrotechnics. After being stimulated by dangerous machinery, the response of pyrotechnics is mainly judged manually based on the results, and it is also impossible to meet the requirements of real-time monitoring and identification. Therefore, the safety management of pyrotechnics during their life cycle urgently needs a method and system for monitoring the excitation and response of dangerous machinery that can meet comprehensive real-time monitoring. Summary of the invention

[0004] In response to the above problems, a method and system for monitoring the excitation and response of dangerous machinery of pyrotechnics are provided. By utilizing the principle of acoustic emission, the excitation and response of dangerous machinery are obtained in real time by monitoring the acoustic signals of pyrotechnics, thereby evaluating the safety hazard level of the excitation and response of dangerous machinery of pyrotechnics and improving the safety of pyrotechnic production, transportation and use.

[0005] The technical solution of the present invention is: a method and system for monitoring the excitation and response of dangerous machinery of explosives, which uses the principle of acoustic emission to obtain the excitation and response of dangerous machinery in real time by monitoring the acoustic signals of explosives, and evaluates the safety hazard level of the excitation and response of dangerous machinery of explosives, including:

[0006] The mechanical excitation generating unit is used to generate or simulate the external dangerous excitation of the pyrotechnic device and act on the pyrotechnic device;

[0007] The acoustic emission monitoring unit is used to set multiple acoustic sensors on the pyrotechnics to monitor the acoustic characteristics of the pyrotechnics in real time and output acoustic emission signals;

[0008] The signal processing unit is used to receive the acoustic emission signal of the acoustic sensor output by the acoustic emission monitoring unit, and process, record and analyze the signal to obtain the energy and position parameters of the dangerous mechanical excitation of the pyrotechnics, and at the same time obtain the response intensity of the pyrotechnics after being subjected to dangerous mechanical excitation, so as to evaluate the safety hazard level of the dangerous mechanical excitation and response of the pyrotechnics.

[0009] Preferably, the mechanical excitation generating unit generates impact excitation through a mechanical excitation source, or generates falling excitation through lifting and falling, or generates bullet or fragment excitation through a bullet or fragment device.

[0010] Preferably, the acoustic emission monitoring unit includes a plurality of acoustic sensors, and acoustic transducers are selected and arranged at different positions of the pyrotechnic device as required. The sensor power supply is connected via cables to monitor the acoustic characteristics of the pyrotechnic device in real time, and the acoustic emission signals are output via cables.

[0011] Preferably, the signal processing unit includes a gain amplifier, an attenuator, an electrical signal separator, a data acquisition and analysis unit and a cable, and is connected to a plurality of acoustic sensors of the acoustic emission monitoring unit through the cable to receive the acoustic emission signal output by the acoustic emission monitoring unit, and processes the acoustic emission signal through the gain amplifier, the attenuator and the electrical signal separator, and finally records and analyzes it by the data acquisition and analysis unit to obtain the energy and position parameters of the dangerous mechanical excitation of the pyrotechnic, and at the same time obtain the response intensity of the pyrotechnic after being subjected to the dangerous mechanical excitation, thereby evaluating the dangerous mechanical excitation and response safety hazard level of the pyrotechnic.

[0012] A method for monitoring the excitation and response of explosive device dangerous machinery using the explosive device dangerous machinery excitation and response monitoring system comprises the following steps:

[0013] S1: Install the pyrotechnic hazardous mechanical excitation and response monitoring system on the pyrotechnic to be tested;

[0014] S2: Turn on the acoustic emission monitoring unit and the signal processing unit to process and record the acoustic emission signal;

[0015] S3: using the excited acoustic emission signal processing algorithm to process the signal obtained in S2, and obtain the dangerous mechanical excitation energy parameters of the explosive device;

[0016] S4: Use the multi-point reconstruction algorithm of the excited acoustic emission signal to process the signal obtained in S2 to obtain the excitation position parameters of the dangerous mechanical equipment of the explosive device;

[0017] S5: using the response acoustic emission signal processing algorithm to process the signal obtained in S2, and obtain the response strength of the evaluation device after being stimulated by the dangerous machinery;

[0018] S6: Based on the analysis results obtained in S3-S5, the safety hazard level of the excitation and response of dangerous machinery of pyrotechnics is evaluated by using the multivariate information fusion identification and evaluation algorithm of the excitation and response of dangerous machinery of pyrotechnics.

[0019] Further, the stimulated acoustic emission signal processing algorithm includes parameters for analyzing the acoustic emission signal characteristic parameters including amplitude, energy, rise time, duration, ring count and event count;

[0020] The amplitude represents the maximum amplitude in the acoustic emission signal waveform;

[0021] The rise time represents the time required for the signal to cross the threshold value for the first time to reach the maximum amplitude;

[0022] The duration refers to the time from when the signal first crosses the threshold value until the signal decays below the threshold value and no longer exceeds the threshold value;

[0023] The ring count refers to the time when the signal crosses the threshold value. Each oscillation is called a ring. The ring count is greatly affected by the threshold value.

[0024] The energy refers to the area below the envelope of the acoustic emission signal, reflecting the overall strength of the acoustic emission signal; the excited acoustic emission signal processing algorithm also includes a spectrum analysis method, which uses Fourier transform to transform the acoustic emission signal from the time domain to the frequency domain, and regards the acoustic emission signal x(t) as a function of the frequency f, thereby obtaining the relationship between the frequency spectrum characteristics of the acoustic emission signal and the material damage;

[0025] The expression of Fourier transform is:

[0026]

[0027] The inverse Fourier transform is:

[0028]

[0029] Among them, X(ω)e jωt dω is an infinitesimal quantity, indicating that the amplitude of the harmonic component of the signal x(t) at the angular frequency ω approaches zero. The harmonic component has a certain magnitude only within a certain frequency range, that is, it has amplitude meaning only after the frequency is integrated within this frequency band.

[0030] The stimulated acoustic emission signal processing algorithm also includes a wavelet transform method to simultaneously extract the time domain and frequency domain information of the signal. The definition of wavelet transform is:

[0031]

[0032] Among them, W ψ is the wavelet function, represents its complex conjugate, a is the scale, b is the translation, 1 / a corresponds to the frequency, t is the time, f(t) is the original function, and represents the time domain signal; different wavelet basis functions are selected according to the acoustic emission signal, including any one of the Db series wavelet, Coif series wavelet, and Morlet wavelet; the denoising of the acoustic emission signal using wavelet transform is divided into three steps, firstly determining the wavelet basis function and the number of decomposition layers; secondly, processing each layer of wavelet coefficients according to the selected threshold function; finally, the processed wavelet coefficients are used to reconstruct the acoustic emission signal; there are three main types of threshold functions, namely hard threshold function, soft threshold function and semi-soft threshold function,

[0033] The hard threshold function is:

[0034]

[0035] The soft threshold function is:

[0036]

[0037] Among them, λ is the threshold.

[0038] Furthermore, the multi-point reconstruction algorithm of the excitation acoustic emission signal reconstructs the acoustic emission signals of multiple acoustic sensors. There are N sensors, and the sensor positions are (x i ,y i )(i=1,2,…,N), acoustic emission source position (x 0 ,y 0 ) at t 0 The signal with a wave speed of c is sent out at time t, and the signal received by the i-th sensor arrives at time t i , then the relationship between the source position and the coordinates of each sensor, wave speed and arrival time is as follows:

[0039] (x i -x 0 ) 2 +(y i -y 0 ) 2 =c 2 (t i -t 0 ) 2

[0040] Assuming the source position coordinates are (x, y), the arrival time of the signal received by the i-th sensor is t i , we can get the emission time τ of the emission source i The estimated value of (x,y) is:

[0041]

[0042] For the true source coordinates, the emission time estimate should be consistent with t 0 The same, that is, when (x, y) takes (x 0 ,y 0 ), we have: τ i (x 0 ,y 0 ) = t 0

[0043] When (x, y) takes other values, the estimated emission time τ i There will be a certain dispersion, and τ i The variance of is used to measure this dispersion, and the variance is:

[0044]

[0045] In the formula, Represents τ i The algebraic mean of

[0046] When it is equal to 0, it corresponds to the real sound source, and the source coordinates are calculated from it; when there are 3 sensors, the sound source can be calculated; when the number of sensors is greater than 3, the 3 sensors can be set as a group, and the arithmetic mean of the positioning results obtained in each group is taken as the final result.

[0047] Furthermore, the response acoustic emission signal processing algorithm is the same as the excitation acoustic emission signal processing algorithm, including the analysis of acoustic emission signal characteristic parameters such as amplitude, energy, rise time, duration, ringing count and event count, as well as spectral analysis and wavelet transform.

[0048] Furthermore, the algorithm for comprehensive identification and evaluation of the excitation and response of dangerous machinery of pyrotechnics is used to solve the problem of comprehensive identification and evaluation of the excitation and response of dangerous machinery of pyrotechnics by constructing a generative adversarial prediction model with Transformer as the backbone; taking advantage of the generative adversarial network framework and integrating the data-driven Transformer network, the Transformer network model architecture is divided into three layers: physical information input layer, state coupling layer, and parameter mapping layer, facing the characteristics of the working mechanism of pyrotechnics. The physical information input layer corresponds to the components and systems of the engine, and is used to learn the working characteristics of the excitation sound signals and the system;

[0049] Among them, the excitation sound signal mainly includes: (1) sub-time series signals at different frequency sequences such as the amplitude of the first trough of the excitation signal, S2 modal energy, and high-frequency energy ratio EP, which characterize the energy parameters of the dangerous mechanical excitation of pyrotechnics; (2) Generate pyrotechnic model vector data through pyrotechnic structural parameters, and obtain the dangerous mechanical excitation position parameters of pyrotechnics according to the signal collection time and the position information of the acoustic emission sensor; (3) Through mechanical excitation tests, obtain the dimensional characteristic values ​​of mechanical excitation intensity of multiple groups of different data dimensions; the coupling layer, as the intermediate layer of feature extraction, has a one-way link relationship with the physical information input layer; the joint work between pyrotechnic components and systems is reflected in the coupling layer, which intervenes in the learning state of the physical information input layer; the top layer of the architecture is the parameter mapping layer, which establishes the relationship between the abstract features of the coupling layer and the pyrotechnics; through the fusion of multiple information such as time series signals, image position vector data and excitation intensity characteristic values, the evaluation of the mechanical excitation results of pyrotechnics includes: (1) the damage mode of the pyrotechnic dangerous mechanical excitation response, that is, pitting or perforation; (2) the damage degree of the pyrotechnic dangerous mechanical excitation response, that is, pit depth or hole diameter;

[0050] According to the above-mentioned comprehensive identification and evaluation algorithm of pyrotechnic hazardous mechanical excitation and response, the damage mode of pyrotechnic hazardous mechanical excitation response is identified, and the degree of damage to pyrotechnic hazardous mechanical excitation response is evaluated, thereby quantifying the safety hazard level of pyrotechnic hazardous mechanical excitation and response.

[0051] The beneficial effects of the present invention are as follows: the method and system for monitoring the excitation and response of dangerous machinery of pyrotechnics of the present invention are based on the principle of acoustic emission, and through generating or simulating external dangerous excitation of pyrotechnics, a plurality of acoustic sensors are arranged on the pyrotechnics to monitor the acoustic emission signals of the pyrotechnics in real time, and the signals are processed, recorded and analyzed, and the parameters such as the excitation energy of dangerous machinery of pyrotechnics and the like are obtained by using the excitation acoustic emission signal processing algorithm, the parameters such as the excitation position of dangerous machinery of pyrotechnics and the like are obtained by using the excitation acoustic emission signal multi-point reconstruction algorithm, and the response intensity of the pyrotechnics after being subjected to dangerous mechanical excitation is evaluated by using the response acoustic emission signal processing algorithm. On this basis, according to the above analysis results, the comprehensive identification and evaluation algorithm for the excitation and response of dangerous machinery of pyrotechnics is used to evaluate the safety hazard level of the excitation and response of dangerous machinery of pyrotechnics, so as to meet the real-time monitoring and evaluation of the excitation and response of dangerous machinery of pyrotechnics and improve the safety of the production, transportation and use of pyrotechnics. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of an excitation and response monitoring system for explosives hazardous machinery in an embodiment of the present invention;

[0053] Figure 2 It is a flow chart of a method for performing dangerous machinery excitation and response monitoring using an pyrotechnic dangerous machinery excitation and response monitoring system in an embodiment of the present invention;

[0054] Figure 3 This is a characteristic parameter analysis diagram of the acoustic emission signal in an embodiment of the present invention;

[0055] Figure 4 The Transform network framework of the present invention has built-in physical constraints. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0057] like Figure 1 As shown, this embodiment provides a pyrotechnic dangerous mechanical excitation and response monitoring system, including a pyrotechnic 1; a mechanical excitation generating unit 2, used to generate or simulate external dangerous excitation of the pyrotechnic, acting on the pyrotechnic 1; an acoustic emission monitoring unit 3, used to set a plurality of acoustic sensors on the pyrotechnic, monitor the acoustic characteristics of the pyrotechnic in real time, and output an acoustic emission signal; a signal processing unit 4, used to receive the acoustic emission signal output by the acoustic emission monitoring unit, and process, record and analyze the signal to obtain parameters such as the energy and position of the pyrotechnic dangerous mechanical excitation, and at the same time obtain the response intensity of the pyrotechnic after being subjected to the dangerous mechanical excitation, so as to evaluate the dangerous mechanical excitation and response safety hazard level of the pyrotechnic.

[0058] The mechanical excitation generating unit may generate impact excitation through the mechanical excitation source 5, may generate drop excitation through lifting and falling, or may generate bullet or fragment excitation through a bullet or fragment device.

[0059] The acoustic emission monitoring unit includes a plurality of acoustic sensors 6, 7, 8 and 9, which may be acoustic transducers, which are arranged at different parts of the pyrotechnic device according to certain requirements, are connected to the sensor power supply through cables, monitor the acoustic characteristics of the pyrotechnic device in real time, and output the acoustic emission signal through the cables;

[0060] Among them, the signal processing unit includes a gain amplifier 10, an attenuator 11, an electrical signal separator 12, a data acquisition and analysis unit 13 and a cable, which is connected to multiple acoustic sensors of the acoustic emission monitoring unit through the cable, receives the acoustic emission signal output by the acoustic emission monitoring unit, and processes the acoustic emission signal through the gain amplifier, attenuator and electrical signal separator. Finally, the data acquisition and analysis unit records and analyzes it to obtain parameters such as the energy and position of the dangerous mechanical excitation of the pyrotechnic, and at the same time obtains the response intensity of the pyrotechnic after being stimulated by the dangerous machinery, so as to evaluate the dangerous mechanical excitation and response safety hazard level of the pyrotechnic.

[0061] This embodiment also provides a method for monitoring the excitation and response of a dangerous machine of explosives, including the following steps:

[0062] S1: Install the pyrotechnic hazardous mechanical excitation and response monitoring system on the pyrotechnic to be tested;

[0063] S2: Turn on the acoustic emission monitoring unit and the signal processing unit to process and record the acoustic emission signal;

[0064] S3: using an excited acoustic emission signal processing algorithm to process the signal obtained in step S2 to obtain parameters such as the excitation energy of dangerous machinery of explosive devices;

[0065] S4: using the multi-point reconstruction algorithm of the excited acoustic emission signal to process the signal obtained in step S2, and obtain parameters such as the excitation position of the dangerous mechanical device of the explosive device;

[0066] S5: using a response acoustic emission signal processing algorithm to process the signal obtained in step S2, so as to evaluate the response strength of the explosive device after being stimulated by dangerous machinery;

[0067] S6: Based on the analysis results obtained in S3-S5, the safety hazard level of the excitation and response of dangerous machinery of pyrotechnics is evaluated by using the multivariate information fusion identification and evaluation algorithm of the excitation and response of dangerous machinery of pyrotechnics.

[0068] Among them, the stimulated acoustic emission signal processing algorithm includes the method for analyzing the characteristic parameters of the acoustic emission signal, including amplitude, energy, rise time, duration, ringing count and event count.

[0069] In the stimulated acoustic emission signal processing algorithm, the amplitude represents the maximum amplitude in the acoustic emission signal waveform;

[0070] The rise time represents the time required for the signal to cross the threshold for the first time to reach the maximum amplitude;

[0071] Duration refers to the time from the first time the signal crosses the threshold value until the signal decays below the threshold value and no longer exceeds the threshold value;

[0072] Ring count refers to the time when the signal crosses the threshold value. Each oscillation is called a ring. The ring count is greatly affected by the threshold value.

[0073] Energy refers to the area below the envelope of the AE signal, which reflects the overall strength of the AE signal.

[0074] Among them, the stimulated acoustic emission signal processing algorithm also includes spectrum analysis, which uses Fourier transform to transform the acoustic emission signal from the time domain to the frequency domain, and regards the acoustic emission signal x(t) as a function of frequency f, thereby obtaining the connection between the spectral characteristics of the acoustic emission signal and material damage. The expression of Fourier transform is:

[0075]

[0076] The inverse Fourier transform is:

[0077]

[0078] Among them, X(ω)e jωt dω is an infinitesimal quantity, indicating that the amplitude of the harmonic component of the signal x(t) at the angular frequency ω approaches zero. The harmonic component has a certain size only within a certain frequency range, that is, it has amplitude meaning only after integrating the frequency within this frequency band.

[0079] Among them, the stimulated acoustic emission signal processing algorithm also includes the wavelet transform method, which can simultaneously extract the time domain and frequency domain information of the signal. The definition of wavelet transform is:

[0080]

[0081] Among them, W ψ is the wavelet function, represents its complex conjugate, a is the scale, b is the translation, 1 / a corresponds to frequency, t is time, f(t) is the original function, and represents the time domain signal. Different wavelet basis functions can be selected according to the acoustic emission signal, including Db series wavelets, Coif series wavelets, Morlet wavelets, etc. At present, in the application field of acoustic emission signals, wavelet transform is mainly used for signal denoising, feature extraction, etc. The use of wavelet transform to denoise acoustic emission signals is generally divided into three steps. First, the wavelet basis function and the number of decomposition layers are determined; secondly, each layer of wavelet coefficients is processed according to the selected threshold function; finally, the processed wavelet coefficients are used to reconstruct the acoustic emission signal. There are three main threshold functions that are currently used, namely hard threshold function, soft threshold function and semi-soft threshold function, among which the hard threshold function is:

[0082]

[0083] The soft threshold function is:

[0084]

[0085] Among them, λ is the threshold.

[0086] Among them, the multi-point reconstruction algorithm of the excitation acoustic emission signal reconstructs the acoustic emission signals of multiple acoustic sensors. There are N sensors, and the sensor positions are (x i ,y i )(i=1,2,…,N), acoustic emission source position (x 0 ,y 0 ) at t 0 The signal with a wave speed of c is sent out at time t, and the signal received by the i-th sensor arrives at time ti , then the relationship between the source position and the coordinates of each sensor, wave speed and arrival time is as follows:

[0087] (x i -x 0 ) 2 +(y i -y 0 ) 2 =c 2 (t i -t 0 ) 2

[0088] Assuming the source position coordinates are (x, y), the arrival time of the signal received by the i-th sensor is t i , we can get the emission time τ of the emission source i The estimated value of (x,y) is:

[0089]

[0090] For the true source coordinates, the emission time (estimate) should be equal to t 0 The same, that is, when (x, y) takes (x 0 ,y 0 ), there are:

[0091] τ i (x 0 ,y 0 ) = t 0

[0092] When (x, y) takes other values, the estimated emission time τ i There will be a certain degree of dispersion. i The variance of is used to measure this dispersion, and the variance is:

[0093]

[0094] In the formula, Represents τ i The algebraic mean of .

[0095] When it is equal to 0, it corresponds to the real sound source, and the source coordinates are calculated from it. When there are 3 sensors, the sound source can be calculated; when the number of sensors is greater than 3, the 3 sensors can be set as a group, and the arithmetic average of the positioning results obtained in each group is taken as the final result.

[0096] Among them, the response acoustic emission signal processing algorithm is the same as the excitation acoustic emission signal processing algorithm, including the analysis of acoustic emission signal characteristic parameters such as amplitude, energy, rise time, duration, ringing count and event count, as well as spectral analysis and wavelet transform.

[0097] Among them, the algorithm for comprehensive identification and evaluation of incentives and responses of dangerous machinery of explosives is used to solve the problem of comprehensive identification and evaluation of incentives and responses of dangerous machinery of explosives by constructing a generative adversarial prediction model with Transformer as the backbone. Figure 4 Taking advantage of the generative adversarial network framework and integrating the data-driven Transformer network, the Transformer network model architecture is divided into three layers: physical information input layer, state coupling layer, and parameter mapping layer, targeting the working mechanism characteristics of pyrotechnics. The physical information input layer corresponds to the components and systems of the engine and is used to learn the excitation sound signals and the operating characteristics of the system.

[0098] Among them, the excitation sound signal mainly includes: (1) sub-time series signals at different frequency sequences such as the amplitude of the first trough of the excitation signal, S2 modal energy, and high-frequency energy ratio EP, which characterize the parameters such as the mechanical excitation energy of the pyrotechnic hazardous material; (2) the pyrotechnic model vector data is generated through the pyrotechnic structural parameters, and the parameters such as the mechanical excitation position of the pyrotechnic hazardous material are obtained according to the signal collection time and the position information of the acoustic emission sensor; (3) through the mechanical excitation test, the dimensional characteristic values ​​of the mechanical excitation intensity of multiple groups of different data dimensions are obtained. The coupling layer, as the intermediate layer of feature extraction, has a one-way link relationship with the physical information input layer. The joint work between the pyrotechnic components and systems is reflected in the coupling layer, which intervenes in the learning state of the physical information input layer. The top layer of the architecture is the parameter mapping layer, which establishes the relationship between the abstract features of the coupling layer and the pyrotechnic. By fusing multiple information such as time series signals, image position vector data, and excitation intensity eigenvalues, the results of mechanical excitation of explosive devices are evaluated, including: (1) the damage mode of mechanical excitation response of explosive devices, i.e., pit formation or perforation; (2) the damage degree of mechanical excitation response of explosive devices, i.e., pit depth or hole diameter.

[0099] According to the above-mentioned comprehensive identification and evaluation algorithm of pyrotechnic hazardous mechanical excitation and response, the damage mode of pyrotechnic hazardous mechanical excitation response is identified, and the degree of damage to pyrotechnic hazardous mechanical excitation response is evaluated, thereby quantifying the safety hazard level of pyrotechnic hazardous mechanical excitation and response.

[0100] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A method and system for monitoring the excitation and response of dangerous machinery of explosives, which uses the principle of acoustic emission to obtain the excitation and response of dangerous machinery in real time by monitoring the acoustic signals of explosives, and evaluates the safety hazard level of the excitation and response of dangerous machinery of explosives, characterized in that: include: The mechanical excitation generating unit is used to generate or simulate the external dangerous excitation of the pyrotechnic device and act on the pyrotechnic device; The acoustic emission monitoring unit is used to set multiple acoustic sensors on the pyrotechnics to monitor the acoustic characteristics of the pyrotechnics in real time and output acoustic emission signals; The signal processing unit is used to receive the acoustic emission signal of the acoustic sensor output by the acoustic emission monitoring unit, and process, record and analyze the signal to obtain the energy and position parameters of the dangerous mechanical excitation of the pyrotechnics, and at the same time obtain the response intensity of the pyrotechnics after being subjected to dangerous mechanical excitation, so as to evaluate the safety hazard level of the dangerous mechanical excitation and response of the pyrotechnics.

2. The method and system for monitoring the excitation and response of dangerous machinery of explosives according to claim 1, characterized in that: The mechanical excitation generating unit generates impact excitation through a mechanical excitation source, generates drop excitation through lifting and falling, or generates bullet or fragment excitation through a bullet or fragment device.

3. The method and system for monitoring the excitation and response of dangerous machinery of explosives according to claim 1, characterized in that: The acoustic emission monitoring unit includes multiple acoustic sensors, which select acoustic transducers and are set at different parts of the pyrotechnics as required. The sensor power supply is connected via cables to monitor the acoustic characteristics of the pyrotechnics in real time and output acoustic emission signals via cables.

4. The method and system for monitoring the excitation and response of dangerous machinery of explosives according to claim 1, characterized in that: The signal processing unit includes a gain amplifier, an attenuator, an electrical signal separator, a data acquisition and analysis unit and a cable, and is connected to multiple acoustic sensors of the acoustic emission monitoring unit through the cable to receive the acoustic emission signal output by the acoustic emission monitoring unit, and processes the acoustic emission signal through the gain amplifier, attenuator and electrical signal separator. Finally, the data acquisition and analysis unit records and analyzes the signal to obtain the energy and position parameters of the dangerous mechanical excitation of the pyrotechnic, and the response intensity of the pyrotechnic after being stimulated by the dangerous machinery, so as to evaluate the dangerous mechanical excitation and response safety hazard level of the pyrotechnic.

5. A method for monitoring the excitation and response of explosive device dangerous machinery using the explosive device dangerous machinery excitation and response monitoring system as claimed in any one of claims 1 to 4, characterized in that: The following steps are involved: S1: Install the pyrotechnic hazardous mechanical excitation and response monitoring system on the pyrotechnic to be tested; S2: Turn on the acoustic emission monitoring unit and the signal processing unit to process and record the acoustic emission signal; S3: using the excited acoustic emission signal processing algorithm to process the signal obtained in S2, and obtain the dangerous mechanical excitation energy parameters of the explosive device; S4: Use the multi-point reconstruction algorithm of the excited acoustic emission signal to process the signal obtained in S2 to obtain the excitation position parameters of the dangerous mechanical equipment of the explosive device; S5: using the response acoustic emission signal processing algorithm to process the signal obtained in S2, and obtain the response strength of the evaluation device after being stimulated by the dangerous machinery; S6: Based on the analysis results obtained in S3-S5, the safety hazard level of the excitation and response of dangerous machinery of pyrotechnics is evaluated by using the multivariate information fusion identification and evaluation algorithm of the excitation and response of dangerous machinery of pyrotechnics.

6. The method for monitoring the excitation and response of dangerous machinery of explosive devices according to claim 5, characterized in that: The stimulated acoustic emission signal processing algorithm includes a method for analyzing the characteristic parameters of the acoustic emission signal, including amplitude, energy, rise time, duration, ring count and event count; The amplitude represents the maximum amplitude in the acoustic emission signal waveform; The rise time represents the time required for the signal to cross the threshold value for the first time to reach the maximum amplitude; The duration refers to the time from when the signal first crosses the threshold value until the signal decays below the threshold value and no longer exceeds the threshold value; The ring count refers to the time when the signal crosses the threshold value. Each oscillation is called a ring. The ring count is greatly affected by the threshold value. The energy refers to the area below the envelope of the acoustic emission signal, reflecting the overall strength of the acoustic emission signal; the excited acoustic emission signal processing algorithm also includes a spectrum analysis method, which uses Fourier transform to transform the acoustic emission signal from the time domain to the frequency domain, and regards the acoustic emission signal x(t) as a function of the frequency f, thereby obtaining the relationship between the frequency spectrum characteristics of the acoustic emission signal and the material damage; The expression of Fourier transform is: The inverse Fourier transform is: Among them, X(ω)e jωt dω is an infinitesimal quantity, indicating that the amplitude of the harmonic component of the signal x(t) at the angular frequency ω approaches zero. The harmonic component has a certain magnitude only within a certain frequency range, that is, it has amplitude meaning only after the frequency is integrated within this frequency band. The stimulated acoustic emission signal processing algorithm also includes a wavelet transform method to simultaneously extract the time domain and frequency domain information of the signal. The definition of wavelet transform is: Among them, W ψ is the wavelet function, represents its complex conjugate, a is the scale, b is the translation, 1 / a corresponds to the frequency, t is the time, f(t) is the original function, and represents the time domain signal; different wavelet basis functions are selected according to the acoustic emission signal, including any one of the Db series wavelet, Coif series wavelet, and Morlet wavelet; the denoising of the acoustic emission signal using wavelet transform is divided into three steps, firstly determining the wavelet basis function and the number of decomposition layers; secondly, processing each layer of wavelet coefficients according to the selected threshold function; finally, the processed wavelet coefficients are used to reconstruct the acoustic emission signal; there are three main types of threshold functions, namely hard threshold function, soft threshold function and semi-soft threshold function, The hard threshold function is: The soft threshold function is: Among them, λ is the threshold.

7. The method for monitoring the excitation and response of dangerous machinery of explosives according to claim 5, characterized in that: The multi-point reconstruction algorithm of the excitation acoustic emission signal reconstructs the acoustic emission signals of multiple acoustic sensors. There are N sensors, and the sensor positions are (x i ,y i )(i=1,2,…,N), the acoustic emission source position (x0,y0) emits a wave signal with a wave speed of c at time t0, and the signal received by the i-th sensor arrives at time t i , then the relationship between the source position and the coordinates of each sensor, wave speed and arrival time is as follows: (x i -x0) 2 +(y i -y0) 2 =c 2 (t i -t0) 2 Assuming the source position coordinates are (x, y), the arrival time of the signal received by the i-th sensor is t i , we can get the emission time τ of the emission source i The estimated value of (x,y) is: For the true source coordinates, the estimated emission time should be the same as t0, that is, when (x, y) takes (x0, y0), we have: τ i (x0,y0)=t0 When (x, y) takes other values, the estimated emission time τ i There will be a certain dispersion, and τ i The variance of is used to measure this dispersion, and the variance is: In the formula, Represents τ i The algebraic mean of When it is equal to 0, it corresponds to the real sound source, and the source coordinates are calculated from it; when there are 3 sensors, the sound source can be calculated; when the number of sensors is greater than 3, the 3 sensors can be set as a group, and the arithmetic mean of the positioning results obtained in each group is taken as the final result.

8. The method for monitoring the excitation and response of dangerous machinery of explosive devices according to claim 5, characterized in that: The response acoustic emission signal processing algorithm is the same as the excitation acoustic emission signal processing algorithm, including the analysis of acoustic emission signal characteristic parameters such as amplitude, energy, rise time, duration, ringing count and event count, as well as spectral analysis and wavelet transform.

9. The method for monitoring the excitation and response of dangerous machinery of explosive devices according to claim 5, characterized in that: The algorithm for comprehensive identification and evaluation of the excitation and response of dangerous machinery with pyrotechnics is constructed by building a generative adversarial prediction model with Transformer as the backbone to solve the problem. The algorithm takes advantage of the generative adversarial network framework and integrates the data-driven Transformer network. Aiming at the working mechanism characteristics of pyrotechnics, the Transformer network model architecture is divided into three layers: physical information input layer, state coupling layer, and parameter mapping layer. The physical information input layer corresponds to the components and systems of the engine and is used to learn the working characteristics of the excitation sound signal and the system. The excitation sound signal mainly includes: (1) sub-time series signals at different frequency sequences such as the first trough amplitude of the excitation signal, S2 modal energy, and high-frequency energy ratio EP, which characterize the excitation energy parameters of dangerous machinery with pyrotechnics; (2) The pyrotechnic product model vector data is generated based on the product structure parameters, and the pyrotechnic product dangerous mechanical excitation position parameters are obtained according to the signal collection time and the acoustic emission sensor position information; (3) Through mechanical excitation tests, the dimensional characteristic values ​​of multiple sets of different data dimensions for mechanical excitation intensity are obtained; the coupling layer, as an intermediate layer for feature extraction, has a one-way link relationship with the physical information input layer; the joint work between pyrotechnic product components and systems is reflected in the coupling layer, which intervenes in the learning state of the physical information input layer; the top layer of the architecture is the parameter mapping layer, which establishes the relationship between the abstract features of the coupling layer and the pyrotechnic product; through the fusion of multiple information such as timing signals, image position vector data and excitation intensity characteristic values, the evaluation of the pyrotechnic product mechanical excitation results includes: (1) the damage mode of the pyrotechnic product dangerous mechanical excitation response, that is, pitting or perforation; (2) the damage degree of the pyrotechnic product dangerous mechanical excitation response, that is, pit depth or hole diameter; According to the above-mentioned comprehensive identification and evaluation algorithm of pyrotechnic hazardous mechanical excitation and response, the damage mode of pyrotechnic hazardous mechanical excitation response is identified, and the degree of damage to pyrotechnic hazardous mechanical excitation response is evaluated, thereby quantifying the safety hazard level of pyrotechnic hazardous mechanical excitation and response.

Images