CN115060803A - Method, device and equipment for sparsely optimizing phased array probe - Google Patents
Method, device and equipment for sparsely optimizing phased array probe Download PDFInfo
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Abstract
The embodiment of the application provides a method, a device and equipment for sparsely optimizing a phased array probe, wherein the method comprises the following steps: acquiring a target array to be sparsely optimized on a phased array probe; the target array comprises N array elements; establishing a directional diagram function of the target array based on the target information of the target array; the target information comprises an aperture L of the array elements, the number N of the array elements, position information of each array element and an angle variable; establishing an optimization model of the target array based on the directional diagram function of the target array; and based on a genetic algorithm, carrying out sparse optimization on the optimization model of the target array to obtain a target array after sparse optimization.
Description
Technical Field
The application relates to the field of phased array ultrasonic detection, in particular to a method, a device and equipment for sparsely optimizing a phased array probe.
Background
In recent years, a Total Focus imaging (TFM) phased array ultrasonic detection technology has been used in nondestructive detection of High Density Polyethylene (HDPE) due to its advantages of good detection effect, High sensitivity, etc., but the TEM in the prior art has the following disadvantages:
1) for some materials with low acoustic speed and high ultrasonic attenuation, such as HDPE, TFM can be computationally expensive, time consuming, and inefficient to image. 2) The imaging effect of TFM is poor due to imaging artifacts and signal-to-noise effects caused by the side beams of the array. 3) The traditional full phased array places array elements at each position of uniform crystal lattices, generally, the lattice points are spaced by half wavelength, and the density of the array elements in the traditional full phased array is high, so that the hardware cost is high.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method, an apparatus, and a device for sparsely optimizing a phased array probe.
The technical scheme of the embodiment of the application is realized as follows:
in a first aspect, an embodiment of the present application provides a method for sparsely optimizing a phased array probe, where the method includes: acquiring a target array to be sparsely optimized on a phased array probe; the target array comprises N array elements; establishing a directional diagram function of the target array based on the target information of the target array; the target information comprises the aperture L of the array elements, the number N of the array elements, the position information of each array element and an angle variable; establishing an optimization model of the target array based on the directional diagram function of the target array; and based on a genetic algorithm, carrying out sparse optimization on the optimization model of the target array to obtain a target array after sparse optimization.
In a second aspect, an embodiment of the present application provides an apparatus for sparsely optimizing a phased array probe, the apparatus including: the acquisition module is used for acquiring a target array to be sparsely optimized on the phased array probe; the target array comprises N array elements; the first establishing module is used for establishing a directional diagram function of the target array based on the target information of the target array; the target information comprises an aperture L of the array elements, the number N of the array elements, position information of each array element and an angle variable theta; the second establishing module is used for establishing an optimization model of the target array based on the directional diagram function of the target array; and the sparse optimization module is used for carrying out sparse optimization on the optimization model of the target array based on a genetic algorithm to obtain a target array after sparse optimization.
In a third aspect, an embodiment of the present application provides a full-focus imaging detection apparatus, where the apparatus includes: the scanning assembly comprises a sparsely optimized phased array probe, array elements on the sparsely optimized phased array probe are arranged according to the target array obtained by the steps in any one of the methods, wherein each array element in the sparsely optimized phased array probe is used for transmitting and receiving ultrasonic signals; the imaging component is connected with the scanning component and is used for carrying out full-focus imaging on the ultrasonic signals received by each array element in the sparse optimized phased array probe; and the monitoring host is connected with the imaging component through a network interface and used for storing and superposing the detection images obtained by each array element in the full-focus imaging.
In the embodiment of the application, under the condition of not reducing the sound field characteristics, the sparsity of the array on the phased array probe reaches a proper level, so that the calculated amount during imaging is reduced, the detection efficiency is improved, and the cost is reduced by reducing array elements; by establishing an optimization model, the side beams of the array are reduced, so that imaging artifacts are less, the signal-to-noise ratio is higher, the obtained imaging effect is better, and analysis and judgment are facilitated.
Drawings
FIG. 1 is an array pattern acquired by a phased array probe that is not sparsely optimized;
fig. 2 is an array directional diagram obtained by using a phased array probe after sparse optimization by a genetic algorithm according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a method for sparsely optimizing a phased array probe according to an embodiment of the present disclosure;
FIG. 4 is a fitness evolution curve provided in an embodiment of the present application;
FIG. 5 is a schematic diagram of a position of an array element after sparse optimization is performed according to an embodiment of the present application;
fig. 6 is a schematic flowchart of a non-destructive testing method based on a sparse optimized phased array probe according to an embodiment of the present application;
fig. 7 is an installation schematic diagram of a full-focus imaging phased array ultrasonic detection apparatus provided in an embodiment of the present application;
fig. 8 is a schematic diagram of a frame of an apparatus for sparsely optimizing a phased array probe according to an embodiment of the present application;
fig. 9 is a schematic physical diagram of a full focus imaging detection apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
It should be noted that the terms "first \ second \ third" referred to in the embodiments of the present application are only used for distinguishing similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under specific ordering or sequence if allowed, so that the embodiments of the present application described herein can be implemented in an order other than that shown or described herein.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of this application belong. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Before the embodiments of the present application are explained in detail, the terms and expressions used in the embodiments of the present application are explained, and the terms and expressions used in the embodiments of the present application are to be interpreted as follows:
genetic Algorithm (GA), a computational model of the biological evolution process that simulates the natural selection and Genetic mechanism of darwinian biological evolution theory, is a method for searching for an optimal solution by simulating the natural evolution process.
HDPE is widely used in the fields of gas transportation and the like because of its advantages such as good corrosion resistance, plasticity, insulation and the like. At present, the HDPE pipelines are connected in a hot-melt butt joint mode, and the butt joint joints of the HDPE pipelines are affected by the surrounding environment of a butt joint occasion or human factors, so that the safe operation of a pipe network system is affected, and huge potential safety hazards are buried. Therefore, nondestructive detection of the pipeline defects of the HDPE pipe is particularly important. The embodiment of the application relates to a nondestructive testing technology for a butt joint of an HDPE pipe, which is influenced by the environment and some human factors of the butt joint of the HDPE pipe, such as: air holes, inclusions, non-fusion and the like, and further can influence the safe operation of a pipe network system. Over time, butt joints of HDPE pipes are prone to aging, leakage and other potential safety hazards, and therefore nondestructive testing needs to be conducted on the butt joints of the HDPE pipes.
The embodiment of the application provides an HDPE pipe joint full-focus imaging method based on a genetic algorithm sparse optimization phased array probe, which comprises the following steps: selecting a proper phased array probe coefficient, and establishing an uneven linear array model; setting a sparse optimization process by adopting the principle of a genetic algorithm, converting the distance between array elements into a value needing sparse optimization, cutting the algorithm to obtain a result after sparse optimization after completing the given cycle number of the genetic algorithm, and connecting a phased array probe after sparse optimization to full-focus imaging equipment; assembling and installing the scanner and the wedge block on the HDPE tube, setting system parameters in the full-focus imaging device, and moving the phased array probe on the HDPE tube after the full-focus imaging device enters a detection interface to obtain the optimal imaging result of a detection target area.
As shown in fig. 1, when θ is detected to be 0 degrees (°), the array pattern acquired by the phased array probe without sparse optimization has an array gain scanned, the array gain at an angle around 0 ° represents the size of a side lobe and a grating lobe of 0 °, and it can be known from fig. 1 that the size of the array gain of the side lobe and the grating lobe is very close to 0 °, and therefore the side lobe and the grating lobe may have some influence on the detection effect. The array directional diagram obtained by the phased array probe after sparse optimization by using the genetic algorithm is shown in fig. 2, after the 64-array element probe is sparsely optimized to 50 array elements, the scanned array gain when theta is 0 degrees is detected, and compared with fig. 1, the wave rate of the array gain from-40 degrees to-15 degrees in fig. 2 is smoother, so that the side lobes and the grating lobes are reduced after probe sparse optimization is performed on the array elements, and the detection effect is better. According to the embodiment of the application, sparse optimization is carried out on the TFM phased array ultrasonic probe, and nondestructive detection is carried out on the butt joint of the HDPE pipe. The sparsely optimized phased array probe can reduce array elements on the probe and reduce cost; the sparse optimized phased array probe reduces the calculated amount and time consumption in the aspect of imaging, reduces imaging artifacts, reduces array side beams, improves the signal to noise ratio, and enables the imaging effect to be better and the detection effect to be better.
The embodiment of the application relates to a nondestructive testing technology for a hot-melt joint of a high-density polyethylene pipe, and aims to provide a full-focusing imaging method for an HDPE pipe joint based on a genetic algorithm sparse optimization phased array probe. The method comprises the following steps: selecting a proper probe coefficient, and establishing an uneven linear array model; converting the distance between the array elements into y, namely, optimizing the value; setting an optimization process by adopting a genetic algorithm principle; finishing given cycle times, meeting the requirements, and obtaining an optimized result by an algorithm; connecting the optimized probe to a full-focus imaging system; assembling and installing the scanner and the wedge block on the HDPE pipe; and setting parameters of a full-focus imaging system, and moving the probe after entering a detection interface to obtain the optimal imaging result of the detection target area.
In the embodiment of the application, array element to on the full focus phased array ultrasonic probe is sparse to be optimized, the hot melt joint to the HDPE pipe carries out nondestructive test, compare in conventional ultrasonic phased array probe, the difference that is used in TFM detection HDPE pipe coupling department lies in, TFM formation of image calculation volume is few, consuming time short sparse array side wave beam is minimum, the formation of image artifact is less, the signal to noise ratio is higher, form the better testing result picture of formation of image effect, can be better carry out analysis and judgement to the HDPE pipe, and probe after sparse optimization can reach and reduce array element, reduce cost. When the internal defect condition of the HDPE pipe hot-melt welding joint is detected, the optimized sparse array not only has better imaging quality than a full array, but also can improve the TFM detection efficiency. The internal defect condition of the HDPE pipe hot-melt welding joint can be quickly and accurately obtained by using the sparsely optimized phased array probe, the quality of the current measured HDPE pipe hot-melt welding joint is qualitatively and quantitatively determined according to the defect shape and position displayed by the detection picture, and whether the HDPE pipe is replaced or the service life of the HDPE pipe is shortened is determined.
The schematic flow chart of the method for sparsely optimizing the phased array probe provided in the embodiment of the present application is shown in fig. 3, and the method is applied to a full focus imaging technology, and the method at least includes the following steps:
step S110, obtaining a target array to be sparsely optimized on the phased array probe; the target array comprises N array elements.
In some embodiments, N may be 16, 32, or 64, etc. In the embodiment of the present application, a phased array probe with 64 array elements to be sparsely optimized is taken as an example, wherein an array formed by 64 array elements is a target array.
Step S120, establishing a directional diagram function of the target array based on the target information of the target array; the target information comprises the aperture L of the array elements, the number N of the array elements, the position information of each array element and an angle variable theta.
In some embodiments, in the case that the target array is a linear array, the position information of the m-th array element includes a distance d between the first array element and the m-th array element m Main wave direction theta of m-th array element 0 (ii) a Wherein d is m The value range is [0, L]And the aperture L is the distance between the first array element and the last array element. Step S120, establishing a directional diagram function of the target array based on the target information of the target array, including: step S121 and step S122.
Step S121, establishing a directional diagram function of the corresponding array element based on the position information of each array element in the target array.
And step S122, determining the directional diagram function of the target array based on the directional diagram function of each array element. The directional diagram function of the target array is shown in formula (1), wherein lambda is wavelength, j is an imaginary unit, pi is a circumferential ratio, and theta is an angle variable.
Converting the value of m into [1, N ] to the value of N into [0, N-1], obtaining:
in the embodiment of the application, the target array is a linear array consisting of 64 array elements with unequal intervals. The target information includes: the aperture L is 38.4mm, the number of the array elements is 64, the position information of the mth array element comprises the distance d between the first array element and the mth array element m The main wave direction of the m-th array element is theta 0 An angle variable theta; wherein d is m The value range is [0, 38.4 ]]。
Based on the distance d of the m-th array element in 64 array elements m And a main wave direction theta 0 And establishing a directional diagram function of the m-th array element according to the directional diagram function of the array element shown in the formula (3).
Where λ is the wavelength of the ultrasonic waves generated by the array elements, and j is an imaginary unit.
Based on the directional pattern function of each of the 64 array elements, a directional pattern function of a linear array is established as shown in formula (1).
Step S130, establishing an optimization model of the target array based on the directional diagram function of the target array.
In some embodiments, step S130, building an optimization model of the target array based on the pattern function of the target array, includes: step S131 to step S133.
Step S131, determining a maximum sidelobe level function of the target array based on the directional diagram function of the target array, as shown in formula (4), where S is θ ═ θ 0 Side lobe region of time target array, F db (theta) sidelobe levels of the target arrayA function.
Wherein S is theta ═ theta 0 And a side lobe area phi of the time target array represents the main beam indication direction of the mth array element.
In some embodiments, the dominant wave direction θ 0 The direction is indicated for the pitching direction of the mth array element.
Step S132, based on the maximum sidelobe level function of the target array, determining the minimum value of the maximum sidelobe level function of the target array.
Step S133, based on the minimum value of the maximum sidelobe level function of the target array, establishing an optimization model of the target array
Wherein y ═ y 1 ,y 2 ,...,y m ,...,y N ] T ＝[d 1 ,d 2 ,...,d m ,...,d N ] T -[0,d c ,...,(N-m)d c ,...,(N-1)d c ] T Each of y m Is given as y 1 ≤y 2 ≤...≤y m ≤...≤y N ∈[0，L-(N-1)d c ]，d c Is a constant. y is m Expressing the value required to be optimized, and calculating d m Conversion to y m Let d be m Value range of [0, L ]]Conversion to y m Value range of [0, L- (N-1) d c ]I.e. the distance between array elements is converted into the value y to be optimized.
In the embodiment of the application, a side lobe level function of the linear array shown in formula (5) is established according to the directional diagram function of the linear array obtained in formula (1).
Determining θ ═ θ based on equation (5) 0 The maximum sidelobe level function of the time-linear array is shown in formula (4), where max is the maximum function, and S is θ ═ θ 0 The sidelobe regions of the linear array.
And (4) determining the minimum value of the maximum sidelobe level function of the linear array based on the formula (4). Defining an optimization model of the linear array based on the minimum value of the maximum sidelobe level function of the linear array
Wherein y ═ y 1 ,y 2 ,...,y m ,...,y N ] T ＝[d 1 ,d 2 ,...,d m ,...,d N ] T -[0,d c ,...,(N-m)d c ,...,(N-1)d c ] T Each of y m Is given as y 1 ≤y 2 ≤...≤y m ≤...≤y N ∈[0，L-(N-1)d c ]。
In some embodiments, in step S133, the optimization model of the target array is established based on the minimum value of the maximum sidelobe level function of the target array
The method comprises the following steps: step S1331 to step S1332.
Step S1331, based on each y in the maximum sidelobe level function of the target array m Determining the minimum value of the maximum sidelobe level function of the target array.
Step S1332, establishing an optimization model of the target array based on the minimum value of the maximum sidelobe level function of the target array
In the embodiment of the application, d m Splitting into values y to be optimized m And (m-1) d c As shown in equation (6); wherein m is more than or equal to 1 and less than or equal to 64. Determining formula (7) based on formula (6); wherein d is c Is a constant.
d m ＝y m +(m-1)d c (6)；
Further, the spacing of each array element can be obtained as shown in equation (6), i.e., the spacing of the array elements is changed from d to y, where N is 64. And (5) converting the formula (6) to obtain a formula (7).
The formula (8) is obtained by converting the formula (7).
y＝[y 1 ,y 2 ,...,y m ,...,y N ] T ＝[d 1 ,d 2 ,...,d m ,...,d N ] T -[0,d c ,...,(N-m)d c ,...,(N-1)d c ] T (8)。
It is assumed that the distance between two adjacent array elements needs to satisfy
Then y is m (m is more than or equal to 1 and less than or equal to 64) is monotonically increased; and the aperture size of the array is ensured to be unchanged based on
Two ends of the linear array are provided with an array element to obtain the position d of the first array element 1 0mm, the 64 th array element position d 64 ＝38.4mm。
Determining the value of y through the formula (8), and calculating the result as shown in the formula (9), namely calculating to obtain
y＝{(y 1 ,y 2 ,...,y m ,...,y N )|y 1 ≤y 2 ≤...≤y m ≤...≤y N ∈[0，L-(N-1)d c ]} (9)。
Wherein d is c To indicate the optimal distance between two array elements, the applicationIn the embodiment
In some embodiments, the speed of sound in the HDPE pipe is 2700 meters per second (m/s), and the ultrasonic transmission frequency can be 2.25 megahertz (Mega Hertz, MHz), in which case
Spacing array elements by the above calculation m Conversion to y m The search distance of the array element spacing is changed from 0, 38.4]Is reduced to
Based on each minimum y m The obtained y with the minimum value is substituted into the formula (4), so that an optimization model is obtained
Wherein the optimization model is the minimum value of the maximum side lobe level obtained by the minimum value y. As shown in fig. 4, as the number of iterations increases,
the function value of (1), namely the objective function value, is gradually increased, but when the number of iterations is increased to 120,
the function value of (a) remains substantially unchanged.
And step S140, based on the genetic algorithm, carrying out sparse optimization on the optimization model of the target array to obtain a target array after sparse optimization.
In some embodiments, the optimization model comprises N array elements arranged regularly; step S140, based on the genetic algorithm, performing sparse optimization on the optimization model of the target array to obtain a sparsely optimized target array, including: step S141 to step S145.
Step S141, based on genetic algorithm, using NP dimensions PReal number vector as intermediate seed group for each y m Encoding, wherein the intermediate population comprises chromosome x i，G (ii) a Wherein, the value of i is an integer between 1 and NP, and G is a genetic algebra.
In the embodiment of the application, based on a genetic algorithm, 64 real number vectors with dimension of 100 are adopted as an intermediate population to encode y, and the value of y is
Wherein the intermediate population comprises 64 chromosomes, each chromosome being denoted x i，G (i ═ 1, 2.., 64), where G is the genetic passage number and NP is the number of chromosomes in the population. Wherein the code may be a binary code.
Step S142, initializing the intermediate population, and determining an initial population of the array element; wherein the initial population of array elements comprises an initial chromosome x of each array element ji,0 (ii) a Wherein j is an integer between 1 and N.
In the embodiment of the application, because the genetic algorithm is an iterative optimization algorithm, a starting point for searching is required, so that the intermediate population needs to be initialized, and the initial population of the array element is determined. Each chromosome in the initial population is randomly generated. Assuming a distribution of all initial populations and corresponding probabilities, the initial chromosome x of each array element is obtained from equation (10) ji,0 ；
x ji,0 ＝rand[0,1]×(100-(N-1)d c ) (10)；
Wherein d is c Can be that
i is an integer between 1 and 64 and j is an integer between 1 and N.
And S143, performing genetic operation on the initial population of the array element, and determining the population of the array element after the genetic operation.
In some embodiments, the genetic manipulation comprises the following sub-manipulations in order: selector operation: based on the applicable criteria to select the probability p i And selecting the chromosomes of the array elements in the array element population, and determining the chromosomes of the reserved array elements. A crossover sub-operation: dividing chromosomes of the reserved array elements into odd bodies x 2i-1,g And even number x 2i,g (ii) a And performing cross pairing on the odd numbered bodies and the even numbered bodies, and determining chromosomes of the array elements after the cross pairing. Mutation sub-operation: carrying out mutation on genes in the chromosomes of the array elements after cross pairing, and determining the chromosomes of the array elements after mutation; and forming a new array element population based on the chromosomes of the mutated array elements. Step S143, performing genetic operation on the initial population of the array element, and determining a population of the array element after the genetic operation, including: and sequentially executing the step selection sub-operation, the cross sub-operation and the mutation sub-operation on the initial population circulation of the array elements until the population of the array elements meeting the termination condition is generated.
In the embodiment of the application, the sub-operation is selected: determining each chromosome x based on applicable criteria i，G The size of the proportion value of the adaptation degree of the chromosome X i，G The proportion value of the adaptation degree determines whether the offspring of the chromosome can be reserved or not, and the chromosome of the reserved array element is obtained. Wherein the fitness of chromosome i is fit i (ii) a The population size N-64, the fraction of the fitness of chromosome i, i.e. the probability with which it is selected, is p, is determined by equation (11) i 。
Wherein i is an integer between 1 and 64.
A crossover sub-operation: selecting chromosomes to be cross-paired based on chromosomes remaining after the selector operation, e.g. chromosomes may be divided into selected odd-numbered bodies x 2i-1,g And even number x 2i,g Two groups, cross-pairing the two genes, and randomly selecting [1, 99 ] from chromosomes to be cross-paired according to the bit string length of 100]As the position of the intersection point; according to the cross probability p c Cross-over operations are performed on chromosomes, p in the examples of this application c May be 0.8. For odd volumes x with cross probability 2i-1,g And even number x 2i,g The genes at the crossing position k of (2) are crossed and the respective genes are interchanged. If the exchanged genes are equal, no crossover is performed, so that new two chromosomes are formed, up to all the odd-numbered bodies x 2i-1,g And even number x 2i,g All the chromosomes are crossed to obtain the chromosomes of the array elements after cross matching.
Mutation sub-operation: by the mutation probability p m Selecting genes to be mutated in the crossed population, wherein the value of i is an integer between 1 and 64; j is an integer between 1 and 64, in the interval [0, 1]]In which a random number p is generated, if p ≦ p m If so, the (j, i) -th gene x (j, i) is a mutated gene. If the value of the selected gene in the chromosome is 1, the value is changed into 0; if the value of the selected gene in the chromosome is 0, it becomes 1. Mutation operation can make chromosomes become diversified, and effectively prevents genetic algorithm from ending in advance. Wherein, the (j, i) th gene x (j, i) is a variant gene according to the formula (9).
x ji,0 ＝rand[0,1]×(100-(N-1)d c ) (10)。
In some embodiments, since the relative numbers of the gene value 1 and the gene value 0 in the crossed and mutated chromosomes may change, it is necessary to process the chromosomes that do not satisfy the requirement of the sparsity ratio, and the specific method of processing is as follows: if the number of the gene values 0 in the chromosome is large, randomly selecting redundant gene values 0 in the chromosome to be 1; if the number of 1 s in the chromosome is large, the value of 1 s in the extra gene in the chromosome is randomly selected to be 0.
The initial population of the array elements can be subjected to genetic operation through the selector operation, the cross operation and the mutation operation, a new population is obtained, and then the next genetic operation is carried out until the population of the array elements meeting the termination condition is generated.
Step S144, based on the array elements after genetic manipulationDetermining the chromosome of each array element in the population, and determining the y corresponding to the chromosome of each array element m 。
In the present example, 64 chromosomes x were assigned based on the population of array elements after genetic manipulation i，G Sparse to 50 array elements, determining each chromosome x i，G Corresponding to y m 。
And S145, determining the target array after sparse optimization based on the x corresponding to the chromosome of each array element.
In some embodiments, step S145, based on the chromosome of each array element in the population of genetically manipulated array elements, determining y corresponding to the chromosome of each array element m The method comprises the following steps: step S1451 to step S1452.
Step S1451, determining a chromosome of each array element in the population of the array elements after genetic manipulation based on the population of the array elements after genetic manipulation.
Step S1452, decoding the chromosome of each array element in the population of the array elements after genetic operation, and determining the y corresponding to the chromosome of each array element m 。
In the embodiment of the application, based on the population of the array elements after genetic manipulation, the chromosome x of 64 array elements is divided into i，G Chromosomes that are sparse up to 50 array elements.
The chromosome x of each array element i，G Decoding to obtain chromosome x of each array element i，G Corresponding to y m (ii) a According to y m And obtaining the positions of the corresponding 50 array elements, namely obtaining the linear array consisting of 50 array elements after sparse optimization. As shown in fig. 5, in order to obtain a linear array with an array element number of 50 array elements after performing sparse optimization on a linear array with 64 array elements, fig. 5 shows the position arrangement of each of the 50 array elements.
The above method is described with reference to a specific embodiment, wherein it should be noted that the specific embodiment is only for better describing the present application and should not be construed as an unlimited number of the present application.
The phased array probe with 64 array elements to be sparsely optimized is taken as an example, the sparse and optimized phased array probe is applied to a full-focusing imaging phased array ultrasonic detection technology, and nondestructive detection is carried out on the butt joint of the HDPE pipe through the full-focusing imaging phased array ultrasonic detection technology.
The embodiment of the application provides a schematic flow chart of a nondestructive testing method based on a sparse optimized phased array probe, as shown in fig. 6, the method at least comprises the following steps:
and step S210, acquiring the sparse optimized phased array probe.
In some embodiments, step S210 includes steps S211 to S213.
Step S211, a directional diagram function is established.
A randomly arranged linear array model is established, the array aperture is set to be L equal to 38.4mm, and N equal to 64 unequal-interval array elements are distributed to form the array. Based on the distance d between the first array element and the mth array element m Main beam direction theta 0 And j is an imaginary number unit, the wavelength lambda of the ultrasonic wave generated by the array element, and an angle variable theta. Taking m as [1, N ]]Conversion to the value of N as [0, N-1]]A directivity pattern function F (θ) as shown in equation (2) is established.
And step S212, establishing an optimization model.
And (3) establishing a side lobe level function of the linear array shown in the formula (5) according to the directional diagram function of the linear array obtained in the formula (1).
Based on equation (5), θ ═ θ is determined 0 The maximum sidelobe level function of the time linear array is shown in formula (4), wherein max is a maximum function, theta represents a pitch angle of the mth array element from the positive direction of the Z axis, and S is theta-theta 0 The side lobe area of the time linear array directional diagram is phi represents the main of the m-th array elementThe beam indicates the direction.
And (4) determining the minimum value of the maximum sidelobe level function of the linear array based on the formula (4). In some embodiments, the maximum sidelobe level function is a maximum sidelobe level function of the directional diagram, which refers to the maximum sidelobe level of the directional diagram, and finally, the maximum sidelobe level of the directional diagram is minimized through the optimized array element position.
d c In order to show the optimal distance between two array elements, the grating lobe can not be generated when the array element interval is half-wave length according to the previous experimental data, and the influence of the grating lobe is eliminated. Thus, in the examples of this application d c Is composed of
And the aperture size of the array is ensured to be unchanged, one array element is arranged at both ends of the linear array element to obtain the first array element position as 0, and the Nth array element position is L, namely
Equation (7) is obtained based on equation (6).
D is to be m Resolution into y m And (m-1) d c As shown in equation (6); wherein m is more than or equal to 1 and less than or equal to 64.
d m ＝y m +(m-1)d c (6)；
It is assumed that the distance between two adjacent array elements needs to satisfy
Then y is m (1. ltoreq. m. ltoreq.64) is monotonically increased. d c Is a constant, two ends of the linear array are provided with an array element to obtain a first array element position d 1 0mm, the 64 th array element position d 64 ＝L＝38.4mm，y m (m is more than or equal to 1 and less than or equal to 64) monotonically increasing; and the aperture size of the array is ensured to be unchanged based on
We hold d m Splitting into y m And (m-1) d c The following can be obtained:
wherein, N is 64,
the formula (8) is obtained by converting the formula (7).
y＝[y 1 ,y 2 ,...,y m ,...,y N ] T ＝[d 1 ,d 2 ,...,d m ,...,d N ] T -[0,d c ,...,(N-m)d c ,...,(N-1)d c ] T (8)。
The value of y is determined through the formula (8), and the calculation result shows that y must be present as shown in the formula (9) 1 ≤y 2 ≤...≤y m ≤...≤y N ∈[0，L-(N-1)d c ]I.e. by
Wherein L is 38.4 mm.
y＝{(y 1 ,y 2 ,...,y m ,...,y N )|y 1 ≤y 2 ≤...≤y m ≤...≤y N ∈[0，L-(N-1)d c ]} (9)。
Wherein d is c To indicate the optimal distance between two array elements, in the embodiment of the present application
Spacing array elements by the above calculation m Conversion to y m The search space of array element spacing is changed from [0, L ]]Reduced to [0, L- (N-1) d c ]I.e. from the search space of array element spacing [0, 38.4 ]]Reduced to
After the search space of the array element position is reduced, the maximum side lobe level of a directional diagram is minimized, namely each minimum y is obtained m Substituting the formed y into the formula (4) to obtain an optimized model
Wherein y ═ y 1 ,y 2 ,...,y m ,...,y N ] T ＝[d 1 ,d 2 ,...,d m ,...,d N ] T -[0,d c ,...,(N-m)d c ,...,(N-1)d c ] T Each of y m Is given a value of y 1 ≤y 2 ≤...≤y m ≤...≤y N ∈[0，L-(N-1)d c ]。
And S213, sparsely optimizing the phased array probe according to the thickness of the sample to be detected based on a genetic algorithm.
In some embodiments, the individual is a chromosome.
And (3) encoding: here, a real numerical vector with an individual number NP of 64 and a dimension of 100 per individual is used as an intermediate population, where each individual is set to x i，G Wherein i is a sequence of the chromosome in the population, G is a genetic algebra, and the genetic algebra needs to be calculated according to parameters given by the ultrasonic phased array probe, and is the number of times after the final optimization is finished.
The population individuals are initially coded, namely initial parameters which are not sparsely optimized at first are determined, so that optimized search points are established. All initial populations are assumed and are in accordance with genetic algorithm sparsity and distribution optimization, i.e. in accordance with probability distribution. After optimization, the parameter variable y can be obtained m Is in the range of [0, L- (N-1) d c ]From the formula (10), the individual initial parameter x can be obtained ji,0 ；
x ji,0 ＝rand[0,1]×(100-(N-1)d c ) (10)；
Wherein d is c Can be that
i is an integer between 1 and 64 and j is an integer between 1 and N.
And sequentially and circularly performing selector operation, cross operation and mutation operation on the individuals in the population to obtain the population meeting the termination condition, wherein the selector operation, the cross operation and the mutation operation are as follows:
a selection sub-operation: using each individual x i，G The percentage value of the adaptation degree determines whether the individual filial generation can be reserved or not, and the selection probability is set as p i In some embodiments p i I.e. the value of the adaptation degree.
Based on each individual x i，G The ratio of the adaptive degree is the value of fit if the adaptive degree of a certain individual i is fit i (ii) a The population size is N-64, then the selection probability of the individual i is
Wherein i is an integer between 1 and 64.
A crossover sub-operation: selecting a selected odd volume x 2i-1,g And even number x 2i,g Performing cross pairing, and matching the individuals with the cross pairing according to a cross rate p c The two genes were replaced with 0.8. According to the length P of the bit string being 100, in the odd-numbered body x to be cross-paired 2i-1,g And even number x 2i,g In the formula, randomly selecting [1, P-1 ]]As the position of the intersection point; according to the cross probability p c Crossover operations were performed at 0.8, and the crossover paired chromosomes exchanged their respective genes at crossover position k, thereby forming a new pair of individuals. Where the bit string length may be dimension P.
Mutation sub-operation: the mutation operation can make individuals become diverse, prevent the genetic algorithm from entering early convergence, and effectively prevent the genetic algorithm from ending in advance. The method comprises the following specific steps: in the population obtained after crossing, the value of i is an integer between 1 and NP; j takes the value of an integer between 1 and N, a random number p is generated in the interval [0, 1], if p is less than or equal to 0.05, 0.05 is represented as a variation probability, and the (j, i) th gene x (j, i) is a variation gene as shown in an equation (10).
x ji,0 ＝rand[0,1]×(100-(N-1)d c ) (10)。
And decoding the population after genetic operation to obtain the spacing of the array elements in sparse optimization arrangement, arranging the array elements on the phased array probe according to the spacing of the array elements after sparse optimization, and obtaining the phased array probe after sparse optimization.
Step S212 yields y { (y) 1 ,y 2 ,...,y m ,...,y N )|y 1 ≤y 2 ≤...≤y m ≤...≤y N ∈[0，L-(N-1)d c ]}。
And S220, performing full-focus imaging phased array ultrasonic detection on the sample to be detected based on the sparse optimized phased array probe.
In some embodiments, the phased array probe is an ultrasonic phased array probe.
In some embodiments, step S220 includes steps S221 through S224.
Step S221, cleaning the sample to be detected.
Here, carry out cleaning treatment with the surperficial filth of the butt joint of the HDPE pipe that will wait to detect, guarantee that its surface accords with the detection requirement, avoid causing harmful effects to follow-up detection.
And step S222, mounting the full-focus imaging phased array ultrasonic detection equipment.
Fig. 7 is an installation schematic diagram of a full-focus imaging phased array ultrasonic testing apparatus provided in an embodiment of the present application, where the full-focus imaging phased array ultrasonic testing apparatus includes: a monitoring host 71, a scanning assembly 73 and a fully focused imaging device 72. When installed, the scanning assembly 73 is fixedly mounted on the HDPE pipe 74. The scanning assembly 73 comprises a sparsely optimized ultrasonic phased array probe 731 and a wedge 732, the wedge 732 is in contact with the HDPE pipe 74, and the sparsely optimized ultrasonic phased array probe 731 is connected to the full focus imaging device 72. The monitoring host 71 sends a detection command to the full-focus imaging device 72 through a network interface, and performs imaging processing on detection data acquired by the full-focus imaging device 72 to obtain a detection image of full-focus imaging; according to the detection command received by the full-focus imaging device 72, the full-focus imaging device 72 controls each array element in the scanning assembly 73 to send ultrasonic waves to the HDPE pipe to be detected for detection. The detection image is used for analyzing the defects of the HDPE pipe detection area; the array element positions on the ultrasonic phased array probe 731 cannot be changed again after being arranged according to sparse optimization.
And step S223, setting system parameters of the full-focus imaging detection equipment based on the physical parameters of the sample to be detected.
And (5) designing the sparsely optimized ultrasonic phased array probe through the step S210 according to the thickness of the HDPE pipe to be detected. For an HDPE pipe with the pipe fitting thickness of about 10mm, a low-frequency probe with the frequency of 2.25 MHz-2.5 MHz can be adopted, in the embodiment of the application, a 64-array phased array probe is adopted for sparse optimization, and the ultrasonic emission frequency of the probe is about 2.25 MHz.
And setting system parameters of the full-focus imaging detection equipment, wherein the system parameters comprise sound velocity, array element spacing, array element number, probe frequency, central array element height, wedge angle and wedge sound velocity. The value range of the sound velocity is also different in HDPE at different temperatures, and is 2000-3000 m/s, the numerical value set in the embodiment of the application comprises the sound velocity of 2700m/s, the frequency of the probe is 2.25 MHz-2.5 MHz, and the angle of the wedge block and the sound velocity of the wedge block are determined according to the joint of the HDPE pipe.
In step S224, a detection image of the full focus imaging is acquired.
And (4) adjusting parameters of the full-focus imaging detection equipment, and then carrying out ultrasonic detection to determine a scanning area of the ultrasonic detection in the HDPE pipe 74. The sparsely optimized ultrasonic phased array probe 731 is moved along the transverse and longitudinal directions of the HDPE pipe 74, and the gain of the full focus imaging assembly is adjusted. When the defect signal in the imaging picture is optimal, storing the image and the original full matrix data obtained by each array element at present, carrying out virtual focusing on each point in the detected area according to the collected original full matrix data to obtain a detection image, and superposing the detection images obtained by each array element to obtain a full-focusing imaging detection image of the HDPE pipe. Wherein the detection image is used for analyzing the defects of the HDPE pipe detection area.
Based on the foregoing embodiments, an apparatus for sparsely optimizing a phased array probe is further provided in an embodiment of the present application, where the apparatus includes modules and sub-modules included in the modules, and may be implemented by a processor in an electronic device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the Processor may be a Central Processing Unit (CPU), a microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
Based on the foregoing embodiment, a schematic diagram of a composition framework of an apparatus for sparsely optimizing a phased array probe provided in an embodiment of the present application is shown in fig. 8, where the apparatus 80 for sparsely optimizing a phased array probe includes:
an obtaining module 81, configured to obtain a target array to be sparsely optimized on the phased array probe; the target array comprises N array elements;
a first establishing module 82, configured to establish a directional pattern function of the target array based on target information of the target array; the target information comprises the aperture L of the array elements, the number N of the array elements, the position information of each array element and an angle variable theta;
a second establishing module 83, configured to establish an optimization model of the target array based on the directional diagram function of the target array;
and a sparse optimization module 84, configured to perform sparse optimization on the optimization model of the target array based on a genetic algorithm, so as to obtain a sparsely optimized target array.
In some embodiments, in the case that the target array is a linear array, the position information of the m-th array element includes a distance d between the first array element and the m-th array element m Main wave direction theta of m-th array element 0 (ii) a Wherein d is m The value range is [0, L](ii) a A first setup module comprising: the first establishing submodule is used for establishing a directional diagram function of corresponding array elements based on the position information of each array element in the target array; the first determining submodule is used for determining a directional diagram function of the target array based on the directional diagram function of each array element; wherein the directional diagram function of the target array is
λ is the wavelength and j is the imaginary unit.
In some embodiments, the second establishing means comprises: a second determining submodule for determining a maximum side lobe level function of the target array based on a directional pattern function of the target array
Wherein S is theta ═ theta 0 The side lobe area of the time target array is phi indicates the main beam indication direction of the mth array element, F db (θ) is a side lobe level function of the target array; a third determining submodule, configured to determine a minimum value of the maximum sidelobe level function of the target array based on the maximum sidelobe level function of the target array; a second establishing submodule for establishing an optimization model of the target array based on the minimum value of the maximum sidelobe level function of the target array
Wherein y ═ y 1 ,y 2 ,...,y m ,...,y N ] T ＝[d 1 ,d 2 ,...,d m ,...,d N ] T -[0,d c ,...,(N-m)d c ,...,(N-1)d c ] T Each of y m Is given as y 1 ≤y 2 ≤...≤y m ≤...≤y N [0，L-(N-1)d c ]}。
In some embodiments, the second establishing sub-module comprises: a determination unit for determining a maximum sidelobe level function based on the target arrayEach y in (1) m Determining the minimum value of the maximum sidelobe level function of the target array; an establishing unit, configured to establish an optimization model of the target array based on a minimum value of a maximum sidelobe level function of the target array
In some embodiments, the optimization model comprises N array elements arranged regularly; a sparse optimization module comprising: a coding submodule for applying NP real number vectors of dimension P as intermediate population to each y based on genetic algorithm m Encoding, wherein the intermediate population comprises chromosome x i，G (ii) a Wherein, the value of i is an integer between 1 and NP, and G is a genetic algebra; a fourth determining submodule, configured to initialize the intermediate population and determine an initial population of the array element; wherein the initial population of array elements comprises an initial chromosome x of each array element ji,0 (ii) a Wherein j is an integer between 1 and N; a fifth determining submodule, configured to perform a genetic operation on the initial population of the array element, and determine a population of the array element after the genetic operation; a sixth determining submodule, configured to determine, based on the chromosome of each array element in the population of array elements after the genetic operation, y corresponding to the chromosome of each array element m (ii) a A seventh determining submodule for determining y corresponding to the chromosome based on each array element m And determining the target array after sparse optimization.
In some embodiments, a fifth determination submodule, comprising: and the circulating unit is used for sequentially executing a selecting sub-operation, a cross sub-operation and a mutation sub-operation on the initial population circulation of the array elements until a population of the array elements meeting the termination condition is generated. The circulation unit includes: a selection sub-operation unit for selecting the probability p based on the applicable criteria i Selecting chromosomes of array elements in the array element population, and determining the chromosomes of the reserved array elements; a crossover sub-operation unit for dividing the chromosomes of the reserved array elements into odd bodies x 2i-1,g And even number x 2i,g (ii) a For the odd number bodyPerforming cross pairing with the even number bodies, and determining chromosomes of array elements after cross pairing; a mutation sub-operation unit, which is used for carrying out mutation on genes in the chromosomes of the array elements after cross pairing and determining the chromosomes of the array elements after mutation; and forming a new array element population based on the chromosomes of the mutated array elements.
In some embodiments, selecting a sub-operational unit comprises: a first determining subunit for selecting the probability p based on an applicable criterion i Determining the fitness ratio value of the chromosome of each array element in the array element population; and the second determining subunit is used for selecting the chromosomes based on the fitness fraction value and determining the chromosomes of the array elements reserved in the array element population.
In some embodiments, the mutation sub-operation unit comprises: a variant subunit, configured to use the (j, i) -th gene in the chromosome of the cross-paired array element as a variant gene; determining a subunit for determining the probability of variation p m Carrying out mutation on the (j, i) th gene x (j, i) and determining a chromosome of a mutated array element; wherein the mutated chromosome is x ji,0 ＝rand[0,1]×(100-(N-1)d c )。
The above description of the apparatus embodiments, similar to the above description of the method embodiments, has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
An embodiment of the present application provides a physical schematic diagram of a full-focus imaging detection apparatus, as shown in fig. 9, the apparatus includes:
the scanning assembly 91 comprises a sparsely optimized phased array probe 911, array elements on the sparsely optimized phased array probe 911 are arranged according to the target array obtained by the method embodiment, wherein each array element in the sparsely optimized phased array probe is used for transmitting and receiving ultrasonic signals.
And the imaging component 92 is connected with the scanning component 91 and is used for performing full-focus imaging on the ultrasonic signals received by each array element in the sparse optimized phased array probe.
And the monitoring host 93 is connected with the imaging component through a network interface and used for storing and superposing the detection images obtained by each array element in the full-focus imaging.
In some embodiments, the scanning assembly further comprises a wedge 912.
In the embodiment of the application, taking HDPE pipe detection as an example, as shown in fig. 9, a full-focus imaging detection device is provided, which includes a scanning component 91, an imaging component 92, and a monitoring host 93, where the scanning component 91 includes a sparsely optimized ultrasonic phased array probe 911 obtained by using a genetic algorithm, where array elements on the phased array probe are arranged according to the pitch of the sparsely optimized array elements, and the obtained sparsely optimized ultrasonic phased array probe 911 is a linear array probe; each array element in the linear array probe can independently transmit signals and receive signals.
The imaging component 92 is connected with the scanning component 91, comprises a full-focusing imaging component, and can directly carry out computer processing on complete full-matrix data to realize high-precision real-time full-focusing imaging detection. The full-focus imaging component is a high-speed hardware imaging technology based on FPGA operation.
The monitoring host 93 is connected with the imaging component 92 through a network interface, and sends a detection command to the imaging component 92, and the imaging component 92 controls each array element in the scanning component 91 to send ultrasonic waves to the HDPE pipe 94 to be detected for detection. The system parameters comprise sound velocity, array element spacing, array element number, probe frequency, central array element height, wedge block angle and wedge block sound velocity. And stores the full matrix data acquired by the imaging component 92, where the full matrix data includes the echo signal acquired by each receiving array element. And aiming at the collected full matrix data, virtually focusing each point in the detected area to obtain a detection image, and finally superposing the detection images obtained by each array element to obtain a full-focusing imaging detection image of the HDPE pipe 94. Wherein the inspection image is used to analyze the HDPE pipe 94 for defects in the inspection area.
The embodiment of the application provides a using method of a full-focus imaging detection device, which is applied to the full-focus imaging detection device, and the method comprises the following steps:
step S310, setting system parameters of the full-focus imaging detection equipment based on physical parameters of a sample to be detected;
step S320, transmitting and receiving ultrasonic signals through the scanning component based on the system parameters;
step S330, based on the received ultrasonic signals, carrying out full-focus imaging on the ultrasonic signals received by each array element through an imaging component;
and step S340, storing and superposing the detection images obtained by each array element in the full-focus imaging through the monitoring host.
In the embodiment of the application, taking detection of an HDPE pipe as an example, a full-focus imaging detection device is installed on the HDPE pipe for detection, firstly, surface dirt of a butt joint of the HDPE pipe to be detected is cleaned, the surface of the HDPE pipe to be detected is ensured to meet detection requirements, and adverse effects on subsequent detection are avoided; the scanner and wedges were assembled and mounted on the HDPE pipe.
The ultrasonic phased array probe is sparsely optimized according to the thickness of the HDPE pipe to be detected, for example, for the HDPE pipe with the pipe fitting thickness of about 10mm, a low-frequency probe with the frequency below 2.25MHz can be adopted. In the embodiment of the application, the linear array with the array element number of 64 and the ultrasonic emission frequency of 2.25 MHz-2.5 MHz is adopted for sparse optimization, the number of the array elements is optimized from 64 to 50, the array elements in the sparse optimized linear array are arranged as shown in fig. 5, and the position arrangement of each array element in the 50 array elements is shown in the figure.
Connecting an ultrasonic phased array probe to a full-focus imaging assembly, setting parameters of the full-focus imaging assembly, including sound velocity, array element distance, array element number, probe frequency, central array element height, wedge block angle and wedge block sound velocity, then entering ultrasonic detection, defining a scanning area, and obtaining a detection result image of full-focus imaging.
And moving the optimized probe along the transverse direction and the longitudinal direction of the HDPE pipe to be detected, and adjusting the gain of the full-focus imaging component. And when the defect signal in the imaging picture is optimal, saving the current image and the original full matrix data.
The foregoing description of the various embodiments is intended to highlight various differences between the embodiments, which are the same or similar and all of which are referenced.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is merely one type of logical division, and an actual implementation may have another division, for example, a unit or a component may be combined or integrated with another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some interfaces, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing an electronic device or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is intended only to illustrate the present application, and should not be taken as limiting the scope of the present application, and any modifications, equivalents, improvements, etc. that are made within the spirit and principle of the present application should be included in the scope of the present application.
Claims (10)
1. A method of sparsely optimizing a phased array probe, the method comprising:
acquiring a target array to be sparsely optimized on a phased array probe; the target array comprises N array elements;
establishing a directional diagram function of the target array based on the target information of the target array; the target information comprises the aperture L of the array elements, the number N of the array elements, the position information of each array element and an angle variable theta;
establishing an optimization model of the target array based on the directional diagram function of the target array;
and based on a genetic algorithm, carrying out sparse optimization on the optimization model of the target array to obtain a target array after sparse optimization.
2. The method of claim 1, wherein the position information of the m-th array element comprises a distance d between the first array element and the m-th array element when the target array is a linear array m Main wave direction theta of m-th array element 0 (ii) a Wherein d is m The value range is [0, L]；
Establishing a directional diagram function of the target array based on the target information of the target array, including:
establishing a directional diagram function of corresponding array elements based on the position information of each array element in the target array;
determining a directional diagram function of the target array based on the directional diagram function of each array element;
wherein the directional diagram function of the target array is
λ is the wavelength and j is the imaginary unit.
3. The method according to claim 1 or 2, wherein the establishing an optimization model of the target array based on the pattern function of the target array comprises:
determining a maximum sidelobe level function of the target array based on a directional diagram function of the target array
Wherein S is theta ═ theta 0 Side lobe region of time target array, F db (theta) is a side lobe level function of the target array, and phi represents the main beam indication direction of the mth array element;
determining a minimum value of the maximum sidelobe level function of the target array based on the maximum sidelobe level function of the target array;
establishing an optimization model of the target array based on the minimum value of the maximum sidelobe level function of the target array
Wherein y ═ y 1 ,y 2 ,...,y m ,...,y N ] T ＝[d 1 ,d 2 ,...,d m ,...,d N ] T -[0,d c ,...,(N-m)d c ,...,(N-1)d c ] T Each of y m Is given as y 1 ≤y 2 ≤...≤y m ≤...≤y N ∈[0，L-(N-1)d c ]。
4. The method of claim 3, wherein the optimization model of the target array is established based on a minimum value of a maximum sidelobe level function of the target array
The method comprises the following steps:
based on each y in a maximum sidelobe level function of the target array m Determining the minimum value of the maximum sidelobe level function of the target array;
establishing an optimization model of the target array based on the minimum value of the maximum sidelobe level function of the target array
5. The method according to claim 1 or 2, wherein the optimization model comprises N array elements arranged regularly;
the sparse optimization of the optimization model of the target array based on the genetic algorithm to obtain the sparsely optimized target array comprises the following steps:
based on genetic algorithm, NP real number vectors with dimension P are used as intermediate population for each y m Encoding, wherein the intermediate population comprises chromosome x i，G (ii) a Wherein, the value of i is an integer between 1 and NP, and G is a genetic algebra;
initializing the intermediate population and determining an initial population of the array elements; wherein the initial population of array elements comprises an initial chromosome x of each array element ji,0 (ii) a Wherein j is an integer between 1 and N;
performing genetic operation on the initial population of the array elements, and determining the population of the array elements after the genetic operation;
determining y corresponding to the chromosome of each array element based on the chromosome of each array element in the population of the array elements after genetic operation m ；
Y corresponding to each array element based on the chromosome m And determining the target array after sparse optimization.
6. The method according to claim 5, characterized in that said genetic manipulation comprises the following sub-manipulations in sequence:
a selection sub-operation: based on the applicable criteria to select the probability p i Selecting chromosomes of array elements in the array element population, and determining the chromosomes of the reserved array elements;
a crossover sub-operation: dividing chromosomes of the reserved array elements into odd bodies x 2i-1,g And even number x 2i,g (ii) a Performing cross pairing on the odd numbered bodies and the even numbered bodies, and determining chromosomes of array elements after cross pairing;
mutation sub-operation: carrying out mutation on genes in the chromosomes of the array elements after cross pairing, and determining the chromosomes of the array elements after mutation; forming a new array element population based on the chromosomes of the mutated array elements;
the performing genetic operation on the initial population of the array element and determining the population of the array element after the genetic operation comprises: and sequentially executing the selector operation, the cross sub operation and the variant sub operation to the initial population cycle of the array elements until a population of the array elements meeting the termination condition is generated.
7. The method of claim 6, wherein the probability p is chosen based on an applicable criterion i Selecting chromosomes of array elements in the array element population, and determining chromosomes of reserved array elements, wherein the method comprises the following steps:
based on the applicable criteria to select the probability p i Determining the fitness ratio value of each array element chromosome in the array element population;
and selecting the chromosomes based on the fitness proportion value, and determining the chromosomes of the array elements reserved in the array element population.
8. The method according to claim 6 or 7, wherein the mutating the genes in the chromosomes of the cross-paired array elements to determine the chromosomes of the mutated array elements comprises:
taking the (j, i) th gene in the chromosome of the array element after cross pairing as a variant gene;
by the mutation probability p m Carrying out mutation on the (j, i) th gene x (j, i) and determining a chromosome of a mutated array element;
wherein the chromosome after mutation is x ji,0 ＝rand[0,1]×(100-(N-1)d c )。
9. An apparatus for sparsely optimizing a phased array probe, the apparatus comprising:
the acquisition module is used for acquiring a target array to be sparsely optimized on the phased array probe; the target array comprises N array elements;
the first establishing module is used for establishing a directional diagram function of the target array based on the target information of the target array; the target information comprises the aperture L of the array elements, the number N of the array elements, the position information of each array element and an angle variable theta;
the second establishing module is used for establishing an optimization model of the target array based on the directional diagram function of the target array;
and the sparse optimization module is used for carrying out sparse optimization on the optimization model of the target array based on a genetic algorithm to obtain a target array after sparse optimization.
10. A full focus imaging detection apparatus, characterized in that the apparatus comprises:
a scanning assembly comprising a sparsely optimized phased array probe having array elements arranged in accordance with a target array obtained by the method of any one of claims 1 to 8, wherein each array element of the sparsely optimized phased array probe is configured to transmit and receive ultrasonic signals;
the imaging assembly is connected with the scanning assembly and is used for carrying out full-focus imaging on the ultrasonic signals received by each array element in the sparse optimized phased array probe;
and the monitoring host is connected with the imaging component through a network interface and used for storing and superposing the detection images obtained by each array element in the full-focus imaging.
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