CN114897015A - Material detection method, device, equipment and storage medium - Google Patents

Abstract

The application provides a material detection method, a device, equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps of obtaining terahertz reflection waveform data of a material to be detected, extracting effective pulse characteristics from the terahertz reflection waveform data, obtaining scanning point indexes corresponding to the effective pulse characteristics, and generating an effective pulse characteristic set and a scanning point index set of the material to be detected; performing characteristic information clustering processing on each effective pulse characteristic in the effective pulse characteristic set by adopting a preset first self-organizing mapping network to generate a first clustering type set; according to the first clustering type set, adopting a preset second self-organizing mapping network to perform pulse type clustering processing on each scanning point in the scanning point index set to generate a second clustering type set; and performing imaging processing on the material to be detected according to the second cluster type set to generate an imaging result graph of the material to be detected. The method can effectively reduce the loss of terahertz spectrum information and has high imaging reliability.

Description

Material testing method, device, equipment and storage medium

technical field

The present application relates to the technical field of material detection, and in particular, to a material detection method, device, equipment and storage medium.

Background technique

With the development of high-tech, various high-performance new materials are widely used in equipment in various fields such as aerospace, electric power and electrical, and during the production, transportation, and operation of equipment, there will be air gaps and holes inside the material. , cracks, inclusions and other defects, these defects cannot be identified from the appearance and will affect the performance of the equipment, cause economic losses, and even casualties. Terahertz imaging technology is one of the most widely used intuitive and fast methods for non-destructive testing of internal defects in specific materials. However, most of the existing terahertz imaging technologies use a single spectral characteristic parameter for imaging. This method will cause a lot of information loss, and cannot effectively use the terahertz spectral information in the entire frequency range for imaging, which is not enough for various types, Complex actual structural defects are effectively imaged, and the imaging reliability is poor.

SUMMARY OF THE INVENTION

In view of this, embodiments of the present application provide a material detection method, device, device, and storage medium, which can effectively utilize terahertz spectral information in the entire frequency range for imaging, reduce the loss of terahertz spectral information, and improve imaging reliability.

A first aspect of the embodiments of the present application provides a material detection method, including:

Obtain the terahertz reflection waveform data of the material to be detected, extract the effective pulse feature from the terahertz reflection waveform data, obtain the scan point index corresponding to the effective pulse feature, and generate the effective pulse feature set of the material to be detected and the scanpoint index set;

Using a preset first self-organizing mapping network to perform feature information clustering processing on each valid pulse feature in the valid pulse feature set, to generate a first cluster type set;

According to the first cluster type set, use a preset second self-organizing mapping network to perform pulse type clustering processing on each scan point in the scan point index set to generate a second cluster type set;

According to the second cluster type set, imaging processing is performed on the material to be detected, and an imaging result map of the material to be detected is generated.

With reference to the first aspect, in a first possible implementation manner of the first aspect, the step of acquiring the terahertz reflection waveform data of the material to be detected further includes:

Using a terahertz imaging device to perform terahertz wave scanning on the material to be detected, to generate bipolar pulse data of the material to be detected;

Deconvolution processing is performed on the bipolar pulse data to generate corresponding unipolar pulse data, and the unipolar pulse data is acquired as terahertz reflection waveform data of the material to be detected.

With reference to the first aspect or the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the step of extracting effective pulse characteristics from the terahertz reflected waveform data includes:

The pulse peak value corresponding to each scan point is extracted from the terahertz reflection waveform data, and the pulse peak value corresponding to each scan point is compared with a preset peak value threshold. If the pulse peak value is greater than the preset peak value threshold, then the pulse peak value is determined as an effective pulse characteristic;

All pulse peaks determined to be effective pulse peaks are assembled to generate an effective pulse peak set, and according to the effective pulse peak set, each terahertz reflection waveform data that is related to the effective pulse peak set is extracted from the terahertz reflection waveform data. The peak time corresponding to each pulse peak value is collected, and the peak time corresponding to each pulse peak value is collected to generate a valid pulse peak time collection.

With reference to the first aspect, in a third possible implementation manner of the first aspect, according to the first clustering type set, a preset second self-organizing mapping network is used to scan each scan point index set The steps of performing pulse type clustering processing on the points to generate the second cluster type set include:

According to the first cluster type set and the scan point index set, encode the terahertz waveform information performed by each scan point in the scan point index set, generate an encoding matrix, and use the encoding matrix as The input vector is input into the second self-organizing map network for pulse type clustering processing to generate a second cluster type set.

With reference to the first aspect, in a fourth possible implementation manner of the first aspect, performing imaging processing on the material to be detected according to the second cluster type set, and generating an imaging result map of the material to be detected ,include:

According to the second cluster type set, the pulse type corresponding to each scan point is converted into an image pixel value, and the image pixel value corresponding to each scan point is filled into the image matrix of the material to be detected according to the scan point , to generate an imaging result map of the material to be detected.

A second aspect of the embodiments of the present application provides a material detection device, and the material detection device includes:

The extraction module is used to obtain the terahertz reflection waveform data of the material to be detected, extract the effective pulse feature from the terahertz reflection waveform data, obtain the scanning point index corresponding to the effective pulse feature, and generate the material to be detected The effective pulse feature set and scan point index set of ;

a first clustering module, configured to use a preset first self-organizing mapping network to perform feature information clustering processing on each valid pulse feature in the valid pulse feature set to generate a first cluster type set;

The second clustering module is configured to, according to the first clustering type set, use a preset second self-organizing mapping network to perform pulse type clustering processing on each scan point in the scan point index set, and generate a second clustering module. Binary cluster type set;

An imaging module, configured to perform imaging processing on the to-be-detected material according to the second cluster type set to generate an imaging result map of the to-be-detected material.

With reference to the second aspect, in a first possible implementation manner of the second aspect, the material detection device further includes:

a scanning sub-module, configured to perform terahertz wave scanning on the material to be detected by using a terahertz imaging device to generate bipolar pulse data of the material to be detected;

The deconvolution submodule is used to perform deconvolution processing on the bipolar pulse data, generate corresponding unipolar pulse data, and obtain the unipolar pulse data as the terahertz reflection of the material to be detected waveform data.

In combination with the second aspect or the first possible implementation manner of the second aspect, in a second possible implementation manner of the second aspect, the material detection device further includes:

A comparison sub-module, configured to extract the pulse peak value corresponding to each scan point from the terahertz reflection waveform data, and compare the pulse peak value corresponding to each scan point with a preset peak threshold value, if the pulse peak value corresponds to each scan point If the peak value is greater than the preset peak value threshold, the pulse peak value is determined as an effective pulse characteristic;

The extraction sub-module is configured to collect all the pulse peaks determined as valid pulse characteristics to generate a valid pulse peak set, and according to the valid pulse peak set, extract from the terahertz reflected waveform data and the valid pulse peaks. For the peak time corresponding to each pulse peak in the pulse peak set, the peak time corresponding to each pulse peak is collected to generate a valid pulse peak time set.

A third aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the electronic device, the processor implementing the first computer program when the processor executes the computer program Each step of the material detection method provided in one aspect.

A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, implements various aspects of the material detection method provided in the first aspect step.

A material detection method, device, electronic device, and storage medium provided by the embodiments of the present application have the following beneficial effects:

In the present application, by acquiring the terahertz reflection waveform data of the material to be detected, the effective pulse feature is extracted from the terahertz reflection waveform data, and the scan point index corresponding to the effective pulse feature is obtained, so as to generate the effective pulse feature set and scan point of the material to be detected. index set; use a preset first self-organizing mapping network to perform feature information clustering processing on each effective pulse feature in the effective pulse feature set, and generate a first cluster type set; according to the first cluster type set, use a preset The set second self-organizing mapping network performs pulse type clustering processing on each scan point in the scan point index set to generate a second cluster type set; according to the second cluster type set, perform imaging processing on the material to be detected to generate Image of the imaging result of the material to be tested. Based on this method, two self-organizing mapping networks are used to successively perform unsupervised clustering processing on pulse features and scanning points, which improves the information utilization rate of terahertz imaging, effectively reduces the loss of terahertz spectrum information, and realizes effective utilization of the entire frequency range of terahertz. Imaging with Hertzian spectral information can effectively image various and complex actual structural defects and improve imaging reliability.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only for the present application. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

Fig. 1 is the realization flow chart of a kind of material detection method that the embodiment of this application provides;

2 is a schematic flowchart of a method for obtaining terahertz reflection waveform data of a material to be detected in the material detection method provided by the embodiment of the present application;

Fig. 3 is a kind of comparison diagram of bipolar pulse and unipolar pulse;

4 is a schematic flowchart of a method for extracting effective pulse features in the material detection method provided by the embodiment of the present application;

Fig. 5 is a kind of structural representation of coding matrix;

6 is a block diagram of the basic structure of a material detection device provided by an embodiment of the present application;

FIG. 7 is a block diagram of the first refined structure of the material detection device provided by the embodiment of the present application;

FIG. 8 is a second refined structural block diagram of the material detection device provided by the embodiment of the present application;

FIG. 9 is a basic structural block diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

The material detection method provided by the embodiment of the present application aims to use the self-organizing mapping algorithm to perform unsupervised clustering of pulses and scanning points successively through two self-organizing mapping networks; Terahertz imaging methods can detect and evaluate internal defects in materials, reducing the possibility of failures and accidents. Based on the material detection method, the imaging parameters can effectively reflect the information of the entire waveform and improve the imaging performance; various types of structural defects can be processed, which is versatile, and does not require manual analysis of the waveform data in advance, reducing workload; using unsupervised The learning method can achieve better results and save workload without requiring a large amount of labeled data; it can realize the imaging of structural defects inside the material, which is helpful for the detection and evaluation of equipment status, and enhances various Device security and stability.

Please refer to FIG. 1 . FIG. 1 is a flowchart for realizing a material detection method provided by an embodiment of the present application.

Details are as follows:

S11: Acquire terahertz reflection waveform data of the material to be detected, extract an effective pulse feature from the terahertz reflection waveform data, acquire a scan point index corresponding to the effective pulse feature, and generate an effective pulse of the material to be detected Feature collection and scan point index collection.

In this embodiment, a terahertz (Terahertz, abbreviated as THz) wave refers to an electromagnetic wave with a frequency in the range of 0.1-10 THz, and its wavelength is between 0.03-3 mm. The principle of terahertz imaging is to scan each scanning point of the material to be detected by using a terahertz incident wave, obtain the reflected wave of each scanning point of the material to be detected, and assign the spectral parameters of each scanning point according to the reflected wave, wherein, The spectral parameters include pulse amplitude, pulse phase, time slice, spectral parameters, etc., and then the imaging of the material to be detected is realized through the spectral parameters of each scanning point. In this embodiment, the material to be detected is firstly scanned by using a terahertz imaging device, and the spectral parameters corresponding to each scanning point are obtained according to the scanning points, and the spectral parameters corresponding to each scanning point are the terahertz of the material to be detected Hertz reflection waveform data. After obtaining the terahertz reflection waveform data of the material to be detected, extract the effective pulse characteristics from the terahertz reflection waveform data and obtain the scan point index corresponding to each effective pulse characteristic, and collect the extracted effective pulse characteristics to generate For the effective pulse feature set of the material to be detected, the scan point index corresponding to each effective pulse feature is collected to generate a scan point index set. Specifically, in this embodiment, the pulse characteristics include a pulse peak value and a peak value time. For the pulse peak value, a set of valid pulse peak values can be obtained correspondingly. For the peak time feature, a valid pulse peak time set can be obtained correspondingly.

In some embodiments of the present application, please refer to FIG. 2 , which is a schematic flowchart of a method for obtaining terahertz reflection waveform data of a material to be detected in the material detection method provided by the embodiment of the present application. Details are as follows:

S21: Using a terahertz imaging device to perform terahertz wave scanning on the material to be detected, to generate bipolar pulse data of the material to be detected;

S22: Perform deconvolution processing on the bipolar pulse data to generate corresponding unipolar pulse data, and obtain the unipolar pulse data as terahertz reflection waveform data of the material to be detected.

In this embodiment, please refer to FIG. 3 , which is a comparison diagram of bipolar pulses and unipolar pulses. As shown in FIG. 3 , the pulses emitted by the terahertz imaging device for scanning are generally bipolar pulses. In this embodiment, in order to facilitate subsequent data clustering processing, when acquiring the terahertz reflection waveform data of the material to be detected, a terahertz imaging device is used to scan the material to be detected with terahertz waves to generate bipolar pulses of the material to be detected After the data is obtained, the obtained bipolar pulse data can be deconvolved to generate corresponding unipolar pulse data, and the unipolar pulse data can be obtained as the terahertz reflection waveform data of the material to be detected.

In some embodiments of the present application, please refer to FIG. 4 , which is a schematic flowchart of a method for extracting effective pulse features in the material detection method provided by the embodiments of the present application. Details are as follows:

S41: Extract the pulse peak value corresponding to each scan point from the terahertz reflection waveform data, and compare the pulse peak value corresponding to each scan point with a preset peak threshold value, if the pulse peak value is greater than the preset peak value the peak value threshold, then the pulse peak value is determined as an effective pulse characteristic;

S42: Collect all pulse peaks determined to be valid pulse characteristics to generate a valid pulse peak set, and according to the valid pulse peak set, extract from the terahertz reflected waveform data and the valid pulse peak set The peak time corresponding to each pulse peak value is collected, and the peak time corresponding to each pulse peak value is collected to generate a valid pulse peak time collection.

In this embodiment, the effective pulse feature set of the material to be detected includes an effective pulse peak value set and an effective pulse peak time set. Due to the background noise in the scanning process of the material to be detected, many "false pulses" with small amplitudes may be generated in the terahertz reflection waveform data. to obtain effective pulse characteristics. Exemplarily, a peak threshold is first set, and then the pulse peak value corresponding to each scan point is extracted from the terahertz reflection waveform data, and the pulse peak value corresponding to each scan point is compared with the peak value threshold one by one. For the pulse peak value corresponding to each scan point, if the pulse peak value is less than the peak value threshold, it indicates that the pulse peak value is a "false pulse" caused by noise, and the pulse peak value is filtered out. If the pulse peak value is greater than the peak value threshold, then Characterizing the peak value of the pulse is not a "false pulse" caused by noise, and at this time, the peak value of the pulse is determined as an effective pulse characteristic. Furthermore, all the pulse peaks determined to be valid pulse characteristics obtained by screening are collected to generate a collection of valid pulse peaks. Since each pulse amplitude in the terahertz reflection waveform data has corresponding time information, after obtaining the effective pulse peak value set, it is possible to extract from the terahertz reflection waveform data and each pulse peak value in the effective pulse peak value set. The corresponding peak time, and then the peak time corresponding to each pulse peak is collected to generate a valid pulse peak time set. Finally, the generated effective pulse peak value set and effective pulse peak time set are configured as the effective pulse feature set of the material to be detected.

S12: Use a preset first self-organizing mapping network to perform feature information clustering processing on each valid pulse feature in the valid pulse feature set to generate a first cluster type set.

In this embodiment, the self-organizing map (SOM) is an unsupervised learning method, which is used to generate a low-dimensional map of the input vector by learning the features of the data in the training set. The self-organizing mapping network specifically includes an input layer and a competitive layer. The number of neurons in the input layer is the same as the dimension of the input vector, and the size of the competitive layer depends on the goal of data analysis. Each neuron in the competition layer is connected with the input layer through a weight vector. In this embodiment, the training process of the self-organizing mapping network includes: firstly, the training set is used as an input vector to input into the self-organizing mapping network for training, and the Euclidean relationship between each input vector and the weight vectors of neurons in all competition layers is calculated. distance, and then the neuron in the competition layer with the closest Euclidean distance to the current input vector is used as the best matching unit (BMU); then, the field range of the best matching unit is updated according to the neuron with the closest Euclidean distance obtained in each iteration The weight vector of the inner neuron, until the upper limit of iteration, that is, the self-organizing map network is trained to a convergent state. Exemplarily, in this embodiment, the formula for updating the weight vector of neurons within the field of the most matching unit is expressed as:

W _v (s+1)=W _v (s)+θ(u,v,s) α(s) (D(t)-W _v (s))

Among them, W _v is the weight vector of neuron v, s is the current iteration number, u is the index number corresponding to the BMU, D is the input vector set, and t is the index number of the current input vector. θ(u, v, s) is the domain function and α(s) is the learning rate. It should be noted that during the training process of the self-organizing map network, the domain function θ(u, v, s) and the learning rate α(s) decrease with the increase of the number of iterations.

In this embodiment, the preset first self-organizing mapping network is a network in which the pulse feature set is used as an input vector to train a convergent state, and is used to cluster all pulses in the pulse feature set according to their respective pulse feature information. In this embodiment, after obtaining the effective pulse feature set and the scanning point index set of the material to be detected, the pulse feature data recorded in the effective pulse feature set of the material to be detected is input as an input vector into the preset first self- In the organizational mapping network, the first self-organizing mapping network is used to perform feature information clustering processing on each effective pulse feature in the effective pulse feature set, and a first cluster type set is generated, and the first cluster type set records There are a number of cluster types obtained by clustering, and the number of cluster types is determined according to the clustering result of the first self-organizing mapping network.

S13: According to the first cluster type set, use a preset second self-organizing mapping network to perform pulse type clustering processing on each scan point in the scan point index set to generate a second cluster type set.

In this embodiment, the preset second self-organizing mapping network is a network that is trained to a convergent state by using the encoding matrix as an input vector, and is used to cluster each scan point in the scan point index set according to the pulse type, so as to realize the Scan points for classification. In this embodiment, please refer to FIG. 5 , which is a schematic structural diagram of an encoding matrix. As shown in FIG. 5 , after the first self-organizing mapping network generates the first cluster type set, it is assumed that the first cluster type set is C ₁ (i) (i=1, 2, . . . , m), that is, the set contains m cluster types. Assuming that the set of scan point indices is N _P (i) (i=1, 2,...,n), that is, the set contains n scan points, at this time, according to the first cluster type set and the set of scan point indices , by encoding the terahertz waveform information of each scan point, an m*n encoding matrix can be constructed and obtained. After the encoding matrix is obtained, the encoding matrix is input into the second self-organizing mapping network as an input vector, so as to use each scan point in the second self-organizing mapping network scan point index set to perform pulse type clustering processing to generate a The second cluster type set, where the pulse type corresponding to each scan point obtained by clustering is recorded in the second cluster type set. It can be understood that, in this embodiment, m=S _X1 ·S _Y1 , and n=N _x ·N _y . Among them, S _X1 and S _Y1 are two size parameters of the competition layer in the first self-organizing mapping network, and N _x and N _y are the scanning steps in two scanning directions during two-dimensional scanning.

Exemplarily, when constructing the encoding moment, if the reflected waveform of the scanning point k contains a valid pulse of type j in the first self-organizing mapping network, the corresponding element in the encoding matrix is assigned a value of 1, otherwise, the value is assigned a value of 0. The specific mathematical expression is as follows:

In this embodiment, after the coding matrix is obtained, the coding matrix can be expressed as a set D ₂ (i)=[P(1,i), P(2,i),...,P(m,i)] ^T (i=1, 2, . . . , n), the set includes valid pulse types contained in all scan points.

S14: Perform imaging processing on the to-be-detected material according to the second cluster type set to generate an imaging result map of the to-be-detected material.

In this embodiment, after the second cluster type set is obtained, the pulse type corresponding to each scan point is recorded in the second cluster type set. In this embodiment, by converting the pulse type corresponding to each scanning point into the image pixel value of the image matrix, when performing imaging processing on the material to be detected, the image matrix of the material to be detected can be filled with corresponding pixels according to the scanning points, The image pixel value corresponding to each scan point is filled to the corresponding scan point position in the image matrix of the material to be detected, and then an imaging result map of the material to be detected can be generated.

It can be understood that the size of the sequence number of each step in the above-mentioned embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any implementation process of the embodiments of the present application. limited.

In some embodiments of the present application, please refer to FIG. 6 , which is a block diagram of the basic structure of a material detection apparatus provided by an embodiment of the present application. In this embodiment, each unit included in the apparatus is used to execute each step in the foregoing method embodiment. For details, please refer to the relevant descriptions in the foregoing method embodiments. For convenience of explanation, only the parts related to this embodiment are shown. As shown in FIG. 6 , the material detection device includes: an extraction module 61 , a first clustering module 62 , a second clustering module 63 and an imaging module 64 . Wherein: the extraction module 61 is used to obtain the terahertz reflection waveform data of the material to be detected, extract the effective pulse feature from the terahertz reflection waveform data, obtain the scan point index corresponding to the effective pulse feature, and generate the Describe the effective pulse feature set and scan point index set of the material to be detected. The first clustering module 62 is configured to use a preset first self-organizing mapping network to perform feature information clustering processing on each valid pulse feature in the valid pulse feature set to generate a first cluster type set. The second clustering module 63 is configured to perform pulse type clustering processing on each scan point in the scan point index set using a preset second self-organizing mapping network according to the first cluster type set, A second set of cluster types is generated. The imaging module 64 is configured to perform imaging processing on the material to be detected according to the second cluster type set, and generate an imaging result map of the material to be detected.

In some embodiments of the present application, please refer to FIG. 7 , which is a block diagram of a first refined structure of the material detection apparatus provided by the embodiments of the present application. As shown in FIG. 7 , the material detection device further includes a scanning sub-module 71 and a deconvolution sub-module 72 . Wherein, the scanning sub-module 71 is configured to perform terahertz wave scanning on the material to be detected by using a terahertz imaging device to generate bipolar pulse data of the material to be detected. The deconvolution sub-module 72 is used to perform deconvolution processing on the bipolar pulse data, generate corresponding unipolar pulse data, and obtain the unipolar pulse data as the data of the material to be detected. Hertz reflection waveform data.

In some embodiments of the present application, please refer to FIG. 8 . FIG. 8 is a second detailed structural block diagram of the material detection apparatus provided by the embodiments of the present application. As shown in FIG. 8 , the material detection device further includes a comparison sub-module 81 and an extraction sub-module 82 . Wherein, the comparison sub-module 81 is configured to extract the pulse peak value corresponding to each scan point from the terahertz reflection waveform data, and compare the pulse peak value corresponding to each scan point with a preset peak value threshold, respectively, If the pulse peak value is greater than a preset peak value threshold, the pulse peak value is determined as an effective pulse characteristic. The extraction sub-module 82 is configured to collect all the pulse peak values determined to be effective pulse characteristics, generate a valid pulse peak value set, and extract the terahertz reflection waveform data from the terahertz reflection waveform data according to the effective pulse peak value set. The peak time corresponding to each pulse peak in the set of valid pulse peak values is collected, and the peak time corresponding to each pulse peak value is collected to generate a valid pulse peak time set.

It should be understood that the above-mentioned material detection device corresponds to the above-mentioned material detection method one by one, and details are not repeated here.

In some embodiments of the present application, please refer to FIG. 9 , which is a basic structural block diagram of an electronic device provided by an embodiment of the present application. As shown in FIG. 9 , the electronic device 9 of this embodiment includes: a processor 91 , a memory 92 , and a computer program 93 stored in the memory 92 and executable on the processor 91 , such as a program for a material detection method . When the processor 91 executes the computer program 93, the steps in each of the above embodiments of the material detection methods are implemented. Alternatively, when the processor 91 executes the computer program 93, the functions of each module in the embodiment corresponding to the above-mentioned material detection apparatus are implemented. For details, please refer to the relevant descriptions in the embodiments, which are not repeated here.

Exemplarily, the computer program 93 may be divided into one or more modules (units), and the one or more modules are stored in the memory 92 and executed by the processor 91 to complete the present invention. Application. The one or more modules may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program 93 in the electronic device 9 . For example, the computer program 93 can be divided into:

The extraction module is used to obtain the terahertz reflection waveform data of the material to be detected, extract the effective pulse feature from the terahertz reflection waveform data, obtain the scanning point index corresponding to the effective pulse feature, and generate the material to be detected The effective pulse feature set and scan point index set of ;

a first clustering module, configured to use a preset first self-organizing mapping network to perform feature information clustering processing on each valid pulse feature in the valid pulse feature set to generate a first cluster type set;

The second clustering module is configured to, according to the first clustering type set, use a preset second self-organizing mapping network to perform pulse type clustering processing on each scan point in the scan point index set, and generate a second clustering module. Binary cluster type set;

An imaging module, configured to perform imaging processing on the to-be-detected material according to the second cluster type set to generate an imaging result map of the to-be-detected material.

The electronic device may include, but is not limited to, a processor 91 and a memory 92 . Those skilled in the art can understand that FIG. 9 is only an example of the electronic device 9, and does not constitute a limitation on the electronic device 9. It may include more or less components than the one shown, or combine some components, or different components For example, the electronic device may further include an input and output device, a network access device, a bus, and the like.

The processor 91 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 92 may be an internal storage unit of the electronic device 9 , such as a hard disk or a memory of the electronic device 9 . The memory 92 may also be an external storage device of the electronic device 9, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) equipped on the electronic device 9 card, flash card (Flash Card) and so on. Further, the memory 92 may also include both an internal storage unit of the electronic device 9 and an external storage device. The memory 92 is used to store the computer program and other programs and data required by the electronic device. The memory 92 can also be used to temporarily store data that has been output or is to be output.

It should be noted that the information exchange, execution process and other contents between the above-mentioned devices/units are based on the same concept as the method embodiments of the present application. For specific functions and technical effects, please refer to the method embodiments section. It is not repeated here.

Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented. In this embodiment, the computer-readable storage medium may be non-volatile or volatile.

The embodiments of the present application provide a computer program product, when the computer program product runs on a mobile terminal, the steps in the foregoing method embodiments can be implemented when the mobile terminal executes the computer program product.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

The integrated modules/units, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable media Excluded are electrical carrier signals and telecommunication signals.

In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the above-mentioned embodiments, those of ordinary skill in the art should understand that: it can still be used for the above-mentioned implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the application, and should be included in the within the scope of protection of this application.

Claims (10)

1. a material detection method, is characterized in that, comprises: Obtain the terahertz reflection waveform data of the material to be detected, extract the effective pulse feature from the terahertz reflection waveform data, obtain the scan point index corresponding to the effective pulse feature, and generate the effective pulse feature set of the material to be detected and the scanpoint index set; Using a preset first self-organizing mapping network to perform feature information clustering processing on each valid pulse feature in the valid pulse feature set, to generate a first cluster type set; According to the first cluster type set, use a preset second self-organizing mapping network to perform pulse type clustering processing on each scan point in the scan point index set to generate a second cluster type set; According to the second cluster type set, imaging processing is performed on the material to be detected, and an imaging result map of the material to be detected is generated. 2. The material detection method according to claim 1, wherein the step of acquiring the terahertz reflection waveform data of the material to be detected further comprises: Using a terahertz imaging device to perform terahertz wave scanning on the material to be detected, to generate bipolar pulse data of the material to be detected; Deconvolution processing is performed on the bipolar pulse data to generate corresponding unipolar pulse data, and the unipolar pulse data is acquired as terahertz reflection waveform data of the material to be detected. 3. The material detection method according to claim 1 or 2, wherein the step of extracting effective pulse characteristics from the terahertz reflected waveform data comprises: The pulse peak value corresponding to each scan point is extracted from the terahertz reflection waveform data, and the pulse peak value corresponding to each scan point is compared with a preset peak value threshold. If the pulse peak value is greater than the preset peak value threshold, then the pulse peak value is determined as an effective pulse characteristic; All pulse peaks determined to be effective pulse peaks are assembled to generate an effective pulse peak set, and according to the effective pulse peak set, each terahertz reflection waveform data that is related to the effective pulse peak set is extracted from the terahertz reflection waveform data. The peak time corresponding to each pulse peak value is collected, and the peak time corresponding to each pulse peak value is collected to generate a valid pulse peak time collection. 4 . The material detection method according to claim 1 , wherein, according to the first cluster type set, a preset second self-organizing mapping network is used for each scanning point in the scanning point index set. 5 . The steps of performing pulse type clustering processing to generate a second cluster type set include: According to the first cluster type set and the scan point index set, encode the terahertz waveform information performed by each scan point in the scan point index set, generate an encoding matrix, and use the encoding matrix as The input vector is input into the second self-organizing map network for pulse type clustering processing to generate a second cluster type set. 5 . The material detection method according to claim 1 , wherein, according to the second cluster type set, performing imaging processing on the to-be-detected material to generate an imaging result map of the to-be-detected material, 6 . include: According to the second cluster type set, the pulse type corresponding to each scan point is converted into an image pixel value, and the image pixel value corresponding to each scan point is filled into the image matrix of the material to be detected according to the scan point , to generate an imaging result map of the material to be detected. 6. A material detection device, characterized in that the material detection device comprises: The extraction module is used to obtain the terahertz reflection waveform data of the material to be detected, extract the effective pulse feature from the terahertz reflection waveform data, obtain the scanning point index corresponding to the effective pulse feature, and generate the material to be detected The effective pulse feature set and scan point index set of ; a first clustering module, configured to use a preset first self-organizing mapping network to perform feature information clustering processing on each valid pulse feature in the valid pulse feature set to generate a first cluster type set; The second clustering module is configured to, according to the first clustering type set, use a preset second self-organizing mapping network to perform pulse type clustering processing on each scan point in the scan point index set, and generate a second clustering module. Binary cluster type set; An imaging module, configured to perform imaging processing on the to-be-detected material according to the second cluster type set to generate an imaging result map of the to-be-detected material. 7. The material detection device according to claim 6, wherein the material detection device further comprises: a scanning sub-module, configured to perform terahertz wave scanning on the material to be detected by using a terahertz imaging device to generate bipolar pulse data of the material to be detected; The deconvolution submodule is used to perform deconvolution processing on the bipolar pulse data, generate corresponding unipolar pulse data, and obtain the unipolar pulse data as the terahertz reflection of the material to be detected waveform data. 8. The material detection device according to claim 6 or 7, wherein the material detection device further comprises: A comparison sub-module, configured to extract the pulse peak value corresponding to each scan point from the terahertz reflection waveform data, and compare the pulse peak value corresponding to each scan point with a preset peak threshold value, if the pulse peak value corresponds to each scan point If the peak value is greater than the preset peak value threshold, the pulse peak value is determined as an effective pulse characteristic; The extraction sub-module is configured to collect all the pulse peaks determined as valid pulse characteristics to generate a valid pulse peak set, and according to the valid pulse peak set, extract from the terahertz reflected waveform data and the valid pulse peaks. For the peak time corresponding to each pulse peak in the pulse peak set, the peak time corresponding to each pulse peak is collected to generate a valid pulse peak time set. 9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the computer program as claimed in the claims Steps of any one of 1 to 5 of the method. 10. A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented .

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