阅读说明：本技术 一种非全时半航空瞬变电磁数据运动噪声去除方法及系统 (Method and system for removing motion noise of non-full-time semi-aviation transient electromagnetic data ) 是由 杨洋 李虎 张衡 王鑫 杜晓峰 周长宇 孙怀凤 于 2021-09-03 设计创作，主要内容包括：本发明提供了一种非全时半航空瞬变电磁数据运动噪声去除方法及系统,所述方法包括以下步骤：获取时域二次场样本数据,及其采样频率和样本数据周期；将所述二次场样本数据包含的多个半周期样本数据延拓至全时长；对全时长的运动噪声基线进行初步拟合,并通过频谱分析得到期望频率；根据所述期望频率,利用离散傅里叶逆变换反演得到全时长的时域运动噪声；从二次场样本数据中将所获得运动噪声剔除。本发明基于半航空瞬变电磁晚期数据进行运动噪声的拟合并从原始数据中去除,能够有效识别并剔除数据中的运动噪声,去噪效果良好。(The invention provides a method and a system for removing motion noise of non-full-time semi-aviation transient electromagnetic data, wherein the method comprises the following steps: acquiring time domain secondary field sample data, and sampling frequency and sample data period thereof; extending a plurality of half-period sample data contained in the secondary field sample data to full time length; performing preliminary fitting on a full-time motion noise baseline, and obtaining expected frequency through spectrum analysis; according to the expected frequency, obtaining time domain motion noise of the full duration by utilizing inverse discrete Fourier transform inversion; and removing the obtained motion noise from the secondary field sample data. The method performs motion noise fitting and removal from the original data based on the semi-aviation transient electromagnetic late data, can effectively identify and remove the motion noise in the data, and has good denoising effect.)

1. A non-full-time semi-aviation transient electromagnetic data motion noise removing method is characterized by comprising the following steps:

acquiring time domain secondary field sample data, and sampling frequency and sample data period thereof;

extending a plurality of half-period sample data contained in the secondary field sample data to full time length;

performing preliminary fitting on a full-time motion noise baseline, and obtaining expected frequency through spectrum analysis;

according to the expected frequency, obtaining time domain motion noise of the full duration by utilizing inverse discrete Fourier transform inversion;

and removing the obtained motion noise from the secondary field sample data.

2. The method of claim 1, wherein extending the secondary field sample data to full time comprises:

and corresponding each data sample point to the real time of acquisition.

3. The method for removing the motion noise of the non-full-time semi-aviation transient electromagnetic data as claimed in claim 1, wherein the method for determining the late data range comprises the following steps:

and superposing all half-period sample data, and determining a low-energy smooth area as a late data range.

4. The method for removing motion noise of non-full-time semi-aviation transient electromagnetic data according to claim 1, characterized in that a Legendre polynomial is adopted for preliminary fitting of a full-time motion noise baseline; and selecting the highest order of the Legendre polynomial based on the fact that the average energy of the residual noise after the polynomial fitting curve of the late data removal is smaller than the set percentage of the average energy relative to the average value before denoising.

5. The method for removing motion noise from non-full-time semi-airborne transient electromagnetic data according to claim 1, wherein obtaining the desired frequency through spectral analysis comprises: and accumulating the frequency energy values in the motion noise frequency range from small to large, and recording the current frequency as the maximum value of the expected frequency when the accumulated value exceeds the set proportion of the sum of the frequency energy values.

6. The method for removing motion noise of non-full-time semi-aviation transient electromagnetic data according to claim 1, wherein obtaining the full-time domain motion noise by inverse discrete fourier transform inversion comprises:

solving a frequency domain coefficient of the noise by constructing an over-determined equation set according to the plurality of time domain sample points and the expected frequency; and the overdetermined equation set is obtained based on Fourier series construction.

7. The method for removing motion noise of non-full-time semi-aviation transient electromagnetic data according to claim 6, wherein the over-determined system of equations is solved by least squares inversion.

8. A non-full time semi-airborne transient electromagnetic data motion noise removal system, comprising:

the sampling data acquisition module is used for acquiring time domain secondary field sample data, sampling frequency and sample data period thereof;

the sampling data processing module is used for extending a plurality of half-period sample data contained in the secondary field sample data to the full time length;

the expected frequency acquisition module is used for carrying out preliminary fitting on a full-time motion noise baseline and obtaining expected frequency through spectral analysis;

the motion noise fitting module is used for obtaining time domain motion noise of the full duration by utilizing inverse discrete Fourier transform inversion according to the expected frequency;

and the motion noise removing module is used for removing the obtained motion noise from the secondary field sample data.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the non-full time semi-airborne transient electromagnetic data motion noise removal method according to any of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that the program, when being executed by a processor, is adapted to carry out a method for motion noise removal of non-full time semi-airborne transient electromagnetic data according to any of the claims 1-7.

Technical Field

The invention relates to the technical field of semi-aviation transient electromagnetic exploration, in particular to a method and a system for removing motion noise of non-full-time semi-aviation transient electromagnetic data.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

Semi-airborne transient electromagnetic method (SATEM) is an emerging geophysical prospecting method by placing a transmitting source on the ground and receiving signals in the air. The ground transmission and aerial reception working mode is adopted, the mode combines the advantages of high-power transmission of a ground Transient Electromagnetic Method (TEM) and aerial rapid exploration of an aeronautical transient electromagnetic method (AEM), and the problems that the traditional ground exploration method cannot be effectively carried out under the condition of complex terrain, and the aeronautical electromagnetic exploration is not suitable and has high cost are solved. The method has the characteristics of secondary field observation, strong adaptability, high sensitivity and strong resolution, and can provide a new exploration technical means in the areas with complex terrain.

In the data acquisition process, the receiving coil and the aircraft are fixed in the air through flexible connection, so that the receiving coil is always in an unstable motion state in the flight process, and motion noise is generated and becomes one of the most serious noises influenced by a semi-aviation transient electromagnetic method. The coil motion noise is induced electromotive force formed by the change of magnetic flux inside a coil due to the cutting of a geomagnetic field by a receiving coil, the noise frequency is low, the noise is mainly concentrated at 0-1000 Hz and exists along with the measurement work all the time, the noise is a main noise source of aviation electromagnetic exploration, and the elimination of the motion noise in electromagnetic data is the key for obtaining a useful and correct exploration result.

In operation of the SATEM system, a primary pulsed field is transmitted from a source at the surface of the earth into the ground at a current. When the current of the excitation source is suddenly cut off, the medium of the underground detection area is excited to form a secondary field which is penetrated annularly and contains the earth electricity information so as to maintain a stable electromagnetic field (primary field) before the current is cut off, and the change characteristic of the secondary field is closely related to the electrical distribution of the underground detection medium, so that the attenuation characteristic of the secondary field is observed in the cut-off gap of the primary field, and the effective exploration of the underground medium is facilitated.

However, since the data acquired when the device is turned off is not full-time, the existing denoising method cannot effectively remove the coil motion noise.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a method and a system for removing motion noise of non-full-time semi-aviation transient electromagnetic data. The motion noise in the data can be effectively identified and eliminated, and the denoising effect is good.

In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:

a non-full-time semi-aviation transient electromagnetic data motion noise removing method comprises the following steps:

acquiring time domain secondary field sample data, and sampling frequency and sample data period thereof;

extending a plurality of half-period sample data contained in the secondary field sample data to full time length;

performing preliminary fitting on a full-time motion noise baseline, and obtaining expected frequency through spectrum analysis;

according to the expected frequency, obtaining time domain motion noise of the full duration by utilizing inverse discrete Fourier transform inversion;

and removing the obtained motion noise from the secondary field sample data.

Further, extending the secondary field sample data to full duration comprises: and corresponding each data sample point to the real time of acquisition.

Further, the late data range determination method comprises the following steps: and superposing all half-period sample data, and determining a low-energy smooth area as a late data range.

Further, a Legendre polynomial is adopted for preliminary fitting of the full-time motion noise baseline; and selecting the highest order of the Legendre polynomial based on the fact that the average energy of the residual noise after the polynomial fitting curve of the late data removal is smaller than the set percentage of the average energy relative to the average value before denoising.

Further, obtaining the desired frequency by spectral analysis includes: and accumulating the frequency energy values in the motion noise frequency range from small to large, and recording the current frequency as the maximum value of the expected frequency when the accumulated value exceeds the set proportion of the sum of the frequency energy values.

Further, obtaining the full-time domain motion noise by inverse discrete fourier transform (dft) inversion comprises:

solving a frequency domain coefficient of the noise by constructing an over-determined equation set according to the plurality of time domain sample points and the expected frequency; and the overdetermined equation set is obtained based on Fourier series construction.

Further, the over-determined system of equations is solved by least squares inversion.

One or more embodiments provide a non-full time semi-airborne transient electromagnetic data motion noise removal system, comprising:

the sampling data acquisition module is used for acquiring time domain secondary field sample data, sampling frequency and sample data period thereof;

the sampling data processing module is used for extending a plurality of half-period sample data contained in the secondary field sample data to the full time length;

the expected frequency acquisition module is used for carrying out preliminary fitting on a full-time motion noise baseline and obtaining expected frequency through spectral analysis;

the motion noise fitting module is used for obtaining time domain motion noise of the full duration by utilizing inverse discrete Fourier transform inversion according to the expected frequency;

and the motion noise removing module is used for removing the obtained motion noise from the secondary field sample data.

One or more embodiments provide an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the non-full time semi-airborne transient electromagnetic data motion noise removal method when executing the program.

One or more embodiments provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the non-full time semi-airborne transient electromagnetic data motion noise removal method.

The above one or more technical solutions have the following beneficial effects:

the method comprises the steps of firstly extending non-full-time electromagnetic data to full time, and then fitting a motion noise baseline based on late data, so as to remove motion noise.

(1) The non-full-time electromagnetic data is extended to full time length, so that the non-full-time electromagnetic data conforms to the continuity rule of electromagnetic signals and motion noise, and a better denoising result can be obtained.

(2) Because the early and middle attenuation characteristics of the semi-aviation transient electromagnetic secondary field can influence the development trend of fitting motion noise, based on the characteristics that effective signals of transient electromagnetic late data are very small and almost submerged by noise, the influence can be eliminated only by utilizing the late data and partial data of the front end of the pulse, and therefore full-time motion noise can be better reconstructed.

(3) In the data acquisition process, as the receiving coil continuously moves and swings, larger motion noise is generated, and the acquired data cannot be directly used. This variation of the coil resembles a simple pendulum motion with a certain periodicity characteristic, while the fourier series has natural advantages for representing a periodic or nearly periodic signal. Therefore, the motion noise in the full time is reconstructed by utilizing the Fourier orthogonal base, and an accurate fitting base line can be obtained from the physical essence generated by the motion noise. And after a fitting base line of the motion noise is obtained, removing the motion noise from the original data to finish denoising. By denoising the simulation data and the measured data, the motion noise in the data can be effectively identified and eliminated, and the denoising effect is good.

And preliminarily fitting the motion noise baseline by adopting a Legendre polynomial, and performing spectrum analysis and energy accumulation so as to obtain more proper Fourier orthogonal fundamental frequency, namely the expected frequency.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.

FIG. 1 is a schematic diagram of semi-airborne transient electromagnetic surveying;

FIG. 2 is a flow diagram of a method for removing motion noise from non-full time semi-airborne transient electromagnetic data in accordance with one or more embodiments of the present invention;

FIG. 3 is a schematic diagram of full-time extension and late stage data extraction of secondary field data in one or more embodiments of the invention;

FIG. 4 is a graph illustrating data attenuation curves for semi-airborne transient electromagnetic secondary fields in accordance with one or more embodiments of the present invention;

FIG. 5 is a schematic representation of Legendre polynomial fitting in one or more embodiments of the invention;

FIG. 6 is a graphical illustration of Legendre fit baseline spectral analysis and energy accumulation in one or more embodiments of the invention;

FIG. 7 is a graphical illustration of a Fourier series fit in accordance with one or more embodiments of the invention;

FIG. 8 is a graph comparing an attenuation curve of raw data with an attenuation curve of denoised data in one or more embodiments of the invention.

Detailed Description

The invention is further described with reference to the following figures and examples.

It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

Example one

The embodiment provides a method for removing motion noise of non-full-time semi-aviation transient electromagnetic data, as shown in fig. 2, comprising the following steps:

step 1: and determining the sampling frequency, the number of sample points, the sample data period and the number of sample points in each period of the time domain secondary field sample data.

Step 2: and determining the real time corresponding to all the sample points, and extending the observed secondary field sample data to the full time. Firstly, dividing each sample point of the response section according to the half cycle to which the sample point belongs, and assuming that a sample points and b half cycles are in total, each half cycle has a/b sample points. The time intervals between the sample points are consistent, and a half-period time interval exists between every two adjacent half periods. Assuming that a half-cycle time interval is unit "1", the measured 1 st, 2 nd and 3 rd 3 … … b half-cycle sample data are respectively moved to the 1 st, 3 rd and 5 … … 2b-1 st half-cycle positions, and the 2 nd, 4 th and 6 … … 2b positions are not processed.

And step 3: determining a late data range according to all half-period data superposition curves, primarily fitting the late data of the secondary field extended to the full duration by using a Legendre polynomial, performing spectrum analysis on the fitted curves, accumulating the energy of the obtained spectrum, and determining the maximum value of the expected frequency (including the frequency of motion noise) by taking 80% accumulated energy sum as a boundary, wherein the accumulated range is 0 Hz-1 kHz.

And 4, step 4: and constructing an overdetermined linear equation set of motion noise of the semi-aviation transient electromagnetic late data based on Fourier series according to the expected frequency determined in the last step, solving the motion noise of the full duration through least square inversion, and removing the obtained motion noise from the original data.

In the step 1, the observation data of one point of semi-aviation transient electromagnetic exploration of the Guangxi Dai tunnel is taken as an example. The sampling frequency is 30000Hz, 300 sampling points are arranged in each half period, 32 half periods are arranged, and 9600 sampling points are arranged in each half period.

In the step 2, as shown in (a) of fig. 3, the secondary field data is extended to full duration, the acquired data is segmented (each half cycle) to correspond to the real acquired time, no interpolation is performed between the last half cycle and the start of the next half cycle, and then the subsequent denoising processing is performed. More specifically, the No. 1 to 300 and No. 301 to 600 … … sample points 9301 to 9600 are moved to the No. 1 to 300 and No. 601 to 900 … … sample points 18901 to 19200, respectively.

As shown in fig. 4 (a), the observed transient electromagnetic response signal decays exponentially, and the decay curve thereof can be divided into three stages according to the characteristics thereof: the signals at the early stage, the middle stage and the late stage are characterized as follows: in the early stage, the signal is strong, the energy is large, the attenuation is fast, and the effective signal and the noise are overlapped; in the middle period, the effective electromagnetic response signal is gradually reduced, the energy is weakened, and the noise influence is gradually increased; in the late stage, the effective electromagnetic response signal is very small, the energy is weak, and the effective electromagnetic response signal is almost submerged by noise. In the actual measurement process, after the primary pulse field is suddenly turned off, a certain process is required for the generation of the secondary field. Therefore, it is actually measured that an energy enhancement section exists before the peak of the secondary field of each half period of data, and the low-energy smooth area at the frontmost end is a noise part, which is similar to the late signal characteristic of the attenuation curve, see (b) in fig. 4. Therefore, the acquired secondary field data is discontinuous in time, and a significant step exists between the end point and the starting point of every two adjacent half cycles, so that the motion noise and the continuity of the effective electromagnetic data disappear.

In the step 3, according to the characteristics of the secondary field signal attenuation curve in three stages, effective signals in late attenuation stage are considered to be weak and almost submerged by noise. Therefore, the basic point of the fitting motion noise is only the late data point of the attenuation of each half-period secondary field, the data in the early stage and the data in the middle stage are not used for fitting the motion noise, and the attenuation characteristics in the early stage and the middle stage are ensured not to influence the development trend of the fitting motion noise.

The range of late data is first circled: and superposing all half-period data, observing the energy of each sample point position, and setting a low-energy smooth area as late-stage data. Then, the late-decay data of each half period and the data of the front end part of the pulse are read and stored in a specific matrix, and the data points are used as known noise points to fit the motion noise in the full time, so that the influence of the decay trend of the early-and-middle-period data on the fit motion noise is reduced. The graph (b) in fig. 4 shows the curves after all half cycles are superimposed, and the 11 th to 50 th sample points are considered to be attenuation early and middle data. And (4) eliminating the position data corresponding to each half period to obtain late data, which is shown in (b) of fig. 3.

The preliminary fitting of the motion noise entails determining the highest order of the legendre polynomial. And selecting the highest order of the Legendre polynomial, wherein the average energy of the residual noise after the late-stage data is attenuated and the polynomial fitting curve is removed is less than 10% of the average energy relative to the average value before denoising.

After the baseline is preliminarily fitted, spectral analysis is performed on the fitted baseline. The motion noise frequency is mainly concentrated in 0-1000 Hz, the frequency energy values in the range are accumulated from small to large, and when the accumulated value exceeds 80% of the sum of the frequency energy values of 0-1000 Hz, the current frequency is recorded as the maximum value of the expected frequency.

As shown in fig. 5, the motion noise is preliminarily fitted using a legendre polynomial of order 10. And (b) performing spectrum analysis and energy accumulation on the obtained base line, wherein the maximum value of the expected frequency is 132Hz, and the frequency interval is half of the sampling frequency divided by the total number of the sample points, namely (30000/2)/9600Hz, as shown in (a) and (b) in FIG. 6 respectively for the limit spectrum analysis result and the energy accumulation result of 0-1000 Hz. The desired frequency of 0-132Hz is used as a parameter for the subsequent overdetermined system of linear equations.

It is noted that this method is equally applicable to full time data.

In step 4, as shown in fig. 7, a fourier series for the discrete signal is constructed by inverse discrete fourier transform, and an overdetermined linear equation set is further constructed to solve the desired frequency domain coefficient value. Then, each element in the expected frequency coefficient vector x obtained by solving is placed at a position corresponding to a frequency domain, a time domain motion noise of the full duration is obtained by utilizing inverse discrete fourier transform inversion, the time domain motion noise is removed from non-full-time electromagnetic data of the corresponding position to achieve the purpose of removing the motion noise, and the result after removing the motion noise is shown in (b) of fig. 7. The comparison result of the overlay curve of the denoised data and the original data is shown in fig. 8.

The baseline fitting is based on a fourier series, which can be used to represent any signal, as shown in the graph (a) of fig. 7, with natural advantages for representing periodic signals or nearly periodic signals.

Constructing an overdetermined linear equation system based on Fourier series:

IDFT basic formula:

where f (m) is the time domain amplitude at the m-th position, F (k) is the frequency domain coefficient value at the k-th position, N is the total number of sample points, and w ═ e^2πi/N，

The IDFT basic formula is expanded into a matrix form:

assuming that f (x) in the equation is continuous sampling of motion noise, f (y) is a discrete frequency domain coefficient corresponding to the motion noise, but only part of the motion noise is actually known accurately, i.e., transient electromagnetic late data, while in the early stage, an effective signal and the motion noise are overlapped and cannot be directly separated, the motion noise generally mainly has a medium-low frequency, and energy is mainly concentrated in a medium-low frequency band, so that the motion noise does not need all frequency domain coefficients, only part of the frequency domain coefficients are needed, especially the frequency domain coefficients corresponding to the low frequency band, and if the number of known points of the noise in a time domain is greater than the number of unknown coefficients in the frequency domain, the frequency domain coefficients of the noise can be solved by constructing an over-determined equation set.

As shown in equation (3), without setting the motion noise to have two desired frequencies in the frequency domain (actually there may be more frequencies), i.e. four frequency domain coefficients, a unique solution can be obtained by selecting any four points in the time domain where the values are accurately known to construct a proper linear equation system. "desired frequencies" refer to those frequencies that contain coil motion noise.

In the formula, right variables F (1), F (2), F (N-2) and F (N-1) are desired frequencies, i.e., frequencies corresponding to motion noise. The left variables f (a), f (b), f (m), and f (n) are selected data points in the time domain.

Since other noises such as gaussian noise exist in the actual data besides the motion noise, an overdetermined linear equation system needs to be constructed by selecting more points in the time domain to solve the motion noise. Assuming that six time domain data points are selected, there are four frequency coefficients to be solved, and the overdetermined linear equation set is shown in equation (4):

in the formula (I), the compound is shown in the specification,F(k₁) Is thatComplex conjugation of (c), m₁……m₆Representing different positions of the sample point in the time domain, k₁、k₂、RepresentsThe desired frequencies are at different locations in the frequency domain.

Rewriting equation (4) to a matrix vector equation, as shown in equation (5):

Ax＝b (5)

wherein

Transposed matrix A of A is left-multiplied on both sides of equation (5)^TTo obtain

A^TAx＝A^Tb (7)

On both sides of equation (7), by left multiplication (A)^TA)^-1Solving the expected frequency coefficient vector x by using least square to obtain

x＝(A^TA)^-1A^Tb (8)

Then, putting each element in the expected frequency coefficient vector x obtained by solving to a position corresponding to a frequency domain, utilizing inverse discrete Fourier transform to perform inversion to obtain a time domain motion noise baseline of full duration, and removing the time domain motion noise baseline from non-full-time electromagnetic data of the corresponding position to achieve the purpose of removing motion noise, wherein a motion noise construction method is shown as a formula (9), namely, the whole time sequence noise is constructed by different 4 frequency coefficients:

more generally, when the mean value of the measured transient electromagnetic data significantly deviates from the position of the secondary field energy 0, a column of first-order legendre polynomials may be added to the rightmost side of the matrix a of the above equation (6) to correct the deviation of the curve, so as to obtain a more accurate result.

The first order Legendre polynomial equation is as follows:

p₁(x)＝x (10)

where x ∈ [ -1, 1], the number of x equals N, the points are spaced 2/(N-1). Solving matrix as follows (11)

In the formula p₁(m₁)……p₁(m₆) A first order Legendre polynomial, P, representing the corresponding position of a sample point in the time domain₁(k) Representing first order legendre polynomial coefficients.

Example two

The object of this embodiment is to provide a non-full-time semi-aviation transient electromagnetic data motion noise removing system based on the method of the first embodiment, where the system includes:

the sampling data acquisition module is used for acquiring time domain secondary field sample data, sampling frequency and sample data period thereof;

the sampling data processing module is used for extending a plurality of half-period sample data contained in the secondary field sample data to the full time length;

the expected frequency acquisition module is used for carrying out preliminary fitting on a full-time motion noise baseline and obtaining expected frequency through spectral analysis;

the motion noise fitting module is used for obtaining time domain motion noise of the full duration by utilizing inverse discrete Fourier transform inversion according to the expected frequency;

and the motion noise removing module is used for removing the obtained motion noise from the secondary field sample data.

EXAMPLE III

The embodiment aims at providing an electronic device.

An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to embodiment one when executing the program.

Example four

An object of the present embodiment is to provide a computer-readable storage medium.

A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to one embodiment.

The steps involved in the second to fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.