Insulating material defect identification method and device based on terahertz spectrum, terminal equipment and storage medium

Abstract

The invention discloses an insulating material defect identification method and device based on a terahertz spectrum, terminal equipment and a storage medium, and belongs to the field of insulating material detection.The method comprises the steps that the matching weight of a target terahertz spectrum is determined according to the terahertz spectrum of each known defect in a defect type library; calculating the matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight; if the matching cost is smaller than a first threshold value, according to the signal intensity and the frequency range of the target terahertz spectrum, calculating a time-varying index of the target terahertz spectrum, judging a difference value between the time-varying index and a sample time-varying index corresponding to the terahertz spectrum sample, and when it is determined that the difference value is smaller than a second threshold value, judging that the to-be-identified area has defects; and if the matching cost is not less than the first threshold value, determining that the to-be-identified area does not have defects. According to the invention, the problem of low detection accuracy caused by manual detection of the defects of the insulating material in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of insulating material detection, and in particular to a method, device, terminal equipment and storage medium for identifying insulating material defects based on terahertz spectroscopy. Background Art

[0002] Insulation materials are key building blocks for the manufacture of electrical and electronic equipment. Their insulation performance is directly linked to the safety of power equipment and even the entire power supply system. During their preparation and use, insulation materials are inevitably affected by various factors, including electrical, thermal, and mechanical factors, which can lead to internal or surface defects. These defects can gradually degrade the insulation material's electrical performance and, in severe cases, even cause insulation failure.

[0003] Existing methods for identifying defects in insulating materials rely primarily on manual judgment, but this is subject to interference from factors such as experience, resulting in insufficient accuracy, strong subjectivity, and a lack of unified standards. For example, different inspectors may interpret the terahertz spectrum of the same insulating material differently, making it difficult to guarantee the reliability of the test results. Summary of the Invention

[0004] The present invention provides a method, apparatus, terminal device and storage medium for identifying defects in insulating materials based on terahertz spectroscopy. The method can solve the problem of low detection accuracy caused by manual detection of defects in insulating materials in the prior art.

[0005] An embodiment of the present invention provides a method for identifying defects in insulating materials based on terahertz spectroscopy, comprising:

[0006] Obtaining a defect type library and a target terahertz spectrum of a target insulating material in an area to be identified; wherein the defect type library stores terahertz spectra of several insulating materials with known defects;

[0007] Comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library, and determining a matching weight of the target terahertz spectrum based on the comparison results;

[0008] Screening out a terahertz spectrum sample from the defect type library; wherein the insulating material corresponding to the terahertz spectrum sample is the same as the material type of the target insulating material;

[0009] Calculating a matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight;

[0010] If the matching cost is less than a preset first threshold, calculating a time-varying index of the target terahertz spectrum based on the signal intensity and frequency range of the target terahertz spectrum, calculating a difference between the time-varying index and a sample time-varying index corresponding to the terahertz spectrum sample, and determining that a defect exists in the to-be-identified area when it is determined that the difference is less than a preset second threshold;

[0011] If the matching cost is not less than the first threshold, it is determined that there is no defect in the area to be identified.

[0012] Furthermore, comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library and determining a matching weight of the target terahertz spectrum based on the comparison result includes:

[0013] dividing the target terahertz spectrum into a plurality of terahertz spectrum segments;

[0014] For each of the terahertz spectrum segments, a feature comparison is performed between the terahertz spectrum segment and the terahertz spectra of each known defect in the defect type library, a terahertz spectrum matching the feature of the terahertz spectrum segment is screened out from the defect type library, and the terahertz spectrum with the matching feature is used as the adapted terahertz spectrum; a matching weight of the terahertz spectrum segment is determined according to a preset weight threshold and a dielectric constant corresponding to the adapted terahertz spectrum;

[0015] The matching weight of the target terahertz spectrum is determined according to the matching weight of each terahertz spectrum segment.

[0016] Furthermore, the calculating the matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight includes:

[0017] Performing time domain alignment of the target terahertz spectrum and the terahertz spectrum sample;

[0018] Traversing the terahertz spectrum segments, during the traversal process, calculating an initial segment matching cost between the current terahertz spectrum segment and the terahertz spectrum of the terahertz spectrum sample in the corresponding time domain, and combining the initial segment matching cost and the matching weight of the current terahertz spectrum segment to obtain a segment matching cost of the current terahertz spectrum segment;

[0019] The segment matching costs are counted to obtain the matching costs between the target terahertz spectrum and the terahertz spectrum sample.

[0020] Furthermore, calculating the time-varying index of the target terahertz spectrum according to the signal intensity and frequency range of the target terahertz spectrum includes:

[0021] Performing a frequency domain transformation operation on the target terahertz spectrum to obtain local terahertz spectra of the target terahertz spectrum in the frequency domain;

[0022] For each of the local terahertz spectra, performing an integral calculation according to the frequency range and signal intensity of the local terahertz spectrum to obtain the integrated intensity of the local terahertz spectrum;

[0023] Calculating an integrated intensity mean value according to the integrated intensity of each local terahertz spectrum;

[0024] The time-varying index of the target terahertz spectrum is calculated based on the integrated intensity mean and the integrated intensity of each local terahertz spectrum.

[0025] Furthermore, after calculating the difference between the time-varying index and the sample time-varying index corresponding to the terahertz spectrum sample, the method further includes:

[0026] When it is determined that the difference is not less than the second threshold, it is determined that there is no defect in the to-be-identified area.

[0027] An embodiment of the present invention further provides an insulating material defect identification device based on terahertz spectroscopy, comprising: a data acquisition module, a matching weight determination module, a terahertz spectrum sample determination module, a matching cost calculation module, and a first defect identification module;

[0028] The data acquisition module is used to acquire a defect type library and a target terahertz spectrum of a target insulating material in an area to be identified; wherein the defect type library stores terahertz spectra of several insulating materials with known defects;

[0029] The matching weight determination module is used to compare the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library, and determine the matching weight of the target terahertz spectrum based on the comparison result;

[0030] The terahertz spectrum sample determination module is used to screen out a terahertz spectrum sample from the defect type library; wherein the insulating material corresponding to the terahertz spectrum sample is the same as the material type of the target insulating material;

[0031] The matching cost calculation module is used to calculate the matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight;

[0032] The first defect identification module is configured to calculate the time-varying index of the target terahertz spectrum based on the signal intensity and frequency range of the target terahertz spectrum when it is determined that the matching cost is less than a preset first threshold, calculate the difference between the time-varying index and the sample time-varying index corresponding to the terahertz spectrum sample, and determine that a defect exists in the area to be identified when it is determined that the difference is less than a preset second threshold; and determine that no defect exists in the area to be identified when it is determined that the matching cost is not less than the first threshold.

[0033] Furthermore, comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library and determining a matching weight of the target terahertz spectrum based on the comparison result includes:

[0034] dividing the target terahertz spectrum into a plurality of terahertz spectrum segments;

[0035] For each of the terahertz spectrum segments, a feature comparison is performed between the terahertz spectrum segment and the terahertz spectra of each known defect in the defect type library, a terahertz spectrum matching the feature of the terahertz spectrum segment is screened out from the defect type library, and the terahertz spectrum with the matching feature is used as the adapted terahertz spectrum; a matching weight of the terahertz spectrum segment is determined according to a preset weight threshold and a dielectric constant corresponding to the adapted terahertz spectrum;

[0036] The matching weight of the target terahertz spectrum is determined according to the matching weight of each terahertz spectrum segment.

[0037] Furthermore, the insulating material defect identification device based on terahertz spectroscopy further includes: a second defect identification module;

[0038] The second defect recognition module is configured to determine that no defect exists in the area to be recognized when it is determined that the difference is not less than the second threshold.

[0039] The present application also provides a terminal device, including:

[0040] one or more processors;

[0041] a memory, coupled to the processor, for storing one or more programs;

[0042] When the one or more programs are executed by the one or more processors, the one or more processors implement the insulating material defect identification method based on terahertz spectroscopy as described in the above-mentioned embodiment of the invention.

[0043] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for identifying defects in insulating materials based on terahertz spectroscopy as described in the above-mentioned embodiment of the invention is implemented.

[0044] The following beneficial effects are achieved by implementing the present invention:

[0045] The present invention provides a method, apparatus, terminal device, and storage medium for identifying defects in insulating materials based on terahertz spectroscopy. The method determines a matching weight for a target terahertz spectrum based on the terahertz spectra of each known defect in a defect type library, and calculates a matching cost between the target terahertz spectrum and a terahertz spectrum sample based on the matching weight. Thus, by assigning corresponding weights to the target terahertz spectrum, the matching cost can better reflect the presence of defects in the target insulating material, thereby avoiding the ambiguity and inaccuracy of manual detection in determining the characteristics of the insulating material.

[0046] Then, based on the matching cost between the target terahertz spectrum and the terahertz spectrum sample, preliminary defect identification is performed on the area to be identified. During the preliminary identification process, a first threshold is set and whether the matching cost exceeds the first threshold is used as a criterion to determine the degree of similarity between the area to be identified and the terahertz spectrum sample;

[0047] When the matching cost is not less than a first threshold, it is determined that the degree of similarity between the area to be identified and the terahertz spectrum sample is low, and it is determined that the area to be identified does not have a defect; conversely, if the matching cost is less than the first threshold, the degree of similarity between the area to be identified and the terahertz spectrum sample is further determined by calculating the time-varying index of the target terahertz spectrum and determining the difference between the time-varying index and the sample time-varying index corresponding to the terahertz spectrum sample; and when it is determined that the difference between the time-varying index of the target terahertz spectrum and the sample time-varying index is less than a preset second threshold, it is determined that the area to be identified has a defect;

[0048] Therefore, through automated weight matching, sample comparison and similarity determination, the problem of low detection accuracy caused by relying on manual detection of insulation material defects is effectively avoided, and the efficiency and accuracy of insulation material defect detection are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] 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.

[0050] Figure 1This is a flow chart of a method for identifying defects in insulating materials based on terahertz spectroscopy provided in one embodiment of the present application;

[0051] Figure 2 This is a schematic structural diagram of an insulating material defect identification device based on terahertz spectroscopy provided in one embodiment of the present application;

[0052] Figure 3 1 is a schematic structural diagram of an insulating material defect identification device based on terahertz spectroscopy provided in another embodiment of the present application;

[0053] Figure 4 This is a schematic diagram of the structure of a terminal device provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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).

[0060] 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.

[0061] See also Figure 1 To address the problem of low detection accuracy in the prior art due to manual detection of insulation material defects, an embodiment of the present invention provides a method for identifying insulation material defects based on terahertz spectroscopy, comprising:

[0062] S1. Obtain a defect type library and a target terahertz spectrum of a target insulating material in an area to be identified; wherein the defect type library stores terahertz spectra of several insulating materials with known defects;

[0063] Specifically, the defect type library includes terahertz spectra of several known defects obtained through experiments; wherein the defect types may include pore defects, impurity mixing defects, delamination defects, etc.;

[0064] Different defect types correspond to different terahertz spectral characteristics. For example, a pore defect may exhibit an absorption peak in a specific frequency band on the terahertz spectrum. Impurity-incorporated defects may cause unique fluctuations or shifts in the spectrum due to the characteristics of the impurities. Delamination defects may exhibit characteristic signals corresponding to different layers in the spectrum. Therefore, collecting and organizing the terahertz spectra of these known defects and constructing a defect type library will facilitate subsequent defect identification in the target terahertz spectrum.

[0065] It should be noted that the defect type library collects a variety of different terahertz spectra. These terahertz spectra correspond to different types of insulating materials, such as ceramic insulating materials, rubber insulating materials, plastic insulating materials, etc. Each material has its corresponding terahertz spectrum included in the library;

[0066] Furthermore, in the terahertz spectra of these different materials, there are cases where, despite the different types of insulating materials, the defect types corresponding to these terahertz spectra are the same. For example, ceramic insulating materials and rubber insulating materials can both have internal bubbles, so the terahertz spectra corresponding to these internal bubble defects in both materials are included in the defect type library.

[0067] S2. comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library, and determining a matching weight of the target terahertz spectrum based on the comparison results;

[0068] In a preferred embodiment, comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library and determining the matching weight of the target terahertz spectrum based on the comparison results includes:

[0069] dividing the target terahertz spectrum into a plurality of terahertz spectrum segments;

[0070] For each of the terahertz spectrum segments, a feature comparison is performed between the terahertz spectrum segment and the terahertz spectra of each known defect in the defect type library, a terahertz spectrum matching the feature of the terahertz spectrum segment is screened out from the defect type library, and the terahertz spectrum with the matching feature is used as the adapted terahertz spectrum; a matching weight of the terahertz spectrum segment is determined according to a preset weight threshold and a dielectric constant corresponding to the adapted terahertz spectrum;

[0071] determining a matching weight of the target terahertz spectrum according to the matching weights of the terahertz spectrum segments;

[0072] Schematically, in order to accurately identify defects in a target terahertz spectrum, the target terahertz spectrum is divided into several terahertz spectrum segments. Then, for each terahertz spectrum segment, a feature comparison is performed with the terahertz spectra of each known defect in the defect type library. Given that different defects have different effects on the dielectric constant of insulating materials, the difference in dielectric constant is used as a key indicator, and the matching weight is determined in combination with the feature comparison results. In this way, the importance of each terahertz spectrum segment in the target terahertz spectrum in the overall defect judgment can be more scientifically considered, thereby improving the accuracy and reliability of defect identification.

[0073] Specifically, when dividing the target terahertz spectrum into several terahertz spectrum segments, the time characteristics or frequency domain characteristics of the target terahertz spectrum need to be used as the basis for division; after the terahertz pulse emitted by the terahertz emission source is incident on the insulating material, part of the pulse will penetrate the material, while the other part of the pulse will be reflected. Therefore, when segmenting according to the time characteristics, the target terahertz spectrum can be segmented according to the arrival time of the echo signal and the time interval of Mps (M is a preset constant); if the frequency domain characteristics are used as the basis for division, the target terahertz spectrum in the time domain needs to be converted to the frequency domain through Fourier transform, and after the conversion is completed, the target terahertz spectrum in the frequency domain is segmented according to the preset frequency domain range using special spectrum analysis software;

[0074] Schematically, for each terahertz spectrum segment, a terahertz spectrum that matches its characteristics is screened out from the defect type library through feature comparison and used as the adapted terahertz spectrum; during the comparison process, for each terahertz spectrum segment, a spectral feature quantification analysis algorithm is used to perform difference calculations with the terahertz spectra in the defect type library from preset dimensions such as frequency, peak-to-valley amplitude, and peak shape, and the terahertz spectrum of the defect type library whose difference calculation results in all preset dimensions exceed the preset thresholds corresponding to each dimension is used as the adapted terahertz spectrum;

[0075] Specifically, this embodiment compares three dimensions: frequency dimension, peak-to-valley amplitude dimension, and peak shape dimension;

[0076] For each terahertz spectrum segment, in the frequency dimension, the difference in frequency values ​​between the terahertz spectrum currently being compared in the defect type library and the current terahertz spectrum segment is calculated; in the peak-to-valley amplitude dimension, the difference in peak height and valley depth between the terahertz spectrum currently being compared in the defect type library and the current terahertz spectrum segment is calculated; in the peak shape dimension, the difference in spectral peak shape and spectral width between the terahertz spectrum currently being compared in the defect type library and the current terahertz spectrum segment is calculated; when comparing, the terahertz spectra whose difference calculation results of all preset dimensions in the defect type library exceed the preset threshold values ​​corresponding to each dimension are screened out as the adapted terahertz spectra;

[0077] Schematically, the matching weight of the terahertz spectrum segment is determined according to a preset weight threshold and a dielectric constant corresponding to the adapted terahertz spectrum;

[0078] Specifically, the more severe the defect, the greater the dielectric constant corresponding to the defective insulating material will be compared to the normal insulating material. Therefore, a preset weight threshold is used as a judgment criterion to determine whether the dielectric constant of the adapted terahertz spectrum significantly deviates from the dielectric constant of the insulating material in a normal state, thereby determining whether to assign a higher weight or a lower weight to the terahertz spectrum segment corresponding to the adapted terahertz spectrum; finally, the overall matching weight of the target terahertz spectrum is determined by combining the matching weights of each terahertz spectrum segment;

[0079] Specifically, the dielectric constant corresponding to the adapted terahertz spectrum is extracted, and it is determined whether the dielectric constant is greater than the weight threshold. If so, the matching weight of the current terahertz spectrum segment is reset to 1; if not, the matching weight of the current terahertz spectrum segment is reset to 0; thereby, the matching weights corresponding to different terahertz spectrum segments are obtained, and thus, by setting 1 and 0, the influence of the severity of the defect on the target terahertz spectrum is increased;

[0080] It should be noted that the dielectric constant corresponding to the adapted terahertz spectrum has been measured in advance through experiments. When constructing the defect type library, a high-precision dielectric constant tester is used to measure each insulating material with known defects under multiple temperature and humidity environmental conditions, and the average value of multiple sets of measurement data is taken as the dielectric constant and stored in the defect type library; the weight threshold is an empirical value.

[0081] S3. Screening out a terahertz spectrum sample from the defect type library; wherein the insulating material corresponding to the terahertz spectrum sample is the same as the material type of the target insulating material;

[0082] Schematically, it is assumed that the material type of the terahertz spectrum sample is cross-linked polyethylene (XLPE), which is consistent with the material type of the target terahertz spectrum;

[0083] Specifically, due to the inherent differences in the terahertz spectral characteristics of different materials, this embodiment will screen out terahertz spectral samples with the same material type as the target terahertz spectrum from the defect type library for subsequent calculation of the matching cost. As a result, the terahertz spectral characteristics of the two are more strongly correlated. Based on this, the matching cost calculated subsequently can more effectively reflect the similarity between the target insulating material and the insulating material sample in the terahertz spectral characteristics, thereby helping to identify defects in the target insulating material.

[0084] S4. Calculating a matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight;

[0085] In a preferred embodiment, the calculating the matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight includes:

[0086] Performing time domain alignment of the target terahertz spectrum and the terahertz spectrum sample;

[0087] Traversing the terahertz spectrum segments, during the traversal process, calculating an initial segment matching cost between the current terahertz spectrum segment and the terahertz spectrum of the terahertz spectrum sample in the corresponding time domain, and combining the initial segment matching cost and the matching weight of the current terahertz spectrum segment to obtain a segment matching cost of the current terahertz spectrum segment;

[0088] Counting the segmented matching costs to obtain the matching costs between the target terahertz spectrum and the terahertz spectrum sample;

[0089] Specifically, first, the target terahertz spectrum and the terahertz spectrum sample need to be aligned in the time domain to ensure that the two are consistent in the time dimension when performing morphological matching later;

[0090] Then, the matching cost MC between the target terahertz spectrum and the terahertz spectrum sample is initialized to 0;

[0091] After the initialization is completed, all terahertz spectrum segments are traversed; taking the terahertz spectrum segment b as an example, the initial segment matching cost mc between the current terahertz spectrum segment b and the terahertz spectrum of the terahertz spectrum sample in the corresponding time domain is calculated by the Euclidean distance measurement method, the spectral angle distance measurement method, etc. b , and combined with the initial segment matching cost mc b And the matching weight of terahertz spectrum segment b, the segment matching cost MC of terahertz spectrum segment b is obtained b , the specific calculation formula is as follows:

[0092] MC b =mc b ×W b ;

[0093] Where W b is the matching weight of THz spectrum segment b, mc b is the initial segment matching cost of THz spectrum segment b;

[0094] Calculate the segment matching cost MC of terahertz spectrum segment b b Then, update the current matching cost MC=MC+MC b ;

[0095] After traversing all the terahertz spectrum segments, the final matching cost MC between the target terahertz spectrum and the terahertz spectrum sample is obtained.

[0096] S5. If the matching cost is less than a preset first threshold, calculating a time-varying index of the target terahertz spectrum based on the signal intensity and frequency range of the target terahertz spectrum, calculating a difference between the time-varying index and a sample time-varying index corresponding to the terahertz spectrum sample, and determining that a defect exists in the to-be-identified area when it is determined that the difference is less than a preset second threshold;

[0097] Schematically, by comparing the matching cost with a preset first threshold, it is preliminarily determined whether the target terahertz spectrum has defects;

[0098] Specifically, since the greater the matching cost, the less similar the target terahertz spectrum is to the terahertz spectrum sample, and since the terahertz spectrum sample is the terahertz spectrum corresponding to the defective insulating material, when the matching cost is not less than the first threshold, it is indicated that the target terahertz spectrum does not have defects. However, if the matching cost is less than the preset first threshold, it is necessary to further calculate the time-varying index of the target terahertz spectrum and determine the difference between the time-varying index and the sample time-varying index corresponding to the terahertz spectrum sample, so as to determine whether the target insulating material has defects.

[0099] In a preferred embodiment, the calculating the time-varying index of the target terahertz spectrum according to the signal intensity and frequency range of the target terahertz spectrum includes:

[0100] Performing a frequency domain transformation operation on the target terahertz spectrum to obtain local terahertz spectra of the target terahertz spectrum in the frequency domain;

[0101] For each of the local terahertz spectra, performing an integral calculation according to the frequency range and signal intensity of the local terahertz spectrum to obtain the integrated intensity of the local terahertz spectrum;

[0102] Calculating an integrated intensity mean value according to the integrated intensity of each local terahertz spectrum;

[0103] Calculating a time-varying index of the target terahertz spectrum according to the integrated intensity mean and the integrated intensity of each local terahertz spectrum;

[0104] Schematically, a frequency domain transformation operation is performed on the target terahertz spectrum, thereby obtaining a local terahertz spectrum corresponding to each terahertz spectrum segment in the target terahertz spectrum under frequency domain representation;

[0105] Specifically, the target terahertz spectrum includes a total of N time periods of local terahertz spectra S={S i}, where i = 1,...,N,S i is the local terahertz spectrum of the i-th time period;

[0106] Assuming that the total frequency range of the target terahertz spectrum is [ν1, ν2], the total integrated intensity expression of the target terahertz spectrum is as follows:

[0107]

[0108] Where R(ν) is the signal intensity;

[0109] Specifically, this embodiment has converted each terahertz spectrum segment in the target terahertz spectrum into a local terahertz spectrum corresponding to the frequency domain representation. Therefore, the integrated intensity X of each local terahertz spectrum is i The calculation expression is as follows:

[0110]

[0111] Among them, the integral interval [vi,v+i] represents the frequency range corresponding to the local terahertz spectrum of the i-th time period;

[0112] Based on the integrated intensity X of each local terahertz spectrum i , calculate the mean integrated intensity The specific calculation expression is as follows:

[0113]

[0114] Then, according to the mean integrated intensity And the integrated intensity X of each local terahertz spectrum i , the time-varying index of the target terahertz spectrum is calculated, and the specific calculation expression is as follows:

[0115]

[0116] Finally, determining a difference between the time-varying index σ(X) and the sample time-varying index corresponding to the terahertz spectrum sample, and determining that a defect exists in the to-be-identified area when determining that the difference is less than a preset second threshold;

[0117] It should be noted that the first threshold and the second threshold are both empirical values.

[0118] In a preferred embodiment, after calculating the difference between the time-varying index and the sample time-varying index corresponding to the terahertz spectrum sample, the method further includes:

[0119] When it is determined that the difference is not less than the second threshold, determining that there is no defect in the to-be-identified area;

[0120] Specifically, when it is determined that the difference is not less than the second threshold, it means that the area to be identified may be subject to interference signals rather than defects.

[0121] S6. If the matching cost is not less than the first threshold, it is determined that there is no defect in the area to be identified;

[0122] Specifically, if the matching cost is not less than the first threshold, it means that the area to be identified receives a normal base signal or a noise signal, and there is no defect.

[0123] See Figure 2 , is an insulating material defect identification device based on terahertz spectroscopy provided by an embodiment of the present invention, comprising: a data acquisition module, a matching weight determination module, a terahertz spectrum sample determination module, a matching cost calculation module, and a first defect identification module;

[0124] The data acquisition module is used to acquire a defect type library and a target terahertz spectrum of a target insulating material in an area to be identified; wherein the defect type library stores terahertz spectra of several insulating materials with known defects;

[0125] The matching weight determination module is used to compare the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library, and determine the matching weight of the target terahertz spectrum based on the comparison result;

[0126] The terahertz spectrum sample determination module is used to screen out a terahertz spectrum sample from the defect type library; wherein the insulating material corresponding to the terahertz spectrum sample is the same as the material type of the target insulating material;

[0127] The matching cost calculation module is used to calculate the matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight;

[0128] The first defect identification module is configured to calculate the time-varying index of the target terahertz spectrum based on the signal intensity and frequency range of the target terahertz spectrum when it is determined that the matching cost is less than a preset first threshold, calculate the difference between the time-varying index and the sample time-varying index corresponding to the terahertz spectrum sample, and determine that a defect exists in the area to be identified when it is determined that the difference is less than a preset second threshold; and determine that no defect exists in the area to be identified when it is determined that the matching cost is not less than the first threshold.

[0129] In a preferred embodiment, comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library and determining the matching weight of the target terahertz spectrum based on the comparison results includes:

[0130] dividing the target terahertz spectrum into a plurality of terahertz spectrum segments;

[0131] For each of the terahertz spectrum segments, a feature comparison is performed between the terahertz spectrum segment and the terahertz spectra of each known defect in the defect type library, a terahertz spectrum matching the feature of the terahertz spectrum segment is screened out from the defect type library, and the terahertz spectrum with the matching feature is used as the adapted terahertz spectrum; a matching weight of the terahertz spectrum segment is determined according to a preset weight threshold and a dielectric constant corresponding to the adapted terahertz spectrum;

[0132] The matching weight of the target terahertz spectrum is determined according to the matching weight of each terahertz spectrum segment.

[0133] See also Figure 3 ,In a preferred embodiment, the insulating material defect identification device based on terahertz spectroscopy further includes: a second defect identification module;

[0134] The second defect recognition module is configured to determine that no defect exists in the area to be recognized when it is determined that the difference is not less than the second threshold.

[0135] See also Figure 4 , an embodiment of the present application further provides a terminal device, including:

[0136] one or more processors;

[0137] a memory, coupled to the processor, for storing one or more programs;

[0138] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for identifying defects in insulating materials based on terahertz spectroscopy.

[0139] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the above-mentioned method for identifying defects in insulating materials based on terahertz spectroscopy. The memory is used to store various types of data to support the operation of the terminal device. For example, these data may include instructions for any application or method used to operate on the terminal device, as well as application-related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0140] In an exemplary embodiment, the terminal device can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the insulation material defect identification method based on terahertz spectroscopy as described in any of the above embodiments and achieve the same technical effect as the above method.

[0141] In another exemplary embodiment, a computer-readable storage medium including a computer program is further provided. When executed by a processor, the computer program implements the steps of the method for identifying insulating material defects based on terahertz spectroscopy as described in any of the aforementioned embodiments. For example, the computer-readable storage medium may be the aforementioned memory including the computer program. The computer program may be executed by a processor of a terminal device to implement the method for identifying insulating material defects based on terahertz spectroscopy as described in any of the aforementioned embodiments, thereby achieving the same technical effects as the aforementioned methods.

[0142] 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 identifying defects in insulating materials based on terahertz spectroscopy, characterized in that: include: Obtaining a defect type library and a target terahertz spectrum of a target insulating material in an area to be identified; wherein the defect type library stores terahertz spectra of several insulating materials with known defects; Comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library, and determining a matching weight of the target terahertz spectrum based on the comparison results; Screening out a terahertz spectrum sample from the defect type library; wherein the insulating material corresponding to the terahertz spectrum sample is the same as the material type of the target insulating material; Calculating a matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight; If the matching cost is less than a preset first threshold, calculating a time-varying index of the target terahertz spectrum based on the signal intensity and frequency range of the target terahertz spectrum, calculating a difference between the time-varying index and a sample time-varying index corresponding to the terahertz spectrum sample, and determining that a defect exists in the to-be-identified area when it is determined that the difference is less than a preset second threshold; If the matching cost is not less than the first threshold, it is determined that there is no defect in the area to be identified.

2. The method for identifying defects in insulating materials based on terahertz spectroscopy according to claim 1, wherein: The comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library, and determining a matching weight of the target terahertz spectrum based on the comparison result, includes: dividing the target terahertz spectrum into a plurality of terahertz spectrum segments; For each of the terahertz spectrum segments, a feature comparison is performed between the terahertz spectrum segment and the terahertz spectra of each known defect in the defect type library, a terahertz spectrum matching the feature of the terahertz spectrum segment is screened out from the defect type library, and the terahertz spectrum with the matching feature is used as the adapted terahertz spectrum; a matching weight of the terahertz spectrum segment is determined according to a preset weight threshold and a dielectric constant corresponding to the adapted terahertz spectrum; The matching weight of the target terahertz spectrum is determined according to the matching weight of each terahertz spectrum segment.

3. The method for identifying defects in insulating materials based on terahertz spectroscopy according to claim 2, wherein: Calculating the matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight includes: Performing time domain alignment of the target terahertz spectrum and the terahertz spectrum sample; Traversing the terahertz spectrum segments, during the traversal process, calculating an initial segment matching cost between the current terahertz spectrum segment and the terahertz spectrum of the terahertz spectrum sample in the corresponding time domain, and combining the initial segment matching cost and the matching weight of the current terahertz spectrum segment to obtain a segment matching cost of the current terahertz spectrum segment; The segment matching costs are counted to obtain the matching costs between the target terahertz spectrum and the terahertz spectrum sample.

4. The method for identifying defects in insulating materials based on terahertz spectroscopy according to claim 3, wherein: Calculating the time-varying index of the target terahertz spectrum according to the signal intensity and frequency range of the target terahertz spectrum includes: Performing a frequency domain transformation operation on the target terahertz spectrum to obtain local terahertz spectra of the target terahertz spectrum in the frequency domain; For each of the local terahertz spectra, performing an integral calculation according to the frequency range and signal intensity of the local terahertz spectrum to obtain the integrated intensity of the local terahertz spectrum; Calculating an integrated intensity mean value according to the integrated intensity of each local terahertz spectrum; The time-varying index of the target terahertz spectrum is calculated based on the integrated intensity mean and the integrated intensity of each local terahertz spectrum.

5. The method for identifying defects in insulating materials based on terahertz spectroscopy according to claim 1, wherein: After calculating the difference between the time-varying index and the sample time-varying index corresponding to the terahertz spectrum sample, the method further includes: When it is determined that the difference is not less than the second threshold, it is determined that there is no defect in the to-be-identified area.

6. A device for identifying defects in insulating materials based on terahertz spectroscopy, characterized in that: include: A data acquisition module, a matching weight determination module, a terahertz spectrum sample determination module, a matching cost calculation module, and a first defect identification module; The data acquisition module is used to acquire a defect type library and a target terahertz spectrum of a target insulating material in an area to be identified; wherein the defect type library stores terahertz spectra of several insulating materials with known defects; The matching weight determination module is used to compare the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library, and determine the matching weight of the target terahertz spectrum based on the comparison result; The terahertz spectrum sample determination module is used to screen out a terahertz spectrum sample from the defect type library; wherein the insulating material corresponding to the terahertz spectrum sample is the same as the material type of the target insulating material; The matching cost calculation module is used to calculate the matching cost between the target terahertz spectrum and the terahertz spectrum sample according to the matching weight; The first defect identification module is configured to calculate the time-varying index of the target terahertz spectrum based on the signal intensity and frequency range of the target terahertz spectrum when it is determined that the matching cost is less than a preset first threshold, calculate the difference between the time-varying index and the sample time-varying index corresponding to the terahertz spectrum sample, and determine that a defect exists in the area to be identified when it is determined that the difference is less than a preset second threshold; and determine that no defect exists in the area to be identified when it is determined that the matching cost is not less than the first threshold.

7. The device for identifying defects in insulating materials based on terahertz spectroscopy according to claim 6, wherein: The comparing the target terahertz spectrum with the terahertz spectra of each known defect in the defect type library, and determining a matching weight of the target terahertz spectrum based on the comparison result, includes: dividing the target terahertz spectrum into a plurality of terahertz spectrum segments; For each of the terahertz spectrum segments, a feature comparison is performed between the terahertz spectrum segment and the terahertz spectra of each known defect in the defect type library, a terahertz spectrum matching the feature of the terahertz spectrum segment is screened out from the defect type library, and the terahertz spectrum with the matching feature is used as the adapted terahertz spectrum; a matching weight of the terahertz spectrum segment is determined according to a preset weight threshold and a dielectric constant corresponding to the adapted terahertz spectrum; The matching weight of the target terahertz spectrum is determined according to the matching weight of each terahertz spectrum segment.

8. The device for identifying defects in insulating materials based on terahertz spectroscopy according to claim 6, wherein: Also includes: a second defect recognition module; The second defect recognition module is configured to determine that no defect exists in the area to be recognized when it is determined that the difference is not less than the second threshold.

9. A terminal device, characterized in that: include: one or more processors; a memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the insulating material defect identification method based on terahertz spectroscopy as described in any one of claims 1 to 5.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying defects in insulating materials based on terahertz spectroscopy according to any one of claims 1 to 5 is implemented.

Images