CN109711333B

CN109711333B - Ultrasonic signal receiving and processing method based on signal section segmentation

Info

Publication number: CN109711333B
Application number: CN201811597279.0A
Authority: CN
Inventors: 齐爱玲; 马宏伟; 张广明; 董明; 郝科伟; 康文惠
Original assignee: Xian University of Science and Technology
Current assignee: Xian University of Science and Technology
Priority date: 2018-12-26
Filing date: 2018-12-26
Publication date: 2022-10-18
Anticipated expiration: 2038-12-26
Also published as: CN109711333A

Abstract

The invention discloses an ultrasonic signal receiving and processing method based on signal section segmentation, which comprises the following steps: 1. acquiring an ultrasonic echo signal and synchronously uploading and receiving; 2. determining a wave crest and a wave trough; 3. removing extreme points; 4. signal division: the data processing equipment is adopted to segment the ultrasonic echo signal F (t), and the process is as follows: 401. determining the time interval between adjacent extreme points; 402. judging the division points and determining the sampling time of the division points; 403. signal segmentation judgment; 404. sorting the division points; 405. and (4) signal division. The method has simple steps, reasonable design, convenient realization and good use effect, realizes the judgment of the segmentation points and the determination of the sampling time of the segmentation points by judging the threshold value of the time interval of the adjacent extreme points, and segments the ultrasonic echo signals according to the determined number of the segmentation points and the sampling time of each segmentation point.

Description

Ultrasonic signal receiving and processing method based on signal section segmentation

Technical Field

The invention belongs to the technical field of ultrasonic flaw detection, and particularly relates to an ultrasonic signal receiving and processing method based on signal section segmentation.

Background

Ultrasonic inspection (also called ultrasonic inspection or ultrasonic inspection) is a nondestructive inspection method for inspecting internal defects of a material by using differences in acoustic properties of the material and its defects against energy changes in reflection conditions and penetration times of ultrasonic propagation waveforms. The principle of ultrasonic flaw detection is that the transmission loss of ultrasonic waves in a solid is small, the detection depth is large, and the ultrasonic waves can generate phenomena such as reflection, refraction and the like on a heterogeneous interface, particularly cannot pass through a gas-solid interface, if defects such as pores, cracks, layering and the like (gas exists in the defects) or impurities exist in metal, the ultrasonic waves (also called ultrasonic signals or ultrasonic signals) can be totally or partially reflected when being transmitted to the interface between the metal and the defects, the reflected ultrasonic signals (also called ultrasonic echo signals, simply called echo signals) are received by a probe, and the depth, the position and the shape of the defects in a workpiece can be judged according to the waveform change characteristics of the received ultrasonic echo signals.

Due to the influence of factors such as the characteristics of metal materials, inherent defects of machining processes and the like, internal defects of different degrees inevitably exist in the production process of industrial mechanical equipment, and common defects comprise cracks, air holes, shrinkage cavities, inclusions, sand inclusion cold shut and the like. For example, the coal mining machinery such as a coal mining machine box body inevitably has the defects in the production process, and as the manufacturing process of the coal mining machinery is complex, and the coal mining machinery works under the conditions of heavy load and alternating load for a long time, the working environment is severe, the stress condition of the equipment is poor, so that important parts of the equipment are gradually damaged from small to large in accidents in the using process, various safety hazards are generated, and particularly, the production is stopped due to the generation of the faults, and the yield of coal and the economic benefit of coal mines are directly influenced.

Nowadays, an ultrasonic detection method is widely used in the defect detection of a box body of a coal mining machine. Ultrasonic testing is an important nondestructive testing method, and an ultrasonic signal is a broadband pulse signal modulated by the center frequency of a probe, and the echo signal of the ultrasonic signal contains a large amount of information related to defects, but the ultrasonic signal (i.e. the ultrasonic echo signal) is often polluted by random noise and related noise of a measuring system and a tested workpiece, especially grain noise in coarse-grained materials, and the noise can make defect identification of the ultrasonic signal difficult, and seriously limits the precision and reliability of defect detection. Therefore, denoising the ultrasonic detection echo signal is very important to ensure the authenticity of the obtained defect signal. The method has important significance for improving the product defect detection rate, ensuring the product quality and prolonging the service life of the product for enterprises.

From the above, in the process of ultrasonic detection of defects of coal mining machines and other coal mining machines, the reliability and quality of the detection result are seriously affected by noise. The extraction of defect signals from signals mixed with various interference noises (i.e. ultrasonic detection signals) is the key to ensure the accuracy of echo signals, and when the defects in the material are detected by using ultrasonic waves, the defect information is represented by the amplitude, frequency or phase of the received ultrasonic echo signals. The defect signal refers to a detected ultrasonic echo signal, and the ultrasonic echo signal contains defect information. However, due to the influence of instrument noise and test environment factors, various interference noises such as various electrical noises, structural noises and pulse noises are accompanied with detection signals, and particularly when the microstructure noise of defect signals is very large or the crystal grains of the material are coarse, the defect and the signal intensity of the noise are weak, and the extraction of the defect signals under the background of strong noise is a difficult problem in the research of the field of ultrasonic signal processing. How to extract the desired information from these signals is a difficult and important issue. Therefore, denoising processing is carried out on the ultrasonic detection echo signal, and the authenticity of the obtained defect signal is guaranteed to be very important.

At present, a plurality of ultrasonic signal extraction methods such as nonlinear filtering, fourier transform, wavelet transform and the like exist, and the methods have a good effect of improving the signal-to-noise ratio of general ultrasonic signals, but have limitations on the extraction of defects under the background of small defects or strong noise, inaccurate detection results and low reliability. The sparse decomposition is a new signal analysis theory, a proper expansion function can be selected in a self-adaptive mode according to the characteristics of a signal to be extracted, the basic characteristics of the extracted signal can be represented by few functions, weak signals can be extracted better under the condition of low signal-to-noise ratio, and the original signal is approached to the maximum. The sparse decomposition algorithm was first proposed by Mal lat, which is a well-known matching pursuit algorithm. Therefore, the method is gradually popularized in the detection of the internal defects of the products. However, the algorithm has two defects, namely, the calculation amount of the sparse decomposition algorithm is large, the calculation time is very large under the existing calculation condition, and real-time detection cannot be carried out; secondly, the sparse decomposition algorithm is an optimal solution obtained under a continuous condition, and the detection precision of weak and small defects is still limited.

In ultrasonic nondestructive testing, an ultrasonic sensor in an ultrasonic flaw detection device sends pulses to pass through a discontinuous interface of a tested object, and received reflected echoes (namely ultrasonic echo signals) contain position information and flaw size information related to flaw characteristics, so that accurate detection of the position and size of a flaw to accurately estimate the flaw is an important content of ultrasonic nondestructive evaluation. The ultrasonic echo signal is represented as a superimposed combination of defect waves (also referred to as defect signals) reflected from different interfaces at different depth positions along the time axis. However, in practical applications, a situation that one ultrasonic echo signal contains multiple defects often occurs, the multiple defects are defects at multiple different positions on a measured object, the multiple defects are independent and do not overlap with each other, the relationship among the multiple defects is a combination relationship, the defect signals corresponding to the multiple defects need to be all divided, otherwise, the defect signals cannot be extracted, and classification and identification of the defects are not performed. After a plurality of defect signals in the ultrasonic echo signal are all divided, signal extraction, feature extraction and defect classification identification are respectively carried out on each divided defect signal. Each extracted defect signal is an ultrasonic echo signal at the position of a defect in the detected object.

Disclosure of Invention

The technical problem to be solved by the present invention is to provide an ultrasonic signal receiving and processing method based on signal segment division, which has the advantages of simple steps, reasonable design, convenient implementation and good use effect, realizes division point judgment and division point sampling time determination by performing threshold judgment on adjacent extreme point time intervals, and divides an ultrasonic echo signal according to the determined number of division points and the sampling time of each division point.

In order to solve the technical problems, the technical scheme adopted by the invention is as follows: an ultrasonic signal receiving and processing method based on signal segment segmentation is characterized by comprising the following steps:

the method comprises the following steps of firstly, obtaining ultrasonic echo signals and synchronously uploading and receiving: ultrasonic detection is carried out on the object to be detected by adopting an ultrasonic flaw detection device, an ultrasonic echo signal F (t) of the object to be detected is obtained, and the obtained ultrasonic echo signal F (t) is synchronously transmitted to data processing equipment; the data processing equipment synchronously stores the received ultrasonic echo signals F (t);

wherein, the first and the second end of the pipe are connected with each other,

t represents a time parameter, t _i The ith sampling time of the ultrasonic flaw detector, f (t) _i ) Is a signal value sampled at the ith sampling time of an ultrasonic flaw detection device, i is a positive integer and i =1, 2, 3, \8230 _z ，N _z Is a positive integer and is the signal length of the ultrasonic echo signal F (t);

step two, determining wave crests and wave troughs: respectively determining all wave crests and all wave troughs of the ultrasonic echo signal F (t) in the step one by adopting data processing equipment, and respectively synchronously recording the sampling time and the signal value of each determined wave crest and each determined wave trough;

in this step, each determined peak and each determined valley are an extreme point of the ultrasonic echo signal F (t);

step three, removing extreme points: adopting data processing equipment and calling a time domain extreme point eliminating module or a frequency domain extreme point eliminating module to eliminate extreme points to obtain M 'eliminated extreme points, and arranging the M' extreme points from front to back according to the sampling time sequence of the extreme points; wherein M' is a positive integer and is the total number of extreme points obtained after the extreme points are removed in the step;

when the data processing equipment is adopted and a time domain extreme point rejecting module is called to reject the extreme points, rejecting the extreme points of which the absolute values of the signal values in all the extreme points determined in the step two are smaller than beta 'to obtain M' number of rejected extreme points; wherein, β ' = α ' × max | F (t) |, α ' is a constant and the value range thereof is 0.1-0.35, max count F (t) | is the maximum value of the absolute value of the signal value in the ultrasonic echo signal F (t);

when the data processing equipment is adopted and a frequency domain extreme point eliminating module is called to eliminate the extreme points, eliminating the extreme points of which the absolute values of the signal values are less than beta in all the extreme points determined in the step two to obtain M' eliminated extreme points; wherein β is a preset rejection threshold and β = α × max | Y (F) |, α is a constant and has a value range of 0.25 to 0.35, Y (F) is a frequency spectrum of the ultrasonic echo signal F (t), and max | Y (F) | is a maximum absolute value of an amplitude value in the frequency spectrum of the ultrasonic echo signal F (t);

step four, signal segmentation: the data processing equipment is adopted to segment the ultrasonic echo signal F (t), and the process is as follows:

step 401, determining the time interval between adjacent extreme points: respectively determining the time intervals of two adjacent extreme points in the M 'extreme points in the third step by adopting data processing equipment to obtain M' -1 time intervals of the adjacent extreme points;

the M 'th of the M' -1 of the adjacent extreme time intervals is denoted as Δ t _m' ，Δt _m' The time interval between the sampling time of the M ' th extreme point in the M ' extreme points and the sampling time of the M ' +1 th extreme point is set; wherein M 'is a positive integer and M' =1, 2, \8230, M '-2, M' -1;

dividing Δ t in M' -1 time intervals of the adjacent extreme points ₁ The time intervals of the other M' -2 adjacent extreme points are all time intervals to be judged, delta t ₁ The time interval between the sampling time of the 1 st extreme point and the sampling time of the 2 nd extreme point in the M' extreme points is set;

step 402, segmentation point judgment and sampling time determination of segmentation points: respectively judging the division points of the M' -2 time intervals to be judged in the step 401 from beginning to end by adopting data processing equipment to obtain L time intervals to be divided; wherein L is an integer and is more than or equal to 0, and L is the total number of the time intervals to be divided determined in the step; each time interval to be divided has a dividing point; in the first step, the number of the segmentation points existing in the ultrasonic echo signal F (t) is the same as the number of the time intervals to be segmented, and the number of the segmentation points existing in the ultrasonic echo signal F (t) is the same as L;

the judgment methods of the M' -2 division points of the time intervals to be judged are the same; for Δ t _m' When the division point is judged, the value of Delta t is measured _m' Whether or not it is greater than c.DELTA.t _m'-1 And (4) judging: when Δ t is reached _m' ＞c·Δt _m'-1 When it is determined to be Δ t _m' Is a time interval to be divided, and Δ t _m' The above existing division pointsAt a sampling time of

Otherwise, the judgment is delta t _m' There is no dividing point above; wherein c is a constant and c > 2.1; t is t _{Total m'} The sum of the sampling time of the M ' th extreme point in the M ' extreme points and the sampling time of the M ' +1 th extreme point;

step 403, signal division and judgment: judging L in step 402: when L =0, judging that the ultrasonic echo signal F (t) does not need to be segmented, and finishing a signal segmentation process; otherwise, judging that the ultrasonic echo signal F (t) needs to be segmented, and entering step 404;

step 404, sorting the segmentation points: sequencing the L division points determined in the step 402 from front to back by adopting data processing equipment according to the sequence of sampling time;

step 405, signal division: dividing the ultrasonic echo signal F (t) in the first step into L +1 signal segments from front to back according to the sampling time of the L sorted dividing points in the step 404, wherein each divided signal segment is a dividing signal.

The ultrasonic signal receiving and processing method based on signal section segmentation is characterized in that: y (F) in the third step is a frequency domain signal obtained by converting the ultrasonic echo signal F (t) into a frequency domain by adopting a time-frequency transformation module, wherein the time-frequency transformation module is a Fourier transformation module;

max | Y (f) | is the maximum absolute value of the signal amplitude in Y (f).

The ultrasonic signal receiving and processing method based on signal segment segmentation is characterized in that: in step 404, the sampling time of the ith division point in the L division points is marked as t _fenl Wherein L is a positive integer and L =1, 2, \8230;

after signal segmentation is performed in step 405, each segmented signal is an ultrasonic echo signal at the position where a defect in the measured object is located;

the 1 st of the L +1 divided signals is denoted as F ₁ (t) ofMiddle F ₁ (t)＝[f(t ₁ ),f(t ₂ ),...,f(t _fen1 )] ^T ；

The L' th of the L +1 divided signals is denoted as F _L' (t) wherein F _L' (t)＝[f(t _fenl' ),f(t _fenl'+1 ),...,f(t _fenL' )] ^T Wherein L 'is a positive integer and L =2, 3, \8230, L-1, L' is a positive integer and L '= L' -1;

the L + 1-th divided signal among the L +1 divided signals is denoted as F _L+1 (t) in which

The ultrasonic signal receiving and processing method based on signal segment segmentation is characterized in that: after the signal division is completed in step 405, signal extraction needs to be performed on the divided L +1 divided signals respectively; the signal extraction methods of the L +1 division signals are the same;

when signal extraction is carried out on any one of the L +1 split signals, signal extraction is carried out on the split signal by adopting data processing equipment, and the split signal is a signal to be processed and is recorded as a signal f (t);

when the data processing equipment is adopted to extract the signal f (t), the process is as follows:

a1, performing signal sparse decomposition based on an optimization algorithm: adopting data processing equipment and calling a sparse decomposition module to carry out iterative decomposition processing on the signal f (t) to be processed in the step one, and converting the signal f (t) to be processed into a signal f (t) to be processed

And obtaining the optimal atom set of iterative decomposition at the moment; the iterative decomposition optimal atom set at this time contains m best matching atoms,

decomposing the nth best matching atom in the best atom set for the iteration;

in the formula R ^m (t) is a residual error quantity of the signal f (t) to be processed after m times of iterative decomposition, wherein m is a preset total iterative decomposition times and m is a positive integer, n is a positive integer and n =1, 2, \ 8230;, m; a is _n The expansion coefficient of the best matching atom after the nth iterative decomposition and the residual error after the last iterative decomposition is obtained;

adopting data processing equipment and calling an optimization algorithm module to find out the best matching atom for the nth iterative decomposition;

is a Gabor atom and

wherein the function ψ (t) is a Gaussian window function and

r _n is composed of

Of time-frequency parameters r _n ＝(s _n ,u _n ,v _n ,w _n )，s _n As a scale parameter, u _n As a displacement parameter, v _n As a frequency parameter, w _n Is a phase parameter;

in this step, the best matching atom is found

According to a preset s _n 、u _n 、v _n And w _n The value range of the adaptive value Fitness (r) is found out by adopting data processing equipment and calling an optimization algorithm module _n ) The maximum optimal time frequency parameter, the found optimal time frequency parameter is the time frequency parameter r _n ；

Wherein, fitness (r) _n ) Is a time-frequency parameter r _n The value of the fitness value of (a) is,

represents R ^n-1 (t) and

inner product of (d); r ^n-1 (t) is the residual error quantity of the signal f (t) to be processed after n-1 times of iterative decomposition, R ⁰ (t)＝f(t)；

Step A2, residual error quantity judgment: judgment | | | R ^m (t)|| ₂ Whether less than ε: when | | | R ^m (t)|| ₂ If the value is less than epsilon, entering a step A4; otherwise, when | | | R ^m (t)|| ₂ When the value is more than or equal to epsilon, entering the step A3;

wherein, | | R ^m (t)|| ₂ For R in the step A1 ^m (t) 2-norm, epsilon is a preset residual quantity judgment threshold;

step A3, optimizing the optimal matching atoms, wherein the process is as follows:

step A31, atom random selection: randomly taking out an optimal matching atom from the iterative decomposition optimal atom set at the moment by adopting data processing equipment as an atom to be optimized, wherein the atom to be optimized is marked as

Wherein j is a positive integer and is more than or equal to 1 and less than or equal to m;

at the moment, m-1 best matching atoms except the atom to be optimized in the iterative decomposition optimal atom set are all atoms to be processed, and m-1 atoms to be processed form the atom set to be processed at the moment;

step A32, finding the best matching atom: the best matching atom found is recorded as

Is recorded as a time-frequency parameter r _j' Time-frequency parameter r _j' ＝(s _j' ,u _j' ,v _j' ,w _j' )；

For the best matching atom

When searching, according to the preset s _j' 、u _j' 、v _j' And w _j' The value range of the adaptive value is found out by adopting data processing equipment and calling the optimizing algorithm module _j' ) The maximum optimal time frequency parameter, the found optimal time frequency parameter is the time frequency parameter r _j' (ii) a According to the formula

Solving for the best matching atom

Wherein the content of the first and second substances,

represent

And with

The inner product of (a) is,

ψ ₀ (t) is the sum of m-1 atoms to be treated in step A31;

step a33, atom replacement judgment and atom replacement: adopting data processing equipment and calling a residual value judging module, a fitness value judging module or a sparsity judging module to judge whether the atoms to be optimized in the step A31 need to be replaced or not, and replacing the atoms to be optimized according to a judging result;

when the data processing device and the residual value judging module are used for judging whether the atoms to be optimized in the step A31 need to be replaced, according to the replaced residual value | | | R _j' ^m (t)|| _ξ Whether it is less than the residue before replacement | | R _j ^m (t)|| _ξ And (4) judging: when | | | R _j' ^m (t)|| _ξ ＜||R _j ^m (t)|| _ξ If so, judging that the atom to be optimized in the step A31 needs to be replaced, and replacing the atom to be optimized in the step A31 with the best matching atom in the step A32

Obtaining the updated iterative decomposition optimal atom set; otherwise, judging that the atoms to be optimized in the step A31 do not need to be replaced, and entering a step A35;

wherein R is _j' ^m (t)＝f(t)-ψ _j' (t)，

R _j ^m (t)＝f(t)-ψ _j (t)，ψ _j (t) is the sum of m best matching atoms in the iterative decomposition best atom set before atom replacement judgment in the step; r | | _j' ^m (t)|| _ξ Represents R _j' ^m Xi-norm of (t, | | R _j ^m (t)|| _ξ Represents R _j ^m Xi-norm of (t), xi is constant and xi is more than or equal to 0 and less than or equal to 1;

adopting data processing equipment and calling a Fitness value judging module to judge whether the atoms to be optimized in the step A31 need to be replaced or not, and then according to a post-replacement Fitness value Fitness (r) _j' ) Whether greater than the pre-replacement Fitness value Fitness (r) _j ) And (4) judging: when Fitness (r) _j' )＞Fitness(r _j ) If so, judging that the atom to be optimized in the step A31 needs to be replaced, and replacing the atom to be optimized in the step A31 with the best matching atom in the step A32

wherein the content of the first and second substances,

represents R ^j-1 (t) and

inner product of (A), R ^j-1 (t)＝f(t)-ψ _j-1 (t)，ψ _j-1 (t) is the sum of the first j-1 best matching atoms in the set of best atoms for this time of the iterative decomposition;

represents R ^j-1 (t) and

inner product of (2);

adopting data processing equipment and calling a sparsity judging module to judge whether to replace the atoms to be optimized in the step A31 or not according to | | R ^j' || _ξ Whether or not less than R ^j || _ξ And (4) judging: when | | | R ^j' || _ξ ＜||R ^j || _ξ If so, judging that the atom to be optimized in the step A31 needs to be replaced, and replacing the atom to be optimized in the step A31 with the best matching atom in the step A32

wherein R is ^j' Is composed of

Amount of residual error of

R ^j Is composed of

Amount of residual error of

||R ^j' || _ξ Represents R ^j' Xi-norm, | | R ^j || _ξ Represents R ^j ξ -norm of;

in this step, after the atom replacement judgment and the atom replacement are completed, the optimization process of the one best matching atom selected in step a31 is completed;

step A34, residual quantity judgment: and B, judging the residual quantity after the optimization of the best matching atom in the step A33: when | | R' _j ^m (t)|| ₂ If the value is less than epsilon, entering a step A4; otherwise, when | | R' _j ^m (t)|| ₂ When the value is more than or equal to epsilon, entering the step A35;

wherein, | R' _j ^m (t)|| ₂ Is R' _j ^m (t) 2-norm; r' _j ^m (t) is a residual quantity after m iterative decompositions are performed on f (t) according to m best matching atoms in the iterative decomposition best atom set at the moment;

step A35, optimizing the next best matching atom: optimizing one of the best matching atoms in the iterative decomposition best atom set which is not optimized according to the method from the step A31 to the step A33;

step A36, residual quantity judgment: and B, judging the residual quantity after the optimization of the best matching atoms in the step A35: when | | | R " _j ^m (t)|| ₂ If the epsilon is less than epsilon, entering a step A4; otherwise, when | | R " _j ^m (t)|| ₂ When the value is more than or equal to epsilon, returning to the step A35;

wherein, | | R " _j ^m (t)|| ₂ Is R' _j ^m (t) a 2-norm; r' _j ^m (t) is the residual quantity after m iterative decompositions are performed on f (t) according to m best matching atoms in the iterative decomposition best atom set at the moment;

step A4, signal reconstruction: obtaining an approximate signal f' (t) of a signal f (t) to be processed by adopting data processing equipment according to the iterative decomposition optimal atom set at the moment; wherein the content of the first and second substances,the approximation signal f "(t) is a signal extracted from the signal f (t) to be processed,

wherein

For this reason, the iteration decomposes the n ' th best matching atom in the best atom set, wherein n ' is a positive integer and n ' =1, 2, \ 8230, m; a is _n' Is composed of

And f (t) is subjected to n '-1 times of iterative decomposition according to the first n' -1 best matching atoms in the iterative decomposition best atom set at the moment, and then the residual error is expanded.

The ultrasonic signal receiving and processing method based on signal section segmentation is characterized in that: after signal sparse decomposition is carried out in the step A1, synchronously storing the iterative decomposition optimal atom set into a data storage by adopting data processing equipment, wherein the data storage is connected with the data processing equipment;

after the atom replacement judgment and the atom replacement are performed in the step a33, the updated iterative decomposition optimal atom set is synchronously stored by using a data processing device.

The ultrasonic signal receiving and processing method based on signal segment segmentation is characterized in that: after signal sparse decomposition is carried out in the step A1, when the optimal atom set of iterative decomposition is synchronously stored in a data memory by adopting data processing equipment, respectively storing m optimal matching atoms in the optimal atom set of iterative decomposition according to the iterative decomposition sequence; wherein, the first and the second end of the pipe are connected with each other,

the best matching atom is found when the nth iteration decomposition is performed on the signal f (t) in the step A1.

The ultrasonic signal receiving and processing method based on signal segment segmentation is characterized in that: when the best matching atoms in the step A3 are optimized, optimizing the best matching atoms in the iterative decomposition best atom set according to the storage sequence;

when the best matching atom in step A3 is optimized, the best matching atom which is optimized first is the 1 st best matching atom in the iterative decomposition best atom set in step A1.

The ultrasonic signal receiving and processing method based on signal section segmentation is characterized in that: in step A1 s _n Has a value range of [1, N ]]And s _n ∈[1,N]，u _n Has a value range of [0, N']And u is _n ∈[0,N]，v _n Has a value range of

And is

w _n Has a value range of [0,2 pi]And w _n ∈[0,2π](ii) a Wherein f is _o Sampling frequency of ultrasonic flaw detector, f _o In MHz; n is a positive integer and it is the signal length of the signal f (t).

The ultrasonic signal receiving and processing method based on signal section segmentation is characterized in that: described in step A1

The best matching atom is found when the nth iteration decomposition is carried out on the signal f (t) in the step A1;

when signal sparse decomposition is carried out in the step A1, finding out m optimal matching atoms in the iterative decomposition optimal atom set in the step A1 from first to last by adopting data processing equipment;

the optimizing algorithm module in the step A1 is an artificial bee colony algorithm module;

to pair

When searching is carried out, data processing equipment is adopted and the artificial bee colony algorithm module is called for searching

Time-frequency parameter r of _n The process is as follows:

step A11, parameter initialization: setting the maximum iteration times MC, the number SN of honey sources, the number of employed bees, the number of observation bees and the maximum exploitation times limit of the honey sources of the artificial bee colony algorithm module by adopting data processing equipment; meanwhile, SN different honey sources are randomly generated by adopting data processing equipment, the SN honey sources are all honey sources to be mined, and the pth honey source in the generated SN honey sources is recorded as a 4-dimensional vector X _p ＝(X _1p ,X _2p ,X _3p ,X _4p ) Each honey source is a time-frequency parameter; the number of the employed bees and the number of the observation bees are SN, and each generated honey source is distributed to one employed bee;

wherein p is a positive integer and p =1, 2, \ 8230; x _1p And s preset in step A1 _n Have the same value range of X _2p And u preset in step A1 _n Have the same value range of X _3p And v is preset in step A1 _n Have the same value range of X _4p And w preset in step A1 _n The value ranges of (A) are the same;

step A12, employing bee neighborhood search: each hiring bee carries out neighborhood search on the allocated honey source, if the fitness value of the searched new honey source is larger than that of the original honey source, the new honey source is used as the honey source to be exploited, which is searched by the hiring bee, and the exploited frequency is set to be 0; otherwise, adding 1 to the mined times of the original honey source;

step A13, search of observation bee neighborhoods: calculating the selection probability of each honey source searched by the employed bees according to the fitness values of all the honey sources searched by the employed bees in the step A12; observing the bees, and selecting honey sources for honey collection from all the honey sources searched by the hiring bees as new honey sources according to the calculated selection probability of each honey source;

the observation bee carries out neighborhood search on the selected honey source, if the fitness value of the searched new honey source is larger than that of the original honey source, the observation bee is changed into a hiring bee, the new honey source is used as the searched honey source to be exploited, and the exploited frequency is set to be 0; otherwise, if the honey source and the employed bees are not changed, adding 1 to the mined times of the original honey source;

step A14, recording the optimal honey source in real time: after the search of the employed bee neighborhood and the search of the observation bee neighborhood are finished, obtaining the optimal honey source at the moment and synchronously recording, wherein the iteration times of the artificial bee colony algorithm module is added with 1;

in the process of hiring bee neighborhood searching and observing bee neighborhood searching, if the mined times of the honey source reach the maximum mined times limit of the honey source, the observing bee is converted into a detecting bee, a new honey source is generated through the detecting bee, and the mined times are set to be 0;

step A15, repeating the step A12 to the step A14 for a plurality of times until the iteration number of the artificial bee colony algorithm module reaches the maximum iteration number MC, wherein the optimal honey source obtained at the moment is

Time-frequency parameter r of _n ，r _n ＝(s _n ,u _n ,v _n ,w _n )；

When the employed bee neighborhood search is performed in step a12 and the observation bee neighborhood search is performed in step a13, the fitness value of any honey source is the Gabor atom and R corresponding to the honey source ^n-1 (t) inner product.

The ultrasonic signal receiving and processing method based on signal segment segmentation is characterized in that: for the best matching atom in step A32

When searching, the data processing equipment is adopted and the optimizing algorithm module is called for searching

Time-frequency parameter r of _j' The optimizing algorithm module is an artificial bee colony algorithm module, and the process is as follows:

step A321, parameter initialization: setting the maximum iteration times MC ', the number SN ' of honey sources, the number of employed bees, the number of observation bees and the maximum exploitation times limit ' of the honey sources of the artificial bee colony algorithm module by adopting data processing equipment; meanwhile, SN 'different honey sources are randomly generated by adopting data processing equipment, the SN' different honey sources are all honey sources to be exploited, and the pth 'honey source in the generated SN' different honey sources is recorded as a 4-dimensional vector X _p' ＝(X _1p' ,X _2p' ,X _3p' ,X _4p' ) Each honey source is a time-frequency parameter; the number of the employed bees and the number of the observation bees are SN', and each generated honey source is distributed to one employed bee;

wherein p ' is a positive integer and p ' =1, 2, \ 8230;, SN '; x _1p' And s preset in step 201 _n Have the same value range of X _2p' And u preset in step 201 _n Have the same value range of X _3p' And v preset in step 201 _n Have the same value range of X _4p' And w preset in step 201 _n The value ranges of (A) are the same;

step A322, employing bee neighborhood search: each hiring bee carries out neighborhood search on the allocated honey source, if the fitness value of the searched new honey source is larger than that of the original honey source, the new honey source is used as the honey source to be exploited, which is searched by the hiring bee, and the exploited frequency is set to be 0; otherwise, adding 1 to the mined times of the original honey source;

step A323, search of observation bee neighborhoods: calculating the selection probability of each honey source searched by the hiring bee according to the fitness values of all the honey sources searched by the hiring bee in the step A322; the observation bees select honey sources for honey collection from all the honey sources searched by the employment bees as new honey sources according to the calculated selection probability of each honey source;

the observation bee carries out neighborhood search on the selected honey source, if the fitness value of the searched new honey source is larger than that of the original honey source, the observation bee is changed into a employment bee, the new honey source is used as the searched honey source to be exploited, and the exploited frequency is set to be 0; otherwise, if the honey source and the employed bees are not changed, adding 1 to the mined times of the original honey source;

step A324, recording the optimal honey source in real time: after the search of the employed bee neighborhood and the search of the observation bee neighborhood are finished, obtaining the optimal honey source at the moment and synchronously recording, wherein the iteration times of the artificial bee colony algorithm module is added with 1;

step A325, repeating the steps A322 to A323 for a plurality of times until the iteration number of the artificial bee colony algorithm module reaches the maximum iteration number MC, and the optimal honey source obtained at the moment is

Time-frequency parameter r of _j' ，r _j' ＝(s _j' ,u _j' ,v _j' ,w _j' )；

When the employed bee neighborhood search is performed in the step A322 and the observation bee neighborhood search is performed in the step A323, the fitness value of any honey source is the Gabor atom and R corresponding to the honey source ^n-1 (t) inner product.

Compared with the prior art, the invention has the following advantages:

1. the method has the advantages of simple steps, reasonable design, convenient implementation and low input cost.

2. The signal segmentation is simple and convenient, and the simple and convenient and quick separation of the segmented signals of the defects at a plurality of different positions in the same ultrasonic echo signal can be simply and conveniently realized.

3. The method has the advantages of convenient realization and good use effect, realizes the judgment of the segmentation points and the determination of the sampling time of the segmentation points by carrying out threshold judgment on the time interval of the adjacent extreme points, and segments the ultrasonic echo signals according to the determined number of the segmentation points and the sampling time of each segmentation point.

4. The adopted determination method of the number of the division points is reasonable in design, simple and convenient to realize and good in use effect, the traveling wave peak and the wave trough are determined firstly, and all extreme points in the ultrasonic echo signal F (t) are obtained correspondingly; and then, effective extreme point elimination is carried out according to a specific extreme point elimination method, so that the calculated amount of signal division is effectively reduced, the divided signals can be more highlighted, and the number L of the divided points in the ultrasonic echo signal F (t) is determined by respectively judging the divided points at the time intervals of M' -1 adjacent extreme points. The determination of the number L of the segmentation points in the ultrasonic echo signal F (t) can be realized only by effectively combining the effective extreme point elimination with the segmentation point judgment, so that the number L of the segmentation points in the ultrasonic echo signal F (t) is neither too large nor too small. Only after effective extreme point elimination, the extreme points without practical analysis significance of a segmentation signal can be eliminated, and only the extreme points with real value in the segmentation signal are reserved, so that the condition that the number L of the determined segmentation points is too large can be effectively avoided; meanwhile, the M' -2 time intervals to be judged are respectively subjected to division point judgment in sequence without leaking any division part, so that the situation that the number L of the determined division points is too small can be effectively avoided, and the accuracy of the number L of the division points in the determined ultrasonic echo signal F (t) is very high.

5. The signal extraction speed is high, the data processor is adopted to automatically complete the signal extraction process, and the signal extraction process can be completed in several minutes or even shorter time, so that the real-time signal extraction is realized.

6. The signal sparse decomposition method based on the optimization algorithm is simple, reasonable in design, convenient to implement and good in using effect, improves the signal extraction speed, can effectively improve the quality and performance indexes of original signals after signal extraction, and particularly plays an important role in ultrasonic nondestructive inspection. Meanwhile, the value range of the frequency parameter v is limited to

And f _o The unit of (A) is MHz, on one hand, the calculated amount of a sparse decomposition algorithm can be effectively reduced, and real-time detection is realized; on the other hand, effectively improve MPDue to the performance of the algorithm (namely, the matching pursuit algorithm), the sparsely represented signal can effectively meet the detection precision of weak and small defects, and the effective information contained in the signal can be obtained more simply and accurately. By limiting the range of values of the frequency parameter v to

Effective information contained in the signals can be further highlighted, the sparsely represented signals can express the effective information more heavily, redundant information is weakened, signal intrinsic characteristics can be expressed more accurately, and signal extraction precision can be effectively guaranteed.

7. And after the signal sparse decomposition, whether the iterative decomposition optimal atomic set meets the preset signal extraction precision requirement is judged through residual quantity, and optimal matching atomic optimization is carried out according to the judgment result, so that the signal extraction accuracy can be further improved, the signal extraction precision is further improved, the extracted signal further approaches to the original signal, the optimal matching with the original signal is realized, and the signal extraction accuracy and the signal extraction speed are improved.

8. The adopted optimal matching atom optimization method is reasonable in design, convenient to achieve and good in using effect, an optimal matching atom is randomly selected from the iterative decomposition optimal atom set for optimization, after the optimal matching atom is optimized, whether the iterative decomposition optimal atom set meets the signal extraction precision requirement or not is judged through the residual quantity, and whether the optimization needs to be continued on the rest optimal matching atoms or not is determined according to the judgment result. Therefore, the method is simple and convenient to realize, can realize the combination of quick optimization and real-time judgment of an optimization result, can effectively simplify the optimization process of the optimal matching atom, can quickly achieve the aim of optimizing the optimal matching atom, and further effectively improves the signal extraction precision. Meanwhile, the adopted atom replacement judgment method is reasonable in design, simple and convenient to realize and good in use effect, any one of the methods of residual value judgment, fitness value judgment or sparsity judgment is adopted for atom replacement judgment, any one of the methods can be selected for atom replacement judgment, the use mode is flexible, and each atom replacement judgment method can realize effective atom replacement judgment.

9. The improved artificial bee colony algorithm is adopted for optimizing to realize the best matching atom search, all atoms in an overcomplete dictionary do not need to be generated before the signal sparse decomposition, only positions of honey sources need to be generated to replace Gabor atoms in an atom library, and the storage space is greatly saved. In addition, the artificial bee colony algorithm searches the best matching atoms in a continuous space, and the matching tracking algorithm searches the atoms in a discrete search space, so that the artificial bee colony algorithm has a wider search range, the extracted atoms can better reflect the characteristics of original signals, the calculation speed is increased, and the accuracy of parameter extraction is improved because the atoms are optimized in the continuous solution space range. Compared with a discrete space range, the method can more accurately extract the optimal matching atoms from signal matching, thereby improving the precision of signal extraction and effectively extracting useful signals under a strong noise background. Particularly for ultrasonic nondestructive inspection, a reliable basis is provided for accurate defect detection, a theoretical basis is provided for qualitative and quantitative analysis of defects, the problem that weak defects are difficult to extract under a strong noise background can be effectively solved, the problems of extraction speed and precision of the weak defects can be solved, defect information under the strong noise background can be accurately extracted, the ultrasonic signal extraction speed is increased, technical support is provided for real-time automatic detection, and therefore the problems of high algorithm complexity, over-matching and the like of the existing matching tracking algorithm can be effectively solved. Therefore, the invention utilizes the artificial bee colony algorithm to select the atom which is optimally matched with the ultrasonic signal from the continuous dictionary library, thereby recovering the signal to be processed.

10. The method has the advantages of good using effect and high practical value, the optimal matching atoms are searched by adopting a signal sparse decomposition method based on an optimization algorithm, meanwhile, the signal extraction precision is judged through the residual quantity, the optimal matching atoms are optimized according to the judgment result, the signal extraction speed can be greatly increased, and the signal extraction precision can be effectively improved.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

FIG. 1 is a block diagram of the process flow of the present invention.

Fig. 2 is a schematic block diagram of a signal division processing system according to the present invention.

Fig. 3 is a flow chart of a method of extracting a signal according to the present invention.

Description of reference numerals:

1-ultrasonic flaw detection device; 2-a data processing device; 3-data memory.

Detailed Description

Fig. 1 shows an ultrasonic signal receiving and processing method based on signal segment segmentation, which includes the following steps:

step one, ultrasonic echo signal acquisition and synchronous uploading and receiving: ultrasonic detection is carried out on a detected object by adopting an ultrasonic flaw detection device 1, an ultrasonic echo signal F (t) of the detected object is obtained, and the obtained ultrasonic echo signal F (t) is synchronously transmitted to a data processing device 2; the data processing device 2 synchronously stores the received ultrasonic echo signals F (t);

t represents a time parameter, t _i The ith sampling time of the ultrasonic flaw detection apparatus 1, f (t) _i ) I is a positive integer and i =1, 2, 3, \ 8230;, N is a signal value sampled at the ith sampling time of the ultrasonic flaw detector 1 _z ，N _z Is a positive integer and is the signal length of the ultrasonic echo signal F (t);

step two, determining wave crests and wave troughs: respectively determining all wave crests and all wave troughs of the ultrasonic echo signal F (t) in the step one by adopting a data processing device 2, and respectively synchronously recording the sampling time and the signal value of each determined wave crest and each determined wave trough;

step three, removing extreme points: adopting data processing equipment 2, calling a time domain extreme point eliminating module or a frequency domain extreme point eliminating module to eliminate extreme points, obtaining M 'number of eliminated extreme points, and arranging the M' number of the extreme points from front to back according to the sampling time sequence of the extreme points; wherein, M' is a positive integer and is the total number of the extreme points obtained after the extreme points are removed in the step;

when the data processing equipment 2 is adopted and a time domain extreme point rejecting module is called to reject the extreme points, rejecting the extreme points of which the absolute values of the signal values in all the extreme points determined in the step two are smaller than beta 'to obtain M' number of rejected extreme points; wherein, β ' = α ' × max | F (t) |, α ' is a constant and its value range is 0.1-0.35, max luminance F (t) | is the absolute value maximum of the signal value in the ultrasonic echo signal F (t);

when the data processing equipment 2 is adopted and a frequency domain extreme point eliminating module is called to eliminate the extreme points, eliminating the extreme points of which the absolute values of the signal values in all the extreme points determined in the step two are smaller than beta to obtain M' eliminated extreme points; wherein β is a preset rejection threshold and β = α × max | Y (F) |, α is a constant and has a value range of 0.25 to 0.35, Y (F) is a frequency spectrum of the ultrasonic echo signal F (t), and max | Y (F) | is a maximum absolute value of an amplitude value in the frequency spectrum of the ultrasonic echo signal F (t);

step four, signal segmentation: the data processing device 2 is used for segmenting the ultrasonic echo signal F (t), and the process is as follows:

step 401, determining the time interval between adjacent extreme points: respectively determining the time intervals of two adjacent extreme points in the M 'extreme points in the third step by adopting a data processing device 2 to obtain M' -1 time intervals of the adjacent extreme points;

the M 'th of said adjacent extreme time intervals of M' -1 of said adjacent extreme time intervals is denoted as Δ t _m' ，Δt _m' The time interval between the sampling time of the mth ' extreme point in the M ' extreme points and the sampling time of the M ' +1 extreme points is set; wherein M 'is a positive integer and M' =1, 2, \8230, M '-2, M' -1;

dividing Δ t in M' -1 time intervals of said adjacent extreme points ₁ The time intervals of the other M' -2 adjacent extreme points are all time intervals to be judged, delta t ₁ The time interval between the sampling time of the 1 st extreme point and the sampling time of the 2 nd extreme point in the M' extreme points is set;

step 402, segmentation point judgment and sampling time determination of segmentation points: respectively judging the division points of the M' -2 time intervals to be judged in the step 401 from first to last by adopting a data processing device 2 to obtain L time intervals to be divided; wherein L is an integer and is more than or equal to 0, and L is the total number of the time intervals to be divided determined in the step; each time interval to be divided has a dividing point; in the first step, the number of the segmentation points existing in the ultrasonic echo signal F (t) is the same as the number of the time intervals to be segmented, and the number of the segmentation points existing in the ultrasonic echo signal F (t) is the same as L;

the judgment methods of the M' -2 division points of the time intervals to be judged are the same; for Δ t _m' When the division point is judged, the value of Delta t is measured _m' Whether or not it is greater than c.DELTA.t _m'-1 And (4) judging: when Δ t is measured _m' ＞c·Δt _m'-1 When it is determined that Δ t is present _m' Is a time interval to be divided, and Δ t _m' The sampling time of the above existing division point is

step 403, signal division and judgment: judging L in step 402: when L =0, judging that the ultrasonic echo signal F (t) does not need to be segmented, and completing a signal segmentation process; otherwise, judging that the ultrasonic echo signal F (t) needs to be segmented, and entering step 404;

step 404, sorting the segmentation points: sequencing the L division points determined in the step 402 from front to back by adopting a data processing device 2 according to the sequence of sampling time;

step 405, signal segmentation: dividing the ultrasonic echo signal F (t) in the first step into L +1 signal segments from front to back according to the sampling time of the L sorted division points in the step 404, wherein each divided signal segment is a division signal.

<xnotran> , [ </xnotran>] ^T Representing the transpose of the matrix.

The step one is

Is a matrix

The transposing of (1).

In this embodiment, in the third step, the data processing device 2 is adopted and the time domain extreme point eliminating module is called to eliminate the extreme point, and the value size of α can be adjusted accordingly according to actual needs.

In practical use, the data processing device 2 can be adopted in the third step, the frequency domain extreme point eliminating module is called to eliminate the extreme points, and the value size of alpha' can be correspondingly adjusted according to actual needs.

Because in the signal sampling process, influence from various factors such as environmental factors and sampling systems is caused, a plurality of non-real extreme points (namely interference extreme points) inevitably exist in the ultrasonic echo signal F (t), and the extreme points are not extreme points of defect signals in the ultrasonic echo signal F (t), so that the non-real extreme points need to be removed, thus not only effectively reducing the calculated amount, but also removing the interference extreme points so as to accurately determine the existing segmentation points and the sampling time thereof.

The two rejection methods of time domain rejection and frequency domain rejection, which are adopted by the invention, can simply, conveniently, quickly and effectively reject the interference extreme points, and the two rejection methods can be automatically completed by adopting the data processing equipment 2, so the method is convenient to realize, the processing speed is high, and the rejection result can be effectively ensured.

When the time domain eliminating method or the frequency domain eliminating method is adopted to eliminate the extreme points, the adopted eliminating threshold value beta' and the eliminating threshold value beta are set reasonably. The determination of the rejection threshold beta' and the rejection threshold beta is directly related to the ultrasonic echo signal F (t), and the determination is performed by the currently processed ultrasonic echo signal F (t) instead of a fixed value aiming at different signals to be processed, so that the method has certain adaptability, can effectively ensure the rejection effect, and can not reject true extreme points too much. In addition, the actual extreme point eliminating effect and the ultrasonic defect signal identification purpose are combined, and the actual using effect of the extreme point eliminating method is very good.

Wherein β ' = α ' × max | F (t) |, β ' is determined by the maximum absolute value of signal value in the ultrasonic echo signal F (t) max | F (t) |, the signal value of the superimposed signal included in the currently processed ultrasonic echo signal F (t) can be known according to max | F (t) |, and extreme points whose absolute value is smaller than β ' are substantially unrelated to the partition signal included in the ultrasonic echo signal F (t), and the meaning of the actual analysis is substantially absent, so that extreme points whose absolute value is smaller than β ' are eliminated.

Accordingly, β = α × max | Y (F) |, β is determined by the maximum absolute value of the amplitude in the ultrasonic echo signal F (t) max | Y (F) |, the amplitude of the superimposed signal included in the currently processed ultrasonic echo signal F (t) can be known according to max | Y (F) |, the extreme points of the signal value smaller than β are substantially not associated with the separation signal included in the ultrasonic echo signal F (t), and the meaning of the actual analysis is substantially absent, so that the extreme points of the signal value smaller than β are eliminated.

When signal segmentation is carried out in the fourth step, the adopted signal segmentation method is reasonable in design, convenient to implement and good in using effect, time intervals of two adjacent extreme points in M 'extreme points after the extreme points are removed are firstly determined, segmentation point judgment is carried out on the obtained time intervals of M' -1 adjacent extreme points respectively, the number L of segmentation points existing in the ultrasonic echo signal F (t) is determined according to the segmentation point judgment result, and the number of segmentation points existing in the ultrasonic echo signal F (t) is determined according to the L.

The method for determining the number of the segmentation points in the ultrasonic echo signal F (t) is reasonable in design, simple and convenient to implement and good in using effect, the traveling wave peak and the wave trough are determined firstly, and all extreme points in the ultrasonic echo signal F (t) are obtained correspondingly; and then, effective extreme point elimination is carried out according to a specific extreme point elimination method, so that the calculated amount of signal division in the fourth step is effectively reduced, the divided signals can be more highlighted, and the number L of the divided points in the ultrasonic echo signal F (t) is determined by respectively judging the divided points at the time intervals of M' -1 adjacent extreme points. The determination of the number L of the segmentation points in the ultrasonic echo signal F (t) can be realized only by effectively combining the effective extreme point elimination with the segmentation point judgment, so that the number L of the segmentation points in the ultrasonic echo signal F (t) is neither too large nor too small. After the extreme points are effectively removed in the third step, the extreme points which have no practical analysis significance of a segmentation signal can be removed, and only the extreme points with real values in the segmentation signal are reserved, so that the condition that the number L of the determined segmentation points is too large can be effectively avoided; meanwhile, in step 402, the M' -2 time intervals to be judged are respectively subjected to division point judgment from first to last without leaking any division part, so that the situation that the number L of the determined division points is too small can be effectively avoided, and the accuracy of the number L of the division points in the determined ultrasonic echo signal F (t) is very high.

And the sampling time of each division point is determined simply, and the middle time of the time interval to be divided is taken as the sampling time of the division point. Therefore, the signal section segmentation can be reasonably and accurately realized.

In this embodiment, Y (F) in step three is a frequency domain signal obtained by converting an ultrasonic echo signal F (t) into a frequency domain using a time-frequency transform module, where the time-frequency transform module is a fourier transform module;

max | Y (f) | is the maximum of the absolute value of the signal amplitude in Y (f).

In this embodiment, the sampling time of the ith division point in the L division points in step 404 is denoted as t _fenl Wherein L is a positive integer and L =1, 2, \8230;

the 1 st of the L +1 divided signals is denoted as F ₁ (t) wherein F ₁ (t)＝[f(t ₁ ),f(t ₂ ),...,f(t _fen1 )] ^T ；

the L +1 th of the L +1 divided signals is denoted as F _L+1 (t) in which

Thus, signal splitting is very simple to implement.

[f(t ₁ ),f(t ₂ ),...,f(t _fen1 )] ^T Is represented by [ f (t) ₁ ),f(t ₂ ),...,f(t _fen1 )]Is transposed, [ f (t) ] _fenl' ),f(t _fenl'+1 ),...,f(t _fenL' )] ^T Is represented by [ f (t) _fenl' ),f(t _fenl'+1 ),...,f(t _fenL' )]The method (2) is implemented by the following steps,

represent

The transposing of (1).

In this embodiment, after the signal division is completed in step 405, signal extraction needs to be performed on L +1 divided signals respectively; the signal extraction methods of the L +1 division signals are the same;

when any one of the L +1 division signals is subjected to signal extraction, the data processing device 2 is adopted to extract the division signal which is a signal to be processed and is marked as a signal f (t);

as shown in fig. 3, when the data processing device 2 is used to extract the signal f (t), the process is as follows:

a1, performing signal sparse decomposition based on an optimization algorithm: adopting data processing equipment 2 and calling a sparse decomposition module to carry out iterative decomposition processing on the signal f (t) to be processed in the step one, and converting the signal f (t) to be processed into a signal f (t) to be processed

And obtaining the optimal atom set of iterative decomposition at the moment; the iterative decomposition of the best set of atoms at this time contains m best matching atoms,

decomposing the nth best matching atom in the best atom set for the iteration;

in the formula R ^m (t) is a residual quantity of the signal f (t) to be processed after m iterative decompositions, wherein m is a preset total iterative decompositions number and m is a positive integer, n is a positive integer and n =1, 2, \8230;, m; a is _n The expansion coefficient of the best matching atom after the nth iterative decomposition and the residual error after the last iterative decomposition is obtained;

adopting the data processing equipment 2 and calling an optimization algorithm module to find out the best matching atom for the nth iteration decomposition;

is a Gabor atom and

wherein the function ψ (t) is a Gaussian window function and

r _n is composed of

Of time-frequency parameters r _n ＝(s _n ,u _n ,v _n ,w _n )，s _n As a scale parameter, u _n As displacement parameter, v _n As a frequency parameter, w _n Is a phase parameter;

in this step, the best matching atom is found

According to a preset s _n 、u _n 、v _n And w _n The data processing device 2 is adopted and an optimization algorithm module is called to find out the Fitness value Fitness (r) _n ) The maximum optimal time frequency parameter, the found optimal time frequency parameter is the time frequency parameter r _n ；

Wherein, fitness (r) _n ) Is a time-frequency parameter r _n The value of the fitness of (a) is,

represents R ^n-1 (t) and

inner product of (2); r ^n-1 (t) is the residual error quantity of the signal f (t) to be processed after n-1 times of iterative decomposition, R ⁰ (t)＝f(t)；

Step A2, residual error amount judgment: judge | | | R ^m (t)|| ₂ Whether less than ε: when | | | R ^m (t)|| ₂ If the value is less than epsilon, entering a step A4; otherwise, when R ^m (t)|| ₂ When the epsilon is more than or equal to epsilon, entering the step A3;

step A31, atom random selection: using the data processing apparatus 2 from this point onRandomly taking out an optimal matching atom from the optimal atom set by iterative decomposition as an atom to be optimized, wherein the atom to be optimized is marked as

For the best matching atom

When searching, according to the preset s _j' 、u _j' 、v _j' And w _j' The data processing device 2 is adopted and the optimizing algorithm module is called to find out the fitness value fitness (r) _j' ) The maximum optimal time frequency parameter, the found optimal time frequency parameter is the time frequency parameter r _j' (ii) a According to the formula

Solving for the best matching atom

Wherein the content of the first and second substances,

represent

And

the inner product of (a) is,

ψ ₀ (t) is the sum of m-1 of the atoms to be treated in step A31;

step a33, atom replacement judgment and atom replacement: adopting a data processing device 2, calling a residual value judging module, a fitness value judging module or a sparsity judging module, judging whether the atoms to be optimized in the step A31 need to be replaced, and replacing the atoms to be optimized according to a judging result;

when the data processing device 2 is adopted and the residual value judging module is called to judge whether the atoms to be optimized in the step A31 need to be replaced, the residual value R after replacement is used _j' ^m (t)|| _ξ Whether it is less than the residue before replacement | | R _j ^m (t)|| _ξ And (4) judging: when | | | R _j' ^m (t)|| _ξ ＜||R _j ^m (t)|| _ξ If so, judging that the atom to be optimized in the step A31 needs to be replaced, and replacing the atom to be optimized in the step A31 with the best matching atom in the step A32

wherein R is _j' ^m (t)＝f(t)-ψ _j' (t)，

R _j ^m (t)＝f(t)-ψ _j (t)，ψ _j (t) is the sum of m best matching atoms in the iterative decomposition best atom set before atom replacement judgment in the step; r | | _j' ^m (t)|| _ξ Represents R _j' ^m Xi-norm, | | | R of (t) _j ^m (t)|| _ξ Represents R _j ^m Xi-norm of (t), xi is constant and is more than or equal to 0 and less than or equal to 1;

adopting the data processing equipment 2 and calling a Fitness value judging module to judge whether the atoms to be optimized in the step A31 need to be replaced or not, and according to the Fitness value Fitness (r) after replacement _j' ) Whether greater than the pre-replacement Fitness value Fitness (r) _j ) And (4) judging: when Fitness (r) _j' )＞Fitness(r _j ) If so, judging that the atom to be optimized in the step A31 needs to be replaced, and replacing the atom to be optimized in the step A31 with the best matching atom in the step A32

represents R ^j-1 (t) and

represents R ^j-1 (t) and

inner product of (d);

adopting the data processing equipment 2 and calling the sparsity judging module to judge whether to replace the atoms to be optimized in the step A31 or not according to the R ^j' || _ξ Whether or not less than R ^j || _ξ And (4) judging: when | | | R ^j' || _ξ ＜||R ^j || _ξ When the atom to be optimized in the step A31 needs to be replaced, the step A31 is judged to be replacedReplacing the atom to be optimized in the step A31 with the best matching atom in the step A32

wherein R is ^j' Is composed of

Amount of residual error of

R ^j Is composed of

Amount of residual error of

||R ^j' || _ξ Represents R ^j' Xi-norm, | | R of (c) ^j || _ξ Represents R ^j ξ -norm of;

in this step, after the atom replacement judgment and the atom replacement are completed, the optimization process of the best matching atom selected in step a31 is completed;

wherein, | R' _j ^m (t)|| ₂ Is R' _j ^m (t) 2-norm; r' _j ^m (t) is the residual quantity after m iterative decompositions are performed on f (t) according to m best matching atoms in the iterative decomposition best atom set at the moment;

step A36, residual quantity judgment: and B, judging the optimized residual quantity of the best matching atom in the step A35: when | | | R " _j ^m (t)|| ₂ If the value is less than epsilon, entering a step A4; otherwise, when | | R " _j ^m (t)|| ₂ When the value is more than or equal to epsilon, returning to the step A35;

step A4, signal reconstruction: according to the iterative decomposition optimal atom set at the moment, an approximate signal f' (t) of the signal f (t) to be processed is obtained by adopting the data processing equipment 2; wherein the approximation signal f "(t) is a signal extracted from the signal f (t) to be processed,

wherein

To this end the iterative decomposition of the n ' th best matching atom in the set of best atoms, n ' being a positive integer and n ' =1, 2, \ 8230; a is a _n' Is composed of

The 2-norm refers to the sum of the squares of the elements of the vector and then the square root (i.e., the L2 norm) is taken, as is well known in the art.

R in step A2 ^m (t) is a vector of dimension Nx 1, | | R ^m (t)|| ₂ Is R ^m (t) the power of 1/2 of the sum of the absolute values of the elements N to the power of 2. Where N is a positive integer and it is the signal length of the signal f (t) to be processed.

R 'in step A34' _j ^m (t) is an Nx 1-dimensional vector, | R' _j ^m (t)|| ₂ Is R' _j ^m And (t) the power of 1/2 of the sum of the absolute values of the N elements to the power of 2.

R in step A36 " _j ^m (t) is an Nx 1-dimensional vector, | | R " _j ^m (t)|| ₂ Is R' _j ^m And (t) the power of 1/2 of the sum of the absolute values of the N elements to the power of 2.

R described in step A33 _j' ^m (t) is a vector of dimension Nx 1, | | R _j' ^m (t)|| _ξ Is R _j' ^m And (t) the power of 1/xi of the xi power sum of the absolute values of the N elements.

Said R _j ^m (t) is a vector of dimension Nx 1, | | R _j ^m (t)|| _ξ Is R _j ^m And (t) the power of 1/xi of the xi power sum of the absolute values of the N elements.

R is as described ^j' Is a vector of dimension Nx 1, | | R ^j' || _ξ Is R ^j' The absolute values of the N elements are 1/xi power of the xi power sum. R is as described ^j Is a vector of dimension Nx 1, | | R ^j || _ξ Is R ^j The absolute values of the N elements are 1/xi to the power of the xi power sum.

In this embodiment, the ultrasonic flaw detector 1 is an a-type digital ultrasonic flaw detector. In practice, other types of ultrasonic flaw detection equipment may be used.

Wherein, in step A1

Before signal sparse decomposition in the step A1, s is subjected to value range determination method of scale parameter, displacement parameter, frequency parameter and phase parameter in time frequency parameter during conventional signal sparse decomposition _n 、u _n 、v _n And w _n Respectively determining the value ranges of the two. Described in step A1

Is the best matching atom when the signal f (t) to be processed is subjected to the nth iteration decomposition.

Before searching the best matching atom in the step A32, s is firstly searched _j' 、u _j' 、v _j' And w _j' Are set, and s is set _j' Is compared with s set in the step A1 _n Are in the same value range, and the set u _j' And u set in the step A1 _n Have the same value range, and set v _j' And v set in step A1 _n Has the same value range, set w _j' And w set in the step A1 _n The value ranges of (A) are the same.

Each Gabor atom corresponds to the time-frequency parameter of the Gabor atom, and each Gabor atom corresponds to the time-frequency parameter of the Gabor atom one to one.

A method for realizing MP-based signal sparseness decomposition by FFT (Author: yi faithful) disclosed in journal of electronics and information, volume 4, 2006 (volume 28, stage 4) is disclosed in the article: "\8230 = (s, u, v, w), and the time-frequency parameters can be discretized according to the following method: r = (α) ^j ,pα ^j Δu,kα ^-j Δ v, i Δ w), where α =2, Δ u =1/2, Δ v = π, Δ w = π/6,0 < j < log ₂ N,0≤p≤N2 ^-j+1 ，0≤k≤N2 ^j+1 I is more than or equal to 0 and less than or equal to 12. The above description gives a specific over-complete atom library ". From the above, the frequency parameter v is based on k α ^-j Delta v is discretized, since k is more than or equal to 0 and less than or equal to N2 ^j+1 、0＜j＜log ₂ N, α =2 and Δ v = π, the frequency parameter v has a very wide range of values, the minimum value of the frequency parameter v is 0 and the maximum value thereof is

The frequency parameter v thus has a value range of

Even if discretized, frequency parametersThe value range of v is still very large.

In this example, s in step A1 _n Has a value range of [1, N ]]And s _n ∈[1,N]，u _n Has a value range of [0, N']And u is _n ∈[0,N]，v _n Has a value range of

And is

w _n Has a value range of [0,2 pi]And w _n ∈[0,2π]. Wherein, f _o Sampling frequency f of the ultrasonic flaw detector 1 _o In MHz. N is a positive integer and it is the signal length of the signal f (t).

According to the common knowledge in the field, the sparse decomposition algorithm (also called MP algorithm) has two defects, one is that the calculation amount of the sparse decomposition algorithm is large, the calculation time is very large under the existing calculation condition, and real-time detection cannot be carried out; secondly, the sparse decomposition algorithm is an optimal solution obtained under a continuous condition, and the detection precision of weak and small defects is still limited.

The purpose of signal sparse representation is to represent a signal by using as few atoms as possible in a given overcomplete dictionary, and a more concise representation mode of the signal can be obtained, so that information contained in the signal can be more easily obtained, and the signal can be more conveniently processed, such as compression, encoding and the like. The research focus of the signal sparse representation direction mainly focuses on the aspects of sparse decomposition algorithm, overcomplete atom dictionary (also called atom library, gabor dictionary) and application of sparse representation. Two major tasks of signal sparse representation are dictionary generation and sparse decomposition of signals. However, existing research has demonstrated that searching for atoms in scale and frequency from a coarse scale to a fine scale can significantly improve the performance of MP algorithms (i.e., matching pursuit algorithms) without increasing the size of the atom pool. Thus, the value range of the frequency parameter v

Advance and advanceOne-step miniaturization can effectively improve the performance of an MP algorithm (namely, a matching pursuit algorithm). Especially for the frequency parameter, the value range has a larger influence on the performance of the MP algorithm (i.e. the matching pursuit algorithm).

Because the value range of the frequency parameter v is related to the actual sampling frequency of the signal, on the basis of the research experience of years of sparse decomposition, the value range of the frequency parameter v and the actual sampling frequency of the processed signal (namely the sampling frequency f of the ultrasonic flaw detection device 1) are obtained after the influence of the value range of the time-frequency parameter on the improvement of the performance of the MP algorithm (namely the matching pursuit algorithm) is fully and long-term researched and verified _o ) Closely related and not completely corresponding one to one, and limits the value range of the frequency parameter v to be the value range of the frequency parameter v from the comprehensive view of simplifying the calculated amount of the sparse decomposition algorithm and refining the value range of the time-frequency parameter and improving the performance of the matching pursuit algorithm

And f _o The unit of (2) is MHz, on one hand, the calculated amount of a sparse decomposition algorithm can be effectively reduced, and real-time detection is realized; on the other hand, the performance of the MP algorithm (namely, the matching pursuit algorithm) is effectively improved, so that the sparsely represented signal can effectively meet the detection precision of weak and small defects, and the purpose of more simply and accurately obtaining effective information contained in the signal is achieved. By limiting the range of values of the frequency parameter v to

According to the common general knowledge in the art, and combining the article "implementing MP-based signal sparse decomposition by FFT" (author: yiloy) published in the journal of electronics and information in 4.2006 (vol.28, no. 4), it is known that, before sparse decomposition is currently performed, four parameters of a time-frequency parameter are usually discretized respectively, and an overcomplete atom library is generated, but the number of atoms in the overcomplete atom library is usually very large, the occupied storage space is very large, the calculation amount is large, the calculation engineering is complex, and all atoms in the overcomplete atom library need to be analyzed and judged respectively, and the best matching atom is found; meanwhile, the parameter value range and the discretization method also have great influence on the generated overcomplete atom library, which inevitably causes poor accuracy of the generated overcomplete atom library (also called an overcomplete dictionary, gabor dictionary), so that the intrinsic characteristics of the signals cannot be accurately expressed, and the signal extraction precision cannot be guaranteed.

Before signal sparse decomposition is carried out in the step A1, all atoms in an over-complete dictionary do not need to be generated, and only the data processing equipment 2 is adopted and the optimization algorithm module is called for optimization, so that the best matching atoms can be simply, conveniently and quickly found one by one, and the storage space is greatly saved. In addition, the optimizing algorithm module searches for the best matching atoms in the value range of each parameter (specifically in a continuous space), and the searching for the best matching atoms in a discrete search space (namely an over-complete dictionary or an over-complete atom library obtained through discretization) is performed with the traditional matching tracking algorithm, so that the searching range of the optimizing algorithm module is wider, the searched best matching atoms can better reflect the characteristics of an original signal, and the accuracy of signal extraction can be further ensured.

The optimizing algorithm module in the step A1 is a genetic algorithm module, an artificial fish swarm algorithm module or an artificial bee swarm algorithm module. In practice, the optimization algorithm module may be other types of optimization algorithm modules. When the genetic algorithm module is called for optimizing, the conventional genetic algorithm is adopted; when an artificial fish school algorithm module is called for optimizing, a conventional artificial fish school algorithm is adopted; and when the genetic algorithm module and the artificial bee colony algorithm module are called for optimizing, the conventional artificial bee colony algorithm is adopted.

The method for determining the best matching atom by adopting the data processing equipment 2 and calling the optimizing algorithm module for optimizing has the following advantages: firstly, the defect that the traditional methods such as Fourier transform, wavelet transform and the like can only carry out decomposition on orthogonal basis is overcome, and the intrinsic characteristics of signals can be more accurately expressed, so that the accuracy of signal extraction is improved; secondly, the generation of local optimal values can be effectively avoided, optimization searching in a continuous space can be carried out, and compared with the optimization searching in a discrete space carried out by the original matching tracking algorithm, the searching range is expanded, so that the accuracy of signal extraction is further effectively improved; thirdly, the optimal matching atoms are found out through optimization of the optimization algorithm module, the method is simple and convenient to realize and high in extraction speed, the problem of high complexity of an original matching algorithm can be effectively solved, the convergence speed of noise reduction processing and the signal extraction speed are greatly improved, and the real-time performance of signal extraction is improved; fourthly, the accuracy of signal extraction can be effectively improved, and the problems of signal extraction under the background of strong noise and extraction of weak and small signals are solved; fifthly, the use effect is good, the detection problems of weak and small defects and the like in the field of ultrasonic nondestructive inspection can be solved, the product quality of production enterprises is improved, and potential safety hazards are avoided; and sixthly, the method is wide in application range, can be effectively applied to the extraction process of various signals, and particularly can effectively extract non-stable difficult-to-detect acoustic signals. Therefore, the method for determining the best matching atoms by calling the optimization algorithm module for optimization has the advantages of reasonable design, good effect and high practical value, not only improves the speed of signal extraction, but also can effectively improve the quality and performance indexes of original signals after signal extraction, and has an important effect particularly in ultrasonic nondestructive inspection.

In this example, s in step A32 _j' Value range of and s _n Have the same value range of u _j' Value range of (a) and (u) _n Have the same value range, v _j' Value range of (d) and v _n Have the same value range, w _j' Value range of and w _n The value ranges of (A) are the same. Thus, s _j' Has a value range of [1, N ]]And s _j' ∈[1,N]，u _j' Has a value range of [0, N']And u is _j' ∈[0,N]，v _j' Has a value range of

And is provided with

w _j' Has a value range of [0,2 pi]And w _j' ∈[0,2π]。

In the actual use process, no matter the conventional matching pursuit algorithm carries out sparse decomposition after establishing an over-complete atom library, or the optimizing algorithm module is utilized to optimize and find out the optimal matching atom to complete signal sparse decomposition, the method has certain limitations, and the optimal matching atom is obtained under certain limiting conditions, so that when the two methods are adopted to carry out signal extraction, the accuracy of signal extraction is only relatively high. When the overcomplete atom library is adopted for sparse decomposition, the value range of each parameter in the time-frequency parameters and the discretization method both have great influence on the generated overcomplete atom library, and the finally determined overcomplete atom library cannot include all atoms and inevitably omits one or more optimal matching atoms, so that the accuracy of signal extraction is influenced. When the optimization algorithm module is used for optimizing and finding out the best matching atoms, although the signal extraction speed can be improved, the searching on a continuous interval can be realized, the found best matching atoms are only the best matching atoms to a certain extent or within a certain range under the influence of the advantages, disadvantages and performances of the algorithm in the optimization algorithm module, such as the searching step length, the searching strategy, the searching termination condition and the like, and the accuracy of signal extraction is also influenced to a certain extent.

From the above, after the signal sparse decomposition is completed in step A1, step A2 is further required to perform residual quantity judgment, and whether the optimal atom set of iterative decomposition at this time meets the preset signal extraction precision requirement is judged, if not, step A3 is required to perform optimal matching atom optimization, so as to further improve the accuracy of signal extraction. Therefore, after the signal sparse decomposition is completed in the step A1, according to the residual error amount judgment result in the step A2, whether the optimal atomic set of iterative decomposition after the signal sparse decomposition in the step A1 meets the preset signal extraction precision requirement is judged, and the verification link of the signal extraction precision is added, so that the signal extraction precision can be further improved, and the extracted signal further approaches to the original signal.

When the optimal matching atom optimization is carried out in the step A3, the adopted optimal matching atom optimization method is reasonable in design, convenient to implement and good in using effect, one optimal matching atom is randomly selected from the iterative decomposition optimal atom set for optimization, after the optimization is completed, whether the iterative decomposition optimal atom set meets the signal extraction precision requirement or not is judged through the residual error quantity, and whether the optimization needs to be continuously carried out on the rest optimal matching atoms or not is determined according to the judgment result. Therefore, the method is simple and convenient to realize, can randomly select an optimal matching atom for optimization, has no limit on the optimization sequence of atoms, can judge the residual quantity once when the optimization process of the optimal matching atom is finished, can realize the combination of quick optimization and real-time judgment of the optimization result, can effectively simplify the optimization process of the optimal matching atom, can quickly achieve the aim of optimizing the optimal matching atom, and effectively improve the signal extraction precision.

When the atom to be optimized is optimized, the method for searching the best matching atom corresponding to the atom to be optimized (i.e., the method for searching the best matching atom in step a 32) is reasonably designed, and the best matching atom better than the atom to be optimized can be simply, conveniently and quickly found out.

Found time frequency parameter r _j' To make the fitness value fitness (r) _j' ) The maximum optimal time-frequency parameter;

due to the fact that

And psi ₀ (t) is the sum of m-1 of said atoms to be treated in step A31, thus

Subtracting the residual error of m-1 atoms to be processed except the atom to be optimized from the signal f (t) (i.e. the original signal), thereby obtaining the final product

Is a residual signal directly related to the atom to be optimized, thus making use of

Finding the time-frequency parameter r as an evaluation _j' The index of (2) is more targeted, and residual signals except m-1 atoms to be processed in the best atom set are iteratively decomposed at the moment

Directly related to the atom to be optimized, and finding out the time-frequency parameter r by using an optimization algorithm module _j' Is not affected by other atoms (i.e., m-1 of the atoms to be processed), and the probability of finding the best matching atom better than the atom to be optimized is higher, while the best matching atom is obtained

The m-1 atoms to be processed in the optimal atom set of iterative decomposition at this time are not affected, signal sparse decomposition is not needed again, only the atom replacement of the atoms to be optimized is completed according to the method in the step A33, and finally the step A204 is directly entered for signal reconstruction, so that the using effect is very good, and the signal extraction precision can be simply, conveniently and quickly improved.

When the atom replacement judgment and the atom replacement are performed in the step a33, any one of the methods of residual value judgment, fitness value judgment or sparsity judgment is adopted for atom replacement judgment, any one of the methods can be selected for atom replacement judgment, the use mode is flexible, and each atom replacement judgment method can realize effective atom replacement judgment.

When the residual value judgment module is called to judge whether the atom to be optimized in the step A31 needs to be replaced, the residual value R after the replacement is carried out _j' ^m (t)|| _ξ Whether it is less than the residue before replacement | | R _j ^m (t)|| _ξ Judging whether to replace the atoms to be optimized or not by the judgment result, selecting the atoms with smaller residual value to ensure that the residual value of the signal is smaller, thereby effectively improving the signal extraction precision and ensuring that the extracted atoms are replacedThe signal further approximates the original signal.

Calling a Fitness value judging module to judge whether the atoms to be optimized in the step A31 need to be replaced or not, and according to the Fitness value Fitness (r) after replacement _j' ) Whether greater than the pre-replacement Fitness value Fitness (r) _j ) And judging whether atoms to be optimized are replaced or not, and selecting atoms with larger fitness value to reduce the residual quantity of the signals, thereby effectively improving the signal extraction precision and enabling the extracted signals to further approach the original signals.

And calling a sparsity judging module to judge whether to replace the atoms to be optimized in the step A31, judging whether to replace the atoms according to the minimum robust support, and selecting the atoms with lower robust support, so that the signal characteristics can be better matched, the representation of the signal is sparser, the purpose of effectively improving the signal extraction precision is achieved, and the extracted signal is further close to the original signal.

Wherein the content of the first and second substances,

R ^j' (t _i ) Is R ^j' The signal value at the ith sampling instant, i.e. R ^j' The ith signal value of (1).

In this embodiment, after signal sparse decomposition is performed in step A1, the optimal atomic set of iterative decomposition is synchronously stored in a data storage 3 by using a data processing device 2, and the data storage 3 is connected to the data processing device 2;

after the atom replacement judgment and the atom replacement are performed in step a33, the updated iterative decomposition optimal atom set is synchronously stored by using the data processing device 2.

The ultrasonic flaw detection device 1, the data processing device 2 and the data memory 3 constitute a signal division processing system, as shown in fig. 2.

The best matching atom optimized in step a35 is one of the best matching atoms in the set of iteratively decomposed best atoms in step A1. The best matching atom that has completed optimization cannot be optimized again.

In this embodiment, after the optimization process of the best matching atom is completed in step a33, the best matching atom selected in step a31 is marked as an optimized atom. Thus, the best matching atom optimized in step a35 is for this time one of the best matching atoms in the best set of atoms other than the optimized atom is decomposed by the iteration. Wherein, at this time, the non-optimized one of the best matching atoms in the best atom set of iterative decomposition is the best matching atom except for the optimized atom in the best atom set of iterative decomposition.

In this embodiment, after performing signal sparse decomposition in step A1, when the optimal atom set of iterative decomposition is synchronously stored in the data storage 3 by using the data processing device 2, the m optimal matching atoms in the optimal atom set of iterative decomposition are respectively stored according to the sequential order of iterative decomposition; wherein the content of the first and second substances,

the best matching atom is found when the nth iterative decomposition is performed on the signal f (t) to be processed in the step A1.

In this embodiment, when optimizing the best matching atoms in step A3, optimizing the best matching atoms in the iterative decomposition best atom set according to the storage order;

In actual use, when the best matching atom in step A3 is optimized, the best matching atom in the iterative decomposition best atom set may also be optimized without the storage order.

The epsilon in the step A2 is a preset residual quantity judgment threshold, and the value of the epsilon can be limited according to specific requirements during actual use.

In this embodiment, ∈ = e in step A2 ^-5 。

In actual use, the value of epsilon can be correspondingly adjusted according to specific requirements.

In this embodiment, ξ =1 is recited in step a 33.

When the device is actually used, the value of xi can be correspondingly adjusted according to specific requirements.

In this example, the procedure described in step A1

The best matching atom is found when the nth iterative decomposition is carried out on the signal f (t) to be processed in the step A1;

when signal sparse decomposition is carried out in the step A1, finding out m optimal matching atoms in the iterative decomposition optimal atom set in the step A1 from first to last by adopting data processing equipment (2);

the optimizing algorithm module in the step A1 is an artificial bee colony algorithm module.

In practical use, the optimization algorithm module can also be other optimization algorithm modules, such as a genetic algorithm module, an artificial fish swarm algorithm module and the like.

In this embodiment, the pair

When searching is carried out, the data processing equipment 2 is adopted and the artificial bee colony algorithm module is called for searching

Time-frequency parameter r of _n The process is as follows:

step A11, parameter initialization: setting the maximum iteration times MC, the number SN of honey sources, the number of employed bees, the number of observation bees and the maximum exploitation times limit of the honey sources of the artificial bee colony algorithm module by adopting the data processing equipment 2; meanwhile, SN different honey sources are randomly generated by adopting the data processing equipment 2, the SN honey sources are all honey sources to be mined, and the pth honey source in the generated SN honey sources is recorded as a 4-dimensional vector X _p ＝(X _1p ,X _2p ,X _3p ,X _4p ) Each of saidThe honey sources are all time-frequency parameters; the number of the employed bees and the number of the observation bees are SN, and each generated honey source is distributed to one employed bee;

wherein p is a positive integer and p =1, 2, \ 8230, SN; x _1p And s preset in step A1 _n Have the same value range of X _2p And u preset in step A1 _n Have the same value range of X _3p And v preset in step A1 _n Have the same value range of X _4p And w preset in step A1 _n The value ranges of (A) are the same;

in this example, X _1p Has a value range of [1, N ]]And X _1p ∈[1,N]，X _2p Has a value range of [0, N']And X _2p ∈[0,N]，X _3p Has a value range of

And is

X _4p Has a value range of [0,2 pi]And X _4p ∈[0,2π]。

step A13, search of the neighborhood of the observation bees: calculating the selection probability of each honey source searched by the employed bees according to the fitness values of all the honey sources searched by the employed bees in the step A12; observing the bees, and selecting honey sources for honey collection from all the honey sources searched by the hiring bees as new honey sources according to the calculated selection probability of each honey source;

in the process of employing bee neighborhood searching and observing bee neighborhood searching, if the mined times of the honey source reach the maximum mined times l imit of the honey source, the observing bee is converted into a detecting bee, a new honey source is generated through the detecting bee, and the mined times are set to be 0;

step A15, repeating the steps A12 to A14 for a plurality of times until the iteration number of the artificial bee colony algorithm module reaches the maximum iteration number MC, wherein the optimal honey source obtained at the moment is

Time-frequency parameter r of _n ，r _n ＝(s _n ,u _n ,v _n ,w _n )；

When the employed bee neighborhood search is performed in the step A12 and the observation bee neighborhood search is performed in the step A13, the fitness value of any honey source is the Gabor atom and R corresponding to the honey source ^n-1 Inner product of (t).

In step A15, the time-frequency parameter r _n The corresponding Gabor atom is

The optimal honey source obtained in the step a14 is the optimal honey source obtained in the one-time iterative process, and the optimal honey source obtained in the step a15 is the optimal honey source with the maximum fitness value among the optimal honey sources obtained in the MC-time iterative process.

In this embodiment, the original honey source is the pth honey source X generated in step a11 _n 。

Wherein the fitness value of the original honey source

Represents R ^n-1 (t) and

inner product of (d);

in this step, the number of the honey sources to be mined, which are searched by the hiring bees, is multiple, and all the honey sources to be mined, which are searched by the hiring bees, are the honey sources searched by the hiring bees.

The fitness value of any one of the new honey sources searched in the step A12 is the Gabor atom and R corresponding to the honey source ^n-1 (t) inner product.

In this embodiment, the best matching atom is selected in step A32

When searching is carried out, the data processing equipment 2 is adopted and the optimizing algorithm module is called to search

step A321, parameter initialization: setting the maximum iteration times MC ', the number SN ' of the honey sources, the number of employed bees, the number of observed bees and the maximum exploitation times limit ' of the honey sources of the artificial bee colony algorithm module by adopting a data processing device (2); meanwhile, SN 'different honey sources are randomly generated by adopting the data processing equipment (2), the SN' different honey sources are all honey sources to be mined, and the pth 'honey source in the generated SN' different honey sources is recorded as a 4-dimensional vector X _p' ＝(X _1p' ,X _2p' ,X _3p' ,X _4p' ) Each honey source is a time-frequency parameter; the number of the employed bees and the number of the observation bees are SN', and each generated honey source is distributed to one employed bee;

wherein p ' is a positive integer and p ' =1, 2, \ 8230;, SN '；X _1p' And s preset in step A1 _n Have the same value range of X _2p' And u is preset in step A1 _n Have the same value range of X _3p' And v is preset in step A1 _n Have the same value range of X _4p' And w preset in step A1 _n The value ranges of (A) are the same;

in this example, X _1p' Has a value range of [1, N ]]And X _1p ∈[1,N]，X _2p' Has a value range of [0, N']And X _2p ∈[0,N]，X _3p' Has a value range of

And is provided with

X _4p Has a value range of [0,2 pi]And X _4p' ∈[0,2π]。

step A323, search of observation bee neighborhoods: calculating the selection probability of each honey source searched by the employed bees according to the fitness values of all the honey sources searched by the employed bees in the step A322; the observation bees select honey sources for honey collection from all the honey sources searched by the employment bees as new honey sources according to the calculated selection probability of each honey source;

step A325, repeating the steps A322 to A323 for multiple times until the iteration number of the artificial bee colony algorithm module reaches the maximum iteration number MC, wherein the optimal honey source obtained at the moment is

Time-frequency parameter r of _j' ，r _j' ＝(s _j' ,u _j' ,v _j' ,w _j' )；

In step A325, the time-frequency parameter r _j' The corresponding Gabor atom is

The optimal honey source obtained in the step a324 is the optimal honey source obtained in one iteration process, and the optimal honey source obtained in the step a325 is the optimal honey source with the maximum fitness value among the optimal honey sources obtained in the MC' iteration process.

In addition, the method adopts border-crossing retracing processing, adopts the hiring bees and the observation bees to carry out neighborhood search, carries out boundary detection on the new honey source after generating the new honey source, and carries out border-crossing retracing operation on the new honey source if the new honey source exceeds the upper and lower bounds. And when the border-crossing retracing operation is carried out on the new honey source, the border-crossing retracing operation is respectively carried out on 4 elements of the new honey source according to the maximum value and the minimum value of the four elements of the honey source. Carrying out boundary detection on the new honey source, and respectively carrying out-of-range judgment on 4 elements of the new honey source according to the maximum value and the minimum value of the four elements of the honey source; and performing border-crossing retracing operation on 4 elements of the new honey source according to the border-crossing judgment result, and obtaining the honey source after the border-crossing retracing operation, thereby avoiding the phenomenon of error search.

Wherein the new honey source

For new honey source

Q element of (2)

When the out-of-range judgment is performed, when

When it is determined that

Does not exceed the boundary, does not need to be aligned

Performing boundary-crossing retracing operation; when the temperature is higher than the set temperature

When it is determined to be

Beyond the lower bound, according to the formula

After obtaining an out-of-range retracing operation

When the temperature is higher than the set temperature

When it is determined to be

Beyond the upper bound, according to the formula

After obtaining an out-of-range retracing operation

In step a13, when the selection probability of each honey source searched by the employed bee is calculated according to the fitness values of all the honey sources searched by the employed bee in step a12, the selection probability of each honey source is calculated according to a roulette mode. The selected probability of any honey source is the ratio of the fitness value of the honey source to the sum of the fitness values of all honey sources searched by the employment bees. And step A13, selecting the honey source with the maximum selected probability as a new honey source when the observation bees select the honey source for honey collection from all the honey sources searched by the employment bees according to the calculated selected probability of each honey source.

Correspondingly, when the selection probability of each honey source searched by the employed bees is calculated according to the fitness values of all the honey sources searched by the employed bees in the step A322 in the step A323, the selection probability of each honey source is calculated according to the roulette mode. The selection probability of any honey source is the ratio of the fitness value of the honey source to the sum of the fitness values of all honey sources searched by the hiring bees. And in the step A323, the observation bees select the honey source with the maximum selected probability as a new honey source when the honey source for honey collection is selected from all the honey sources searched by the employment bees according to the calculated selected probability of each honey source.

When the observer neighborhood searching is performed in the step A13 and the observer neighborhood searching is performed in the step A323, in order to accelerate the searching speed, the searching mode is changed from the random searching to the following searching mode: judging whether the fitness value of the honey source searched next randomly is larger than the fitness value of the honey source at the center position of the bee colony at the moment, and taking the honey source searched next randomly as a new honey source when the fitness value of the honey source searched next randomly is larger than the fitness value of the honey source at the center position of the bee colony at the moment; otherwise, the honey source at the center position of the bee colony at the moment is used as a new honey source to improve the searching speed of the algorithm. And the honey source at the central position of the bee colony at the moment is the average value of the sum of all the honey sources searched at the moment.

Because the distance from the optimal atom is closer and closer along with the increase of the search times of the bee colony, in order to accelerate the optimization speed and avoid trapping in local optimization, the honey source concentration (namely the fitness value) of the next search position and the central position of the bee is compared when the observation bee is searched, and a new honey source is determined according to the comparison result, so that the search step length is increased, and the speed of the bee moving towards the optimal atom direction is accelerated.

In this embodiment, when the parameter initialization is performed in step a11 and step a321, the initial bee colony is generated by using a uniform distribution method.

The randomness of initial swarm distribution in the original artificial swarm algorithm can cause uncertainty of a search space, and if the initial swarm search space does not contain a global optimal solution and cannot cover a region of the global optimal solution in limited searches, premature convergence can be caused. The initial bee colony is generated by adopting a uniform distribution method, so that the premature convergence phenomenon can be effectively avoided.

In this embodiment, in the parameter initialization process in step a11, when SN honey sources are generated, the SN honey sources are generated according to a formula

Calculating the qth element X of the pth honey source in SN honey sources _qp Wherein q is a positive integer and q =1, 2, 3 or 4; x _qup Is the maximum value of the qth element of the honey source, X _qlow Is the minimum value of the qth element of the honey source.

Wherein the 1 st element of the honey source has a maximum value of N' and a minimum value of 1, thus X _1up = N' and X _1low And =1. The maximum value of the 2 nd element of the honey source is N' and its minimum value is 0, thus X _2up = N' and X _2low And =0. The maximum value of the 3 rd element of the honey source is

And its minimum value is 0, thus

And X _3low And =0. The maximum value of the 4 th element of the honey source is 2 pi and the minimum value thereof is 0, thus X _4up =2 π and X _4low ＝0。

In this embodiment, during the neighborhood search of the employed bee in step a12, the employed bee performs neighborhood search near the current honey source position and generates a new honey source, and the new honey source position is according to formula X _p* ＝X _p +φ _p (X _p -X _l ) Making a determination wherein X _p For the currently searched source of raw honey, phi _p Is [ -1,1 [ ]]A random number in the range, X _l Is a random honey source, X _p* Is a new source of honey, passes through phi _p The range of new honey sources is limited.

In this embodiment, during the parameter initialization process in step a321, when SN '(i.e. SN') honey sources are generated, according to a formula

Calculating the qth element X of the pth honey source in SN honey sources _qp' 。

In this embodiment, during neighborhood search of the hiring bee in step a322, neighborhood search is performed near the current honey source position by the hiring bee and a new honey source is generated, where the new honey source position is generated according to formula X _p'* ＝X _p' +φ _p (X _p'* -X _l ) Making a determination wherein X _p' For the currently searched source of raw honey, phi _p Is [ -1,1 [ ]]A random number in the range, X _l Is a random honey source, X _p'* Is a new source of honey, passes through phi _p The range of new honey sources is limited.

In this example, R 'in step A34' _j ^m (t) according to the formula

To carry outIs calculated, wherein

To this end, the iterative decomposition of the n1 st of the best matching atoms in the set of best atoms, n1 being a positive integer and n1=1, 2, \ 8230;, m; a is _n1 Is composed of

And f (t) is subjected to n1-1 times of iterative decomposition according to the first n1-1 best matching atoms in the iterative decomposition best atom set at the moment, and then the expansion coefficient of the residual quantity is obtained;

r' described in step A36 " _j ^m (t) according to the formula

Performing a calculation in which

To this end the iterative decomposition of the n2 th best matching atom in the set of best atoms, n2 being a positive integer and n2=1, 2, \ 8230;, m; a is _n2 Is composed of

And performing n2-1 times of iterative decomposition on f (t) according to the first n2-1 best matching atoms in the iterative decomposition best atom set at the moment to obtain the expansion coefficient of the residual quantity.

In this embodiment, R in step A33 ^j-1 And (t) is the residual quantity after j-1 times of iterative decomposition is carried out on f (t) according to the first j-1 best matching atoms in the iterative decomposition best atom set before atom replacement judgment in the step.

For R in step A33 ^j-1 (t) when calculating, iteratively decomposing the optimal set of atoms and

performing a calculation, wherein k is a positive integer and k =1, 2, \8230;, j-1, k < j;

for the kth best matching atom in the iterative decomposition best atom set before atom replacement judgment in the step, a _k Is composed of

And performing k-1 times of iterative decomposition on f (t) according to the first k-1 best matching atoms in the iterative decomposition best atom set before atom replacement judgment in the step to obtain an expansion coefficient of a residual quantity.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all simple modifications, changes and equivalent structural changes made to the above embodiment according to the technical spirit of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An ultrasonic signal receiving and processing method based on signal segment segmentation is characterized by comprising the following steps:

step one, ultrasonic echo signal acquisition and synchronous uploading and receiving: ultrasonic detection is carried out on a detected object by adopting an ultrasonic flaw detection device (1), an ultrasonic echo signal F (t) of the detected object is obtained, and the obtained ultrasonic echo signal F (t) is synchronously transmitted to a data processing device (2); the data processing device (2) synchronously stores the received ultrasonic echo signals F (t);

wherein the content of the first and second substances,

t represents a time parameter, t _i Is the ith sampling time f (t) of the ultrasonic flaw detection device (1) _i ) Is a signal value sampled at the ith sampling time of an ultrasonic flaw detector (1), i is a positive integer and i =1, 2, 3, \ 8230, N _z ，N _z Is a positive integer and is the signal length of the ultrasonic echo signal F (t);

step two, determining wave crests and wave troughs: determining all wave crests and all wave troughs of the ultrasonic echo signal F (t) in the step one by adopting a data processing device (2), and synchronously recording the sampling time and the signal value of each wave crest and each wave trough which are determined respectively;

step three, removing extreme points: adopting data processing equipment (2) and calling a time domain extreme point eliminating module or a frequency domain extreme point eliminating module to eliminate extreme points to obtain M 'number of eliminated extreme points, and arranging the M' number of the extreme points from front to back according to the sampling time sequence of the extreme points; wherein M' is a positive integer and is the total number of extreme points obtained after the extreme points are removed in the step;

when the data processing equipment (2) is adopted and a time domain extreme point rejecting module is called to reject the extreme points, rejecting the extreme points of which the absolute values of the signal values in all the extreme points determined in the step two are smaller than beta 'to obtain M' number of rejected extreme points; wherein, β ' = α ' × max | F (t) |, α ' is a constant and the value range thereof is 0.1-0.35, max count F (t) | is the maximum value of the absolute value of the signal value in the ultrasonic echo signal F (t);

when the data processing equipment (2) is adopted and a frequency domain extreme point rejecting module is called to reject extreme points, rejecting extreme points of which the absolute values of the signal values in all the extreme points determined in the step two are smaller than beta to obtain M' extreme points after rejection; wherein β is a preset rejection threshold and β = α × max | Y (F) |, α is a constant and has a value range of 0.25 to 0.35, Y (F) is a frequency spectrum of the ultrasonic echo signal F (t), and max | Y (F) | is a maximum absolute value of an amplitude value in the frequency spectrum of the ultrasonic echo signal F (t);

step four, signal segmentation: the data processing device (2) is adopted to segment the ultrasonic echo signal F (t), and the process is as follows:

step 401, determining the time interval between adjacent extreme points: respectively determining the time intervals of two adjacent extreme points in the M 'extreme points in the third step by adopting data processing equipment (2) to obtain M' -1 time intervals of the adjacent extreme points;

step 402, segmentation point judgment and sampling time determination of segmentation points: respectively judging the division points of the M' -2 time intervals to be judged in the step 401 from first to last by adopting a data processing device (2) to obtain L time intervals to be divided; wherein L is an integer and is more than or equal to 0, and L is the total number of the time intervals to be divided determined in the step; each time interval to be divided has a dividing point; in the first step, the number of the segmentation points existing in the ultrasonic echo signal F (t) is the same as the number of the time intervals to be segmented, and the number of the segmentation points existing in the ultrasonic echo signal F (t) is the same as L;

Otherwise, the judgment is delta t _m' There is no dividing point; wherein c is a constant and c > 2.1; t is t _{Total m'} The sum of the sampling time of the mth ' extreme point in the M ' extreme points and the sampling time of the M ' +1 extreme points;

step 403, signal division and judgment: judging the L in the step 402: when L =0, judging that the ultrasonic echo signal F (t) does not need to be segmented, and finishing a signal segmentation process; otherwise, judging that the ultrasonic echo signal F (t) needs to be segmented, and entering step 404;

step 404, sorting the segmentation points: sequencing the L division points determined in the step 402 from front to back by adopting data processing equipment (2) according to the sequence of sampling time;

step 405, signal division: according to the sampling time of the L sorted division points in the step 404, dividing the ultrasonic echo signal F (t) in the step one into L +1 signal segments from front to back, wherein each divided signal segment is a division signal;

after the signal division is completed in step 405, signal extraction needs to be performed on the divided L +1 divided signals respectively; the signal extraction methods of the L +1 division signals are the same;

when signal extraction is carried out on any one of the L +1 divided signals, signal extraction is carried out on the divided signal by adopting a data processing device (2), the divided signal is a signal to be processed and is recorded as a signal f (t);

when the data processing device (2) is adopted to extract the signal f (t), the process is as follows:

a1, performing signal sparse decomposition based on an optimization algorithm: adopting data processing equipment (2) and calling a sparse decomposition module to carry out iterative decomposition processing on the signal f (t) to be processed in the step one, and converting the signal f (t) to be processed into a signal f (t) to be processed

And obtaining the iterative decomposition optimal atom set at the moment; the iterative decomposition of the best set of atoms at this time contains m best matching atoms,

decomposing the nth best matching atom in the best atom set for the iteration;

in the formula R ^m (t) is the signal f (t) to be processed through m iterationsResidual quantity after generation decomposition, wherein m is a preset iteration decomposition total number, m is a positive integer, n is a positive integer, and n =1, 2, \ 8230; a is a _n The expansion coefficient of the best matching atom after the nth iterative decomposition and the residual error after the last iterative decomposition is obtained;

adopting data processing equipment (2) for nth iterative decomposition and calling an optimization algorithm module to find out the best matching atom;

is a Gabor atom and

wherein the function ψ (t) is a Gaussian window function and

r _n is composed of

in this step, the best matching atom is found

According to a preset s _n 、u _n 、v _n And w _n The value range of the adaptive value Fitness (r) is found out by adopting the data processing equipment (2) and calling an optimization algorithm module _n ) The maximum optimal time frequency parameter, the found optimal time frequency parameter is the time frequency parameter r _n ；

represents R ^n-1 (t) and

Step A2, residual error quantity judgment: judgment | | | R ^m (t)|| ₂ Whether less than ε: when | | | R ^m (t)|| ₂ If the epsilon is less than epsilon, entering a step A4; otherwise, when R ^m (t)|| ₂ When the value is more than or equal to epsilon, entering the step A3;

step A31, atom random selection: randomly taking out a best matching atom from the iterative decomposition best atom set at the moment by adopting a data processing device (2) as an atom to be optimized, wherein the atom to be optimized is marked as

Wherein j is a positive integer and j is more than or equal to 1 and less than or equal to m;

step A32, finding the best matching atom: the best matching atom found is denoted as

For the best matching atom

When searching, according to preset s _j' 、u _j' 、v _j' And w _j' The value range of the adaptive value is found out by adopting the data processing equipment (2) and calling the optimizing algorithm module _j' ) The maximum optimal time frequency parameter, the found optimal time frequency parameter is the time frequency parameter r _j' (ii) a According to the formula

Solving for the best matching atom

represent

And

the inner product of (a) is,

ψ ₀ (t) is the sum of m-1 of the atoms to be treated in step A31;

step a33, atom replacement judgment and atom replacement: adopting data processing equipment (2) and calling a residual value judging module, an adaptability value judging module or a sparsity judging module to judge whether the atoms to be optimized in the step A31 need to be replaced or not, and replacing the atoms to be optimized according to the judging result;

adopting a data processing device (2) and calling a residual value judging module to judge whether the atoms to be optimized in the step A31 need to be replaced or not, and according to the replaced residual value | | R _j' ^m (t)|| _ξ Whether it is less than the residue before replacement | | R _j ^m (t)|| _ξ And (4) judging: when | | | R _j' ^m (t)|| _ξ ＜||R _j ^m (t)|| _ξ When the optimization process is carried out, judging that the atom to be optimized in the step A31 needs to be replaced, replacing the atom to be optimized in the step A31 with the best matching atom in the step A32

wherein R is _j' ^m (t)＝f(t)-ψ _j' (t)，

R _j ^m (t)＝f(t)-ψ _j (t)，ψ _j (t) is the sum of m best matching atoms in the iterative decomposition best atom set before atom replacement judgment in the step; | | R _j' ^m (t)|| _ξ Represents R _j' ^m Xi-norm of (t, | | R _j ^m (t)|| _ξ Represents R _j ^m Xi-norm of (t), xi is constant and xi is more than or equal to 0 and less than or equal to 1;

adopting data processing equipment (2) and calling a Fitness value judging module to judge whether the atoms to be optimized in the step A31 need to be replaced or not, and then according to the replaced Fitness value Fitness (r) _j' ) Whether greater than the pre-replacement Fitness value Fitness (r) _j ) And (4) judging: when Fitness (r) _j' )＞Fitness(r _j ) When the utility model is used, the water is discharged,judging that the atom to be optimized in the step A31 needs to be replaced, and replacing the atom to be optimized in the step A31 with the best matching atom in the step A32

represents R ^j-1 (t) and

inner product of (2), R ^j-1 (t)＝f(t)-ψ _j-1 (t)，ψ _j-1 (t) is the sum of the first j-1 best matching atoms in the best atom set for that time in the iterative decomposition;

represents R ^j-1 (t) and

inner product of (d);

adopting data processing equipment (2) and calling a sparsity judging module to judge whether to replace the atoms to be optimized in the step A31 or not according to | | | R ^j' || _ξ Whether less than R ^j || _ξ And (4) judging: when | | | R ^j' || _ξ ＜||R ^j || _ξ When the optimization process is carried out, judging that the atom to be optimized in the step A31 needs to be replaced, replacing the atom to be optimized in the step A31 with the best matching atom in the step A32

wherein R is ^j' Is composed of

Amount of residual error of

R ^j Is composed of

Amount of residual error of

step A36,And (3) residual error amount judgment: and B, judging the residual quantity after the optimization of the best matching atoms in the step A35: when | | | R " _j ^m (t)|| ₂ If the value is less than epsilon, entering a step A4; otherwise, when | | | R " _j ^m (t)|| ₂ When the value is more than or equal to epsilon, returning to the step A35;

wherein, | | R " _j ^m (t)|| ₂ Is R' _j ^m (t) 2-norm; r' _j ^m (t) is the residual quantity after m iterative decompositions are performed on f (t) according to m best matching atoms in the iterative decomposition best atom set at the moment;

step A4, signal reconstruction: according to the iterative decomposition optimal atom set at the moment, an approximate signal f' (t) of a signal f (t) to be processed is obtained by adopting a data processing device (2); wherein the approximation signal f "(t) is a signal extracted from the signal to be processed f (t),

wherein

2. A method for receiving and processing an ultrasonic signal based on signal segment division according to claim 1, wherein: y (F) in the third step is a frequency domain signal obtained by converting the ultrasonic echo signal F (t) into a frequency domain by adopting a time-frequency transformation module, wherein the time-frequency transformation module is a Fourier transformation module;

max | Y (f) | is the maximum absolute value of the signal amplitude in Y (f).

3. A method for receiving and processing an ultrasonic signal based on signal segment division according to claim 1 or 2, wherein: in step 404, the sampling time of the ith division point in the L division points is denoted as t _fenl Wherein L is a positive integer and L =1, 2, \8230;

4. A method for receiving and processing an ultrasonic signal based on signal segment division according to claim 1, wherein: after signal sparse decomposition is carried out in the step A1, synchronously storing the iterative decomposition optimal atom set into a data memory (3) by adopting data processing equipment (2), wherein the data memory (3) is connected with the data processing equipment (2);

and after atom replacement judgment and atom replacement are carried out in the step A33, the updated iterative decomposition optimal atom set is synchronously stored by adopting data processing equipment (2).

5. The method for receiving and processing an ultrasonic signal based on signal segment division according to claim 4,the method is characterized in that: after signal sparse decomposition is carried out in the step A1, when the iterative decomposition optimal atom set is synchronously stored into a data storage (3) by adopting data processing equipment (2), m optimal matching atoms in the iterative decomposition optimal atom set are respectively stored according to the iterative decomposition sequence; wherein the content of the first and second substances,

6. The signal segment division based ultrasonic signal receiving and processing method of claim 5, wherein: when the best matching atoms in the step A3 are optimized, optimizing the best matching atoms in the iterative decomposition best atom set according to the storage sequence;

when the best matching atom is optimized in step A3, the best matching atom which is optimized first is the 1 st best matching atom in the iterative decomposition best atom set in step A1.

7. A method for receiving and processing an ultrasonic signal based on signal segment division according to claim 1, wherein: in step A1 s _n Has a value range of [1, N ]]And s _n ∈[1,N]，u _n Has a value range of [0, N']And u is _n ∈[0,N]，v _n Has a value range of

And is

w _n Has a value range of [0,2 π]And w _n ∈[0,2π](ii) a Wherein f is _o Is the sampling frequency, f, of the ultrasonic flaw detection device (1) _o In MHz; n is a positive integer and it is the signal length of the signal f (t).

8. A method for receiving and processing an ultrasonic signal based on signal segment division according to claim 1, wherein: described in step A1

The best matching atom is found when the signal f (t) is subjected to the nth iterative decomposition in the step A1;

for is to

When searching is carried out, the data processing equipment (2) is adopted and the artificial bee colony algorithm module is called to search

Time-frequency parameter r of _n The process is as follows:

step A11, parameter initialization: setting the maximum iteration times MC, the number SN of honey sources, the number of employed bees, the number of observation bees and the maximum exploitation times limit of the honey sources of the artificial bee colony algorithm module by adopting a data processing device (2); meanwhile, SN different honey sources are randomly generated by adopting the data processing equipment (2), the SN honey sources are all honey sources to be mined, and the pth honey source in the generated SN honey sources is recorded as a 4-dimensional vector X _p ＝(X _1p ,X _2p ,X _3p ,X _4p ) Each honey source is a time-frequency parameter; the number of the employed bees and the number of the observation bees are SN, and each generated honey source is distributed to one employed bee;

wherein p is a positive integer and p =1, 2, \ 8230; x _1p And s preset in step A1 _n Have the same value range of X _2p And u is preset in step A1 _n Phase of value range ofIn the same way, X _3p And v preset in step A1 _n Have the same value range of X _4p And w preset in step A1 _n The value ranges of (A) are the same;

step A13, search of the neighborhood of the observation bees: calculating the selection probability of each honey source searched by the employed bees according to the fitness values of all the honey sources searched by the employed bees in the step A12; the observation bees select honey sources for honey collection from all the honey sources searched by the employment bees as new honey sources according to the calculated selection probability of each honey source;

step A14, recording the optimal honey source in real time: after the employed bee neighborhood searching and the observation bee neighborhood searching are finished, obtaining the optimal honey source at the moment and synchronously recording, wherein the iteration times of the artificial bee colony algorithm module are increased by 1;

in the process of hiring bee neighborhood searching and observing bee neighborhood searching, if the mined times of the honey source reach the maximum mined times limit of the honey source, the observing bee is converted into an investigation bee, a new honey source is generated through the investigation bee, and the mined times are set to be 0;

Time-frequency parameter r of _n ，r _n ＝(s _n ,u _n ,v _n ,w _n )；

9. A method for receiving and processing an ultrasonic signal based on signal segment division according to claim 1, wherein: for the best matching atom in step A32

When searching, the data processing equipment (2) is adopted and the optimizing algorithm module is called to search

wherein p ' is a positive integer and p ' =1, 2, \8230, SN '; x _1p' And s preset in step 201 _n Have the same value range of X _2p' And u preset in step 201 _n Have the same value range of X _3p' Value range ofV is preset in step 201 _n Have the same value range of X _4p' And w preset in step 201 _n The value ranges of (A) are the same;

the observation bee carries out neighborhood search on the selected honey source, if the fitness value of the searched new honey source is larger than that of the original honey source, the observation bee is changed into a employment bee, the new honey source is used as the searched honey source to be exploited, and the exploited frequency is set to be 0; otherwise, if the honey source and the hiring bee remain unchanged, adding 1 to the mined times of the original honey source;

step A324, recording the optimal honey source in real time: after the employed bee neighborhood searching and the observation bee neighborhood searching are finished, obtaining the optimal honey source at the moment and synchronously recording, wherein the iteration times of the artificial bee colony algorithm module are increased by 1;

Time-frequency parameter r of _j' ，r _j' ＝(s _j' ,u _j' ,v _j' ,w _j' )；