An intelligent charge column and its crack monitoring system

Abstract

The present disclosure relates to an intelligent charge and its crack monitoring system, and relates to the field of charge crack monitoring technology. It is used to solve the problem that the existing technology cannot monitor the occurrence of cracks in the charge in real time. By arranging a crack signal sensing module and a waveguide module between the explosive and the ammunition shell, the elastic stress waves generated when the charge cracks are generated are collected, and the elastic stress waves are adjusted into high-frequency signals for wireless transmission through the waveguide module. By arranging a signal receiving module and a signal processing module outside the charge, the charge crack information emitted by the crack signal sensing module can be received and processed and analyzed to obtain detailed information such as whether cracks have occurred and the size of the cracks, thereby solving the problem that the charge cannot be monitored in real time to determine whether cracks have occurred.

Description

Technical Field

[0001] The present disclosure relates to the technical field of grain crack monitoring, and in particular to an intelligent grain and a crack monitoring system thereof. Background Art

[0002] Solid rocket motor grains are the heart of a rocket engine and require extremely high stability. During the pouring, demolding, cooling, and assembly processes, defects such as unevenness, cracks, pores, and inclusions can occur. Alternatively, when subjected to external forces, the grains may deform and expand, leading to fuel leaks. These factors can affect proper combustion during ignition, causing accidents such as crossfire. In severe cases, they can even cause the rocket engine to explode and lead to launch failure. Therefore, timely and non-destructive detection of leaks and real-time monitoring of grain performance are crucial issues for ensuring safety.

[0003] Generally, the detection methods for drug columns are carried out by various non-contact and non-destructive methods. They mainly include:

[0004] (1) Ultrasonic non-destructive testing method: Aerospace engine propellant is a polymer composite material that strongly attenuates sound waves. As the ultrasonic frequency increases, the attenuation coefficient increases. When the appropriate ultrasonic frequency is selected, tiny cracks can be detected while having good propagation performance in the grain. The advantages of this method are high sensitivity, and it can simultaneously detect debonding of the coating layer and internal defects of the grain, making it easy to achieve automated large-scale testing. At the same time, the ultrasonic non-destructive testing method has been widely used due to its advantages of accurate positioning, low manufacturing and maintenance costs, no radiation, and no pollution.

[0005] However, the ultrasonic non-destructive method has low sensitivity for small cracks and has certain blind spots. It has poor detection capabilities for surface and near-surface defects and reflective surfaces that are not perpendicular to the axis of the sound beam. In addition, the sound path is long and the attenuation is large. At the same time, it is difficult to achieve real-time monitoring.

[0006] (2) Laser holographic detection method: Laser holographic detection technology is based on the principle of light interference and uses the double exposure method in laser holographic interferometry to detect the deformation of the grain coating. The displacement field distribution of the grain surface deformation is determined by the interference characteristic fringes that appear on the hologram before and after deformation, thereby determining the location and size of the defect.

[0007] However, this method is cumbersome to operate, time-consuming to process, and cannot achieve real-time monitoring. It is difficult to detect cracks inside the grain that is covered by the external coating.

[0008] (3) X-ray method: X-rays are used to penetrate the grain, during which the energy of the X-rays will attenuate. The degree of attenuation is related to the wavelength of the X-rays and the thickness, density, and shape of the material being penetrated. When the grain has defects or cracks, there will be a certain thickness difference, resulting in different degrees of attenuation. The attenuation image is recorded on film and processed to determine the location of the crack.

[0009] However, due to the uneven intensity of X-rays, the X-ray detection images have low contrast and blurred defect edges. Moreover, under complex conditions, it is difficult to correctly extract and segment defect information from the detection images.

[0010] Extensive research has been conducted on grain crack monitoring, and systems are becoming increasingly sophisticated. However, these non-destructive testing techniques require disassembling the grain and performing testing in a laboratory environment. This makes it impossible to monitor the grain in real time, and to immediately detect cracks. This poses a potential risk during use. Summary of the Invention

[0011] To address the inability of existing systems to monitor grain cracks in real time, this paper proposes an intelligent grain crack monitoring system. By placing a crack signal sensing module between the explosive and the ammunition casing, this system collects elastic stress waves generated by grain cracks, converts these stress waves into high-frequency signals for wireless transmission, and processes and analyzes them, thereby obtaining grain crack information.

[0012] In order to solve the above technical problems, the technical solutions of the present invention are as follows.

[0013] In a first aspect, a smart charge is provided, wherein a crack signal sensing module and a waveguide module are provided between the explosive and the ammunition casing of the charge, and the crack signal sensing module and the waveguide module are connected to each other; during the explosive filling process, the crack signal sensing module and the ammunition casing are squeezed by the extrusion effect generated by the slurry and the ammunition casing, so that the waveguide module and the ammunition casing are bonded and contacted; the crack signal sensing module is configured to collect elastic stress waves generated when cracks occur in the charge, and the elastic stress waves are transmitted outward by the waveguide module via wireless radio frequency.

[0014] In the above technical solution, a crack signal sensing module is set between the ammunition and the cartridge case to monitor the propellant. Without removing the propellant, the propellant can be monitored in real time so that the occurrence of cracks can be known at the first time. The crack signal sensing module is integrated with the ammunition casing through the waveguide module to ensure that the signal corresponding to the generated elastic stress wave is transmitted wirelessly.

[0015] In one embodiment, the crack signal sensing module is composed of a six-layer structure. Between the charge and the ammunition shell, there are, in order, the package bottom surface, the bottom electrode layer, the flexible polymer piezoelectric film layer, the surface electrode layer, the package surface, and the flexible circuit board layer; the material of the flexible polymer piezoelectric film layer is P (VDF-TrFE); the surface electrode layer and the bottom electrode layer are printed on the flexible polymer piezoelectric film layer by spraying; the package bottom surface and the package surface are both PE materials, and are integrally packaged with the bottom electrode layer, the surface electrode layer and the flexible polymer film layer by plastic sealing; the surface electrode layer and the bottom electrode layer are connected to the flexible circuit board layer through a lead-out structure; the flexible circuit board layer is composed of a conditioning circuit and a harmonic transmitting antenna; the flexible piezoelectric sensor is used to collect the elastic stress wave generated when the charge cracks, and convert the elastic stress wave into a high-frequency voltage signal, which is adjusted into a high-frequency signal by the conditioning circuit and transmitted outward through the harmonic transmitting antenna via the waveguide structure.

[0016] In this embodiment, the crack signal sensing module is flexibly mounted on the propellant, with the bottom surface of the package facing the propellant, and the flexible circuit board layer facing the ammunition casing, connected to the waveguide module. The flexible piezoelectric sensor utilizes a novel flexible piezoelectric film, and electrodes can be prepared by screen printing or vacuum sputtering. The bottom and surface of the package are both made of PE material. The harmonic transmitting antenna is implemented by a flexible antenna structure printed on the flexible circuit board substrate, allowing the crack signal sensing module to be attached to the interior of the cartridge case via a viscous medium. As ammunition is continuously added, the flexible piezoelectric sensor is partially squeezed and pressed against the outer surface of the propellant, allowing real-time monitoring of the state and extent of cracks in the ammunition. In one embodiment, the harmonic transmitting antenna has a serpentine structure. It is understood that the parameters of the harmonic transmitting antenna match those of the waveguide module to transmit high-frequency RF signals outward through the waveguide module.

[0017] As an improvement to the above technical solution, a passive tag is provided on the flexible circuit board layer so that when wireless transmission is blocked, signal information when a crack occurs can be read by an external card reader.

[0018] In a second aspect, the present invention proposes an intelligent charge crack monitoring system, which includes a crack signal sensing module, a waveguide module, a signal receiving module, and a signal processing module; the crack signal sensing module and the waveguide module are connected, and the two are located between the explosive of the charge and the ammunition shell; during the explosive filling process, the extrusion effect generated by the slurry and the ammunition shell causes the crack signal sensing module to be bonded and contacted with the ammunition shell, so that the waveguide module is connected to the ammunition shell; the crack signal sensing module is configured to collect the elastic stress wave generated when the charge cracks, and the waveguide module transmits the signal corresponding to the elastic stress wave to the outside through wireless radio frequency; the signal receiving module is configured to receive the high-frequency harmonic signal transmitted by the waveguide structure through the harmonic receiving antenna, and restore the received high-frequency harmonic signal to the high-frequency signal generated by the charge crack through the demodulation circuit; the signal processing module is configured to analyze and process the high-frequency signal to obtain crack information.

[0019] In the above technical solution, when cracks occur in the grain, the system uses a crack signal sensing module to collect the generated elastic stress waves and then transmits them wirelessly. The system then receives and processes the signals outside the grain to achieve real-time monitoring of the grain crack generation and expansion status, as well as rough information on the crack expansion size, thereby resolving the current difficulty in real-time grain crack monitoring. The present invention can be applied to various industries requiring pressure vessels, such as the military and manufacturing industries, and is particularly useful for real-time grain monitoring and crack status assessment of pressure vessels containing hazardous agents, enabling lifespan prediction and risk assessment. It has broad application prospects and strong promotional value.

[0020] In one embodiment, the crack signal sensing module is composed of a six-layer structure. Between the charge and the ammunition shell, there are, in order, the package bottom surface, the bottom electrode layer, the flexible polymer piezoelectric film layer, the surface electrode layer, the package surface, and the flexible circuit board layer; the material of the flexible polymer piezoelectric film layer is P (VDF-TrFE); the surface electrode layer and the bottom electrode layer are printed on the flexible polymer piezoelectric film layer by spraying; the package bottom surface and the package surface are both PE materials, and are integrally packaged with the bottom electrode layer, the surface electrode layer and the flexible polymer film layer by plastic sealing; the surface electrode layer and the bottom electrode layer are connected to the flexible circuit board layer through a lead-out structure; the flexible circuit board layer is composed of a conditioning circuit and a harmonic transmitting antenna; the flexible piezoelectric sensor is used to collect the elastic stress wave generated when the charge cracks, and convert the elastic stress wave into a high-frequency voltage signal, which is adjusted into a high-frequency signal by the conditioning circuit and transmitted outward through the harmonic transmitting antenna via the waveguide structure.

[0021] In this embodiment, the crack signal sensing module utilizes a flexible piezoelectric film to form a flexible piezoelectric sensor and a flexible circuit substrate, allowing the module to be attached to the interior of the cartridge case via a viscous medium. As ammunition is continuously added, the flexible piezoelectric sensor is partially squeezed against the outer surface of the charge, enabling real-time monitoring of the state and extent of cracks in the ammunition. Electrodes can be fabricated on the flexible piezoelectric film through screen printing or vacuum sputtering. A conditioning circuit and harmonic transmitting antenna are then printed on the flexible circuit substrate and connected to the polymer flexible piezoelectric film coated with the electrode and circuit film. When cracks develop in the charge, the flexible piezoelectric film detects the stress wave signal generated by the crack and converts this elastic stress wave into a voltage signal. This signal is converted into a high-frequency signal by the conditioning circuit and wirelessly transmitted using a waveguide module. This signal is then transmitted to the exterior of the charge for analysis and processing based on signal processing methods, or further displayed.

[0022] As an improvement to the above technical solution, a passive tag is further provided on the flexible circuit board layer, and the system further includes an external card reader. When wireless transmission is blocked, the signal information when the crack occurs can be read from the passive tag via the external card reader.

[0023] In one embodiment, the signal processing module includes a noise reduction and reconstruction unit and a crack identification unit. The noise reduction and reconstruction unit separates the original elastic stress wave signal-related components in the high-frequency signal through a signal noise reduction and reconstruction algorithm, and performs noise reduction and reconstruction. In one embodiment, the noise reduction and reconstruction adopts an atomic basis function-based reconstruction method, uses a series of standard atomic basis functions and stress wave feature matching pursuit to reconstruct the elastic stress wave signal, and realizes noise reduction processing of the signal. The crack identification unit converts the reconstructed elastic stress wave time domain signal into a time-frequency data array, determines the characteristic signal frequency range based on the grain crack signal and the prior signal feature library, obtains crack characteristics in combination with a preset machine learning model, and determines crack information. In one embodiment, the input of the training data of the preset machine learning model is the experimentally obtained grain crack size and stress wave signal characteristics, and the output is information including semi-quantitative judgment of whether the grain is damaged, the crack development speed, and the degree of grain damage.

[0024] In one embodiment, the machine learning model takes the reconstructed stress wave signal and its power spectrum as input and outputs crack information; the crack information includes whether the grain is damaged, the crack development speed, and semi-quantitative judgment information for the degree of grain damage.

[0025] In one embodiment, the signal processing module further includes a display module configured to display crack information of the charge, including the presence or absence of cracks, crack depth, crack growth rate, etc.

[0026] As a further improvement of the above technical solution, the system also includes a remote information center, which is configured to collaboratively control the charge crack signal perception module, signal receiving module and signal processing module, and store and manage the identification results obtained by analysis and processing, and automatically upgrade the relevant algorithms for signal processing and identification and / or update the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] Figure 1 , is a schematic diagram of the structure of a smart drug column in one embodiment;

[0029] Figure 2 , is a schematic structural diagram of a crack monitoring system in one embodiment;

[0030] Figure 3 , is a schematic diagram of a six-layer structure of a crack signal sensing module in one embodiment;

[0031] Figure 4 , is a schematic diagram of a cross-sectional structure of a crack signal sensing module in one embodiment;

[0032] Figure 5 , is a schematic diagram of the workflow of a signal processing module in one embodiment;

[0033] Among them: 1 is the intelligent charge, 2 is the external terminal system; 101 is the crack signal sensing module, 102 is the ammunition shell, 103 is the charge, 104 is the waveguide module; 201 is the signal receiving module, 202 is the signal processing module, 203 is the remote information center; 111-flexible piezoelectric sensor, 112-conditioning circuit, 113-passive tag, 114-harmonic transmitting antenna; 201 is the signal receiving module, 211 is the harmonic receiving antenna, 212 is the signal demodulation module, 202 is the signal processing and display module, 221 is the noise reduction and reconstruction unit, 222 is the crack identification unit; 301 is the bottom surface of the package, 302 is the bottom electrode layer, 303 is the flexible polymer piezoelectric film layer, 304 is the surface electrode layer, 305 is the package surface, and 306 is the flexible circuit board layer. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0035] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "install", "connect", and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0036] The purpose of the present invention is to monitor the damage and leakage of the grain in real time without disassembling the grain, so as to obtain semi-quantitative information on whether there are cracks and the size of the cracks in the first place, so as to ensure the real-time performance of the grain.

[0037] Figure 1 The present invention is a schematic diagram of a wireless flexible charge crack monitoring system in one embodiment. In this embodiment, the system consists of two parts: an intelligent charge (1) and an external terminal system (2). The intelligent charge (1) and the external terminal system (2) communicate and transmit data via wireless radio frequency.

[0038] The intelligent explosive column (1) comprises a crack signal sensing module (101), an ammunition shell (102), an explosive column (103), and a waveguide module (104). The crack signal sensing module (101) and the waveguide module (104) are connected, and both are located between the explosive column (103) and the ammunition shell (102), and adopt an integrated design. The crack signal sensing module (101) and the waveguide module (104) are located and installed on the inner side of the ammunition shell (102), between the explosive column (103) and the ammunition shell (102). During the explosive filling process, the extrusion effect generated by the slurry and the ammunition shell causes the crack signal sensing module (101) and the explosive column (103) to be bonded and contacted, so that the waveguide structure is closely attached to the shell, thereby realizing the integration of the installation of the crack signal sensing module (101) and the waveguide module (104) with the explosive column, and is not affected by the surface of the explosive column, and is easy to manufacture.

[0039] The external terminal system (2) includes a signal receiving module (201), a signal processing module (202) and a remote information center (203). The signal receiving module (201) receives the high-frequency harmonic signal sent by the intelligent drug column (1) and demodulates the original stress wave signal to transmit it to the signal processing and display module (202). The signal processing module (202) analyzes and processes the elastic stress wave to display the damage status and development speed information of the drug column. The remote information center (203) remotely coordinates the work of various parts and accumulates the data generated by the cracks for data accumulation in the later upgrade algorithm.

[0040] like Figure 2As shown, when a crack occurs in the charge, the elastic stress wave generated by the crack generates a voltage signal on the flexible piezoelectric sensor (111). The voltage signal is filtered and amplified by the conditioning circuit (112). The generated conditioning signal is transmitted to the harmonic transmitting antenna (114) and transmitted to the signal receiving module through the waveguide module (104).

[0041] A passive tag (113) can be provided in the crack signal sensing module (101) to store the conditioned signal of the conditioning circuit (112). When wireless transmission is blocked and cannot work, the signal can be read by an external RFID reading system, and the conditioned signal can be sent to an external terminal system (2) for processing and / or display via an external power supply and transmission device. In this case, the system also includes an external card reader for reading signal information when a crack is generated from the passive tag.

[0042] The harmonic receiving antenna (211) of the signal receiving module receives the signal from the harmonic transmitting antenna (114) and sends it to the signal demodulation module (212) to separate the signal wave and the carrier wave, and then sends the grain crack damage signal to the signal processing module (202). After the signal undergoes noise reduction and reconstruction processing by the noise reduction and reconstruction unit in the signal processing module (202), the established machine learning algorithm is used in the crack identification unit (222) to perform grain crack feature recognition and analysis, and finally outputs parameters such as the presence or absence of cracks, crack depth, and crack development factor, and outputs these crack information. In one embodiment, the system is provided with a display module. These crack information is output to the display module, and the crack status information is displayed on the display screen. The remote information center (203) is used to remotely coordinate the operation of various parts, store data information, and accumulate data for future algorithm upgrades and model reconstruction.

[0043] like Figure 3 , Figure 4As shown, the crack signal sensing module (101) consists of a six-layer structure. They are, in order, the package bottom surface, the bottom electrode layer, the flexible polymer piezoelectric film layer, the surface electrode layer, the package surface, and the flexible circuit board layer. Among them, (301) is the package bottom surface, which is close to the drug column (103); followed by the bottom electrode layer (302), the flexible polymer piezoelectric film layer (303), the surface electrode layer (304), the package surface (305), and the flexible circuit board layer (306), which is connected to the ammunition shell (102) through the waveguide module (104). The bottom electrode layer (302) and the surface electrode layer (304) are connected to the flexible circuit board (306) through the lead-out structure. By connecting the electrodes printed on the flexible polymer piezoelectric film to the harmonic circuit printed on the flexible substrate, and then through the waveguide module based on the ammunition shell, the real-time transmission of crack information is achieved. In the above embodiment, the flexible polymer piezoelectric film is specifically P(VDF-TrFE). Compared with the traditional PVDFA piezoelectric film, it has better piezoelectric performance, longer service life, and higher response sensitivity, and can realize long-term monitoring of cracks in the drug column.

[0044] In the above embodiment, the signal transmission process is as follows:

[0045] When a crack develops in the charge, the flexible piezoelectric sensor detects the stress wave signal generated by the crack and converts this elastic stress wave into a voltage signal. This voltage signal, after passing through a conditioning circuit, is then transmitted via a harmonic transmitting antenna through a waveguide module. The on-site processing module demodulates and reconstructs the signal, applying an established machine learning algorithm to identify and analyze the crack characteristics of the charge. It ultimately outputs parameters such as the presence of cracks, crack depth, and crack growth factor, and transmits this information to the display module. This conditioned voltage signal is stored and backed up in a passive tag. In the event of a wireless transmission failure, an external read / write module directly reads the information from the passive tag, ensuring efficient and accurate monitoring.

[0046] In the above embodiment, the signal processing algorithm involved demodulates the signal demodulation module in the signal receiving module to complete the demodulation of the grain crack information to obtain the restored elastic acoustic wave signal, and uses the atomic basis function reconstruction noise reduction algorithm to demodulate and restore the wireless signal. Figure 5 As shown in FIG, the denoising algorithm based on atomic basis function reconstruction includes the following steps:

[0047] The recovered elastic acoustic wave signal is subjected to morphological matching tracking using a library of constructed atomic basis functions, and then reconstructed using a nonlinear reconstruction function to obtain the elastic acoustic signal. The resulting elastic acoustic wave signal achieves noise reduction. The time domain signal of the reconstructed elastic acoustic wave signal is then converted into a time-frequency data array. The frequency range of the characteristic crack signal is automatically determined based on the grain crack signal and the prior signal feature library. Power spectrum integration is performed within different frequency windows to obtain energy density within different frequency bands. The established grain crack state recognition machine learning algorithm model is used to identify features such as crack depth and output corresponding crack feature recognition information. This includes the following steps:

[0048] (1) Construct an overcomplete matching atom library G = (g0, g1, ..., g n ), where each atom is a wave packet signal with similar characteristics to the original signal wave packet.

[0049] (2) Calculate the inner product of the monitoring signal f(t) and each atom in the atom library in turn, and select the atom with the largest absolute value of the inner product:

[0050]

[0051] (3) Subtract the signal component matched by g from the signal f(t) to obtain the residual signal Rf:

[0052] Rf=f(t)- <f(t),g i >g

[0053] Where:

[0054] (4) The residual signal is recorded as the initial signal to be decomposed f(t), and the process jumps to step 2 to continue. The number of matches P is limited or an amplitude threshold Q is set for the residual signal. When the amplitude of the residual signal is less than Q or the number of iterations is greater than P, the iteration terminates.

[0055] (5) Add the best matching atoms obtained from each matching to obtain the reconstructed signal.

[0056] (6) Then, the acoustic wave time domain signal Ft and the acoustic wave signal power spectrum Pt are used as inputs, and the preset support vector machine machine learning model h is used to automatically identify the presence of cracks (St), crack depth (Sd), and development speed (Sv) of the charge. Among them, St output is a binary output, and Sd and Sv are continuous variable outputs. The model is expressed as:

[0057] Y(St,Sd,Sv,…)=h(Ft,Pt)

[0058] (7) Finally, the crack information is transmitted to the display module for display, so that the crack status of the grain can be visually seen to prevent misjudgment. The crack information is also sent to the remote information center for further data processing and management. Furthermore, when a crack is detected, the system provides a text or sound prompt.

[0059] In the above steps, the preset support vector machine machine learning model can be used to demodulate and identify crack signals, automatically obtain information such as the presence or absence of cracks, crack depth, and expansion speed, and realize real-time intelligent monitoring.

[0060] In summary, the present invention uses a flexible piezoelectric film to prepare a flexible piezoelectric sensor, especially a new type of flexible piezoelectric film P (VDF-TrFE). Compared with the traditional PVDFA piezoelectric film, it has better piezoelectric performance, longer service life, and higher response sensitivity, which can achieve long-term monitoring of cracks in the projectile. Furthermore, each layer in the crack signal sensing module uses a flexible material, and the surface electrode layer and the bottom electrode layer are printed by spraying, so that the crack signal sensing module can fit the ammunition shell. When judging the crack situation of the projectile, there is no need to disassemble the projectile, thereby achieving real-time non-destructive monitoring. Furthermore, the present invention uses a machine learning algorithm to obtain crack information, thereby realizing intelligent monitoring of cracks.

[0061] Through the above description of the embodiments, those skilled in the art will clearly understand that the content of the method of the present disclosure can be implemented by software plus necessary general-purpose hardware. Of course, it can also be implemented by dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. In general, any function performed by a computer program can be easily implemented by corresponding hardware. Moreover, the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the present disclosure, in most cases, software program implementation is the preferred embodiment.

[0062] Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields. The above-mentioned specific embodiments are merely illustrative and instructive, and are not restrictive. A person skilled in the art, guided by this specification and without departing from the scope of protection of the claims of the present invention, may also devise various forms, all of which fall within the scope of protection of the present invention.

Claims

1. A smart drug column, characterized by: A crack signal sensing module and a waveguide module are provided between the explosive of the charge and the ammunition casing, and the crack signal sensing module and the waveguide module are connected; During the explosive filling process, the extrusion effect between the slurry and the ammunition casing causes the crack signal sensing module to bond with the ammunition casing, thereby connecting the waveguide module to the ammunition casing. The crack signal sensing module is configured to collect elastic stress waves generated when cracks occur in the charge, and the waveguide module transmits the elastic stress waves outwards via wireless radio frequency. The crack signal sensing module consists of a six-layer structure, which is composed of the package bottom surface, the bottom electrode layer, the flexible polymer piezoelectric film layer, the surface electrode layer, the package surface, and the flexible circuit board layer between the charge and the ammunition shell. The material of the flexible polymer piezoelectric film layer is P(VDF-TrFE); The surface electrode layer and the bottom electrode layer are printed on the flexible polymer piezoelectric film layer by spraying; The bottom and surface of the package are made of PE material, and are encapsulated together with the bottom electrode layer, surface electrode layer and flexible polymer film layer through plastic sealing; The surface electrode layer and the bottom electrode layer are connected to the flexible circuit board layer through a lead-out structure; The flexible circuit board layer consists of a conditioning circuit and a harmonic transmitting antenna.

2. The drug column according to claim 1, characterized in that: There are also passive tags on the flexible circuit board layer.

3. A wireless flexible charge crack monitoring system, characterized in that: The system includes a crack signal sensing module, a waveguide module, a signal receiving module, and a signal processing module; The crack signal sensing module is connected to the waveguide module, and the two are located between the explosive of the charge and the ammunition casing; During the explosive filling process, the extrusion effect between the slurry and the ammunition casing causes the crack signal sensing module to come into contact with the ammunition casing, thereby connecting the waveguide module to the ammunition casing. The crack signal sensing module is configured to collect elastic stress waves generated when cracks occur in the charge, and the waveguide module transmits the signals corresponding to the elastic stress waves to the outside through wireless radio frequency. The signal receiving module is configured to receive the high-frequency harmonic signal transmitted by the waveguide structure through the harmonic receiving antenna, and restore the received high-frequency harmonic signal to the high-frequency signal generated by the crack of the grain through the demodulation circuit; The signal processing module is configured to analyze and process the high-frequency signal to obtain crack information; The crack signal sensing module consists of a six-layer structure. Between the charge and the ammunition shell, there are, in order, the package bottom surface, the bottom electrode layer, the flexible polymer piezoelectric film layer, the surface electrode layer, the package surface, and the flexible circuit board layer. The material of the flexible polymer piezoelectric film layer is P (VDF-TrFE). The surface electrode layer and the bottom electrode layer are printed on the flexible polymer piezoelectric film layer by spraying; The bottom and surface of the package are made of PE material, and are encapsulated together with the bottom electrode layer, surface electrode layer and flexible polymer film layer through plastic sealing; The surface electrode layer and the bottom electrode layer are connected to the flexible circuit board layer through a lead-out structure; The flexible circuit board layer consists of a conditioning circuit and a harmonic transmitting antenna.

4. The system according to claim 3, wherein: There is also a passive tag on the flexible circuit board layer, and the system also includes an external card reader; The external card reader is used to read signal information when a crack occurs from the passive tag.

5. The system according to claim 3, wherein: The signal processing module includes a noise reduction and reconstruction unit and a crack identification unit; The noise reduction and reconstruction unit separates the original elastic stress wave signal-related components in the high-frequency signal through a signal noise reduction and reconstruction algorithm, and performs noise reduction and reconstruction; The crack identification unit converts the reconstructed time domain signal of the elastic stress wave into a time-frequency data array, determines the characteristic signal frequency range based on the charge crack signal and the prior signal feature library, obtains the crack characteristics in combination with the preset machine learning model, and determines the crack information.

6. The system according to claim 5, characterized in that: The machine learning model takes the reconstructed stress wave signal and its power spectrum as input and outputs crack information; The crack information includes whether the grain is damaged, the crack growth rate, and semi-quantitative judgment information for the degree of grain damage.

7. The system according to claim 5, characterized in that: The noise reduction reconstruction adopts an atomic basis function-based reconstruction method.

8. The system according to claim 3, wherein: The signal processing module also includes a display module configured to display the crack information of the charge.

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