CN117113008B - X-ray-based plate thickness measuring method - Google Patents
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Abstract
The invention relates to the technical field of thickness measurement, and provides a thickness measurement method based on X-rays, which comprises the following steps: acquiring a measurement interference detection matrix; obtaining an estimated value of a noise source in the plate thickness measurement process based on a row vector in the measurement interference detection matrix; acquiring a measurement noise influence weighting coefficient of each row vector according to angle information and an estimated value of the sound sensor compared with a thickness gauge receiver; obtaining a measurement interference mapping sample according to a projection result of the measurement interference sample on an influence weighted coordinate system; acquiring a measurement interference coefficient according to the clustering result of all measurement interference mapping samples; obtaining a decomposition threshold according to the measured interference coefficient; and obtaining a denoising current data sequence based on the decomposition threshold by utilizing a wavelet denoising algorithm, and obtaining a plate thickness measurement result according to the denoising current data sequence. According to the invention, the decomposition threshold value in the wavelet denoising algorithm is determined by analyzing the influence of interference noise superposition on the electric signal in the measuring process, so that the accuracy of plate thickness measurement is improved.
Description
Technical Field
The invention relates to the technical field of thickness measurement, in particular to a thickness measurement method based on X-rays.
Background
The X-ray thickness gauge calculates the thickness of the measured material by utilizing the change of the ray intensity when the X-ray is traditional to be measured, and is a non-contact measuring instrument, and the X-ray thickness gauge is widely applied to the detection of medical images, the thickness measurement of metal plates such as aluminum plates and copper plates, and the thickness measurement of films, lithium battery diaphragms and the like at the present stage.
The problem of lower measurement accuracy exists in the use process of the current X-ray thickness gauge for complex scenes. The improvement of the measurement precision of the X-ray thickness gauge is mostly the parameter compensation of the equipment, and the measurement precision is improved by reducing the influence of interference factors of the equipment. However, in the using process of the X-ray thickness measurement, the attenuation signal of the X-ray energy is converted into a current signal for measurement, a great amount of noise caused by external interference exists in the measuring environment, the noise signals generated by different noise sources seriously affect the authenticity of thickness data, the measuring precision of the X-ray thickness measurement system is reduced, and the noise reduction treatment should be carried out on the electric signal at the receiver of the thickness meter, so that the influence of the noise of different noise sources on the measuring result is eliminated.
Disclosure of Invention
The invention provides a plate thickness measuring method based on X rays, which aims to solve the problem that noise in the working environment of an X-ray thickness meter causes errors to X-ray thickness measurement data, and adopts the following technical scheme:
an embodiment of the invention is an X-ray based plate thickness measurement method, comprising the steps of:
acquiring a measurement interference detection matrix composed of a plurality of monitoring data in a measurement environment;
obtaining an estimated value of a noise source in the plate thickness measurement process based on a row vector in a measurement interference detection matrix by using a MUSIC algorithm;
acquiring a measurement noise influence weighting coefficient of each row vector according to the angle information of the sound sensor compared with the thickness gauge receiver and the estimated value;
taking each column element in the measurement interference detection matrix as a measurement interference sample, and acquiring a measurement interference mapping sample according to the projection result of the measurement interference sample on an influence weighted coordinate system; acquiring a measurement interference coefficient according to the clustering result of all measurement interference mapping samples;
obtaining a decomposition threshold according to the measured interference coefficient; and obtaining a denoising current data sequence based on the decomposition threshold by utilizing a wavelet denoising algorithm, and obtaining a plate thickness measurement result according to the denoising current data sequence.
Preferably, the method for obtaining the measurement interference detection matrix composed of multiple monitoring data in the measurement environment comprises the following steps:
a preset number of monitoring points are determined around a receiver of the X-ray thickness gauge, and a noise monitoring data sequence is obtained at each monitoring point by utilizing a sound sensor;
collecting a sequence formed by current signals in an ionization chamber of the X-ray thickness gauge by using a current sensor as a current monitoring data sequence;
and taking a matrix formed by all the noise monitoring data sequences and the current monitoring data sequences as a measurement interference detection matrix.
Preferably, the method for obtaining the estimated value of the noise source in the plate thickness measurement process based on the row vector in the measurement interference detection matrix by using the MUSIC algorithm comprises the following steps:
using all noise monitoring data sequences as input of a MUSIC algorithm, and performing sound source localization on noise received in the measuring process of the X-ray thickness gauge by using the MUSIC algorithm to obtain the number of noise sources and the angle value of each noise source;
the estimated value of the noise source consists of two parts, namely the number of noise sources and the angle value of the noise sources.
Preferably, the method for obtaining the weighting coefficient of the measured noise influence of each row vector according to the angle information of the acoustic sensor compared with the thickness gauge receiver and the estimated value comprises the following steps:
taking each noise monitoring data sequence as a row vector, taking the position of any sound sensor as a reference direction, and taking the angle of each sound sensor, which is determined according to the clockwise sequence, relative to the reference direction as the relative reference angle of each sound sensor;
taking the absolute value of the difference between the relative reference angle of each sound sensor and the average value of all noise source angle values as the acquisition deviation angle of each sound sensor;
taking the sum of the collection deviation angles of all the sound sensors as a denominator, and taking the ratio of the collection deviation angle of each sound sensor to the denominator as a scale factor of each sound sensor;
and taking the difference value of the preset parameter and the scale factor of each sound sensor as a measurement noise influence weighting coefficient of the corresponding row vector of each sound sensor.
Preferably, the method for obtaining the measurement interference mapping sample according to the projection result of the measurement interference sample on the influence weighted coordinate system comprises the following steps:
acquiring an influence weighted coordinate system according to the measured noise influence weighted coefficients of all the row vectors;
and taking the mapping result of each measurement interference sample in the influence weighted coordinate system as a measurement interference mapping sample corresponding to each measurement interference sample.
Preferably, the method for obtaining the influence weighted coordinate system according to the measured noise influence weighted coefficients of all row vectors comprises the following steps:
and taking the sequencing results of the measured noise influence weighting coefficients of the row vectors corresponding to all the sound sensors according to the descending order as a weighted sequencing sequence, respectively utilizing data in the noise monitoring data sequences corresponding to the first element and the second element in the weighted sequencing sequence to form an abscissa and an ordinate, and taking a coordinate system formed by the abscissa and the ordinate as an influence weighted coordinate system.
Preferably, the method for obtaining the measurement interference coefficient according to the clustering result of all the measurement interference mapping samples comprises the following steps:
acquiring a measurement interference association coefficient and a short-time energy sequence of each cluster according to the clustering result of all measurement interference mapping samples;
taking the sum of the measured interference association coefficient of each cluster and the preset parameter as a first composition factor; taking the product of the normalized average value of all elements in the short-time energy sequence of each cluster and the first composition factor as a first accumulation factor;
taking the average value of the first accumulation factors on all the clusters as a measured interference coefficient.
Preferably, the method for obtaining the measurement interference association coefficient and the short-time energy sequence of each cluster according to the clustering result of all the measurement interference mapping samples comprises the following steps:
taking a sequence formed by data in a noise monitoring data sequence corresponding to a first element and a second element in a weighted sequencing sequence in each cluster according to the ascending order of time as a measurement interference correlation sequence of each cluster, and taking a similarity measurement result between the measurement interference correlation sequences as a measurement interference correlation coefficient of each cluster;
and carrying out short-time energy analysis on each measurement interference association sequence of each cluster, and taking a sequence formed by the energy values of each measurement interference association sequence according to the ascending order as a short-time energy sequence corresponding to each measurement interference association sequence.
Preferably, the method for obtaining the decomposition threshold according to the measured interference coefficient comprises the following steps:
taking the difference value between the preset parameter and the measured interference coefficient as the adjustment quantity of each layer of wavelet component; acquiring an initial threshold value of each layer of wavelet component by using a visual Shrink algorithm;
the product of the adjustment amount of each layer wavelet component and the initial threshold value is taken as a decomposition threshold value of each layer wavelet.
Preferably, the method for obtaining the plate thickness measurement result according to the denoising current data sequence comprises the following steps:
taking the current monitoring data sequence as the input of a wavelet denoising algorithm, and recording the denoised current monitoring data sequence obtained by the wavelet denoising algorithm based on the decomposition threshold value of each layer of wavelet as a denoised current data sequence;
the denoising current data sequence is used as a receiving signal of an X-ray thickness gauge receiver, and a result obtained by processing the denoising current sequence through a conversion unit and a computer is used as a plate thickness measuring result.
The beneficial effects of the invention are as follows: according to the method, the direction of the working environment noise source of the X-ray thickness gauge is analyzed, the correlation influence degree of the environment noise in different directions on the current signal received by the receiver is considered, an influence weighted coordinate system is constructed according to the correlation influence characteristics of the noise in different directions and the current signal, the measurement interference index is calculated through the mapping result of the monitoring data on the influence weighted coordinate system, the influence of the noise monitoring data at different acquisition moments can be eliminated by the influence weighted coordinate system, and the sound sensor closest to the noise source is conveniently selected; and secondly, the adjustment quantity of the threshold value in each layer of wavelet component is obtained based on the measured interference index, and the method has the advantages that the proper decomposition threshold value can be selected to process each layer of wavelet component in consideration of the noise association influence characteristics, so that a more accurate noise reduction result is obtained, the reliability of the current signal in the conversion unit fed into the thickness gauge is improved, and the accuracy of metal thickness measurement is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a method for X-ray based plate thickness measurement according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a linear array of acoustic sensors according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for measuring a thickness of a plate based on X-rays according to an embodiment of the invention is shown, the method includes the following steps:
and S001, acquiring a measurement interference detection matrix composed of various monitoring data in the measurement environment.
And selecting a metal plate to be measured with a proper size, stably placing the metal plate to be measured between a transmitter and a receiver of the X-ray thickness gauge, and transmitting X-rays to the metal plate to be measured by the transmitter until the converted current signal is received by the ionization chamber. In the measuring process, n monitoring points are uniformly selected around a receiver, each monitoring point is provided with a sound sensor for collecting environmental noise of the receiver in the measuring process, a current sensor is arranged in an ionization chamber and used for collecting current signals received by the receiver, the size of n is a tested value 6, the time interval between two adjacent signal collection is t, each sensor collects m times of data, the size of t is a tested value 2s, the size of m is a tested value 800, a sequence formed by the data collected by each sound sensor according to a time ascending order is used as a noise monitoring data sequence of each sound sensor, the sequence formed by the data collected by the current sensor according to the time ascending order is recorded as a current monitoring data sequence, and a measuring interference detection matrix C is constructed by using the monitoring data sequence.
Wherein,、/>noise monitoring data sequences of the first and the second sound sensor, respectively,/->Is a current monitoring data sequence, wherein +.>、/>The voltage value output by the first and the second sound sensor respectively, +.>The current sensor collects the current for the 1 st time, and the definition of the rest elements is similar to the definition of the elements.
Thus, a measurement interference detection matrix is obtained and is used for subsequently acquiring the estimated value of the noise source localization.
Step S002, obtaining the estimated value of the noise source, and obtaining the measured noise influence weighting coefficient based on the angle information of the sound sensor compared with the thickness gauge receiver and the estimated value.
In the process of measuring the thickness of the metal plate to be detected, if noise exists in the working environment of the thickness meter, the interference degree of noise sources at different positions on the current signal received by the thickness meter receiver is different, so that the invention aims to analyze the noise signal source from the integral characteristic, further analyze the correlation degree of the mutual influence of the noise signal and the current signal, determine the decomposition threshold value of each layer of wavelet component in the wavelet denoising algorithm according to the correlation degree, and realize the thickness measurement of the metal plate to be detected.
Specifically, all the sound sensors are arranged on the receiver of the X-ray thickness gauge as a linear array model, as shown in fig. 2, the position of one sound sensor is taken as the reference direction, namely, the 0 DEG direction, the angle of each sound sensor relative to the reference direction is determined in the clockwise direction of the reference direction, the angle of each sound sensor relative to the reference direction is recorded as the relative reference angle, and the i-th relative reference angle is recorded as the i-th relative reference angle。
And secondly, taking each noise monitoring data sequence in the measurement interference detection matrix C as a row vector, taking 6 noise monitoring data sequences as input of a MUSIC algorithm, adopting DOA estimation based on the MUSIC algorithm to process, setting the number of array elements in the MUSIC algorithm to be 6, setting the prior number to be 4, obtaining an estimated value of an environmental noise source of an X-ray thickness gauge, wherein the DOA estimation based on the MUSIC algorithm is a known technology, and a specific process is not repeated.
Further, the angle value of each noise source is used as the noise source angle sequence according to the sequence formed by sorting from small to largeWherein->Refers to the angle value of the 1 st noise source. And analyzing the influence degree of the superposition of the environmental noise of the X-ray thickness gauge according to the noise source angle sequence, specifically, the closer the noise source is to the receiver in the environment, the stronger the energy of the noise signal and the larger the interference influence on the thickness measurement. Taking into consideration the acquisition of the measurement noise influence weighting coefficient V of each sound sensor by using the relative reference angle of the sound sensor and the noise source angle, calculating the measurement noise influence weighting coefficient +_ of the ith sound sensor>：
Where n is the number of acoustic sensors in the present invention, n is a checked value of 6,、/>the ith and the h relative reference angle, respectively +.>Is the noise source angle sequence->Average of all elements in the list.
Wherein the position of the ith sound sensor is deviated from the positioning result of the noise sourceThe greater the degree of separation, the smaller the influence of noise source on the ith sound sensor, the greater the deviation of the angle value of the ith relative reference angle and the noise source, and the acquisition deviation angleThe larger the value of (2), the scale factor +.>The greater the value of (2), the corresponding, +.>The smaller the value of (2).
Further, if the sound sensors are close to different noise sources, the influence degree of the noise signals on the current signals is different, and the measurement noise influence weighting coefficients of the time sequence of the monitoring data acquired by each sound sensor are taken into consideration of the difference of the influence degree of the intensity of the noise sources on the current signals as measurement noise influence judgment sequences according to sequences formed by sorting from big to small. The noise superposition of different sources is already contained in the sound sensor in the working process of the X-ray thickness gauge when the data are collected, so that the monitoring data collected by the sound sensor close to the position where the noise source is stronger are selected, and the interference of the association influence degree between the measuring noise signals on the current signals can be further analyzed.
Thus, the measurement noise influence weighting coefficient of each sound sensor is obtained and used for calculating the subsequent measurement interference coefficient.
Step S003, a measurement interference mapping sample is obtained according to the projection result of the measurement interference sample on the influence weighted coordinate system, and a measurement interference coefficient is obtained according to the clustering result of the measurement interference mapping sample.
In order to avoid analysis of noise interference by data acquired at different acquisition moments, mapping processing is carried out on a noise monitoring data sequence. Specifically, the sequence of each column of elements in the measurement interference detection matrix C is used as a measurement interference sample, e.gThe mth measurement interference sample representing the measurement interference matrix is respectively used +.>、/>Elements in the noise monitoring data sequences of the 1 st and 2 nd sound sensors form an abscissa and an ordinate, a coordinate system constructed by the abscissa and the ordinate is marked as an influence weighted coordinate system, and a mapping result of each measurement interference sample in the influence weighted coordinate system is used as a measurement interference mapping sample corresponding to each measurement interference sample. Judging sequence +.>Middle->、Two sound sensors representing the closest noise source by +.>、/>The corresponding noise monitoring data sequences acquired by the two sound sensors are used for mapping the measurement interference samples, so that the associated influence degree of noise interference on the current signals can be further analyzed.
Further, clustering is performed on all measurement interference mapping samples by using a fuzzy C-means clustering algorithm to obtain a clustering result of the measurement interference mapping samples, the number of clusters is recorded as K, the fuzzy C-means clustering algorithm is a known technology, and the specific process is not repeated. Analyzing each cluster according to the obtained clustering result of the measurement interference mapping sample, and monitoring the noise collected by the 1 st and 2 nd sound sensors in each clusterThe data in the data sequence respectively form a measurement interference association sequence according to the time ascending order、/>, />、/>Respectively as noise monitoring data sequences in each cluster that are more relevant to the influence of the current signal at the receiver, thus by +_ in each cluster>、/>The degree of the associated influence on the current signal is further analyzed.
Specifically, each cluster is formed by、/>The pearson correlation coefficient between as the measured interference correlation coefficient r for each cluster. Second, for->And->Performing short-time energy analysis to obtain +.>And->Short-term energy value of each element of (a) will be +.>And->The short-term energy values of all elements in (a) are taken as ++>And->Is>And->The specific implementation process of pearson correlation coefficient and short-time energy analysis is a known technology, and will not be described in detail.
Calculating a measurement interference coefficient according to short-time energy sequences among different clusters：
Where K is the number of clusters of measurement interference mapped samples,is a normalization function->Is the measured interference correlation coefficient of the kth cluster,/->、/>The short-term energy sequences in the kth cluster are +.>、/>Mean value of all elements in>Is a parameter regulating factor, and is a herb of Jatropha curcas>The function of (a) is to avoid the influence on the calculation result when the interference correlation coefficient measured in a single cluster is 0,/b)>The size of (2) is 0.01.
Wherein, the measurement interference associated sequence corresponding to the kth cluster、/>The stronger the positive correlation between them, the measuring interference correlation coefficient +.>The larger the value of (2), the first composition factor +.>The greater the value of (2); measuring interference related sequence->、/>The more pronounced the energy characteristic of the short-time energy sequence of (2) is, the average energy value +.>、/>The larger the value of (a) is, the first accumulation factorThe greater the value of (2); i.e. < ->The larger the value of (c) is, the more severe the superposition of ambient noise at the receiver is, the more severe the noise source-related interference conditions at different locations are, with greater impact on the X-ray thickness measurement.
Thus, the measured interference coefficient is obtained, and the subsequent determination of the decomposition threshold value of each layer is performed.
And S004, obtaining a decomposition threshold according to the measured interference coefficient, obtaining a denoising current data sequence based on the decomposition threshold by utilizing a wavelet denoising algorithm, and obtaining a measurement result according to the denoising current data sequence.
According to the invention, the influence of noise sources at different positions on the current monitoring data sequence is eliminated by utilizing a wavelet denoising algorithm, and the superposition influence degree is different at different acquisition moments due to the superposition interference of noise sources at different angles, so that the decomposition threshold value of each layer of wavelet components is self-adaptively obtained according to the measurement interference coefficient when the current monitoring data sequence is decomposed by utilizing wavelet denoising. Furthermore, the initial threshold value of each layer of wavelet component is obtained by using the visual Shrink algorithm, the processing mode of the hard threshold value is adopted for processing, the initial threshold value of each layer of wavelet component is adjusted according to the measurement association index, and the specific calculation process of the visual Shrink algorithm is a known technology and is not repeated.
Obtaining the decomposition threshold of each wavelet component according to the measured interference coefficient, and obtaining the decomposition threshold of the M-layer wavelet component：
In the method, in the process of the invention,is the initial threshold value of the M-th layer wavelet component obtained by using the VisuShrink algorithm,/and->Is a parameter-adjusting coefficient,/->Is to prevent occurrence of bad noise canceling effect due to excessive decomposition threshold value, < ->The size of (2) is 1.5.
Wherein, the more serious the superposition phenomenon of the environmental noise of different sound sources at the receiver, the greater the interference degree of the environmental noise to the current signal at the receiver, the measured interference coefficient is calculatedThe larger the decomposition threshold should be lowered>The noise reduction effect is enhanced.
According to the steps, the decomposition threshold value of each layer of wavelet component is obtained, the haar wavelet is used as a basis function in a wavelet denoising algorithm, the wavelet denoising algorithm is used for denoising the current monitoring data sequence, interference of different noise sources is eliminated, the denoised current monitoring data sequence is recorded as a denoised current data sequence, and the wavelet denoising algorithm is a known technology and is not repeated in a specific process.
Further, the denoising current data sequence is used as a current signal acquired by a receiver in the X-ray thickness gauge, the denoising current data sequence is sent to a conversion unit and a computer of the X-ray thickness gauge, a thickness measurement result of the metal plate to be detected is obtained by using a thickness calculation formula, the calculation formula between the thickness and the ray intensity in the X-ray thickness gauge is a known technology, and the specific process is not repeated.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. An X-ray based plate thickness measuring method, characterized in that the method comprises the steps of:
acquiring a measurement interference detection matrix composed of a plurality of monitoring data in a measurement environment;
obtaining an estimated value of a noise source in the plate thickness measurement process based on a row vector in a measurement interference detection matrix by using a MUSIC algorithm;
acquiring a measurement noise influence weighting coefficient of each row vector according to the angle information of the sound sensor compared with the thickness gauge receiver and the estimated value;
taking each column element in the measurement interference detection matrix as a measurement interference sample, and acquiring a measurement interference mapping sample according to the projection result of the measurement interference sample on an influence weighted coordinate system; acquiring a measurement interference coefficient according to the clustering result of all measurement interference mapping samples;
obtaining a decomposition threshold according to the measured interference coefficient; obtaining a denoising current data sequence based on a decomposition threshold by utilizing a wavelet denoising algorithm, and obtaining a plate thickness measurement result according to the denoising current data sequence;
the method for acquiring the measurement interference detection matrix composed of a plurality of monitoring data in the measurement environment comprises the following steps: a preset number of monitoring points are determined around a receiver of the X-ray thickness gauge, and a noise monitoring data sequence is obtained at each monitoring point by utilizing a sound sensor; collecting a sequence formed by current signals in an ionization chamber of the X-ray thickness gauge by using a current sensor as a current monitoring data sequence; taking a matrix formed by all noise monitoring data sequences and current monitoring data sequences as a measurement interference detection matrix;
the method for obtaining the estimated value of the noise source in the plate thickness measuring process based on the row vector in the measuring interference detection matrix by using the MUSIC algorithm comprises the following steps: using all noise monitoring data sequences as input of a MUSIC algorithm, and performing sound source localization on noise received in the measuring process of the X-ray thickness gauge by using the MUSIC algorithm to obtain the number of noise sources and the angle value of each noise source; the estimated value of the noise source consists of two parts, namely the number of noise sources and the angle value of the noise sources;
the method for obtaining the decomposition threshold according to the measured interference coefficient comprises the following steps: taking the difference value between the preset parameter and the measured interference coefficient as the adjustment quantity of each layer of wavelet component; acquiring an initial threshold value of each layer of wavelet component by using a visual Shrink algorithm; taking the product of the adjustment quantity of each layer of wavelet component and the initial threshold value as a decomposition threshold value of each layer of wavelet;
the method for obtaining the plate thickness measuring result according to the denoising current data sequence comprises the following steps: taking the current monitoring data sequence as the input of a wavelet denoising algorithm, and recording the denoised current monitoring data sequence obtained by the wavelet denoising algorithm based on the decomposition threshold value of each layer of wavelet as a denoised current data sequence; the denoising current data sequence is used as a receiving signal of an X-ray thickness gauge receiver, and a result obtained by processing the denoising current sequence through a conversion unit and a computer is used as a plate thickness measuring result.
2. The X-ray-based plate thickness measurement method according to claim 1, wherein the method of acquiring the measured noise influence weighting coefficient of each row vector from the angle information of the acoustic sensor compared to the thickness gauge receiver and the estimated value is:
taking each noise monitoring data sequence as a row vector, taking the position of any sound sensor as a reference direction, and taking the angle of each sound sensor, which is determined according to the clockwise sequence, relative to the reference direction as the relative reference angle of each sound sensor;
taking the absolute value of the difference between the relative reference angle of each sound sensor and the average value of all noise source angle values as the acquisition deviation angle of each sound sensor;
taking the sum of the collection deviation angles of all the sound sensors as a denominator, and taking the ratio of the collection deviation angle of each sound sensor to the denominator as a scale factor of each sound sensor;
and taking the difference value of the preset parameter and the scale factor of each sound sensor as a measurement noise influence weighting coefficient of the corresponding row vector of each sound sensor.
3. The X-ray based plate thickness measurement method according to claim 1, wherein the method for obtaining the measurement interference mapping sample according to the projection result of the measurement interference sample on the influence weighted coordinate system is as follows:
acquiring an influence weighted coordinate system according to the measured noise influence weighted coefficients of all the row vectors;
and taking the mapping result of each measurement interference sample in the influence weighted coordinate system as a measurement interference mapping sample corresponding to each measurement interference sample.
4. The X-ray-based plate thickness measurement method according to claim 3, wherein the method of obtaining the influence weighted coordinate system from the measurement noise influence weighted coefficients of all the row vectors is:
and taking the sequencing results of the measured noise influence weighting coefficients of the row vectors corresponding to all the sound sensors according to the descending order as a weighted sequencing sequence, respectively utilizing data in the noise monitoring data sequences corresponding to the first element and the second element in the weighted sequencing sequence to form an abscissa and an ordinate, and taking a coordinate system formed by the abscissa and the ordinate as an influence weighted coordinate system.
5. The X-ray based plate thickness measurement method according to claim 1, wherein the method for obtaining the measurement interference coefficient from the clustering result of all the measurement interference mapping samples is:
acquiring a measurement interference association coefficient and a short-time energy sequence of each cluster according to the clustering result of all measurement interference mapping samples;
taking the sum of the measured interference association coefficient of each cluster and the preset parameter as a first composition factor; taking the product of the normalized average value of all elements in the short-time energy sequence of each cluster and the first composition factor as a first accumulation factor;
taking the average value of the first accumulation factors on all the clusters as a measured interference coefficient.
6. The X-ray based plate thickness measurement method according to claim 5, wherein the method for obtaining the measurement interference correlation coefficient and the short-time energy sequence of each cluster according to the clustering result of all the measurement interference mapping samples comprises the following steps:
taking a sequence formed by data in a noise monitoring data sequence corresponding to a first element and a second element in a weighted sequencing sequence in each cluster according to the ascending order of time as a measurement interference correlation sequence of each cluster, and taking a similarity measurement result between the measurement interference correlation sequences as a measurement interference correlation coefficient of each cluster;
and carrying out short-time energy analysis on each measurement interference association sequence of each cluster, and taking a sequence formed by the energy values of each measurement interference association sequence according to the ascending order as a short-time energy sequence corresponding to each measurement interference association sequence.
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