Thin-wall structure impact positioning method and system based on dense array and intelligent optimization strategy

Abstract

The invention discloses a thin-wall structure impact positioning method and system based on a dense array and an intelligent optimization strategy, and the method comprises the steps: obtaining an impact stress wave signal, and extracting an impact stress narrow-band wave signal; calculating the propagation group velocity and the wave number of the impact stress narrow-band wave signal; setting initial parameters of an intelligent optimization algorithm; obtaining a fitness function of the intelligent optimization strategy according to a spatial beam focusing algorithm; formulating a population screening mechanism of a directional fitness function; constructing a population screening mechanism of an intelligent optimization strategy, and calculating individuals, wherein the angles between the individuals and the optimal particles and reference array elements in the dense array are smaller than the half-power beam width during each iteration, and the coordinates of the individuals are within the monitoring area range; iteratively searching the reserved individuals meeting the condition; and estimating and predicting the impact source coordinates by adopting a DBSCAN density clustering algorithm and Gaussian kernel density. According to the method and the system provided by the invention, accurate positioning can be realized at a low sampling rate, and a blind area does not exist.

Description

Technical Field

[0001] The present invention belongs to the field of structural health monitoring, and in particular relates to an impact positioning method and system for a thin-walled structure based on a dense array and an intelligent optimization strategy. Background Art

[0002] During their service, aviation structures will inevitably be subjected to various forms of external impact loads, such as bird strikes, hail, runway gravel, etc. Multiple impacts may cause structural damage or even failure. Most aviation structures are thin-walled structures, and the response signals generated by the impact are mainly propagated in the form of elastic waves, which have the characteristics of long propagation distance and low attenuation. Piezoelectric sensors have a wide frequency response range and fast dynamic response speed, and are often used to obtain impact response signals. With the increasing requirements of the industry for lightweight sensor networks and lead regularity, as well as the restrictions of complex structures on the sensor layout area, the use of dense array sensor layout has become a trend. Therefore, the development of shock location algorithms based on dense sensor arrays is of great significance for the research on shock monitoring of aviation structures.

[0003] At present, the impact location algorithm based on dense array still faces many problems, mainly including: First, the sampling rate of the data acquisition device is directly related to the resolution of the collected signal. A lower sampling rate will result in poor resolution of the collected signal, which makes the spatial characteristics of the signals received by each array element in the dense array not very different. A single dense array can only estimate the relative direction of the impact source, but it is difficult to estimate its accurate position, and there is a positioning blind spot in the monitoring area. Therefore, the conventional dense array positioning method usually has high requirements for the sampling rate of the data acquisition device (usually in MHz), and the operation and maintenance costs of the monitoring system are greatly increased. How to balance the positioning blind spot and the system equipment cost so that all-round blind spot monitoring can be achieved at a lower sampling rate is one of the problems to be solved in the impact location technology. Second, the classic dense array positioning algorithm usually maps the characteristic information such as the phase and wave number of the impact signal from the spatial spectrum to all discrete spatial grids on the structure to measure the possibility of impact. For large real structures, this method of scanning spatial grids one by one is very time-consuming and difficult to meet real-time online monitoring. In addition, the accuracy of positioning and imaging depends on the number and size of the divided spatial grids. Summary of the invention

[0004] The purpose of the invention is to solve the above-mentioned problems and provide an improved thin-wall structure impact positioning method and system.

[0005] To this end, some embodiments of the present application provide a thin-walled structure impact positioning method based on a dense array and an intelligent optimization strategy, the method comprising the steps of: arranging at least two dense sensor arrays on the monitoring area of ​​the monitored structure, acquiring the impact stress wave signal of each of the sensor arrays; extracting the impact stress narrowband wave signal at a specific center frequency using Morlet wavelet transform, Shannon wavelet transform or short-time Fourier transform, and calculating the propagation group velocity and wave number of the impact stress narrowband wave signal; setting the initial parameters of the intelligent optimization algorithm; obtaining the fitness function of the intelligent optimization strategy according to the spatial beam focusing algorithm; constructing a directional fitness function, including respectively calculating the phase difference between different array elements in the two dense arrays, constructing the directional vector of each array element, and deriving the dense array output power function P with the individual position of the population as the independent variable according to the spatial beam focusing algorithm. SBF ; and constructing a path fitness function, including calculating the time difference of arrival between the array elements at corresponding positions in at least two dense arrays, and obtaining the imaging function P on the path according to the hyperbolic Gaussian distribution imaging algorithm HL ; Formulate a population screening mechanism for the directional fitness function; deduce the half-power beam width characteristics of the dense array based on the specific array element distribution shape, array element spacing and array element number of the dense array. Construct a population screening mechanism for the intelligent optimization strategy, calculate the individuals whose angles with the optimal particle and the reference array element in the dense array are less than the half-power beam width at each iteration and whose coordinates are within the monitoring area, retain these individuals, and release the remaining individuals that do not meet the conditions; iteratively search for retained individuals that meet the conditions; and use the intelligent optimization algorithm to iteratively find the retained individuals with the maximum fitness function value and that meet the screening conditions; and use the DBSCAN density clustering algorithm and Gaussian kernel density estimation to predict the coordinates of the impact source;

[0006] In some embodiments, the intelligent optimization algorithms that can be selected include but are not limited to gray wolf, bat, particle swarm optimization algorithms, etc.

[0007] In some embodiments, Morlet wavelet transform, Shannon wavelet transform or short-time Fourier transform is used to extract the impact stress narrowband wave signal at a specific center frequency, and the propagation group velocity and wave number of the impact stress narrowband wave signal are calculated.

[0008] In some embodiments, constructing the directional fitness function includes respectively calculating the phase difference between different array elements in any two dense arrays, constructing the directional vector of each array element, and deriving the dense array output power function with the individual position of the population as the independent variable according to the spatial beam focusing algorithm, and using it as the directional fitness function of the optimization algorithm.

[0009] In some embodiments, constructing the path fitness function includes calculating the arrival time difference between array elements at corresponding positions in at least two dense arrays, obtaining an imaging function on the path according to a hyperbolic Gaussian distribution imaging algorithm, and using it as the path fitness function.

[0010] In some embodiments, individuals who do not meet the conditions are released.

[0011] In some embodiments, the retained individuals obtained by searching the direction fitness function are defined as direction individuals, and the individuals obtained by searching the path fitness function are defined as path individuals.

[0012] In some embodiments, the DBSCAN density clustering algorithm is used to cluster all directional individuals and path individuals. The approximate location of the impact source can be locked by the distribution area of ​​all individuals retained by clustering, and then the Gaussian kernel density estimation method is used to calculate the probability density distribution of the individual position coordinates in the clustering area. The coordinates corresponding to the maximum probability are the predicted impact position coordinates.

[0013] This application also proposes a corresponding system.

[0014] The present invention is to develop an online impact position recognition technology that can achieve no blind area, high accuracy and real-time performance at a low sampling rate by combining dense arrays, and the beneficial technical effects brought by it include:

[0015] Firstly, the method and system proposed in the present invention can achieve accurate positioning at a low sampling rate without any blind spots.

[0016] Second, the present invention overcomes the shortcoming of the traditional method that all spatial grids need to be scanned one by one, the positioning time is greatly shortened, and the real-time performance of impact monitoring is improved.

[0017] Third, the present invention uses the beam width characteristics of dense arrays as constraints for screening individuals in the intelligent optimization algorithm, taking into account the ambiguity and uncertainty of the positioning results caused by the dense array's own beam width characteristics.

[0018] Fourth, the present invention designs a multi-channel synchronous data acquisition module, in which each channel of a single module synchronously acquires data, each module is responsible for the signal acquisition of a dense array, and the modules do not interfere with each other. Compared with the data acquisition system of the traditional method, the module is more compact and lightweight. In addition, it is not necessary to keep all data acquisition channels in the monitoring area synchronized, which improves the real-time performance of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1A , Figure 1B and Figure 1CA flowchart of a thin-walled structure impact positioning method based on a dense array and an intelligent optimization strategy according to the present invention;

[0020] Figure 2 A flowchart of a DBSCAN density clustering algorithm in a method according to the present invention;

[0021] Figure 3 A schematic diagram of the arrangement of a monitored structure, a passive monitoring system and a sensor cluster thereof in an embodiment of the present invention;

[0022] Figure 4 is the array pattern of a sensor cluster with 6 array elements;

[0023] Figure 5 is the half-power beamwidth of the sensor cluster with 6 array elements M;

[0024] Figure 6 The time domain signal diagram of the impact stress wave collected by each sensor;

[0025] Figure 7 for Figure 6 The time domain signal diagram of the impact stress wave is obtained by Fourier transforming the frequency domain diagram of the impact stress wave;

[0026] Figure 8 Graphs showing the distribution of retained individuals of the 2nd, 3rd, 4th and 5th generations according to the method of the present invention.

[0027] Fig. 9A Schematic diagram for all retained individuals.

[0028] Fig. 9B Schematic diagram of the process of clustering all retained individuals using the DBSCAN density clustering algorithm.

[0029] Fig. 9C Schematic diagram of the approximate locations of the regions distributed for all individuals retained by clustering.

[0030] Fig.9D is the probability density distribution of the impact position finally calculated using the Gaussian kernel density estimation method. DETAILED DESCRIPTION

[0031] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0032] like Figure 1A , Figure 1B and Figure 1CAs shown in the figure, a thin-walled structure impact positioning method based on dense array and intelligent optimization strategy can be divided into five parts according to the functions implemented, mainly including: preprocessing of impact stress wave signal and algorithm parameters, obtaining the fitness function of the intelligent optimization strategy according to the spatial beam focusing algorithm, formulating the population screening mechanism of the directional fitness function, iteratively searching for retained individuals that meet the conditions, and using density clustering algorithm and Gaussian kernel density estimation to predict the coordinates of the impact source.

[0033] Each part is implemented according to the following steps:

[0034] Step S1: Acquisition of shock stress wave signals and preprocessing of algorithm parameters.

[0035] N dense sensor arrays (N≥2) are arranged on the monitoring area of ​​the monitored structure, and the impact stress wave signal of each array element is obtained by using data acquisition equipment. The shape of the dense sensor array can be arranged in a circular, rectangular, etc., and the number can be customized according to needs. The array element PZT in the nth dense sensor array used to receive the impact stress wave signal excited by the impact source i n The position coordinates are marked as Where I is the total number of elements in the nth dense sensor array. The radius of the circular array is denoted by R s . The array element PZT in the nth dense sensor array i n The received shock stress wave signal is recorded as According to the frequency distribution information of the impact stress wave signal collected by each array element, the specific center frequency f is extracted c Narrowband wave signal

[0036] The specific center frequency can be determined by the following steps: first, Fourier transform is performed to obtain the spectrum of the signal, and the extracted center frequency needs to be within the spectrum range. For continuous wavelets, the higher the frequency of the signal, the higher its time resolution; for the mode of Lamb wave signals, the higher the frequency of the signal, the more complex the signal mode. The specific center frequency can be selected based on comprehensive considerations of different scenarios.

[0037] Then, the group velocity v of the impact stress wave propagating at this specific frequency is calculated by using methods such as Morlet wavelet transform, Shannon wavelet transform or short-time Fourier transform. g During imaging, the wave number k and wavelength λ of the impact stress wave are calculated based on formula (1).

[0038]

[0039] Step S2, intelligent algorithm initialization step. The intelligent optimization algorithm in the method of the present invention can use optimization algorithms such as gray wolf, bat and particle swarm. Here, the gray wolf optimization algorithm is used as an example. Initialize the parameters set based on the principle of the gray wolf optimization algorithm: the maximum evolutionary generation g used to control the algorithm cutoff m , initialize the number of individuals in the population M, the individual orientation control parameter A, and the individual distance control parameter C. The two-dimensional space vector δ∈(x,y) of the impact positioning area of ​​large thin-walled structures. Formula (2) is used to calculate the wolf pack p g Initialization, when the monitoring area is in x∈[x min ,x max ]mm, y∈[y min ,y max ]mm, the upper and lower limits of the space vector in the monitoring area are δ U =(x max ,y max ) and δ L =(x min ,y min ). r Indicates the individual spatial resolution during the search process, usually ζ r =(1,1). At this time, the population is initialized with a 1mm space interval x∈[x min ,x max ]mm, y∈[y min ,y max It should be understood that, although this step is marked as S2 for the convenience of description, the intelligent algorithm initialization only needs to be performed before the actual execution of the intelligent algorithm.

[0040]

[0041] Step S3: Obtaining the fitness function of the intelligent optimization strategy according to the spatial beam focusing algorithm.

[0042] According to the individual positions (x pg ,y pg ), based on formula (3), the relative distance r of the individual to each element in the nth dense array is calculated i (x pg ,y pg ).

[0043]

[0044] The steering vector of each element in the nth dense array can be calculated by formula (4), r i is the relative distance of the individual from each element in the nth dense array. iIt is the steering vector of the array element in the array, which refers to the direction from the center of the array to the signal source. It is usually expressed in spatial coordinates and reflects the angle or direction of the signal when it arrives at the array:

[0045]

[0046] The signal received by each element in the nth dense array can be expressed as follows:

[0047] F n (t) = A n (x pg ,y pg )V n (t)+N n (t) (5)

[0048] in,

[0049]

[0050] The output power of the nth dense array is constructed according to the spatial beam focusing algorithm based on beamforming theory. Use it as the directional fitness function of the optimization algorithm:

[0051]

[0052] Based on the normalized RMS amplitude method described in equations (8) and (9), the PZT of the array element in the nth dense array is calculated as i n and the array element PZT in the mth dense array i m The normalized RMS sequence of .

[0053]

[0054] Where V represents the length of the impact stress wave signal, S(t w ) represents the signal at t w The signal amplitude at time Indicates at 0 <t<t v The average amplitude of a signal over time.

[0055]

[0056] Then select a suitable normalized RMS threshold κ and measure the PZT of the array element in the nth dense array. i n and the array element PZT in the mth dense array i m Time Difference Where m,n=1,2,...,N.

[0057] According to the group velocity of the wave, the array element PZT in the nth dense array i n and the array element PZT in the mth dense array i m The theoretical time difference of arrival is calculated from the coordinates of

[0058]

[0059] Finally, the imaging function on the path is obtained by the hyperbolic Gaussian distribution imaging algorithm, referred to as the hyperbolic imaging algorithm As a path fitness function:

[0060]

[0061] Step S4: Formulate a population screening mechanism for the directional fitness function.

[0062] Based on formulas (12) and (13), the directional pattern function E of a uniform circular array with 8 elements is derived: n (φ):

[0063]

[0064] Extract the angle value φ corresponding to the 3dB beam width 3dB , define the parameters of the retained individual constraints

[0065]

[0066] Screen the individuals in each iteration to meet the constraints of the retained individuals The individuals that meet the requirements are retained, and the individuals that do not meet the requirements are eliminated.

[0067] Step S5: Iteratively search for retained individuals that meet the conditions, including:

[0068] Step S51, according to the directional fitness function obtained in the above steps, calculate the directional fitness of all gray wolf individuals in the current dense array, save the top three wolves α, β and δ with the highest fitness, and update the current gray wolf individual position. Filter the direction to retain the gray wolf individuals. Update A and C, and calculate the direction fitness value and position of the retained gray wolf individuals. Until the number of iterations reaches g m And search to obtain the direction-preserving individual set obtained under all dense arrays

[0069] Step S52, according to the path fitness function obtained in the above steps, calculate the path fitness of all gray wolf individuals in the current dense array, save the top three wolves α, β and δ with the highest fitness, and update the current gray wolf individual position. Update A and C, and calculate the directional fitness value and position of the retained gray wolf individual. Until the number of iterations reaches g m And search for all dense arrays to obtain the path to retain the individual set

[0070] Step S6: Use density clustering algorithm and Gaussian kernel density estimation to predict the impact source coordinates.

[0071] Including through integration and Obtain all retained individuals in the monitoring area, and use Figure 2 The DBSCAN density clustering algorithm shown in FIG. 1 clusters all direction individuals and path individuals. The approximate location of the impact source can be locked by clustering the distribution area of ​​all individuals retained, step S61; and then using the Figure 2 The Gaussian kernel density estimation method shown calculates the probability density distribution of individuals in the cluster area, and the position with the maximum probability is the predicted impact position, step S62.

[0072] In another embodiment, the present application also provides a thin-walled structure impact positioning system based on a dense array and an intelligent optimization strategy, which includes at least two dense sensor arrays arranged in the monitoring area of ​​the monitored structure to obtain the impact stress wave signal of each array element; a multi-channel data synchronization acquisition module to collect the impact stress wave signal; and also includes a memory and a processor, wherein the memory stores computer instructions, and the processor executes the following method steps after executing the computer instructions: extracting the impact stress narrowband wave signal from the impact stress wave signal; algorithm parameter preprocessing, including calculating the propagation group velocity and wave number of the impact stress narrowband wave signal; setting the initial parameters of the intelligent optimization algorithm; obtaining the fitness function of the intelligent optimization strategy according to the spatial beam focusing algorithm, including The invention comprises constructing a directional fitness function and constructing a path fitness function; formulating a population screening mechanism of the directional fitness function; deducing the half-power beam width characteristic of the dense array according to the array element distribution shape, array element spacing and array element number of each dense array; constructing a population screening mechanism of an intelligent optimization strategy, calculating the individuals whose angles with the optimal particle and the reference array element in the dense array are less than the half-power beam width and whose coordinates are within the monitoring area at each iteration, and retaining the calculated individuals; iteratively searching for the retained individuals that meet the conditions, including iteratively searching for the retained individuals with the maximum fitness function value and meeting the screening conditions by using an intelligent optimization algorithm; and predicting the coordinates of the impact source by using a DBSCAN density clustering algorithm and a Gaussian kernel density estimation.

[0073] like Figure 3 The schematic diagram of a monitored thin-walled plate, a sensor cluster, and a multi-channel data synchronous acquisition module used in a specific embodiment of the method and system of the present application is shown. The size of the monitored structure is 900mm×1250mm×3mm. A sensor cluster with a radius of 13mm is formed by using piezoelectric sensors with a diameter of 8mm and a thickness of 0.5mm. The sensor cluster is the above-mentioned dense sensor array, such as sensor cluster 1 and sensor cluster 2.

[0074] like Figure 4 The array pattern of the sensor cluster 1 when pointing at 90° is shown. Figure 5 The half-power beam width is calculated to be 58°. A Cartesian coordinate system is established with the lower left vertex of the structure under test 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 coordinates (382mm, 830mm) on the structure. Each dense sensor array is connected to a multi-channel data synchronization acquisition module, such as Figure 3 The system sampling rate of module 1 and module 2 shown in the figure is set to 25kHz. The multi-channel data synchronization acquisition module can also realize the acquisition of required signals together with the existing data storage module, power management module and signal conditioning module.

[0075] like Figure 6 The figure shows the time domain diagram of a typical impact stress wave signal received by sensor 1. The Fourier transform is used to obtain Figure 7 As shown in the spectrum diagram, it can be seen that the main frequency components of the signal are concentrated within 3kHz. According to step S1, the Morlet wavelet transform is used to extract the impact stress narrowband wave signal with a center frequency of 1kHz, and the group velocity is calculated to be 113m / s and the wave number is 8.813m / s. -1 , the wavelength is 0.113m. Initialize the intelligent optimization algorithm, the maximum evolutionary number g m =5. Initialize the number of individuals in the population M = 3000. According to step S2 and step S3, obtain the fitness function of the intelligent optimization strategy according to the spatial beam focusing algorithm, and formulate a population screening mechanism of the directional fitness function.

[0076] like Figure 8 As shown, according to step S4, the direction-retaining individual set is obtained and path-preserving individual sets According to step S5, Fig. 9A All the retained individuals shown in the figure are clustered using the DBSCAN density clustering algorithm for all direction individuals and path individuals, as shown in Fig. 9B As shown, the approximate location of the impact source can be locked by clustering the distribution areas of all individuals retained, as Fig. 9CAs shown, the Gaussian kernel density estimation method is finally used to calculate the probability density distribution of individuals in the cluster area, as shown in Fig.9D As shown, the predicted impact position coordinates are (386mm, 833mm).

[0077] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0078] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronously linked dynamic random access memory (SLDRAM) and direct memory bus random access memory (DR RAM).

[0079] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0080] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0081] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0082] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0083] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0085] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0086] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0087] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0088] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0089] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

Claims

1. A thin-walled structure impact positioning method based on dense array and intelligent optimization strategy, characterized in that: The method includes: At least two dense sensor arrays are arranged on the monitoring area of ​​the monitored structure to obtain the impact stress wave signal of each array element and extract the impact stress narrowband wave signal; Algorithm parameter preprocessing, including calculating the propagation group velocity and wave number of the impact stress narrowband wave signal; Set the initial parameters of the intelligent optimization algorithm; Obtaining the fitness function of the intelligent optimization strategy according to the spatial beam focusing algorithm, including constructing a direction fitness function and constructing a path fitness function; Formulate a population screening mechanism for the directional fitness function; including deducing the half-power beam width characteristics of the dense array according to the array element distribution shape, array element spacing and array element number of each dense array; construct a population screening mechanism for the intelligent optimization strategy, calculate the individuals whose angles with the optimal particle and the reference array element in the dense array are less than the half-power beam width at each iteration and whose coordinates are within the monitoring area, and retain the calculated individuals; Iteratively searching for the retained individuals that meet the conditions, including iteratively searching for the retained individuals with the maximum fitness function value and that meet the screening conditions using an intelligent optimization algorithm; The DBSCAN density clustering algorithm and Gaussian kernel density estimation are used to predict the impact source coordinates.

2. The thin-walled structure impact positioning method based on dense array and intelligent optimization strategy according to claim 1 is characterized in that: The intelligent optimization algorithms include but are not limited to gray wolf, bat, and particle swarm optimization algorithms.

3. The thin-walled structure impact positioning method based on dense array and intelligent optimization strategy according to claim 1 is characterized in that: Morlet wavelet transform, Shannon wavelet transform or short-time Fourier transform is used to extract the impact stress narrowband wave signal at a specific center frequency, and the propagation group velocity and wave number of the impact stress narrowband wave signal are calculated.

4. The thin-walled structure impact positioning method based on dense array and intelligent optimization strategy according to claim 1 is characterized in that: The construction of the directional fitness function includes respectively calculating the phase difference between different array elements in any two dense arrays, constructing the direction vector of each array element, and deriving the dense array output power function with the individual position of the population as the independent variable according to the spatial beam focusing algorithm, and using it as the directional fitness function of the optimization algorithm.

5. The thin-walled structure impact positioning method based on dense array and intelligent optimization strategy according to claim 1 is characterized in that: The construction of the path fitness function includes calculating the arrival time difference between array elements at corresponding positions in at least two dense arrays, obtaining an imaging function on the path according to a hyperbolic Gaussian distribution imaging algorithm, and using the imaging function as the path fitness function.

6. The thin-walled structure impact positioning method based on dense array and intelligent optimization strategy according to claim 1 is characterized in that: Individuals who do not meet the requirements will be released.

7. The thin-walled structure impact positioning method based on dense array and intelligent optimization strategy according to claim 1 is characterized in that: The retained individuals obtained by searching the direction fitness function are defined as direction individuals, and the individuals obtained by searching the path fitness function are defined as path individuals.

8. The thin-walled structure impact positioning method based on dense array and intelligent optimization strategy according to claim 1 is characterized in that: The DBSCAN density clustering algorithm is used to cluster all directional individuals and path individuals. The approximate location of the impact source is locked through the distribution area of ​​all individuals retained by clustering. The Gaussian kernel density estimation method is then used to calculate the probability density distribution of individual position coordinates in the clustering area. The coordinates corresponding to the maximum probability are the coordinates of the predicted impact location.

9. Thin-walled structure impact positioning system based on dense array and intelligent optimization strategy, characterized by: It includes at least two dense sensor arrays arranged in the monitoring area of ​​the monitored structure to obtain the impact stress wave signal of each array element; A multi-channel data synchronous acquisition module for acquiring the impact stress wave signal; The invention also includes a memory and a processor, wherein the memory stores computer instructions, and the processor executes the following method steps after executing the computer instructions: Extracting an impact stress narrowband wave signal from the impact stress wave signal; Algorithm parameter preprocessing, including calculating the propagation group velocity and wave number of the impact stress narrowband wave signal; Set the initial parameters of the intelligent optimization algorithm; Obtaining the fitness function of the intelligent optimization strategy according to the spatial beam focusing algorithm, including constructing a direction fitness function and constructing a path fitness function; Formulate a population screening mechanism for the directional fitness function; including deducing the half-power beam width characteristics of the dense array according to the array element distribution shape, array element spacing and array element number of each dense array; construct a population screening mechanism for the intelligent optimization strategy, calculate the individuals whose angles with the optimal particle and the reference array element in the dense array are less than the half-power beam width at each iteration and whose coordinates are within the monitoring area, and retain the calculated individuals; Iteratively searching for the retained individuals that meet the conditions, including iteratively searching for the retained individuals with the maximum fitness function value and meeting the screening conditions using an intelligent optimization algorithm; and The DBSCAN density clustering algorithm and Gaussian kernel density estimation are used to predict the impact source coordinates.

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