CN117349597A - Intelligent background noise reduction method for intelligent digital accurate pointing instrument - Google Patents
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Abstract
The invention relates to the technical field of data processing, and provides an intelligent background noise reduction method for an intelligent digital accurate pointing instrument, which comprises the following steps: acquiring monitoring data of an intelligent digital accurate pointing instrument; calculating an environment comprehensive noise influence coefficient according to the overall change characteristics of the data in the monitoring data; calculating a magnetic field change interference coefficient according to local change characteristics of data acquired at different time points in the monitoring data; calculating a noise interference evaluation coefficient according to the environment comprehensive noise influence coefficient and the magnetic field change interference coefficient; calculating a fixed-point internal error coefficient according to the local change characteristics of the data acquired at each time point in the monitoring data; and calculating a threshold parameter according to the noise interference evaluation coefficient and the fixed-point internal error coefficient, and carrying out noise reduction processing on the monitored fault signal based on discrete wavelet transformation through the threshold parameter. According to the invention, the parameters in discrete wavelet transformation are adjusted through the threshold parameters, so that the noise reduction effect on the monitored fault signals is improved, and the positioning precision of the fixed point instrument is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent background noise reduction method for an intelligent digital accurate pointing instrument.
Background
With the acceleration of the urban process, the construction of underground cable pipelines is rapidly increasing. Various defects can occur in the underground cable in the long-term use process due to aging, external force damage and the like, if the defects cannot be found in time, large-area power failure can be caused once the cable breaks down, and huge economic loss is caused. The acoustic-magnetic synchronous intelligent digital accurate fixed-point instrument integrates acoustic emission technology and magnetic field technology, can perform omnibearing acoustic emission detection on an underground cable, and accurately positions the defect position of the cable through magnetic field detection. The method realizes the rapid detection and accurate positioning of the defects of the underground cable, and improves the capability of preventing and positioning the cable faults.
However, due to the quality of the signal source, external noise in the signal transmission path, noise interference in the system, and the like, a large amount of noise is doped in the signal received by the staff when receiving the signal, and the noise affects the detection result of the cable defect. Because the underground cable is complicated in laying, and the change characteristics of magnetic field signals are complex, the noise reduction effect of the traditional discrete wavelet transformation and other noise reduction technologies on the acquisition of defect signals in the cable defect detection process is poor, and the defect positioning and detection precision of the underground cable by adopting the intelligent digital accurate fixed point instrument is low.
Disclosure of Invention
The invention provides an intelligent background noise reduction method for an intelligent digital accurate pointing instrument, which aims to solve the problem of poor background noise reduction effect in the intelligent digital accurate pointing instrument, and adopts the following technical scheme:
one embodiment of the invention provides an intelligent background noise reduction method for an intelligent digital accurate pointing instrument, which comprises the following steps:
acquiring monitoring data of the intelligent digital accurate fixed point instrument according to the intelligent digital accurate fixed point instrument and the ultrasonic sensor;
constructing a monitoring matrix of the fixed point instrument according to the monitoring data of the intelligent digital accurate fixed point instrument; calculating the environmental comprehensive noise influence coefficient of the fixed point instrument monitoring matrix according to the integral characteristic of the data change in the fixed point instrument monitoring matrix; calculating a magnetic field pipeline winding influence coefficient according to local characteristic differences at different acquisition moments in the fixed point instrument monitoring matrix, and acquiring a magnetic field pipeline winding influence sequence of the fixed point instrument monitoring matrix based on the magnetic field pipeline winding influence coefficient; calculating the magnetic field change characteristic value of each row of elements of the fixed point instrument monitoring matrix according to the magnetic field change characteristic of each row of elements in the fixed point instrument monitoring matrix; calculating the magnetic field change interference coefficient of the monitoring matrix of the fixed point instrument according to the magnetic field change characteristic value and the magnetic field winding influence sequence of each row of elements of the monitoring matrix of the fixed point instrument; calculating a noise interference evaluation coefficient of the monitoring matrix of the fixed point instrument according to the comprehensive influence coefficient of the environmental noise and the interference coefficient of the magnetic field change of the monitoring matrix of the fixed point instrument; calculating a fixed-point internal error coefficient of the fixed-point instrument monitoring matrix according to the change characteristics of the data corresponding to each acquisition moment in the fixed-point instrument monitoring matrix;
calculating a threshold parameter according to the noise interference evaluation coefficient of the fixed point instrument monitoring matrix and the fixed point internal error coefficient; and adopting a discrete wavelet change algorithm to perform noise reduction treatment on fault signals acquired by the intelligent digital accurate fixed point instrument according to the threshold parameters.
Preferably, the method for constructing the monitoring matrix of the fixed point instrument according to the monitoring data of the intelligent digital accurate fixed point instrument comprises the following steps:
the monitoring data of the intelligent digital accurate fixed point instrument comprises a sequence formed by the arrangement sequence of the sound propagation time difference, the sound intensity, the geomagnetic field data, the magnetic field change rate, the space magnetic field sensing data, the position information, the nearest pipeline distance, the surrounding pipeline distribution data and the time mark data, wherein the sequence formed by the arrangement sequence of the monitoring data corresponding to each acquisition time according to the sound propagation time difference, the sound intensity, the geomagnetic field data, the magnetic field change rate, the space magnetic field sensing data, the position information, the nearest pipeline distance, the surrounding pipeline distribution data and the time mark data is used as a fixed point instrument monitoring sequence at each acquisition time, the fixed point instrument monitoring sequence at each acquisition time is used as one row element of a matrix, and the matrix formed by the fixed point instrument monitoring sequences corresponding to all the acquisition times is used as a fixed point instrument monitoring matrix.
Preferably, the method for calculating the environmental comprehensive noise influence coefficient of the fixed point instrument monitoring matrix according to the integral characteristic of the data change in the fixed point instrument monitoring matrix comprises the following steps:
in the method, in the process of the invention,the comprehensive influence coefficient of the environmental noise of the monitoring matrix of the fixed point instrument is represented; />Indicating the 1 st row in the monitoring matrix of the pointing instrument>Column data; />Indicating +.>Line->Column data; />Representation->Output results of the calculation results of (2) in a logarithmic function with a natural constant as a base; />Indicating the number of elements per column in the monitor matrix of the pointing device,indicating the number of elements per row in the monitor matrix of the pointing device,/-, for example>Representing the adjustment parameters.
Preferably, the method for calculating the magnetic field pipeline winding influence coefficient according to the local characteristic difference at different acquisition moments in the fixed point instrument monitoring matrix and obtaining the magnetic field pipeline winding influence sequence of the fixed point instrument monitoring matrix based on the magnetic field pipeline winding influence coefficient comprises the following steps:
and taking geomagnetic field data, a magnetic field change rate, a nearest pipeline distance and surrounding pipeline distribution in each row of elements in the fixed point instrument monitoring matrix as inputs of Fisher criterion functions, taking output results of the Fisher criterion functions as magnetic field pipeline winding influence coefficients of each row of elements, and taking normalized results of a sequence formed by sequencing all corresponding magnetic field pipeline winding influence coefficients in the fixed point instrument monitoring matrix from small to large as a magnetic field pipeline winding influence sequence of the fixed point instrument monitoring matrix.
Preferably, the method for calculating the magnetic field variation characteristic value of each row of elements of the fixed point instrument monitoring matrix according to the magnetic field variation characteristic of each row of elements of the fixed point instrument monitoring matrix comprises the following steps:
in the method, in the process of the invention,indicating +.>A magnetic field variation characteristic value of the row element; />、/>、/>、/>Respectively represent the +.f in the monitoring matrix of the fixed point instrument>The 4 th, 7 th, 8 th and 10 th data in the row correspond to the +.>The magnetic field change rate, the nearest pipeline distance, the surrounding pipeline distribution information and the farthest pipeline distance obtained in the sub-sampling process.
Preferably, the method for calculating the magnetic field change interference coefficient of the monitor matrix of the pointing device according to the magnetic field change characteristic value and the magnetic field winding influence sequence of each row of elements of the monitor matrix of the pointing device comprises the following steps:
and taking a sequence of which all corresponding magnetic field change characteristic values in the fixed point instrument monitoring matrix are formed from small to large as a magnetic field change characteristic value sequence of the fixed point instrument monitoring matrix, adopting a normalization algorithm to obtain a normalization result of the magnetic field change characteristic value sequence, taking the normalization result as a magnetic field change characteristic value updating sequence of the fixed point instrument monitoring matrix, and taking the product of the average value of all elements of the magnetic field change characteristic value updating sequence and the average value of all elements of a magnetic field pipeline winding influence sequence of the fixed point instrument monitoring matrix as a magnetic field change interference coefficient of the fixed point instrument monitoring matrix.
Preferably, the method for calculating the noise interference evaluation coefficient of the fixed point instrument monitoring matrix according to the environmental noise comprehensive influence coefficient and the magnetic field change interference coefficient of the fixed point instrument monitoring matrix comprises the following steps:
and taking the product of the environmental noise comprehensive influence coefficient and the magnetic field change interference coefficient of the fixed point instrument monitoring matrix as the noise interference evaluation coefficient of the fixed point instrument monitoring matrix.
Preferably, the method for calculating the fixed-point internal error coefficient of the fixed-point instrument monitoring matrix according to the change characteristics of the data corresponding to each acquisition time in the fixed-point instrument monitoring matrix comprises the following steps:
taking a sequence formed by ordering all elements corresponding to position information in a fixed point instrument monitoring matrix according to a time sequence as a first internal system influence sequence of the fixed point instrument monitoring matrix, taking the average value of all elements in the first internal system influence sequence as a first internal influence characteristic value, taking the absolute value of the difference value between each element and the first internal influence characteristic value as a molecule for each element in the first internal system influence sequence, taking the absolute value of each element as a denominator, taking the ratio of the molecule and the denominator as a second internal influence characteristic value of each element, and taking the average value of the second internal influence characteristic value corresponding to all elements in the first internal system influence sequence as a first internal error coefficient of the fixed point instrument monitoring matrix;
taking a sequence formed by ordering all elements corresponding to event mark data in a fixed point instrument monitoring matrix according to a time sequence as a second internal system influence sequence of the fixed point instrument monitoring matrix, taking the average value of all elements in the second internal system influence sequence as a third internal influence characteristic value, taking the absolute value of the difference value between each element and the third internal influence characteristic value as a molecule for each element in the second internal system influence sequence, taking the absolute value of each element as a denominator, taking the ratio of the molecule and the denominator as a fourth internal influence characteristic value of each element, and taking the average value of the fourth internal influence characteristic value corresponding to all elements in the second internal system influence sequence as a second internal error coefficient of the fixed point instrument monitoring matrix; taking the average value of the first internal error coefficient and the second internal error coefficient of the fixed point instrument monitoring matrix as the fixed point internal error coefficient of the fixed point instrument monitoring matrix.
Preferably, the method for calculating the threshold parameter according to the noise interference evaluation coefficient of the fixed point instrument monitoring matrix and the fixed point internal error coefficient comprises the following steps:
in the method, in the process of the invention,representing a threshold parameter; />Representing an initial threshold parameter; />And->Respectively representing a noise interference evaluation coefficient and a fixed-point internal error coefficient of a fixed-point instrument monitoring matrix; />Representing the adjustment parameters.
Preferably, the method for noise reduction processing of the fault signal acquired by the intelligent digital accurate fixed point instrument by adopting a discrete wavelet change algorithm according to the threshold parameter comprises the following steps:
and inputting a sound signal generated by the threshold parameter and cable faults acquired by the intelligent digital accurate fixed point instrument, and carrying out noise reduction treatment on the sound signal by adopting discrete wavelet change.
The beneficial effects of the invention are as follows: the intelligent digital accurate fixed point instrument and the ultrasonic sensor are used for collecting monitoring data of the intelligent digital accurate fixed point instrument, a fixed point instrument monitoring matrix is formed according to the monitoring data of the intelligent digital accurate fixed point instrument, environment comprehensive noise influence coefficients are calculated according to the change characteristics of different collecting moments in the fixed point instrument monitoring matrix, magnetic field pipeline winding influence coefficients are calculated according to local characteristic differences of different collecting moments, magnetic field change characteristic values are obtained according to the magnetic field pipeline winding influence coefficients, magnetic field change interference coefficients are obtained according to the magnetic field change characteristic values, noise interference evaluation coefficients are calculated according to the magnetic field change interference coefficients and the environment comprehensive influence coefficients, fixed point internal error coefficients are calculated according to the fixed point instrument internal difference characteristics corresponding to each collecting moment in the fixed point instrument monitoring matrix, and threshold parameters are calculated based on the fixed point internal error coefficients and the noise interference evaluation coefficients.
Drawings
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 an intelligent background noise reduction method for an intelligent digital accurate pointing device according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an ultrasonic sensor and a position distribution of detection points according to an embodiment of the 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 an intelligent background noise reduction method for an intelligent digital accurate pointing device according to an embodiment of the present invention is shown, the method includes the following steps:
and S001, acquiring monitoring data of the intelligent digital accurate pointing instrument.
The intelligent digital accurate fixed point instrument uses an acousto-magnetic time difference positioning technology, takes the propagation time difference between a magnetic field signal and a sound signal generated when a cable fault point discharges as a positioning judgment basis, combines a space magnetic field sensing technology, presents the azimuth, test data and the like of a tested cable, and acquires data such as sound signal data, magnetic field signal data, space magnetic field sensing data, position information, signal values, event marks and the like in the process, wherein the sound signal data comprises sound propagation time difference and sound intensity, and the magnetic field signal data comprises geomagnetic field data and magnetic field change rate; the time mark data is that the event mark data is set to 1 when the fault discharge point is detected, and the event mark data is set to 0 when the fault discharge point is not detected. The space magnetic field sensing data and the position information are input, the UTF-8 (Unicode Transformation Format) coding technology is adopted to obtain coding results of the space magnetic field sensing data and the position information, and the coding results of the space magnetic field sensing data and the position information are respectively converted into decimal numbers to serve as the space magnetic field sensing coding data and the position information coding data.
Further, as shown in FIG. 2, a deployment is performed near each detection pointThe method comprises the steps of (taking 10 checked values) ultrasonic sensors, inputting data acquired by all the ultrasonic sensors, position information of all the ultrasonic sensors and position information of detection points, acquiring position information of underground pipelines by adopting a triangulation method, calculating the distance between each underground pipeline position and each detection point, taking a set formed by the distances between all the underground pipeline positions and the detection points as an underground pipeline position distance set, taking the minimum value in the underground pipeline position distance set as the nearest pipeline distance, and taking the maximum value in the underground pipeline position distance set as the farthest pipeline distance.
Advancing oneStep, centering on each detection point, and radius isThe search range of (the size of 5 meters is taken as the search area of each detection point, and the ratio of the number of underground pipe position information in the search area of each detection point to the number of all underground pipe position information acquired by each detection point is taken as the surrounding pipe distribution data. The number of sampling per detection point is +.>The data collected in each sampling process are respectively sound propagation time difference, sound intensity, geomagnetic field data, magnetic field change rate, spatial magnetic field sensing data, position information, nearest pipeline distance, surrounding pipeline distribution data, event sign data and farthest pipeline distance (the empirical value 50).
Thus, intelligent digital accurate fixed point instrument monitoring data are obtained, wherein the monitoring data comprise sound propagation time difference, sound intensity, geomagnetic field data, magnetic field change rate, space magnetic field sensing data, position information nearest pipeline distance, surrounding pipeline distribution data, event mark data and farthest pipeline distance.
And step S002, constructing a fixed point instrument monitoring matrix according to the monitoring data of the intelligent digital accurate fixed point instrument, calculating an environmental noise comprehensive influence coefficient according to the relative change characteristics of elements in the fixed point instrument monitoring matrix, calculating a magnetic field pipeline winding influence coefficient according to the correlation of magnetic field signal data corresponding to each data in the fixed point instrument monitoring matrix and pipeline distribution information, and acquiring a magnetic field pipeline winding influence sequence according to the magnetic field pipeline difference winding influence coefficient.
The monitoring data of the intelligent digital accurate pointing device comprises a sound propagation time difference, sound intensity, geomagnetic field data, magnetic field change rate, spatial magnetic field sensing coding data, position information coding data, nearest pipeline distance, surrounding pipeline distribution data, time mark data and farthest pipeline distance, wherein a sequence formed by the sequence of the monitoring data corresponding to each acquisition time according to the sound propagation time difference, sound intensity, geomagnetic field data, magnetic field change rate, spatial magnetic field sensing coding data, position information coding data, nearest pipeline distance, surrounding pipeline distribution data, time mark data and farthest pipeline distance is used as a pointing device monitoring sequence of each acquisition time, the pointing device monitoring sequence of each acquisition time is used as one row element of a matrix, the matrix formed by the pointing device monitoring sequences corresponding to all the acquisition times is used as a pointing device monitoring matrix, and the specific pointing device monitoring matrix is as follows:
wherein, the fixed point instrument monitors the matrixMiddle->Line element->Middle->Sequentially indicate +.>Sound propagation time difference, sound intensity, geomagnetic field data, magnetic field change rate, spatial magnetic field perception coding data, position information coding data, nearest pipeline distance, surrounding pipeline distribution data, time mark data and farthest pipeline distance corresponding to each acquisition time.
Further, the sound signal data may be noisy due to changes in air density, temperature and humidity in sound wave propagation and environmental or other sound sources, the magnetic field signal data and the spatial magnetic field sensing data may be noisy due to errors of external magnetic field sources, electromagnetic interference, the position information may be noisy due to errors of multipath propagation and position sensors, and the event markers may be noisy due to the sensor accuracy of internal systems, stability of triggers and errors. Therefore, the environmental comprehensive noise influence coefficient of the monitoring matrix of the fixed point instrument is calculated according to the change characteristics of the acquired data corresponding to different acquisition moments, and a specific calculation formula is as follows:
in the method, in the process of the invention,the comprehensive influence coefficient of the environmental noise of the monitoring matrix of the fixed point instrument is represented; />Indicating the 1 st row in the monitoring matrix of the pointing instrument>Column data; />Indicating +.>Line->Column data; />Representation->Output results of the calculation results of (2) in a logarithmic function with a natural constant as a base; />Indicating the number of elements per column in the monitor matrix of the pointing device,indicating the number of elements per row in the monitor matrix of the pointing device,/-, for example>Representation adjustmentThe parameter, the magnitude takes the empirical value of 1.
If the noise change in the environment is large in the monitoring process of the intelligent digital accurate pointing instrument, the method comprises the following steps ofAnd->The greater the difference, i.e. calculated +.>And->The larger the value of (2), the calculated environmental noise integrated influence coefficient of the monitor matrix of the pointing instrument +.>The larger the value of (2) is, the more serious the influence of environmental noise is on the working process of the intelligent digital accurate pointing instrument.
Further, factors such as liquid or gas flowing in the underground pipeline, magnetic characteristics of pipeline materials and the like, and changes of magnetic field signals all generate interference or noise when monitoring signals, and certain relations exist between magnetic field change data and pipeline data, for example, materials and magnetic characteristics of the underground pipeline, distances of the pipeline and liquid or gas flowing through the pipeline can influence a magnetic field, so that the strength and the direction of the magnetic field change, however, because the pipeline is buried underground and the relation between the pipeline and the magnetic field relates to a plurality of aspects, the interaction winding effect generated by the interaction between the underground pipeline and the magnetic field cannot be accurately analyzed.
Therefore, the magnetic field pipeline winding influence coefficients are obtained according to the geomagnetic field data, the magnetic field change rate, the nearest pipeline distance and the associated change characteristics of surrounding pipeline distribution data corresponding to different acquisition moments, the magnetic field pipeline winding influence sequence of the monitoring matrix of the pointing instrument is obtained based on the magnetic field pipeline winding influence coefficients, and the influence degree of the magnetic field and pipeline distribution at each acquisition moment on the monitoring process of the pointing instrument is reflected through the magnetic field pipeline winding influence coefficients. Specifically, the geomagnetic field data, the magnetic field change rate, the nearest pipeline distance and the elements corresponding to the distribution of surrounding pipelines in each row of elements in the fixed point instrument monitoring matrix are used as inputs of Fisher criterion functions, output results of the Fisher criterion functions are used as magnetic field pipeline winding influence coefficients of the elements in each row, normalization results of sequences formed by sequencing all the corresponding magnetic field pipeline winding influence coefficients in the fixed point instrument monitoring matrix from small to large are used as magnetic field pipeline winding influence sequences of the fixed point instrument monitoring matrix, and the specific calculation process of the Fisher criterion functions is a known technology and is not repeated.
So far, the magnetic field pipeline winding influence sequence and the environment noise comprehensive influence coefficient of the fixed point instrument monitoring matrix are obtained.
Step S003, calculating a magnetic field change interference coefficient according to the magnetic field pipeline winding influence sequence and the fixed point instrument monitoring matrix, calculating a noise interference evaluation coefficient according to the environment noise comprehensive influence coefficient and the magnetic field change interference coefficient, and calculating a fixed point internal error coefficient according to the local characteristics of each row of elements in the fixed point instrument monitoring matrix.
Calculating the magnetic field change characteristic value of each row of elements of the fixed point instrument monitoring matrix according to the magnetic field change characteristic of each row of elements in the fixed point instrument monitoring matrix, wherein a specific calculation formula is as follows:
in the method, in the process of the invention,indicating +.>A magnetic field variation characteristic value of the row element; />、/>、/>、/>Respectively represent the +.f in the monitoring matrix of the fixed point instrument>The 4 th, 7 th, 8 th and 10 th data in the row correspond to the +.>The magnetic field change rate, the nearest pipeline distance, the surrounding pipeline distribution information and the farthest pipeline distance obtained in the sub-sampling process.
If the nearest pipeline distance from the detection point is closer, thenThe smaller the value of (2), i.e +.>The larger the value of (2), the more the distribution of the surrounding pipeline is near the detection point and the larger the difference of the magnetic field change is, the +.>And->The larger the value of (2), the more>Magnetic field variation characteristic value of row element +.>The larger the value of (2), the more ∈>The magnetic field change and pipeline distribution at each acquisition time have a larger influence on the acquisition of the monitoring data.
Further, a sequence of which all magnetic field change characteristic values are formed by small to large in the fixed point instrument monitoring matrix is used as a magnetic field change characteristic value sequence of the fixed point instrument monitoring matrix, a maximum and minimum normalization algorithm is adopted to obtain a normalization result of the magnetic field change characteristic value sequence, the normalization result is used as a magnetic field change characteristic value updating sequence of the fixed point instrument monitoring matrix, and the product of the average value of all elements of the magnetic field change characteristic value updating sequence and the average value of all elements of a magnetic field pipeline winding influence sequence of the fixed point instrument monitoring matrix is used as a magnetic field change interference coefficient of the fixed point instrument monitoring matrix. The influence of the magnetic field change and the pipeline distribution in the environment during the monitoring process on the monitoring process is reflected by the magnetic field change interference coefficient.
Further, a noise interference evaluation coefficient is calculated according to the environmental noise comprehensive influence coefficient and the magnetic field change interference coefficient, specifically, the product of the environmental noise comprehensive influence coefficient and the magnetic field change interference coefficient of the fixed point instrument monitoring matrix is used as the noise interference evaluation coefficient of the fixed point instrument monitoring matrix, and the influence degree of the interactive winding effect of the environmental noise, the magnetic field and the pipeline on the signals in the using process of the intelligent digital accurate fixed point instrument is reflected through the noise interference evaluation coefficient.
Further, the accuracy limitations and errors of the position sensor may lead to inaccuracy of the position information, which means that the position coordinates of the detection points may have errors of small magnitude, while the generation of the event markers may be affected by the stability and errors of the triggers, and the triggering of certain events (such as the detection of cable defects) may have uncertainty, resulting in inaccuracy of the time stamps of the event markers, and when the position information and the event markers are used together with the sound signal data, their errors and uncertainty may interact, resulting in noise generated during the listening of the signal, for example, the errors of the position information may lead to inaccuracy of the recorded detection coordinates, and the uncertainty of the event markers may lead to inaccuracy of the time stamps of the events, thereby affecting the interpretation and analysis of the signal.
Further, calculating a fixed-point internal error coefficient according to the change characteristics of position information coding data and event mark data corresponding to different acquisition moments in a fixed-point instrument monitoring matrix, specifically, taking a sequence formed by sequencing all elements corresponding to position information in the fixed-point instrument monitoring matrix according to time sequence as a first internal system influence sequence of the fixed-point instrument monitoring matrix, taking the average value of all elements in the first internal system influence sequence as a first internal influence characteristic value, taking the absolute value of the difference value between each element and the first internal influence characteristic value as a molecule, taking the absolute value of each element as a denominator, taking the ratio of the molecule and the denominator as a second internal influence characteristic value of each element, and taking the average value of the second internal influence characteristic value corresponding to all elements in the first internal system influence sequence as the first internal error coefficient of the fixed-point instrument monitoring matrix;
taking a sequence formed by ordering all elements corresponding to event mark data in a fixed point instrument monitoring matrix according to a time sequence as a second internal system influence sequence of the fixed point instrument monitoring matrix, taking the average value of all elements in the second internal system influence sequence as a third internal influence characteristic value, taking the absolute value of the difference value between each element and the third internal influence characteristic value as a molecule for each element in the second internal system influence sequence, taking the absolute value of each element as a denominator, taking the ratio of the molecule and the denominator as a fourth internal influence characteristic value of each element, and taking the average value of the fourth internal influence characteristic value corresponding to all elements in the second internal system influence sequence as a second internal error coefficient of the fixed point instrument monitoring matrix; taking the average value of the first internal error coefficient and the second internal error coefficient of the fixed point instrument monitoring matrix as the fixed point internal error coefficient of the fixed point instrument monitoring matrix.
If the stability of the position information coding data and the event mark data corresponding to all the acquisition moments is lower, the larger the value of the fixed-point internal error coefficient of the calculated fixed-point instrument monitoring matrix is, the larger the fluctuation of the acquired data is, the sound intensity of the sampling point is influenced by larger internal system errors or other interference, and the reliability of the data is lower.
Thus, a noise interference evaluation coefficient and a fixed-point internal error coefficient are obtained.
Step S004, calculating a threshold parameter according to the noise interference evaluation coefficient and the fixed point internal error coefficient, and carrying out noise reduction processing on a sound signal generated by discharging when the cable fails by adopting wavelet transformation based on the threshold parameter.
The method comprises the steps of adopting a discrete wavelet change algorithm to conduct noise reduction processing on cable fault sound signals acquired in the detection process of an intelligent digital accurate fixed point instrument, adopting a haar wavelet function as a wavelet basis function, enabling the number of decomposition layers to be 5, adopting a soft threshold processing method to process each layer of wavelet components of decomposition, determining threshold parameters in the soft threshold processing method according to noise interference evaluation coefficients and fixed point internal error coefficients, and adopting a specific calculation formula as follows:
in the method, in the process of the invention,representing a threshold parameter; />Representing an initial threshold parameter; />And->Respectively representing a noise interference evaluation coefficient and a fixed-point internal error coefficient of a fixed-point instrument monitoring matrix; />Representing the adjustment parameters, the magnitude takes the checked value 2.
If the noise influence generated by the internal system is larger and the noise influence degree in the environment is larger when the intelligent digital accurate fixed point instrument is sampled, the noise interference evaluation coefficient of the fixed point instrument monitoring matrix is calculatedThe larger the value of (2) and the fixed point internal error coefficient +.>The greater the value of (2), the threshold parameter +.>The larger the value of the (B) is, the over-compression of the signal in a high noise environment is avoided, so that the noise is effectively removed, and the accuracy of the background noise reduction treatment in the use process of the intelligent digital accurate fixed point instrument is improved.
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 above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. An intelligent background noise reduction method for an intelligent digital accurate pointing device is characterized by comprising the following steps:
acquiring monitoring data of the intelligent digital accurate fixed point instrument according to the intelligent digital accurate fixed point instrument and the ultrasonic sensor;
constructing a monitoring matrix of the fixed point instrument according to the monitoring data of the intelligent digital accurate fixed point instrument; calculating the environmental comprehensive noise influence coefficient of the fixed point instrument monitoring matrix according to the integral characteristic of the data change in the fixed point instrument monitoring matrix; calculating a magnetic field pipeline winding influence coefficient according to local characteristic differences at different acquisition moments in the fixed point instrument monitoring matrix, and acquiring a magnetic field pipeline winding influence sequence of the fixed point instrument monitoring matrix based on the magnetic field pipeline winding influence coefficient; calculating the magnetic field change characteristic value of each row of elements of the fixed point instrument monitoring matrix according to the magnetic field change characteristic of each row of elements in the fixed point instrument monitoring matrix; calculating the magnetic field change interference coefficient of the monitoring matrix of the fixed point instrument according to the magnetic field change characteristic value and the magnetic field winding influence sequence of each row of elements of the monitoring matrix of the fixed point instrument; calculating a noise interference evaluation coefficient of the monitoring matrix of the fixed point instrument according to the comprehensive influence coefficient of the environmental noise and the interference coefficient of the magnetic field change of the monitoring matrix of the fixed point instrument; calculating a fixed-point internal error coefficient of the fixed-point instrument monitoring matrix according to the change characteristics of the data corresponding to each acquisition moment in the fixed-point instrument monitoring matrix;
calculating a threshold parameter according to the noise interference evaluation coefficient of the fixed point instrument monitoring matrix and the fixed point internal error coefficient; and adopting a discrete wavelet change algorithm to perform noise reduction treatment on fault signals acquired by the intelligent digital accurate fixed point instrument according to the threshold parameters.
2. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for constructing a pointing device monitoring matrix according to the monitoring data of the intelligent digital accurate pointing device is as follows:
the monitoring data of the intelligent digital accurate fixed point instrument comprises a sequence formed by the arrangement sequence of the sound propagation time difference, the sound intensity, the geomagnetic field data, the magnetic field change rate, the space magnetic field sensing data, the position information, the nearest pipeline distance, the surrounding pipeline distribution data and the time mark data, wherein the sequence formed by the arrangement sequence of the monitoring data corresponding to each acquisition time according to the sound propagation time difference, the sound intensity, the geomagnetic field data, the magnetic field change rate, the space magnetic field sensing data, the position information, the nearest pipeline distance, the surrounding pipeline distribution data and the time mark data is used as a fixed point instrument monitoring sequence at each acquisition time, the fixed point instrument monitoring sequence at each acquisition time is used as one row element of a matrix, and the matrix formed by the fixed point instrument monitoring sequences corresponding to all the acquisition times is used as a fixed point instrument monitoring matrix.
3. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for calculating the environmental integrated noise influence coefficient of the pointing device monitoring matrix according to the overall characteristics of the data change in the pointing device monitoring matrix is as follows:
in the method, in the process of the invention,the comprehensive influence coefficient of the environmental noise of the monitoring matrix of the fixed point instrument is represented; />Indicating the 1 st row in the monitoring matrix of the pointing instrument>Column data; />Indicating +.>Line->Column data; />Representation->Output results of the calculation results of (2) in a logarithmic function with a natural constant as a base; />Indicating the number of elements in each column of the monitor matrix of the pointing device,/-, for example>Indicating the number of elements per row in the monitor matrix of the pointing device,/-, for example>Representing the adjustment parameters.
4. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for calculating the magnetic field pipeline winding influence coefficient according to the local feature differences at different acquisition moments in the pointing device monitoring matrix and obtaining the magnetic field pipeline winding influence sequence of the pointing device monitoring matrix based on the magnetic field pipeline winding influence coefficient is as follows:
and taking geomagnetic field data, a magnetic field change rate, a nearest pipeline distance and surrounding pipeline distribution in each row of elements in the fixed point instrument monitoring matrix as inputs of Fisher criterion functions, taking output results of the Fisher criterion functions as magnetic field pipeline winding influence coefficients of each row of elements, and taking normalized results of a sequence formed by sequencing all corresponding magnetic field pipeline winding influence coefficients in the fixed point instrument monitoring matrix from small to large as a magnetic field pipeline winding influence sequence of the fixed point instrument monitoring matrix.
5. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for calculating the magnetic field variation characteristic value of each row element of the pointing device monitoring matrix according to the magnetic field variation characteristic of each row element of the pointing device monitoring matrix is as follows:
in the method, in the process of the invention,indicating +.>A magnetic field variation characteristic value of the row element; />、/>、/>、/>Respectively represent the +.f in the monitoring matrix of the fixed point instrument>The 4 th, 7 th, 8 th and 10 th data in the row correspond to the +.>The magnetic field change rate, the nearest pipeline distance, the surrounding pipeline distribution information and the farthest pipeline distance obtained in the sub-sampling process.
6. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for calculating the magnetic field variation interference coefficient of the pointing device monitoring matrix according to the magnetic field variation characteristic value and the magnetic field winding influence sequence of each row of elements of the pointing device monitoring matrix is as follows:
and taking a sequence of which all corresponding magnetic field change characteristic values in the fixed point instrument monitoring matrix are formed from small to large as a magnetic field change characteristic value sequence of the fixed point instrument monitoring matrix, adopting a normalization algorithm to obtain a normalization result of the magnetic field change characteristic value sequence, taking the normalization result as a magnetic field change characteristic value updating sequence of the fixed point instrument monitoring matrix, and taking the product of the average value of all elements of the magnetic field change characteristic value updating sequence and the average value of all elements of a magnetic field pipeline winding influence sequence of the fixed point instrument monitoring matrix as a magnetic field change interference coefficient of the fixed point instrument monitoring matrix.
7. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for calculating the noise interference evaluation coefficient of the pointing device monitoring matrix according to the environmental noise comprehensive influence coefficient and the magnetic field variation interference coefficient of the pointing device monitoring matrix comprises the following steps:
and taking the product of the environmental noise comprehensive influence coefficient and the magnetic field change interference coefficient of the fixed point instrument monitoring matrix as the noise interference evaluation coefficient of the fixed point instrument monitoring matrix.
8. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for calculating the internal error coefficient of the pointing device monitoring matrix according to the change characteristics of the data corresponding to each acquisition time in the pointing device monitoring matrix is as follows:
taking a sequence formed by ordering all elements corresponding to position information in a fixed point instrument monitoring matrix according to a time sequence as a first internal system influence sequence of the fixed point instrument monitoring matrix, taking the average value of all elements in the first internal system influence sequence as a first internal influence characteristic value, taking the absolute value of the difference value between each element and the first internal influence characteristic value as a molecule for each element in the first internal system influence sequence, taking the absolute value of each element as a denominator, taking the ratio of the molecule and the denominator as a second internal influence characteristic value of each element, and taking the average value of the second internal influence characteristic value corresponding to all elements in the first internal system influence sequence as a first internal error coefficient of the fixed point instrument monitoring matrix;
taking a sequence formed by ordering all elements corresponding to event mark data in a fixed point instrument monitoring matrix according to a time sequence as a second internal system influence sequence of the fixed point instrument monitoring matrix, taking the average value of all elements in the second internal system influence sequence as a third internal influence characteristic value, taking the absolute value of the difference value between each element and the third internal influence characteristic value as a molecule for each element in the second internal system influence sequence, taking the absolute value of each element as a denominator, taking the ratio of the molecule and the denominator as a fourth internal influence characteristic value of each element, and taking the average value of the fourth internal influence characteristic value corresponding to all elements in the second internal system influence sequence as a second internal error coefficient of the fixed point instrument monitoring matrix; taking the average value of the first internal error coefficient and the second internal error coefficient of the fixed point instrument monitoring matrix as the fixed point internal error coefficient of the fixed point instrument monitoring matrix.
9. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for calculating the threshold parameter according to the noise interference evaluation coefficient of the pointing device monitoring matrix and the internal error coefficient of the pointing device is as follows:
in the method, in the process of the invention,representing a threshold parameter; />Representing an initial threshold parameter; />And->Respectively representing a noise interference evaluation coefficient and a fixed-point internal error coefficient of a fixed-point instrument monitoring matrix; />Representing the adjustment parameters.
10. The intelligent background noise reduction method for an intelligent digital accurate pointing device according to claim 1, wherein the method for performing noise reduction processing on the fault signal acquired by the intelligent digital accurate pointing device by adopting a discrete wavelet change algorithm according to the threshold parameter is as follows:
and inputting a sound signal generated by the threshold parameter and cable faults acquired by the intelligent digital accurate fixed point instrument, and carrying out noise reduction treatment on the sound signal by adopting discrete wavelet change.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7253627B1 (en) * | 2006-07-19 | 2007-08-07 | Univ King Fahd Pet & Minerals | Method for removing noise from nuclear magnetic resonance signals and images |
| US20120105073A1 (en) * | 2010-11-03 | 2012-05-03 | Hagenuk Kmt Kabelmesstechnik Gmbh | Arrangement for Eliminating Interference from Acoustic Signals in a Ground-Borne Sound Locating Process |
| CN105606953A (en) * | 2015-09-09 | 2016-05-25 | 西安电子科技大学 | Automatic mute and intelligent background noise reduction reset sound and magnetism synchronization fixing point instrument |
| CN106885971A (en) * | 2017-03-06 | 2017-06-23 | 西安电子科技大学 | A kind of intelligent background noise-reduction method for Cable fault examination fixed point apparatus |
| US20180045768A1 (en) * | 2015-03-25 | 2018-02-15 | Optasense Holdings Limited | Detecting Failure Locations in Power Cables |
| CN109655714A (en) * | 2019-01-22 | 2019-04-19 | 西安电子科技大学 | Dual sensor type cable failure positioning detection system and method, Cable test platform |
| CN112684285A (en) * | 2020-12-11 | 2021-04-20 | 国网辽宁省电力有限公司抚顺供电公司 | Intelligent positioning device and positioning method for cable fault |
| CN117093857A (en) * | 2023-10-19 | 2023-11-21 | 深圳市魔样科技有限公司 | Rapid processing method for intelligent ring test data |
| CN117113008A (en) * | 2023-10-23 | 2023-11-24 | 北京华力兴科技发展有限责任公司 | X-ray-based plate thickness measuring method |
-
2023
- 2023-12-04 CN CN202311640693.6A patent/CN117349597B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7253627B1 (en) * | 2006-07-19 | 2007-08-07 | Univ King Fahd Pet & Minerals | Method for removing noise from nuclear magnetic resonance signals and images |
| US20120105073A1 (en) * | 2010-11-03 | 2012-05-03 | Hagenuk Kmt Kabelmesstechnik Gmbh | Arrangement for Eliminating Interference from Acoustic Signals in a Ground-Borne Sound Locating Process |
| US20180045768A1 (en) * | 2015-03-25 | 2018-02-15 | Optasense Holdings Limited | Detecting Failure Locations in Power Cables |
| CN105606953A (en) * | 2015-09-09 | 2016-05-25 | 西安电子科技大学 | Automatic mute and intelligent background noise reduction reset sound and magnetism synchronization fixing point instrument |
| CN106885971A (en) * | 2017-03-06 | 2017-06-23 | 西安电子科技大学 | A kind of intelligent background noise-reduction method for Cable fault examination fixed point apparatus |
| CN109655714A (en) * | 2019-01-22 | 2019-04-19 | 西安电子科技大学 | Dual sensor type cable failure positioning detection system and method, Cable test platform |
| CN112684285A (en) * | 2020-12-11 | 2021-04-20 | 国网辽宁省电力有限公司抚顺供电公司 | Intelligent positioning device and positioning method for cable fault |
| CN117093857A (en) * | 2023-10-19 | 2023-11-21 | 深圳市魔样科技有限公司 | Rapid processing method for intelligent ring test data |
| CN117113008A (en) * | 2023-10-23 | 2023-11-24 | 北京华力兴科技发展有限责任公司 | X-ray-based plate thickness measuring method |
Non-Patent Citations (3)
| Title |
|---|
| C.K. JUNG ET AL.: "A study on the fault location algorithm on underground power cable system", 《IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, 2005》, pages 1 - 7 * |
| 洪炎森: "电力电缆故障定位及检测信号去噪分析", 《无线互联科技》, no. 14, pages 111 - 112 * |
| 米建伟 等: "非平稳背景噪声下声音信号增强技术", 《仪器仪表学报》, vol. 38, no. 1, pages 17 - 22 * |
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