CN117169815A - Microphone array optimization method, microphone array optimization equipment, electronic terminal and storage medium - Google Patents
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Abstract
The invention discloses a microphone array optimization method, microphone array optimization equipment, an electronic terminal and a storage medium. The microphone array optimization method adopts the following technical scheme: inputting pre-collected typical abnormal acoustic signal parameters of the GIS, acoustic monitoring distance and azimuth parameters of the GIS into an optimized microphone array model, performing simulation analysis on the performance of the microphone array model, and evaluating main lobe and side lobe parameters in an array response map; judging whether to iteratively optimize the microphone array model according to the evaluation result, and outputting the microphone array model parameters until the ideal result is reached. Because the typical abnormal sound acoustic signal parameters of the GIS and the acoustic monitoring distance and azimuth parameters of the GIS are collected in advance and are obtained according to the GIS actual operation data, the microphone array model has strong actual pertinence, the finally optimized microphone array model is suitable for abnormal sound monitoring of GIS power station equipment, the abnormal sound monitoring has strong pertinence, and the monitoring effect can be improved.
Description
Technical Field
The invention relates to the technical field of abnormal sound detection and positioning of power equipment, in particular to a microphone array optimization method, equipment, an electronic terminal and a computer readable storage medium for monitoring GIS abnormal sound.
Background
The gas insulated metal enclosed equipment (GIS) has the advantages of high voltage, high current, compact structure, flexible arrangement mode, stable operation, long service life, excellent technical index, no external influence and the like, and has been widely applied to domestic and foreign electric power systems. Gas insulated metal enclosed equipment (GIS) often has abnormal sound to produce when breaking down, if the trouble can not be in time found and solved, probably back to serious incident.
For detecting and positioning the abnormal sound of the equipment caused by mechanical defects, the most effective method is obviously to directly measure the acoustic signal, but the abnormal sound is submerged by the background noise of the equipment due to the complex environmental noise of the transformer substation, and the abnormal sound is difficult to capture and position by a human ear or an independent acoustic sensor. The method for detecting and positioning the abnormal sound of the power equipment and involved in the sound source identification requires a microphone array (acoustic array) to be used for positioning the sound source, related parameters of the microphone array, the size of the injected microphone array, the spacing arrangement of array elements and other related parameters have great influence on the sound source positioning result, therefore, the microphone array used by the GIS is required to be optimally designed, and the monitoring effect of the microphone array is improved.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, an object of the present invention is to provide a microphone array optimization method, device, electronic terminal and computer readable storage medium for monitoring GIS abnormal sound, which optimally designs an acoustic microphone array to improve the monitoring effect of the microphone array.
In order to achieve the above and other related objects, the present invention adopts the following technical scheme: a microphone array optimization method for monitoring GIS abnormal sound comprises the following steps:
1) Simplifying the microphone array model;
2) Optimizing the microphone array to obtain an optimized microphone array model;
3) Inputting pre-collected typical abnormal acoustic signal parameters of the GIS, acoustic monitoring distance and azimuth parameters of the GIS into an optimized microphone array model, performing simulation analysis on the performance of the microphone array model, and evaluating main lobe and side lobe parameters in an array response map;
4) If the evaluation result of the step 3) does not reach the standard, repeating the steps 1) to 3); and if the evaluation result in the step 3) meets the standard, outputting the microphone array model parameters and ending.
Preferably, the step 1) includes:
1.1 Initializing the radius range of the microphone array ring: defining minimum radius interval of adjacent two circular rings of microphone array as ρ min Setting the number of the circular rings as M1 to obtain the radius range of the microphone array model;
1.2 Initializing a microphone array element interval range: adjacent array element spacing minimum distance d for defining each ring of microphone array min And in the process of generating new array element individuals for each circular ring, setting the number of array elements on the circular ring as M2 to obtain the array element interval range of the microphone array model.
Preferably, the step 2) includes:
2.1 Initializing the population: initializing parameters of the radius of each microphone array ring or/and the array element interval on each ring and generating corresponding target vectors and variation vectors;
2.2 Cross: performing two-term intersection on the radius of each microphone array ring or/and the target vector and the variation vector of the array element interval on each ring to obtain a test vector;
2.3 Boundary condition processing.
Preferably, the evaluating main lobe and side lobe parameters in the array response spectrum in the step 3) includes: the width and amplitude of the main lobe and the width and amplitude of the side lobe are evaluated.
Preferably, the parameters of the abnormal acoustic signals typical to the GIS in the step 3) include sound source frequency and signal to noise ratio.
Preferably, the typical abnormal sound acoustic signal parameters of the GIS further include acoustic signal parameters of shielding case loosening, contact loosening, bolt loosening, internal foreign matters and abnormal sound modes of transformer noise.
Preferably, in the step 4), if the difference between the evaluation results obtained in the steps 1) to 3) is within a set range, the microphone array model parameters are output and ended.
Correspondingly, the invention also provides microphone array optimizing equipment for realizing the microphone array optimizing method for monitoring GIS abnormal sound.
Correspondingly, the invention also provides an electronic terminal which comprises a storage unit and a processing unit, wherein the storage unit is used for storing the pre-collected typical abnormal sound acoustic signal parameters of the GIS, the processing unit is used for executing the steps 1) to 4) in the technical scheme, and the storage unit is also used for storing the microphone array model parameters output by the processing unit.
Correspondingly, the invention also provides a computer readable storage medium which stores a computer program, and the computer program realizes the microphone array optimization method for monitoring GIS abnormal sound according to the technical scheme when being executed by a processor.
As described above, the microphone array optimization method for monitoring GIS abnormal sound of the present invention has the following beneficial effects: inputting the pre-collected typical abnormal acoustic signal parameters of the GIS, the acoustic monitoring distance and the azimuth parameters of the GIS into an optimized microphone array model, performing simulation analysis on the performance of the microphone array model, and evaluating the main lobe and side lobe parameters in an array response map; judging whether to iteratively optimize the microphone array model according to the evaluation result, and outputting the microphone array model parameters until the ideal result is reached. Because the typical abnormal sound acoustic signal parameters of the GIS, the acoustic monitoring distance and the azimuth parameters of the GIS are obtained according to the GIS actual operation data, the microphone array model is very strong in actual pertinence, so that the finally optimized microphone array model is suitable for abnormal sound monitoring of GIS power station equipment, the abnormal sound monitoring has very strong pertinence, and the monitoring effect can be improved.
The microphone array optimizing device, the electronic terminal and the computer readable storage medium for monitoring the GIS abnormal sound are used for executing the microphone array optimizing method for monitoring the GIS abnormal sound, which has the beneficial effects and is not repeated here.
Drawings
FIG. 1 is a flow chart of a microphone array optimization method for monitoring GIS abnormal sound according to the present invention;
FIGS. 2 and 3 are graphs of directivity functions of microphone arrays in polar coordinates and rectangular coordinates, respectively, according to embodiments of the present invention;
fig. 4 is a schematic diagram of an electronic terminal according to the present invention.
The element numbers illustrate 1-memory units, 2-processing units.
Detailed Description
The following specific examples are presented to illustrate the present invention, and those skilled in the art will readily appreciate the additional advantages and capabilities of the present invention as disclosed herein. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the illustrations, not according to the number, shape and size of the components in actual implementation, and the form, number and proportion of each component in actual implementation may be arbitrarily changed, and the layout of the components may be more complex.
Referring to fig. 1, the invention provides a microphone array optimization method for monitoring GIS abnormal sound, which comprises the following steps:
1) Simplifying the microphone array model;
2) Optimizing the microphone array to obtain an optimized microphone array model;
3) Inputting pre-collected typical abnormal acoustic signal parameters of the GIS, acoustic monitoring distance and azimuth parameters of the GIS into an optimized microphone array model, performing simulation analysis on the performance of the microphone array model, and evaluating main lobe and side lobe parameters in an array response map;
4) If the evaluation result of the step 3) does not reach the standard, repeating the steps 1) to 3); and if the evaluation result in the step 3) meets the standard, outputting the microphone array model parameters and ending.
In the microphone array optimization method for monitoring GIS abnormal sound, the characteristic abnormal sound acoustic signal parameters of GIS, the acoustic monitoring distance of GIS and the azimuth parameters which are collected in advance are used for inputting the parameters into an optimized microphone array model to carry out simulation analysis on the performance of the microphone array model, and main lobe and side lobe parameters in an array response map are evaluated; judging whether to iteratively optimize the microphone array model according to the evaluation result, and outputting the microphone array model parameters until the ideal result is reached. Because the typical abnormal sound acoustic signal parameters of the GIS, the acoustic monitoring distance and the azimuth parameters of the GIS are obtained according to the GIS actual operation data, the microphone array model is very strong in actual pertinence, so that the finally optimized microphone array model is suitable for abnormal sound monitoring of GIS power station equipment, the abnormal sound monitoring has very strong pertinence, and the monitoring effect can be improved.
The directivity of a microphone array refers to the characteristic of the amplitude of an acoustic signal received by the array as a function of azimuth angle. When the microphone array is focused in a certain direction, the ratio between the output of the array in any direction and the output of the array in the direction of the main maximum is the directivity function of the array. Fig. 2 and 3 are graphs of array directivity functions, simply referred to as directional diagrams. In the array pattern, a beam in which a maximum value of the directivity function in the reference direction is located is referred to as a main lobe, a beam in which a directivity function in other directions is equal to the main beam maximum value is referred to as a grating lobe, and a series of beams in which the maximum value of the directivity function in the reference direction is smaller is referred to as side lobes. A microphone array should have a small main lobe width and side lobes for higher resolution and better interference rejection.
The reasonable optimization design of the microphone under the constraint conditions of the aperture of the microphone array, the number of array elements and the like can meet the requirement of a desired directional diagram by optimizing the number of arrays, the positions of the arrays, the excitation phases and the amplitudes of the array elements and other related parameters. The microphone array optimization design can not only effectively reduce the cost of the array microphone by using the minimum array element number, but also can meet the requirements of low side lobe performance and high resolution.
The differential evolution algorithm is a global optimization method based on population intelligence, does not depend on characteristic information for solving problems, adopts floating point real number coding, searches the whole population space through the difference information among population individuals to guide population evolution, and finally obtains the optimal solution of the problems through corresponding operation and multiple iterative processes of the differential evolution algorithm.
In the microphone array optimization method for monitoring GIS abnormal sound, firstly, a microphone array model is simplified, and in the process of synthesizing a lean concentric circular ring array, the constraint condition is that an array aperture R is limited, and the minimum radius interval of adjacent circular rings is limited to be rho min Limiting the array element spacing value on each ring to be [ d ] min ,d max ]And the adjacent array elements of each ring are ensured to be spaced equally.
In a microphone array optimization method for monitoring GIS abnormal noise of the present invention, as a preferred embodiment, the step 1) includes:
1.1 Initializing the radius range of the microphone array ring: defining minimum radius interval of adjacent two circular rings of microphone array as ρ min Setting the number of the circular rings as M 1 Obtaining the radius range of the microphone array model;
1≤j＜i≤M 1 ，ρ min is a constant, then there is min { ρ } i -ρ j }≥ρ min Then the radius constraint vector is constructed as follows:
ρ″＝[ρ min ,2·ρ min ,…(M 1 -1)ρ min ,0]
thereby reducing the search space of the radius of the circle from the original R to SR, which is expressed as:
SR＝R-M 1 ·ρ min
in the course of generating new individuals by initializing the radius of the circular ring, in the search space [0, SR]Up-randomly generating M 1 Random number and store this M 1 The indirect radius vector is obtained by arranging the number from small to largeAdding it to the radius constraint variable ρ "forms a radius vector ρ, namely:
ρ＝ρ′+ρ″
1.2 Initializing a microphone array element interval range: adjacent array element spacing minimum distance d for defining each ring of microphone array min In the process of generating new array element individuals for each circular ring, setting the number of array elements on the circular ring as M 2 And obtaining the array element interval range of the microphone array model.
Constraint condition of array element interval is to define minimum distance d of adjacent array elements min Introducing an array element interval constraint vector:
in the course of generating new individuals at intervals of array elements, in the search space [0, d ] max -d min ]Up-randomly generating M 2 Random number and store this M 2 The indirect array element interval vector is obtained by arranging the number from small to largeAnd adding the element spacing constraint vector d 'with the element spacing constraint vector d' to form an element spacing vector d, namely:
d＝d′+d″
by initializing the radius of the circular ring and the intervals of the array elements, a new individual is generated, and the constraint condition is well satisfied. In the simulation of the embodiment, the optimization variable is divided into the radius of the circular ring, the array element interval on each circular ring, the radius of the circular ring and the array element interval, and when only the radius of the circular ring is optimized, the optimization variable is expressed as v=ρ; when only the array element interval is optimized, the optimization variable is denoted v=d; when the radius of the torus and the array element spacing are jointly optimized, the optimization variable is expressed as v= [ ρ, d ].
And when the improved differential evolution algorithm is applied and the radius of the circular ring and the interval of array elements are optimized, the action process of the vector is restrained. In the optimization process of the improved differential algorithm, the search space of the radius of the circular ring is reduced from R to SR due to the proposal of constraint vectors rho 'and d', and the search space of the array element spacing is reduced from [0, d ] max ]Reduced to [0, d max -d min ]When the differential algorithm is initialized, only the reduced search space is needed to randomly generate indirect individuals v '= [ rho', d ] "]The indirect individual v' is the individual v= [ ρ, d ] of each individual in the population]Subtracting constraint vector v "= [ ρ", d ""]The indirect individual v' is used for variation, crossover and selection of the differential algorithm.
Two cases require the addition of an indirect individual v 'to a constraint vector v': firstly, when calculating the fitness function, an indirect individual is added with a constraint vector to form a real individual, a corresponding peak sidelobe level is obtained, and then the selection operation is carried out; and secondly, when a final optimization result is obtained, calculating the peak sidelobe level of a final optimal individual and drawing a corresponding direction diagram.
In the microphone array optimization method for monitoring GIS abnormal noise of the present invention, the microphone array is optimized by applying the improved differential evolution algorithm, preferably, the step 2) includes:
2.1 Initializing the population: initializing parameters of the radius of each microphone array ring or/and the array element interval on each ring and generating corresponding target vector and variation vector
When the improved differential evolution algorithm is applied to the sparse concentric circular array to only optimize the circular radius of the microphone array, the initial population consists of real-value parameter vectors with the number of population individuals NP and the dimension M1 (namely the number of circular rings). Ith individual in initial population
ρ i (0)＝{ρ i1 (0),ρ i2 (0)…ρ iM (0)}(i＝1,2…NP,j＝1,2…M 1 Element initialization of 1):
ρ ij (0)＝(R-M 1 ρ min -0)·rand+0
from the above, ρ ij (0) Constitute a value of [0, R-M 1 ρ min ]Upper M 1 -1 random number, also for this M 1 -1 number of values arranged from small to small ρ ij (0) The value of the M-th dimension of (c) is ρ iM (0) And R, the array aperture. In practice, only the radius is optimized, only the individual first M 1 -1 dimension, the value of the last dimension being a constant value.
When the improved differential evolution algorithm is applied to the sparse concentric circular array to only optimize the array element spacing, initializing a population, wherein the population number of individuals is NP, and each individual contains M 2 Individual variable, i-th individual in initial population
d i (0)＝{d i1 (0),d i2 (0)…d iM (0)}(i＝1,2…NP,j＝1,2…M 2 ) The element initialization of (a) is as follows:
d ij (0)＝(d max -d min )·rand+0
d ij (0) Form a value of [0, d max -d min ]Upper M 2 The random numbers of the individual need not be arranged from small to large.
When the radius of the circular ring and the array element interval on each circular ring are optimized in a combined mode, initializing each individual dimension from 1 to M 1 To optimize the radius of the circle, the individual dimension is defined by M 1 +1 to M 1 +M 2 To optimize the ring from 1 to M 2 Array element spacing of (2), namely:
X i (0)＝{x ij (0)}＝{ρ i (0),d i (0) Initialization of the values is as described above and will not be described again here.
For each X i (0)＝{x ij (t) } (i=1, 2 … NP, j=1, 2 … N) individuals, variant vector productionThe following were generated:
V i (t+1)＝X p (t)+F·[X j (t)-X k (t)]
randomly selected numbers i, j, p and k are different from each other and are integers within [1, NP ], so NP must be greater than 4,F is a scaling factor, which is a real constant factor.
2.2 Cross: performing two-term intersection on the target vector and the variation vector of the radius of each microphone array ring or/and the array element interval on each ring to obtain a test vector
Test vector U using two-term crossover i (t+1)＝{U ij (t+1) } by the target vector X i (0)＝{x ij (t) } and variation vector V i (t+1)＝{v ij (t) } performing a cross operation, when rand is equal to or less than CR or j=j rand When U i The j-th dimension component of (t+1) is defined by V i The j-th dimensional component of (t+1) is provided, otherwise U i The j-th dimensional component of (t+1) is provided. j (j) rand Is [ l, N ]]Random numbers on the interval, CR, represent the crossover probability factor. The equation for the two-term crossover operation is:
2.3 Boundary condition processing
When the optimization variable is only the radius of the ring, the radius of the ring must satisfy the minimum distance ρ between the rings min The search space of the radius of the circular ring is reduced from the original R to SR, U i (t+1)＝{u ij (t+1)}(i∈[1,NP]j∈[1,M 1 ]) Is an individual generated after operations such as mutation, crossover and the like, t is the current evolution algebra, and the element u is ij If (t+1) exceeds [0, SR]Ranges of (u) ij (t+1) is generated as follows:
u ij (t+1)＝SR·rand(1) i＝1,2…NP,j＝1,2…M 1 -1
when the optimization variable is the array element spacing on each ring, if u ij (t+1) exceeds [0, d max -d min ]Ranges of (u) ij (t+1) is generated as follows:
u ij (t+1)＝(d max -d min )·rand(1) i＝1,2…NP,j＝1,2…M
when the optimization variable is the radius of the circular ring, each element of the individual needs to be arranged from small to large after the boundary processing is finished, and when the optimization variable is the array element interval on each circular ring, the arrangement from small to large is not needed.
The radius vector of the optimal sparse concentric ring array obtained by jointly optimizing the concentric radius and the array element spacing through the improved differential evolution algorithm is r= (0.0455,0.0985,0.155), and the number of the array elements on each optimal ring is N= (20, 26, 26).
In the microphone array optimization method for monitoring GIS abnormal sound of the present invention, the optimization effect of the microphone array is determined by evaluating the main lobe and side lobe parameters in the microphone array response spectrum, preferably, the evaluating the main lobe and side lobe parameters in the array response spectrum in the step 3) includes: the width and amplitude of the main lobe and the width and amplitude of the side lobe are evaluated.
The abnormal sound acoustic characteristics of the GIS power station equipment have stronger specificity, the abnormal sound of the GIS power station equipment is generally in the range of 100 Hz-800 Hz, the abnormal sound belongs to a low-frequency noise source, the frequency characteristics of the abnormal sound are stronger, and preferably, the typical abnormal sound acoustic signal parameters of the GIS in the step 3) comprise the frequency of the source and the signal to noise ratio. The abnormal sound source of the GIS transformer substation is mainly generated by factors such as transformer noise, part looseness, internal foreign matters and the like, and preferably, the typical abnormal sound acoustic signal parameters of the GIS comprise acoustic signal parameters of shielding cover looseness, contact looseness, bolt looseness, internal foreign matters and transformer noise abnormal sound modes.
In the microphone array optimization method for monitoring GIS abnormal sound, the pre-collected GIS typical abnormal sound acoustic signal parameters, the GIS acoustic monitoring distance and the azimuth parameters are input into the optimized microphone array model, after simulation analysis is carried out on the performance of the microphone array model, whether optimization meets the standard or not is determined through evaluation on main lobe and side lobe parameters in a microphone array response map, but the fact that an optimal result meeting the evaluation standard is difficult to obtain through the optimization model due to extreme cases or certain factors is not excluded, and a program needs to obtain a reasonable result at the moment to finish the operation step, so that in the step 4), as a preferred implementation mode, if the difference value of the evaluation results obtained in the steps 1) to 3) is within a set range, the microphone array model parameters are output and finished. If the difference between the two continuous evaluation results is very small, it can be considered that no further obvious optimization effect can be obtained, and the program can be finished to output the optimized microphone array model parameters.
Correspondingly, the invention also provides an electronic terminal, as shown in fig. 4, comprising a storage unit and a processing unit, wherein the storage unit is used for storing pre-collected abnormal acoustic signal parameters typical of the GIS, the processing unit is used for executing the steps 1) to 4) in the technical scheme, and the storage unit is also used for storing microphone array model parameters output by the processing unit.
Correspondingly, the invention also provides a computer readable storage medium which stores a computer program, and the computer program realizes the microphone array optimization method for monitoring GIS abnormal sound according to the technical scheme when being executed by a processor.
Based on the technical scheme of the specific embodiment, the microphone array optimization method for monitoring GIS abnormal sound can optimize the microphone array model to be suitable for abnormal sound monitoring of GIS power station equipment, has strong pertinence, and can improve the monitoring effect.
The electronic terminal and the computer readable storage medium are used for executing the microphone array optimization method for monitoring GIS abnormal sound, which has the beneficial effects and is not repeated here.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (10)
1. The microphone array optimization method is characterized by comprising the following steps of:
1) Simplifying the microphone array model;
2) Optimizing the microphone array to obtain an optimized microphone array model;
3) Inputting pre-collected typical abnormal acoustic signal parameters of the GIS, acoustic monitoring distance and azimuth parameters of the GIS into an optimized microphone array model, performing simulation analysis on the performance of the microphone array model, and evaluating main lobe and side lobe parameters in an array response map;
4) If the evaluation result of the step 3) does not reach the standard, repeating the steps 1) to 3); and if the evaluation result in the step 3) meets the standard, outputting the microphone array model parameters and ending.
2. The microphone array optimization method as claimed in claim 1, wherein the step 1) includes:
1.1 Initializing the radius range of the microphone array ring: defining minimum radius interval of adjacent two circular rings of microphone array as ρ min Setting the number of the circular rings as M 1 Obtaining the radius range of the microphone array model;
1.2 Initializing a microphone array element interval range: adjacent array element spacing minimum distance d for defining each ring of microphone array min In the process of generating new array element individuals for each circular ring, setting the number of array elements on the circular ring as M 2 And obtaining the array element interval range of the microphone array model.
3. The microphone array optimization method as claimed in claim 1, wherein the step 2) includes:
2.1 Initializing the population: initializing parameters of the radius of each microphone array ring or/and the array element interval on each ring and generating corresponding target vectors and variation vectors;
2.2 Cross: performing two-term intersection on the radius of each microphone array ring or/and the target vector and the variation vector of the array element interval on each ring to obtain a test vector;
2.3 Boundary condition processing.
4. The microphone array optimization method as claimed in claim 1, wherein the evaluating of the main lobe and side lobe parameters in the array response spectrum in the step 3) includes: and evaluating the width and the amplitude of the main lobe and evaluating the width and the amplitude of the side lobe.
5. The microphone array optimization method as claimed in claim 1, wherein the GIS-typical abnormal acoustic signal parameters in the step 3) include a sound source frequency and a signal-to-noise ratio.
6. The microphone array optimization method of claim 1, wherein the acoustic signal parameters of the GIS typical abnormal sound include acoustic signal parameters of shield loosening, contact loosening, bolt loosening, internal foreign objects, and transformer noise abnormal sound modes.
7. The microphone array optimization method according to claim 1, wherein in the step 4), if the difference between the evaluation results obtained by repeating the steps 1) to 3) twice before and after is within a set range, the microphone array model parameters are outputted and ended.
8. Microphone array optimization device, characterized in that it is adapted to implement the microphone array optimization method according to any of the claims 1-7.
9. An electronic terminal comprising a storage unit and a processing unit, wherein the storage unit is used for storing pre-collected parameters of typical abnormal acoustic signals of a GIS, the processing unit is used for executing the steps 1) to 4) of claim 1, and the storage unit is also used for storing model parameters of a microphone array output by the processing unit.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the microphone array optimization method of any of claims 1-7.
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