Structural impact positioning method and system based on adaptive time-reversal focusing imaging and image fusion

Abstract

The present application discloses a method for structural shock location based on adaptive time-reversal focusing imaging and image fusion, the method comprising extracting multiple narrowband wave signals of different frequencies of the shock stress wave signal through complex Morlet wavelet transform; setting the initial parameters of the algorithm, for each of the narrowband wave signals, obtaining the adaptive time-reversal focusing imaging result images of all narrowband wave signals based on the set initial parameters of the algorithm, and then calculating the image focusing index of each imaging result image, setting the focusing index threshold of the imaging result image according to the actual shock location imaging accuracy requirements, multiplying and fusion all the imaging result images above the focusing index threshold, and obtaining the grid point coordinates corresponding to the maximum pixel value of the fused image as the predicted shock position. The shock location accuracy of the present application can reach a relatively high level. The present application also discloses a corresponding system.

Description

Technical Field

[0001] The present invention belongs to the field of structural health monitoring, and in particular relates to a structural impact positioning method and system based on adaptive time-reversal focusing imaging and image fusion. Background Art

[0002] Composite materials are widely used in aerospace and other fields due to their advantages such as high specific strength, high specific stiffness, and strong designability. However, composite materials have weak impact resistance. During service and maintenance, they will inevitably be affected by low-speed impact events such as bird strikes, hail, runway debris, and falling maintenance tools, causing invisible damage inside the composite materials. This tiny damage will cause the structural bearing capacity to decrease and produce sudden damage, which seriously threatens the stability and safety of the aircraft service. Impact monitoring technology collects accurate impact response signals through a sensor network, and combines relevant signal processing methods and positioning algorithms to identify the impact position. After determining the impact position, the identified impact position area is then damaged and safety assessed. Compared with the traditional non-destructive testing technology that uses scanning of large structural areas, impact monitoring technology can shorten the time for damage location and damage identification and reduce the cost of structural maintenance. Therefore, it is very necessary to carry out real-time online impact monitoring of composite materials.

[0003] Shock stress wave signals can be collected by piezoelectric, optical fiber or acceleration sensors. At present, common shock location algorithms include time difference method, virtual time reversal method, multiple signal classification method, etc. The time-frequency domain characteristic parameters contained in the shock stress wave signal and the prior knowledge of the wave propagation medium are the key to realizing the location algorithm, such as wave arrival time, wave propagation speed in the structure and frequency component information. The time difference method establishes multiple mathematical equations through the geometric position relationship of the sensor to achieve shock location. Its key is to obtain accurate wave arrival time, but in some complex structures or extreme environments, it is difficult to obtain accurate wave arrival time, which seriously affects the accuracy of shock location results. The virtual time reversal method based on phase synthesis realizes shock source identification by compensating the phase difference of narrowband wave signals and then superimposing the response amplitude. However, this method requires the advance determination of the wave propagation speed in the structure. Due to the anisotropic characteristics of composite materials, their wave speed varies with the propagation direction. The accuracy of wave speed acquisition directly affects the final positioning accuracy. Multiple signal classification performs shock location based on the orthogonality of the signal direction vector and the noise subspace, but this algorithm consumes a large number of sensors and has problems such as scanning blind spots and complex calculations. The machine learning-based method can realize the impact location at any position of the structure by obtaining signal characteristic parameters as input data sets for sample training. The reference database method requires the collection of dense training points in the monitoring area to establish a reference database for impact location. However, this method requires a huge amount of training samples to be collected, which is time-consuming and difficult to apply to real structural health monitoring systems.

[0004] In summary, based on the shortcomings of existing technologies and the needs of engineering applications, there is an urgent need for an impact location method that does not require the acquisition of precise signal characteristic parameters such as wave arrival time and wave velocity and structural prior knowledge, and can achieve real-time and efficient monitoring. Summary of the invention

[0005] In order to overcome the shortcomings of existing impact positioning methods, the present invention provides a structural impact positioning method based on adaptive time reversal focusing imaging and image fusion, the method comprising: step 1: marking the sensor position coordinates and recording the impact stress wave signal; step 2: extracting multiple narrowband wave signals of different frequencies of the impact stress wave signal through complex Morlet wavelet transform; step 3: setting the initial parameters of the algorithm, including setting the initial value of group velocity iteration, the iteration step and the total number of iterations; step 4: for each of the narrowband wave signals, according to the envelope amplitude characteristics of the narrowband wave signal, applying the time reversal theory to establish an imaging function with the grid point coordinates and the group velocity value in the monitoring area as variables, substituting the set group velocity iteration initial value into the imaging function, calculating the pixels of all grid points for imaging, iterating with the set iteration step, and obtaining the imaging result of each iteration image, and mark the maximum value of the image pixel of each imaging result image until the set total number of iterations is reached; then compare the maximum value of the image pixel of each imaging result image, take the group velocity corresponding to the maximum pixel as the group velocity of the narrowband wave signal of the frequency, and take the imaging result image where the maximum pixel is located as the adaptive time reversal focusing imaging result image of the narrowband wave signal of the frequency; and step 5: repeat step 4 to obtain the adaptive time reversal focusing imaging result images of all narrowband wave signals, and then calculate the image focusing index of each imaging result image, set the focusing index threshold of the imaging result image according to the actual impact positioning imaging accuracy requirement, discard the imaging result images below the focusing index threshold, and perform cumulative fusion on all imaging result images above the focusing index threshold, and obtain the grid point coordinates corresponding to the maximum pixel value of the fused image as the predicted impact position.

[0006] In some embodiments, step 1 includes dividing the monitoring area of ​​the monitored structure into grids of moderate size and uniform distribution and establishing a Cartesian coordinate system, marking the position coordinates of the sensor for receiving the impact stress wave signal, and recording the collected impact stress wave signal.

[0007] In some embodiments, step 2 includes extracting multiple narrowband wave signals of the impact stress wave within the frequency spectrum range using complex Morlet wavelet transform according to the frequency spectrum range information of the impact stress wave signal collected by the sensor.

[0008] In some embodiments, the complex Morlet wavelet transform is a cmor1-1 wavelet basis transform.

[0009] In some embodiments, step 4 includes obtaining adaptive time-reversal focusing imaging for each narrowband wave signal, specifically including applying the time reversal theory to establish an imaging function with the grid point coordinates and group velocity values ​​in the monitoring area as variables according to the amplitude characteristics of the narrowband wave signal of the specific frequency, substituting the set initial value of the group velocity iteration into the imaging function, calculating the pixels of all grid points for imaging, iterating with the set iteration step size, obtaining the imaging result image of each iteration, and marking the maximum value of the image pixel of each imaging result image until the set total number of iterations is reached; then comparing the maximum value of the image pixel of each imaging result image, taking the group velocity corresponding to the maximum pixel as the group velocity of the narrowband wave signal of the frequency, and taking the imaging result image where the maximum pixel is located as the imaging result image of the adaptive time-reversal focusing imaging of the narrowband wave signal of the frequency. The relationship between the adaptive inversion focusing result of a certain frequency and the group velocity value of the narrowband wave of the frequency is: Among them, Cg f is the group velocity.

[0010] In some embodiments, the time delay of the sensor is compensated in the order of the impact stress waves received by the sensor to obtain the signal of each sensor after the time delay is compensated; the preset group velocity initial value is substituted into the imaging function, and the cumulative sum of the maximum amplitudes of the signals of each sensor after the time delay compensation at all grid points in the monitoring area is calculated, and the sum is used as a pixel for imaging.

[0011] In some embodiments, the time delay of the specific sensor is determined in the following manner: assuming that a grid point of the coordinate system is an assumed impact source; calculating the propagation distance of the impact stress wave from the grid point where the assumed impact source is located to each sensor: based on the distance difference between the distance the impact stress wave propagates to the specific sensor and the distance it propagates to other sensors and the average group velocity of the narrowband wave signal propagating in the measured structure at a specific frequency, the time delay between the narrowband wave signal propagating to each sensor and propagating to the specific sensor at the specific frequency is calculated.

[0012] In some embodiments, the monitored structure is a composite material plate, in particular a composite material plate with reinforcing ribs.

[0013] Some embodiments of the present application also provide a structural impact positioning system based on adaptive time-reversal focusing imaging and image fusion, the system comprising a processor and a memory, the memory storing a computer program, and the program executing any one of the above-mentioned structural impact positioning methods based on adaptive time-reversal focusing imaging and image fusion.

[0014] The beneficial technical effects of one or more embodiments of the present application include but are not limited to:

[0015] On the one hand, the present invention does not require prior knowledge of the wave propagation medium, can realize adaptive time-reversal focusing imaging when the stress wave velocity information is unknown, and can predict the wave velocity.

[0016] On the other hand, the present invention introduces an image focusing index to fuse the multi-frequency adaptive time-reversal focusing imaging results, and further predicts the impact position, thereby reducing the interference of environmental noise during practical applications.

[0017] On the other hand, the impact positioning accuracy of the present invention can reach a relatively high level, and is applicable to plate-like structures, and particularly to the impact positioning of plate-like structures containing reinforcing ribs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the method of the present invention;

[0019] Figure 2 This is a schematic diagram of the impact positioning principle of the present invention;

[0020] Figure 3A It is a schematic diagram of the passive monitoring system, the composite material stiffened plate and the arrangement of the sensors thereof in an embodiment of the present invention;

[0021] Figure 3B is a schematic diagram of a composite material reinforced plate according to an embodiment of the present invention;

[0022] Figure 4 It is the time domain signal diagram of the impact stress wave collected by the sensor;

[0023] Figure 5 The impact stress wave spectrum collected by the sensor;

[0024] Figure 6 It is the time domain signal diagram of the impact stress wave at the predetermined center frequency extracted by Morlet wavelet;

[0025] Figure 7 It is a three-dimensional schematic diagram of multiple impact imaging results obtained by iterating the initial wave velocity range;

[0026] Figure 8 is an impact positioning imaging diagram of a narrowband wave signal obtained by iteration in an embodiment of the present invention;

[0027] Fig. 9 is the group velocity result of the narrowband wave signal predicted by iteration in an embodiment of the present invention;

[0028] Fig.10 This is a diagram showing the impact imaging results of multiple frequency narrowband wave signals in an embodiment of the present invention;

[0029] Fig.11This is a diagram of the focused imaging positioning result of multi-frequency fusion in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0031] The specific structural and functional details disclosed herein are merely representative and are for the purpose of describing exemplary embodiments of the present application. However, the present application can be implemented in many alternative forms and should not be interpreted as being limited to only the embodiments set forth herein.

[0032] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0033] The terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "one", "one" and "item" used herein are also intended to include plural numbers. It should also be understood that the terms "include" and / or "comprise" used herein specify the existence of stated features, integers, steps, operations, units and / or components, without excluding the existence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0034] It should also be noted that, in some alternative implementations, the functions / actions mentioned may occur in a different order than that indicated in the accompanying drawings. For example, depending on the functions / actions involved, two figures shown in succession may actually be performed substantially simultaneously or may sometimes be performed in the reverse order.

[0035] The structural shock location method based on adaptive time-reversal focusing imaging and image fusion of the present application can be based on such a test environment, that is, a sensor array is set on the monitored structure to receive shock stress wave signals, such as a piezoelectric sensor array, referred to as sensor or PZT.

[0036] like Figure 1 As shown, according to an embodiment of the present application, a method for structural impact positioning based on adaptive time-reversal focusing imaging and image fusion may specifically include the following steps:

[0037] Step 1: Establish a Cartesian coordinate system in the monitoring area of ​​the monitored structure, mark the position coordinates of the sensor for receiving the impact stress wave signal on the coordinate system, and record the impact stress wave signal received by the sensor.

[0038] like Figure 2 As shown, step 1 specifically includes dividing the monitoring area of ​​the monitored structure into M grids of moderate size and uniform distribution (side length is 1-5 mm). A Cartesian coordinate system is established on the monitoring area of ​​the monitored structure, with the length direction of the structure as the positive direction of the x-axis and the width direction of the structure as the positive direction of the y-axis; any sensor PZT used to receive the impact stress wave signal excited by the impact source is used i The position coordinates are marked as (Sx i ,Sy i ), i = 1, 2, ..., I, where I is the total number of sensors. i The received shock stress wave signal is recorded as S i (t), i=1,2,…,I.

[0039] Step 2: Extract the multi-frequency narrow-band wave signal in the impact stress wave through complex Morlet wavelet transform.

[0040] The shock stress wave signal S collected by all sensors i (t) Perform Morlet wavelet transform:

[0041]

[0042] Among them, "CWT()" is the wavelet transform function, a is the scaling parameter of the wavelet transform, and b is the translation scale parameter of the wavelet transform. "<>" represents the inner product, "*" represents the conjugate, and ψ(t) is the Morlet wavelet function:

[0043]

[0044] Among them, f b is the wavelet bandwidth, f c is the wavelet center frequency.

[0045] By adjusting the scale factor a, the wavelet center frequency f c and wavelet bandwidth f b , extract the multi-frequency narrow-band Lamb wave signal V from the impulse response signal of all sensors i f (t),f=1,2,...,K. Wherein, K is the total number of narrowband wave signals.

[0046] Step 3: Set the initial parameters of the algorithm.

[0047] Set the group velocity iteration parameters of the impact location algorithm, including the group velocity iteration range of the impact stress wave [Cg min ,Cg max ], namely, the initial value and maximum value of the group velocity iteration, the iteration step size ΔCg and the number of iteration steps k, where k=1.

[0048] Step 4: Adaptive time-reversal focusing imaging.

[0049] Assume that the grid point (x, y) is the impact source, and the distance from this point to each sensor is L i (x,y):

[0050]

[0051] Thus, the grid point (x, y) to the sensor PZT i With sensor PZT 1 The distance difference between them is D i (x,y):

[0052] D i (x,y)=L i (x,y)-L 1 (x,y) (Formula 4)

[0053] Narrowband Lamb wave signal V i f (t) propagates from the grid point (x, y) to each sensor PZT i With sensor PZT 1 The time delay between Satisfying formula (5):

[0054]

[0055] Among them, Cg f is the narrowband Lamb wave signal V i f (t) is the group velocity.

[0056] Take the time length Δ containing the direct wave and convert all the narrow-band impact stress wave signals V i f (t) time reversal is performed, and the time delay is compensated according to the principle of "first come, last sent, last come, first sent", and the signal can be synthesized at the grid point (x, y)

[0057]

[0058] Synthesized signal The maximum value of the envelope amplitude is taken as the pixel value of the grid point (x, y), and the coordinates of the monitoring area grid points and the group velocity Cg of the narrowband Lamb wave are constructed.f P is the virtual time-reversal imaging function of the variable f (Cg f ,x,y):

[0059]

[0060] Wherein, “^” indicates taking the envelope of the signal. f (Cg f ,x,y) indicates that when the group velocity of the narrowband Lamb wave is Cg f , the pixel value of the virtual time-reversal imaging at the grid point (x, y).

[0061] Since the group velocity Cg of the narrowband Lamb wave f Unknown, so according to the algorithm initial parameters set in step 3, the iterative group velocity Cg is determined according to formula (8): f :

[0062] C f =Cg min +(k-1)·ΔCg (Formula 8)

[0063] Substituting equation (8) into equation (7) iteratively calculates different group velocities Cg f Virtual time-reversal focusing imaging is performed on all the pixels at the grid points in the monitoring area.

[0064] If Cg f <Cg max ,but:

[0065] k=k+1 (Formula 9)

[0066] Iteration continues. When Cg f =Cg max , the iteration is terminated. Only under the real narrow-band Lamb wave group velocity and impact position can the virtual time-reversal focusing imaging result with the best focusing effect be obtained, so according to different group velocities Cg f The corresponding maximum pixel curve of virtual time-reversal imaging can determine the group velocity of the real narrow-band Lamb wave

[0067]

[0068] The adaptive time-reversal focusing image is

[0069]

[0070] Step 5: Image fusion to predict the actual impact position.

[0071] calculate The focusing index E f :

[0072]

[0073] Wherein, m is the number of grid points whose pixel values ​​are higher than 85% of the maximum pixel value among the M grid points of the adaptive time-reversal focusing image.

[0074] According to the aggregation index E of all images f Set the focus index threshold E 0 , the threshold E 0 It can be set to any percentage as needed, such as 70%, 80%, 85%, 90%, 95%.

[0075] The image focusing index is calculated according to the following formula: Among them, Pix is ​​the pixel higher than a certain set value Pix threshold The number of grid points is , and M is the total number of grid points.

[0076] According to claim 1, the structural impact based on adaptive time reversal focusing imaging and image fusion determines the focusing index E f Below the focus index threshold E 0 The adaptive time-reversal focused image is discarded, and all images above the focus threshold E 0 The adaptive time-reversal focused image is retained and fused by cumulative multiplication to obtain Y f :

[0077]

[0078] Where V is the total number of retained adaptive time-reversal focusing images. The grid point coordinates corresponding to the maximum pixel of the fused image are used as the predicted actual impact position.

[0079] Experimental example:

[0080] like Figure 3A , Figure 3BAs shown, it is a schematic diagram of the composite material reinforced plate 100, the arrangement of the sensor 200 and the passive monitoring system 300 used in the embodiment. The size of the composite material reinforced plate is 800mm×400mm×3.5mm. Two L-shaped reinforcing strips with a thickness of 3.5mm are pasted on the lower surface of the wall panel. The left side reinforcing strip is 300mm away from the left composite material plate boundary, and the span of the strip is 300mm. The piezoelectric sensor adopts a P-51 piezoelectric ceramic disc with a diameter of 16mm and a thickness of 0.5mm. The L-shaped strip and the wall panel material are both quasi-isotropic T700SC-12k carbon fiber fabrics, and the layering method is [0 / 90]s5. According to step 1, a Cartesian coordinate system is established with the lower left vertex of the stiffened plate as the origin, and the coordinates of the piezoelectric sensor and the impact source are recorded. A drop hammer device is used to trigger the impact event at the position of the composite stiffened plate with coordinates (300mm, 275mm). The passive dynamic signal acquisition system is used to collect and record the impact stress wave signal received by the piezoelectric sensor. The system sampling rate is set to 200kHz. Figure 4 The figure shows the time domain diagram of the typical impact stress wave signal received by the No. 7 piezoelectric sensor. Figure 5 The figure shows the normalized spectrum of the typical impact stress wave signal received by the No. 7 piezoelectric sensor. From the spectrum, we can see that the main frequency component of the impact stress wave signal is between 0 and 10 kHz. According to step 2, the complex Morlet wavelet transform is used to extract the following Figure 6 The impact stress narrowband wave signal with a center frequency of 5kHz is shown. According to step 3, the group velocity iteration range of the impact stress wave propagating in the measured structure is set to [600,1400]m / s, the iteration step is 20m / s, and the total number of iteration steps is 41. According to step 4, the narrowband wave signal with a center frequency of 5kHz is substituted into the iteration process for time-reversed focusing imaging, as shown in Figure 7 The following is a three-dimensional schematic diagram of all imaging results obtained by iteration. Figure 8 The image with the largest pixel is taken as the final impact positioning imaging result. At this time, the group velocity corresponding to the maximum pixel curve of the virtual time reversal imaging is the group velocity of the real impact stress narrowband wave, as shown in Fig. 9 As shown in the figure, the 740m / s corresponding to “☆” is the group velocity value obtained by the final iteration prediction. Fig.10 The figure shows the result of impact imaging at the position with coordinates (300mm, 275mm) according to step 4 using six frequency narrowband wave signals of 5kHz, 6kHz, 7kHz, 8kHz, 9kHz and 10kHz. In the figure, "○" represents the actual impact position and "×" represents the predicted impact position. The imaging focus threshold set by the present invention is 85%. Fig.10The imaging results shown in the figure show that the impact image focusing index of the narrowband wave signals with frequencies of 6kHz, 7kHz, and 8kHz is less than 85%, so they are discarded. The impact image focusing index of the narrowband wave signals with frequencies of 5kHz, 9kHz, and 10kHz is greater than 85%, and is taken as a valid image, and image fusion is performed on it, as shown in Fig.11 The positioning result of multi-frequency fusion imaging is shown in FIG. 1. It can be seen that the imaging result has good focusing and high positioning accuracy, indicating that the method of the present invention can be well applied to complex composite stiffened plate structures.

[0081] It should be understood that extracting multiple narrowband wave signals of different frequencies can extract, for example, six narrowband wave signals of 5kHz, 6kHz, 7kHz, 8kHz, 9kHz and 10kHz for a spectrum of 0-10KHz, and multiple narrowband waves of 1kHz, 5KHz, 10kHz and 15kHz for a spectrum of 1-15KHz. Without loss of generality, other multiple frequencies can be extracted as long as the frequencies within the spectrum range are in the category. In one embodiment of the present invention, a structural impact positioning system based on adaptive time-reversal focusing imaging and image fusion is also provided. The system includes a processor and a memory, and the memory stores a computer program. After the program is run, the structural impact positioning method based on adaptive time-reversal focusing imaging and image fusion in any of the above embodiments is executed.

[0082] In one embodiment of the present invention, a storage medium is further provided, wherein the storage medium stores computer program instructions, and the computer program instructions are executed according to the sleep aid control method described in Embodiment 1 or Embodiment 2.

[0083] In a typical configuration of the present invention, the storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be a device of computer-readable instructions, data structures, programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory, and other types of random access memory (RAM).

[0084] (EEPROM), flash memory or other memory technology, compact disk-read only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0085] In one embodiment of the present invention, a computing device is further provided, comprising: a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the computing device is triggered to execute the method described in Embodiment 1 or Embodiment 2 of the present invention. In a typical configuration of the present invention, the computing devices each include one or more processors (CPU), an input / output interface, a network interface, and a memory.

[0086] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0087] The computing devices referred to in the present invention include but are not limited to any electronic product that can perform human-computer interaction with a user (for example, human-computer interaction through a touchpad), such as smart phones, tablet computers and other mobile electronic products. The mobile electronic products can use any operating system, such as Android operating system, iOS operating system, etc.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the present invention. Those skilled in the art should understand that changes, modifications, additions or substitutions made within the substantial technical scope of the present invention should also fall within the scope of protection of the present invention.

Claims

1. Structural impact location method based on adaptive time-reversal focusing imaging and image fusion, Features: The following steps are involved: Step 1: Divide the monitoring area of ​​the monitored structure into a grid and establish a Cartesian coordinate system, mark the position coordinates of the sensor for receiving the impact stress wave signal on the Cartesian coordinate system, and record the impact stress wave signal received by the sensor; Step 2, extracting multiple narrowband wave signals of different frequencies of the impact stress wave by complex Morlet wavelet transform; Step 3, setting the initial parameters of the impact location algorithm, including the initial value of the group velocity iteration of the impact stress wave, the iteration step size and the total number of iterations; Step 4, performing impact positioning adaptive time-reversal focusing imaging of each narrowband wave signal based on the initial parameters to obtain an adaptive time-reversal focusing imaging image corresponding to each narrowband wave signal; as well as Step 5: Calculate the image focusing index of each adaptive time-reversal focusing imaging image, and set the image focusing index threshold according to the actual impact positioning accuracy requirements and image focusing requirements. E 0 , discard the adaptive time-reversal focusing images whose image focusing index is lower than the image focusing index threshold, and perform cumulative fusion on all the adaptive time-reversal focusing images whose image focusing index is higher than the image focusing index threshold, wherein the grid point coordinates corresponding to the maximum pixel of the fused image are used as the predicted actual impact position; Among them, step 4 obtains the adaptive time-reversal focusing imaging image corresponding to each of the narrowband wave signals, specifically including establishing an imaging function with the grid point coordinates and group velocity values ​​in the monitoring area as variables according to the amplitude characteristics of each narrowband wave signal, substituting the set initial value of the group velocity iteration into the imaging function, calculating the pixels of all grid points for imaging, iterating with the set iteration step size, obtaining the imaging image of each iteration, and marking the maximum value of the image pixels of each imaging image until the set total number of iterations is reached; then comparing the maximum value of the image pixels of each imaging image, taking the group velocity corresponding to the maximum pixel as the group velocity of the narrowband wave signal of this frequency, and the imaging image where the maximum pixel is located as the adaptive time-reversal focusing imaging image of the narrowband wave signal of this frequency.

2. The structural impact positioning method based on adaptive time-reversal focusing imaging and image fusion according to claim 1, Features: The image focusing index is calculated according to the following formula: ,in, Pix For pixels above a certain set value Pix threshold The number of grid points, M is the total number of grid points.

3. The structural impact positioning method based on adaptive time-reversal focusing imaging and image fusion according to claim 1, Features: in, Step 1 includes dividing the monitoring area of ​​the monitored structure into grids of moderate size and uniform distribution and establishing a Cartesian coordinate system, marking the position coordinates of the sensor for receiving the impact stress wave signal, and recording the collected impact stress wave signal.

4. The structural impact positioning method based on adaptive time-reversal focusing imaging and image fusion according to claim 1, Features: in, Step 2 includes extracting a plurality of narrowband wave signals of the impact stress wave within the frequency spectrum range using complex Morlet wavelet transform according to the frequency spectrum range information of the impact stress wave signal collected by the sensor.

5. The method for structural impact positioning based on adaptive time-reversal focusing imaging and image fusion according to claim 1, Features: in, The complex Morlet wavelet transform is a cmor1-1 wavelet basis transform.

6. The method for structural impact positioning based on adaptive time-reversal focusing imaging and image fusion according to claim 1, Features: in, The time delay of the sensor is compensated in the order of the impact stress waves received by the sensor to obtain the signal of each sensor after the time delay is compensated; the preset group velocity iteration initial value is substituted into the imaging function, and the cumulative sum of the maximum amplitude of the signal of each sensor after the time delay is compensated at all grid points in the monitoring area is calculated, and it is used as a pixel for imaging.

7. The method for structural impact location based on adaptive time-reversal focusing imaging and image fusion according to claim 1, Features: The monitored structure is a composite material panel.

8. The method for structural impact positioning based on adaptive time-reversal focusing imaging and image fusion according to claim 7, Features: The composite material plate is a composite material plate with reinforcing ribs.

9. Structural impact positioning system based on adaptive time-reversal focusing imaging and image fusion, Features: The system comprises a processor and a memory, wherein the memory stores a computer program, and after the program is run, the method for locating structural impact based on adaptive time-reversal focusing imaging and image fusion according to any one of claims 1 to 8 is executed.

Images