Insulating material defect detection method and device based on terahertz waves, terminal equipment and storage medium

Abstract

The invention discloses an insulation material defect detection method and device based on terahertz waves, terminal equipment and a storage medium, and belongs to the technical field of insulation material defect detection.The method comprises the steps that frequency dispersion characteristic parameters, a polarization electric field and a measurement distance of a to-be-detected insulation material piece are obtained; calculating an effective wave number, a spatial frequency spectrum at each measurement angle, a maximum recoverable spatial frequency and a signal-to-noise ratio; then, aiming at each measurement angle, constructing a filtering function and a compensation function, and compensating and filtering the spatial frequency spectrum; and then reconstructing the compensation frequency spectrum, performing multi-angle fusion according to the comprehensive electric field distribution at all measurement angles and the corresponding signal-to-noise ratio to obtain a three-dimensional defect distribution image, and determining a defect detection result of the to-be-detected insulating material part. By implementing the method and the device, the problem of inaccurate defect detection result caused by serious attenuation of evanescent waves in spatial frequency and reduction of resolution in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of defect detection of insulating materials, and in particular to a method, apparatus, terminal equipment and storage medium for defect detection of insulating materials based on terahertz waves. Background Art

[0002] As electronic and electrical equipment become increasingly miniaturized and integrated, internal defect detection in insulating materials has become critical for ensuring safe and stable operation. Terahertz waves, due to their strong penetration into non-polar materials and low-photon-energy non-destructive testing capabilities, have attracted significant attention in the field of insulating material testing. Currently, terahertz testing of insulating materials primarily relies on traditional near-field scanning techniques, such as near-field photoconductive antennas (NFPAs).

[0003] However, under standard laboratory conditions, the NFPA antenna is typically hundreds of microns away from the object being measured, in the radiation near-field region (the distance between λ / 2π and λ). At this point, the evanescent wave of the spatial frequency has been significantly attenuated, and the resolution is severely limited, resulting in the loss of a large amount of detail in the detected signal. For multi-layer defects inside insulating materials, high resolution is required to distinguish the defect position and morphology of different layers. Insufficient resolution makes it difficult to accurately distinguish and locate multi-layer defects during reconstruction.

[0004] Therefore, the existing technology has the problem that the evanescent wave in the spatial frequency is severely attenuated, the resolution is reduced, and the defect detection result is inaccurate. Summary of the Invention

[0005] The present invention provides a method, apparatus, terminal device and storage medium for detecting defects in insulating materials based on terahertz waves, which can solve the problem in the prior art of inaccurate defect detection results due to severe attenuation of evanescent waves in spatial frequency and reduced resolution.

[0006] An embodiment of the present invention provides a method for detecting defects in insulating materials based on terahertz waves, comprising:

[0007] Obtaining dispersion characteristic parameters of the insulating material piece to be measured, polarization electric fields of the terahertz wave at several measurement angles at a preset measurement point, and a measurement distance between the preset measurement point and the insulating material piece to be measured;

[0008] Calculating the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio based on the polarized electric field, the measurement distance, and the dispersion characteristic parameters;

[0009] For each measurement angle, a filter function is constructed based on the maximum recoverable spatial frequency, signal-to-noise ratio, and spatial frequency spectrum.

[0010] According to the above-mentioned measurement distance, effective wave number and spatial frequency spectrum, a compensation function is constructed;

[0011] Compensating and filtering the spatial frequency spectrum according to the filtering function and the compensation function to obtain a compensated frequency spectrum;

[0012] Reconstruct the above compensation frequency spectrum to obtain the comprehensive electric field distribution at the corresponding measurement angle;

[0013] Based on the comprehensive electric field distribution at all measurement angles and the corresponding signal-to-noise ratio, multi-angle fusion is performed to obtain a three-dimensional defect distribution image, and based on the above three-dimensional defect distribution image, the defect detection result of the above-mentioned insulating material piece to be tested is determined.

[0014] Furthermore, the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio are calculated based on the polarized electric field, the measurement distance, and the dispersion characteristic parameters, including:

[0015] Obtaining the wave number of the terahertz wave in the free space in the insulating material piece to be tested;

[0016] Calculate the complex dielectric constant of the insulating material to be tested based on the above-mentioned dispersion characteristic parameters;

[0017] The effective wave number is calculated based on the complex dielectric constant and the wave number;

[0018] For each measurement angle, a two-dimensional fast Fourier transform is performed on the polarized electric field to obtain a spatial frequency spectrum;

[0019] The above spatial frequency spectrum is divided according to the above effective wave number to obtain propagating waves and evanescent waves;

[0020] Determining the signal-to-noise ratio based on the propagating wave and the evanescent wave;

[0021] The maximum recoverable spatial frequency is calculated based on the signal-to-noise ratio, measurement distance, and effective wave number.

[0022] Furthermore, before performing two-dimensional fast Fourier transform on the polarized electric field, the method further includes: performing low-pass filtering to remove noise on the polarized electric field.

[0023] Furthermore, the above-mentioned spatial frequency spectrum is divided according to the above-mentioned effective wave number to obtain propagating waves and evanescent waves, including:

[0024] determining each spatial frequency component of the polarized electric field according to the spatial frequency spectrum;

[0025] The region in the spatial frequency spectrum where the sum of the squares of all spatial frequency components is greater than the square of the effective wave number is regarded as an evanescent wave;

[0026] The area in the above spatial frequency spectrum where the sum of the squares of all spatial frequency components is not greater than the square of the above effective wave number is regarded as the propagating wave.

[0027] Furthermore, determining the signal-to-noise ratio based on the propagating wave and the evanescent wave includes:

[0028] Calculating the noise value corresponding to the evanescent wave and calculating the signal value corresponding to the propagating wave;

[0029] The signal-to-noise ratio is calculated based on the noise value and the signal value.

[0030] Furthermore, the above-mentioned compensation frequency spectrum is reconstructed to obtain the comprehensive electric field distribution at the corresponding measurement angle, including:

[0031] Obtaining the layer thickness and layer admittance of each layer in the insulating material piece to be tested;

[0032] Performing a two-dimensional inverse Fourier transform on the compensation frequency spectrum to obtain a reconstructed electric field distribution;

[0033] The layer weight corresponding to each layer is calculated based on the above layer thickness and layer admittance;

[0034] The above-mentioned comprehensive electric field distribution is calculated based on the above-mentioned layer weights and the reconstructed electric field distribution.

[0035] Furthermore, the above multi-angle fusion is performed based on the comprehensive electric field distribution at all measurement angles and the corresponding signal-to-noise ratio to obtain a three-dimensional defect distribution image, including:

[0036] For the comprehensive electric field distribution at each measurement angle, the electric field weight of the comprehensive electric field distribution is calculated according to the signal-to-noise ratio corresponding to the comprehensive electric field distribution;

[0037] The three-dimensional defect distribution image is calculated based on all comprehensive electric field distributions and all electric field weights.

[0038] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0039] The present invention provides an insulating material defect detection device based on terahertz waves, comprising:

[0040] Data acquisition module, data calculation module, filter function construction module, compensation function construction module, spatial frequency spectrum compensation filter module, electric field reconstruction module and detection result determination module;

[0041] The data acquisition module is used to obtain the dispersion characteristic parameters of the insulating material to be measured, the polarization electric field of the terahertz wave at several measurement angles at a preset measurement point, and the measurement distance between the preset measurement point and the insulating material to be measured;

[0042] The data calculation module is used to calculate the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio based on the polarized electric field, the measurement distance, and the dispersion characteristic parameters;

[0043] The filter function construction module is used to construct a filter function for each measurement angle according to the maximum recoverable spatial frequency, signal-to-noise ratio and spatial frequency spectrum;

[0044] The compensation function construction module is used to construct a compensation function based on the measurement distance, effective wave number and spatial frequency spectrum;

[0045] The spatial frequency spectrum compensation filter module is used to compensate and filter the spatial frequency spectrum according to the filtering function and the compensation function to obtain a compensated frequency spectrum;

[0046] The electric field reconstruction module is used to reconstruct the compensation frequency spectrum to obtain the comprehensive electric field distribution at the corresponding measurement angle;

[0047] The detection result determination module is used to perform multi-angle fusion based on the comprehensive electric field distribution and the corresponding signal-to-noise ratio at all measurement angles to obtain a three-dimensional defect distribution image, and determine the defect detection result of the above-mentioned insulating material part to be tested based on the above-mentioned three-dimensional defect distribution image.

[0048] Based on the above method embodiment, the present invention provides a corresponding terminal device embodiment;

[0049] The present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-mentioned method for detecting defects in insulating materials based on terahertz waves according to any embodiment of the present invention.

[0050] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0051] The present invention provides a storage medium comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for detecting defects in insulating materials based on terahertz waves according to any embodiment of the present invention is implemented.

[0052] The embodiments of the present invention have the following beneficial effects:

[0053] The present invention provides a method, apparatus, terminal device and storage medium for detecting defects in insulating materials based on terahertz waves. The method comprises: obtaining dispersion characteristic parameters of an insulating material piece to be tested, a polarization electric field of a terahertz wave at several measurement angles at a preset measurement point, and a measurement distance between the preset measurement point and the insulating material piece to be tested; then, based on the polarization electric field, the measurement distance and the dispersion characteristic parameters, calculating the effective wave number of the insulating material piece to be tested, a spatial frequency spectrum at each measurement angle, a maximum recoverable spatial frequency and a signal-to-noise ratio; then, for each measurement angle, calculating the maximum recoverable spatial frequency and a signal-to-noise ratio based on the maximum recoverable spatial frequency. The method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: first, the method comprises the following steps: second, the method comprises the following steps: BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 The present invention is a flowchart of a method for detecting defects in insulating materials based on terahertz waves, provided in accordance with an embodiment of the present invention.

[0056] Figure 2 The figure is a schematic structural diagram of an insulating material defect detection device based on terahertz waves provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0057] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0059] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0060] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0061] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0062] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0063] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0064] See also Figure 1 In order to solve the problem in the prior art that the evanescent wave in the spatial frequency is severely attenuated, the resolution is reduced, and the defect detection results are inaccurate. An embodiment of the present invention provides a method for detecting defects in insulating materials based on terahertz waves, comprising:

[0065] Step S101: obtaining dispersion characteristic parameters of the insulating material to be measured, polarization electric fields of the terahertz wave at several measurement angles at a preset measurement point, and a measurement distance between the preset measurement point and the insulating material to be measured;

[0066] Specifically, the dispersion parameters include static dielectric constant, high-frequency limiting dielectric constant, and relaxation time. The polarized electric field is measured using a terahertz time-domain spectrometer at a predetermined measurement point on the detection plane z = L, where L is the measurement distance and z represents the detection plane. The polarized electric field is divided into x-polarized electric field and y-polarized electric field, both in V / m.

[0067] Step S102: Calculating the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio according to the polarized electric field, the measurement distance, and the dispersion characteristic parameters;

[0068] In a preferred embodiment, the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio are calculated based on the polarized electric field, the measurement distance, and the dispersion characteristic parameters, including:

[0069] Obtaining the wave number of the terahertz wave in the free space in the insulating material piece to be tested;

[0070] Calculate the complex dielectric constant of the insulating material to be tested based on the above-mentioned dispersion characteristic parameters;

[0071] Specifically, the complex dielectric constant of insulating materials follows the Debye model as it changes with frequency, so the complex dielectric constant can be calculated using the following formula:

[0072] ε r,eff (ω)=ε ∞+(ε s -ε ∞ ) / (1+iωτ)

[0073] ω=2πf

[0074] Where ω represents the angular frequency, ε r,eff (ω) represents the complex dielectric constant when the angular frequency is ω, ε ∞ Represents the high-frequency limiting dielectric constant in the dispersion characteristic parameters, ε s represents the static dielectric constant in the dispersion characteristic parameters, τ represents the relaxation time in the dispersion characteristic parameters, i represents an imaginary number, and f represents the frequency of the terahertz wave.

[0075] The effective wave number is calculated based on the complex dielectric constant and the wave number;

[0076] Specifically, the effective wave number in the insulating material can be calculated by the following formula:

[0077]

[0078] k o =2π / λ

[0079] Where k oeff represents the effective wave number, k o represents the universal wave number in free space, ε r,real represents the real part of the complex dielectric constant, i εr,img represents the imaginary part of the complex dielectric constant, λ represents the wavelength in vacuum, and the unit is m.

[0080] For each measurement angle, a two-dimensional fast Fourier transform is performed on the polarized electric field to obtain a spatial frequency spectrum;

[0081] The above spatial frequency spectrum is divided according to the above effective wave number to obtain propagating waves and evanescent waves;

[0082] Determining the signal-to-noise ratio based on the propagating wave and the evanescent wave;

[0083] The maximum recoverable spatial frequency is calculated based on the signal-to-noise ratio, measurement distance, and effective wave number.

[0084] Specifically, the maximum recoverable spatial frequency is calculated using the following formula:

[0085]

[0086] Where k max represents the maximum recoverable spatial frequency, λ eff It represents the effective wavelength of the insulating material, L represents the measurement distance, and SNR represents the signal-to-noise ratio.

[0087] In this preferred embodiment, the effective wave number, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency and the signal-to-noise ratio are calculated through the polarization electric field, the measurement distance and the dispersion characteristic parameters.

[0088] In another preferred embodiment, before performing two-dimensional fast Fourier transform on the polarized electric field, the method further includes: performing low-pass filtering on the polarized electric field to remove noise.

[0089] Specifically, since the polarized electric field directly measured using a terahertz time-domain spectrometer often contains noise, it is necessary to perform denoising preprocessing on the two polarized electric fields. The denoising process for the two polarized electric fields is the same. Schematically, the x-polarized electric field can be denoised using the following formula:

[0090] E x,cleam (x,y,L)=LPF[E x (x,y,L)]

[0091] Where, E x,cleam (x, y, L) represents the x-polarized electric field after denoising, and LPF represents a low-pass filtering operation to filter out high-frequency noise.

[0092] Specifically, after the polarized electric field is denoised, a two-dimensional fast Fourier transform is performed on it to obtain a spatial frequency spectrum. Schematically, the two-dimensional fast Fourier transform of the x-polarized electric field is implemented by the following formula (the two-dimensional fast Fourier transform process of the y-polarized electric field is the same as that of the x-polarized electric field):

[0093]

[0094] Where, represents the spatial frequency spectrum corresponding to the x-polarized electric field, (k x ,k y ) represents the spatial frequency component in the spatial frequency spectrum, and FFT2D represents two-dimensional fast Fourier transform.

[0095] In this preferred embodiment, denoising is achieved by low-pass filtering the polarized electric field before performing a two-dimensional fast Fourier transform.

[0096] In another preferred embodiment, the above-mentioned division of the spatial frequency spectrum according to the above-mentioned effective wave number to obtain the propagating wave and the evanescent wave includes:

[0097] determining each spatial frequency component of the polarized electric field according to the spatial frequency spectrum;

[0098] The region in the spatial frequency spectrum where the sum of the squares of all spatial frequency components is greater than the square of the effective wave number is regarded as an evanescent wave;

[0099] The area in the above spatial frequency spectrum where the sum of the squares of all spatial frequency components is not greater than the square of the above effective wave number is regarded as the propagating wave.

[0100] Specifically, the x-polarized electric field and the y-polarized electric field each correspond to a spatial frequency spectrum, so the two spatial frequency spectra are divided respectively to obtain the evanescent wave and propagating wave corresponding to each polarized electric field.

[0101] Specifically, when the spatial frequency component satisfies: When , it corresponds to the evanescent wave; when the spatial frequency satisfies: , corresponding to the propagation wave.

[0102] In this preferred embodiment, after dividing the spatial frequency spectrum based on the effective wave number, propagating waves and evanescent waves are obtained.

[0103] In another preferred embodiment, determining the signal-to-noise ratio based on the propagating wave and the evanescent wave includes:

[0104] Calculating the noise value corresponding to the evanescent wave and calculating the signal value corresponding to the propagating wave;

[0105] Specifically, the signal values ​​of the spatial frequency spectra corresponding to the x-polarized electric field and the y-polarized electric field are calculated respectively, and the calculation methods for both are the same. Schematically, for the x-polarized electric field, the signal value is calculated using the following formula:

[0106]

[0107] Where A signal Indicates the signal value.

[0108] Specifically, the noise values ​​of the spatial frequency spectra corresponding to the x-polarized electric field and the y-polarized electric field are calculated respectively, and the calculation methods for both are the same. Schematically, for the x-polarized electric field, the noise value is calculated using the following formula:

[0109]

[0110] Where A noise Indicates the noise value.

[0111] The signal-to-noise ratio is calculated based on the noise value and signal value corresponding to each polarization electric field.

[0112] Specifically, the signal-to-noise ratio is defined as:

[0113] SNR = 20log 10 (A signal / A noise )

[0114] Where SNR represents the signal-to-noise ratio.

[0115] In this preferred embodiment, the signal-to-noise ratio is determined based on the propagating wave and the evanescent wave.

[0116] Step S103: for each measurement angle, construct a filter function according to the maximum recoverable spatial frequency, signal-to-noise ratio, and spatial frequency spectrum;

[0117] Specifically, a Gaussian soft cutoff filter is designed based on the maximum recoverable spatial frequency and signal-to-noise ratio, and the filter function is obtained:

[0118]

[0119] σ f =α SNR ×k max / (SNR+β SNR )

[0120] Where k represents the spatial frequency modulus, H(k x ,k y ) represents the filter function, σ f represents the soft cutoff parameter, α SNR and β SNR represents the empirical parameter, α SNR =0.1,β SNR =5.

[0121] Preferably, the filter function is used to suppress noise amplification after virtual superlens processing and is directly applied in the virtual superlens algorithm to ensure that the algorithm can both recover useful high-frequency information and suppress noise amplification.

[0122] Step S104: constructing a compensation function according to the above-mentioned measurement distance, effective wave number and spatial frequency spectrum;

[0123] Specifically, the core of this step is the implementation of the virtual superlens algorithm. First, the propagation constant of each spatial frequency point is calculated:

[0124]

[0125] Where k z (k x ,k y ) represents the propagation constant.

[0126] Then, a compensation function is constructed based on the above propagation constant to reverse the propagation process:

[0127] C(k x ,k y )=exp[ik z (k x ,ky )L]

[0128] In the formula, C(k x ,k y ) represents the compensation function.

[0129] Preferably, the above-mentioned complementary function is used to perform phase compensation for the propagating wave, and the following formula is used to achieve exponential amplification for the evanescent wave:

[0130] exp[ik z L]=exp[i·i|k z |L]=exp[|k z |L]

[0131] Preferably, by analyzing the propagation and attenuation law of the evanescent wave in free space, a theoretical basis is provided for the construction of the above compensation function: when the spatial frequency component satisfies When , corresponding to the evanescent wave, its z-direction propagation constant is a pure imaginary number:

[0132]

[0133] Where k z represents the propagation constant of the evanescent wave in the z direction.

[0134] The attenuation function of the evanescent wave after the propagation distance (i.e. the above-mentioned measurement distance) is L is:

[0135]

[0136] The characteristic decay length is defined as:

[0137]

[0138] Where, L d (k x ,k y ) represents the characteristic attenuation length, A(k x ,k y ,L) represents the above attenuation function, which quantitatively describes the signal strength of different spatial frequency components at the measurement distance L. Therefore, based on this theoretical basis, we can guide how to accurately perform reverse compensation for different spatial frequencies and then construct the above compensation function. z | represents the attenuation constant of the evanescent wave, with the unit being rad / m.

[0139] Step S105: compensating and filtering the spatial frequency spectrum according to the filtering function and the compensation function to obtain a compensated frequency spectrum;

[0140] Specifically, the spatial frequency spectra of the x-polarized electric field and the y-polarized electric field are both compensated and filtered. The compensation and filtering methods are the same for both. The resulting compensated frequency spectrum restores the high-frequency information of the original defect and suppresses noise. Schematically, the spatial frequency spectrum of the x-polarized electric field is compensated and filtered using the following formula to obtain the compensated frequency spectrum:

[0141]

[0142] Where, represents the compensation frequency spectrum corresponding to the x-polarized electric field.

[0143] Step S106: reconstructing the compensation frequency spectrum to obtain a comprehensive electric field distribution at a corresponding measurement angle;

[0144] Specifically, since the previous steps are all related processing operations performed in the frequency domain, the results need to be converted back to the spatial domain, that is, the compensation frequency spectrum needs to be reconstructed.

[0145] In a preferred embodiment, the above-mentioned reconstruction of the compensation frequency spectrum to obtain the comprehensive electric field distribution at the corresponding measurement angle includes:

[0146] Obtaining the layer thickness and layer admittance of each layer in the insulating material piece to be tested;

[0147] Performing a two-dimensional inverse Fourier transform on the compensation frequency spectrum to obtain a reconstructed electric field distribution;

[0148] Specifically, a two-dimensional inverse Fourier transform is performed on the compensation frequency spectra of the x-polarized electric field and the y-polarized electric field. The reconstructed electric field distribution contains the subwavelength detail information of the original defect. The two-dimensional inverse Fourier transform of the compensation frequency spectra of the x-polarized electric field and the y-polarized electric field is performed using the following formula:

[0149]

[0150] Where, E SLx (x,y) represents the reconstructed electric field distribution corresponding to the x-polarized electric field, IFFT 2D Represents the two-dimensional inverse Fourier transform.

[0151] The layer weight corresponding to each layer is calculated based on the above layer thickness and layer admittance;

[0152] Specifically, the transmission matrix corresponding to each layer is first calculated by layer thickness and layer admittance, and then the transmission matrix of each layer is cascaded to obtain the total transmission matrix, and then the layer weight corresponding to each layer is calculated based on the total transmission matrix. The transmission matrix corresponding to each layer is calculated using the following formula:

[0153] Tn =[cos(γ n d n )-isin(γ n d n ) / Y n ][-iY n sin(γ n d n )cos(γ n d n )]

[0154]

[0155] Where, T n represents the transmission matrix of the nth layer, γ n represents the propagation constant of the nth layer, d n represents the layer thickness of the nth layer, Y n represents the layer admittance of the nth layer, ε r,n represents the complex dielectric constant of the nth layer, μ r,n Indicates the relative magnetic permeability of the nth layer of material.

[0156] Then, the transfer matrix is ​​concatenated by the following formula to obtain the total transfer matrix:

[0157] T total =T1×T2×…×T N

[0158] Where N represents the total number of layers, T total represents the total transmission matrix.

[0159] Specifically, the weight corresponding to each layer is calculated by the following formula:

[0160]

[0161] Where w n Indicates the weight corresponding to the nth layer.

[0162] The above-mentioned comprehensive electric field distribution is calculated based on the above-mentioned layer weights and the reconstructed electric field distribution.

[0163] Specifically, the comprehensive electric field distribution is calculated by the following formula:

[0164]

[0165] Where, E multilayer (x, y) represents the comprehensive electric field distribution, N represents the total number of layers, E SL,n (x,y) represents the reconstructed electric field distribution corresponding to the nth layer.

[0166] In this preferred embodiment, the comprehensive electric field distribution at the corresponding measurement angle is obtained by reconstructing the compensation frequency spectrum.

[0167] Step S107: performing multi-angle fusion based on the comprehensive electric field distribution at all measurement angles and the corresponding signal-to-noise ratio to obtain a three-dimensional defect distribution image, and determining the defect detection result of the insulating material piece to be tested based on the three-dimensional defect distribution image.

[0168] In a preferred embodiment, the above-mentioned multi-angle fusion is performed based on the comprehensive electric field distribution at all measurement angles and the corresponding signal-to-noise ratio to obtain a three-dimensional defect distribution image, including:

[0169] For the comprehensive electric field distribution at each measurement angle, the electric field weight of the comprehensive electric field distribution is calculated according to the signal-to-noise ratio corresponding to the comprehensive electric field distribution;

[0170] Specifically, the coordinates of the comprehensive electric field distribution obtained from multiple measurement angles are first unified:

[0171] [x ′ ]=[cosθ i -sinθ i ][x]

[0172] [y ′ ]=[sinθ i cosθ i ][y]

[0173] Where θ i Indicates the i-th measured angle, x ′ Indicates the horizontal coordinate of the unified i-th measurement angle, y ′ represents the ordinate of the i-th measurement angle after unification, x represents the abscissa of the i-th measurement angle before unification, and y represents the ordinate of the i-th measurement angle before unification.

[0174] Then, the electric field weight of the comprehensive electric field distribution is calculated according to the following formula:

[0175]

[0176] Where w′ i Indicates the electric field weight corresponding to the i-th measurement angle, SNR i It represents the signal-to-noise ratio corresponding to the i-th measurement angle, M represents the total number of measurements, and one measurement angle corresponds to one measurement.

[0177] The three-dimensional defect distribution image is calculated based on all comprehensive electric field distributions and all electric field weights.

[0178] Specifically, the three-dimensional defect distribution image is obtained by fusion through the following formula:

[0179]

[0180] Where, E 3D (x, y, z) represents the three-dimensional defect distribution image, E SL,i (x′, y′) represents the integrated electric field distribution corresponding to the i-th measurement angle after coordinate unification.

[0181] Preferably, the three-dimensional defect distribution image can provide complete spatial information of the defects.

[0182] In this preferred embodiment, multi-angle fusion is performed based on the comprehensive electric field distribution at all measurement angles and the corresponding signal-to-noise ratio to obtain a three-dimensional defect distribution image.

[0183] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0184] like Figure 2 As shown, an embodiment of the present invention provides an insulating material defect detection device based on terahertz waves, comprising:

[0185] Data acquisition module, data calculation module, filter function construction module, compensation function construction module, spatial frequency spectrum compensation filter module, electric field reconstruction module and detection result determination module;

[0186] The data acquisition module is used to obtain the dispersion characteristic parameters of the insulating material to be measured, the polarization electric field of the terahertz wave at several measurement angles at a preset measurement point, and the measurement distance between the preset measurement point and the insulating material to be measured;

[0187] The data calculation module is used to calculate the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio based on the polarized electric field, the measurement distance, and the dispersion characteristic parameters;

[0188] The filter function construction module is used to construct a filter function for each measurement angle according to the maximum recoverable spatial frequency, signal-to-noise ratio and spatial frequency spectrum;

[0189] The compensation function construction module is used to construct a compensation function based on the measurement distance, effective wave number and spatial frequency spectrum;

[0190] The spatial frequency spectrum compensation filter module is used to compensate and filter the spatial frequency spectrum according to the filtering function and the compensation function to obtain a compensated frequency spectrum;

[0191] The electric field reconstruction module is used to reconstruct the compensation frequency spectrum to obtain the comprehensive electric field distribution at the corresponding measurement angle;

[0192] The detection result determination module is used to perform multi-angle fusion based on the comprehensive electric field distribution and the corresponding signal-to-noise ratio at all measurement angles to obtain a three-dimensional defect distribution image, and determine the defect detection result of the above-mentioned insulating material part to be tested based on the above-mentioned three-dimensional defect distribution image.

[0193] It should be noted that the device embodiments described above are merely schematic, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without making any creative effort. The above schematic diagram is only an example of an insulating material defect detection device based on terahertz waves, and does not constitute a limitation on an insulating material defect detection device based on terahertz waves. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0194] Based on the above method embodiment, the present invention provides a corresponding terminal device embodiment.

[0195] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-mentioned method for detecting defects in insulating materials based on terahertz waves in any embodiment of the present invention.

[0196] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the device.

[0197] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, or a cloud server. The device may include, but is not limited to, a processor and a memory;

[0198] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the device, connecting the various parts of the device using various interfaces and lines.

[0199] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0200] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0201] Another embodiment of the present invention provides a storage medium, which includes a stored computer program. When the computer program is running, the device where the storage medium is located is controlled to execute the above-mentioned method for detecting defects in insulating materials based on terahertz waves according to any embodiment of the present invention.

[0202] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, an executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0203] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for detecting defects in insulating materials based on terahertz waves, characterized in that: include: Obtaining dispersion characteristic parameters of the insulating material piece to be measured, polarization electric fields of the terahertz wave at several measurement angles at a preset measurement point, and a measurement distance between the preset measurement point and the insulating material piece to be measured; Calculating the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio according to the polarized electric field, the measurement distance, and the dispersion characteristic parameters; For each measurement angle, construct a filter function according to the maximum recoverable spatial frequency, the signal-to-noise ratio, and the spatial frequency spectrum; constructing a compensation function according to the measurement distance, the effective wave number and the spatial frequency spectrum; Compensating and filtering the spatial frequency spectrum according to the filtering function and the compensation function to obtain a compensated frequency spectrum; Reconstructing the compensation frequency spectrum to obtain a comprehensive electric field distribution at a corresponding measurement angle; Multi-angle fusion is performed based on the comprehensive electric field distribution at all measurement angles and the corresponding signal-to-noise ratio to obtain a three-dimensional defect distribution image, and the defect detection result of the insulating material piece to be tested is determined based on the three-dimensional defect distribution image.

2. The method for detecting defects in insulating materials based on terahertz waves according to claim 1, characterized in that: The calculating, based on the polarized electric field, the measurement distance, and the dispersion characteristic parameters, of the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio includes: Obtaining the wave number of the terahertz wave in the insulating material piece to be tested in free space; Calculate the complex dielectric constant of the insulating material to be tested according to the dispersion characteristic parameters; Calculating the effective wave number according to the complex dielectric constant and the wave number; For each measurement angle, performing a two-dimensional fast Fourier transform on the polarized electric field to obtain a spatial frequency spectrum; dividing the spatial frequency spectrum according to the effective wave number to obtain propagating waves and evanescent waves; determining the signal-to-noise ratio based on the propagating wave and the evanescent wave; The maximum recoverable spatial frequency is calculated according to the signal-to-noise ratio, the measurement distance, and the effective wave number.

3. The method for detecting defects in insulating materials based on terahertz waves according to claim 2, characterized in that: Before performing two-dimensional fast Fourier transform on the polarized electric field, the method further includes: performing low-pass filtering and noise removal on the polarized electric field.

4. The method for detecting defects in insulating materials based on terahertz waves according to claim 3, characterized in that: The dividing the spatial frequency spectrum according to the effective wave number to obtain propagating waves and evanescent waves includes: determining each spatial frequency component of the polarized electric field according to the spatial frequency spectrum; The region in the spatial frequency spectrum where the sum of the squares of all spatial frequency components is greater than the square of the effective wave number is regarded as an evanescent wave; An area in the spatial frequency spectrum where the sum of the squares of all spatial frequency components is not greater than the square of the effective wave number is taken as a propagation wave.

5. The method for detecting defects in insulating materials based on terahertz waves according to claim 4, characterized in that: Determining the signal-to-noise ratio according to the propagating wave and the evanescent wave includes: Calculating a noise value corresponding to the evanescent wave and calculating a signal value corresponding to the propagating wave; The signal-to-noise ratio is calculated according to the noise value and the signal value.

6. The method for detecting defects in insulating materials based on terahertz waves according to claim 5, characterized in that: The reconstructing the compensation frequency spectrum to obtain a comprehensive electric field distribution at a corresponding measurement angle includes: Obtaining the layer thickness and layer admittance of each layer in the insulating material piece to be tested; Performing a two-dimensional inverse Fourier transform on the compensation frequency spectrum to obtain a reconstructed electric field distribution; Calculate the layer weight corresponding to each layer according to the layer thickness and layer admittance; The comprehensive electric field distribution is calculated based on the layer weights and the reconstructed electric field distribution.

7. The method for detecting defects in insulating materials based on terahertz waves according to claim 6, characterized in that: The method of performing multi-angle fusion based on the comprehensive electric field distribution and the corresponding signal-to-noise ratio at all measurement angles to obtain a three-dimensional defect distribution image includes: For the comprehensive electric field distribution at each measurement angle, the electric field weight of the comprehensive electric field distribution is calculated according to the signal-to-noise ratio corresponding to the comprehensive electric field distribution; The three-dimensional defect distribution image is calculated based on all comprehensive electric field distributions and all electric field weights.

8. A terahertz wave-based insulation material defect detection device, characterized in that: include: Data acquisition module, data calculation module, filter function construction module, compensation function construction module, spatial frequency spectrum compensation filter module, electric field reconstruction module and detection result determination module; The data acquisition module is used to obtain the dispersion characteristic parameters of the insulating material to be measured, the polarization electric field of the terahertz wave at several measurement angles at a preset measurement point, and the measurement distance between the preset measurement point and the insulating material to be measured; The data calculation module is used to calculate the effective wave number of the insulating material to be measured, the spatial frequency spectrum at each measurement angle, the maximum recoverable spatial frequency, and the signal-to-noise ratio according to the polarized electric field, the measurement distance, and the dispersion characteristic parameter; The filter function construction module is used to construct a filter function for each measurement angle according to the maximum recoverable spatial frequency, the signal-to-noise ratio and the spatial frequency spectrum; The compensation function construction module is used to construct a compensation function according to the measurement distance, the effective wave number and the spatial frequency spectrum; The spatial frequency spectrum compensation filtering module is used to compensate and filter the spatial frequency spectrum according to the filtering function and the compensation function to obtain a compensated frequency spectrum; The electric field reconstruction module is used to reconstruct the compensation frequency spectrum to obtain the comprehensive electric field distribution at the corresponding measurement angle; The detection result determination module is used to perform multi-angle fusion based on the comprehensive electric field distribution and the corresponding signal-to-noise ratio at all measurement angles to obtain a three-dimensional defect distribution image, and determine the defect detection result of the insulating material part to be tested based on the three-dimensional defect distribution image.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for detecting defects in insulating materials based on terahertz waves according to any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute the insulating material defect detection method based on terahertz waves according to any one of claims 1 to 7.

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