CN113032907B - Method and system for correcting shaking car disease data deviation based on waveform correlation - Google Patents
Method and system for correcting shaking car disease data deviation based on waveform correlation Download PDFInfo
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Abstract
The invention provides a vehicle shaking disease data deviation correcting method and system based on waveform correlation, wherein the method calculates correlation coefficients between vehicle shaking data waveforms and vehicle shaking disease data waveforms, which are obtained by each historical detection in a set mileage range, by acquiring vehicle shaking disease data waveforms corresponding to vehicle shaking disease points to be corrected and vehicle shaking data waveforms corresponding to mileage ranges of the vehicle shaking disease points to be corrected in multiple historical detection, and judges whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold; if so, judging that the vehicle shaking disease point to be rectified is invalid; if not, judging that the vehicle shaking disease point to be rectified is effective. The method can identify effective vehicle shaking disease points, eliminates ineffective vehicle shaking disease points, improves the reliability of detection data, does not need to recheck the diseases again on site by an instrument, reduces the workload of site personnel, and improves the working efficiency.
Description
Technical Field
The invention relates to the field of track state detection data deviation correction, in particular to a vehicle shaking disease data deviation correction method and system based on waveform correlation.
Background
The rolling stock and the rail are coupled vibration systems, and the vertical acceleration and the horizontal acceleration of the rolling stock can represent the geometrical irregularity state of the rail. The line quality inspection instrument obtains horizontal acceleration and vertical acceleration data (called shaking data for short) of a train body by measuring vibration conditions of the locomotive and the train in the running process so as to judge the geometrical irregularity state of the railway track. The road repair rules of the railway head office have clear regulations on the allowable deviation management values of the vertical acceleration and the horizontal acceleration of the vehicle body, and the sloshing disease data refer to the vertical acceleration and the horizontal acceleration data of the vehicle body exceeding the regulated allowable deviation management values.
Different from a large-scale rail inspection vehicle, the line quality inspection instrument has the advantages of small volume, low detection cost, high detection frequency and simple and convenient use, and is a real-time in-transit monitoring means commonly adopted by railway systems. Because the detection period is short, the line quality inspection instrument can generate rich line shake data, such as the vehicle-mounted line quality inspection instrument can detect the track quality state for a plurality of times in a day. The proportion of the track shaking data in the track equipment detection data is large, and the track shaking data is one of key data for comprehensively evaluating the track irregularity state.
Vehicle body sway is affected by track irregularity and rolling stock characteristics simultaneously: (1) The vertical acceleration and the horizontal acceleration of the locomotive body can exceed a threshold value due to the self reasons or abnormal operation of the locomotive body, and the corresponding shaking disease data are false alarm data because the problem caused by the geometrical overrun of the track is not solved, and the measuring deviation exists and needs to be removed from the detection data. (2) The geometric irregularity of the track is a disturbance source of a wheel track system, is a main cause for generating vibration of rolling stock, and the corresponding vehicle shaking disease data are effective vehicle shaking disease data.
Due to the limitation of the instrument and the influence of external environment, the current line quality inspection instrument shakes vehicle data to have serious mileage deviation and measurement deviation problems, and the false alarm of the data is frequent, which brings great trouble to field work. In the actual working of a railway, workers are generally arranged to go to the site to check the vehicle shaking diseases detected by the railway quality inspection instrument by means of other detection instruments such as a rail inspection instrument, whether the vehicle shaking diseases are real rail geometric overrun diseases or not is judged, the accurate disease mileage positions of the vehicle shaking diseases are determined, and the corresponding vehicle shaking disease causes are diagnosed. When checking the vehicle shaking diseases, the staff needs to spend a great deal of time and energy, so that the working efficiency is reduced and the maintenance cost is increased. Therefore, it is necessary to provide a vehicle-shaking disease data detection method with reliable detection data and high working efficiency.
Disclosure of Invention
The invention aims to provide a vehicle shaking disease data deviation correcting method and system based on a waveform correlation, which can improve the reliability of detection data and the working efficiency.
In order to achieve the above object, the present invention provides the following solutions:
the vehicle shaking disease data deviation correcting method based on the waveform correlation is characterized by comprising the following steps of:
obtaining a vehicle shaking disease data waveform corresponding to a vehicle shaking disease point to be corrected, wherein the vehicle shaking disease data waveform is a mileage-acceleration value waveform, the mileage range constraint in the vehicle shaking disease data waveform is within a mileage range C_FD set before and after the vehicle shaking disease point to be corrected, and the acceleration value in the vehicle shaking disease data waveform is an acceleration measurement value of each measurement point within the mileage range C_FD constraint;
obtaining a vehicle shaking data waveform corresponding to a vehicle shaking disease point to be corrected in multiple historical detection, wherein the vehicle shaking data waveform is a mileage-acceleration value waveform, the mileage range constraint in the vehicle shaking data waveform is within a preset mileage range before and after the vehicle shaking disease point to be corrected, the acceleration value in the vehicle shaking data waveform is an acceleration measurement value of each measurement point in the mileage range constraint, and the time constraint C_FT of the historical detection is within a preset time before the measurement time of the vehicle shaking disease point to be corrected;
calculating a correlation coefficient between the vehicle shaking data waveform and the vehicle shaking disease data waveform obtained through each historical detection;
judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not;
if so, judging that the vehicle shaking disease point to be rectified is invalid;
if not, judging that the vehicle shaking disease point to be rectified is effective.
Optionally, the vehicle shaking disease data deviation correcting method based on the waveform correlation further includes:
dividing a railway track into a plurality of sections of equal length;
determining total deduction of the diseases of the section according to the disease grade determined by the peak value of each effective shaking disease point in the section and the number of the effective shaking disease points;
the first n sections where the total knot of disease is high are determined to be weak sections.
Optionally, the total number of the vehicle shaking disease points to be rectified is I, and the ith disease point alpha in the vehicle shaking disease points to be rectified is i The corresponding shaking waveform data sequence is Represents the disease point alpha i A vehicle shaking waveform data sequence set in a front-back C_FD mileage range; collection setClose->The total number of elements n=2×c_fd×f, f representing the sampling frequency of the line quality inspection instrument, +.>Representing a train wave data sequence +.>Peak point in>The mileage position of (a) represents the disease point alpha i Is to correct the position of the ith disease point alpha in the vehicle shaking disease points to be corrected i Corresponding sloshing waveform data +.>Is +.>
Optionally, the calculating a correlation coefficient between the vehicle-shaking data waveform and the vehicle-shaking disease data waveform obtained by each historical detection specifically includes:
obtaining shaking waveform data corresponding to shaking disease points to be rectified in historical P-th (P is more than or equal to 1 and less than or equal to P) detection, and integrating the shaking waveform data points for useA representation;
using the formulaCalculating a correlation coefficient between the shaking vehicle waveform data and the shaking vehicle disease waveform data obtained by the p-th detection of the history;
using the formulaAcquiring a minimum correlation coefficient;
wherein P represents the historical detection total times of the vehicle shaking disease point to be rectified within the time constraint C_FT,representing the shaking waveform data obtained by historical p-th detection and the ith disease point alpha i Corresponding correlation coefficients between the vehicle-shaking disease waveform data cov (M 0 i ,M p i ) Representing variable M 0 i And M is as follows p i Covariance of->Representing variable M 0 i 、M p i In (d) represents the minimum value in the variables (. Cndot.),. Cndot.,>and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained through each historical detection.
In order to achieve the above object, the present invention further provides a vehicle-shaking disease data deviation correcting system based on a waveform correlation, the vehicle-shaking disease data deviation correcting system based on a waveform correlation comprising:
the vehicle shaking disease waveform data acquisition module is used for acquiring vehicle shaking disease waveform data corresponding to a vehicle shaking disease point to be corrected, wherein the vehicle shaking disease waveform data is mileage-acceleration value waveform data, the mileage range constraint in the vehicle shaking disease waveform data is within a mileage range C_FD set before and after the vehicle shaking disease point to be corrected, and the acceleration value in the vehicle shaking disease waveform data is an acceleration measurement value of each measurement point within the mileage range constraint;
the historical shaking waveform data acquisition module is used for acquiring shaking waveform data corresponding to the shaking disease point to be corrected in multiple historical detection, wherein the shaking waveform data is mileage-acceleration value waveform data, the mileage range constraint in the shaking waveform data is within a mileage range C_FD set before and after the shaking disease point to be corrected, the acceleration value in the shaking waveform data is an acceleration measurement value of each measurement point in the mileage range constraint, and the time constraint C_FT of the historical detection is within a set time before the measurement time of the shaking disease point to be corrected;
the correlation coefficient calculation module is used for calculating the correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained through each historical detection;
the vehicle shaking disease point correction module is used for judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not; if so, judging that the vehicle shaking disease point to be rectified is invalid; if not, judging that the vehicle shaking disease point to be rectified is effective.
Optionally, the system for correcting the shaking car disease data deviation based on the waveform correlation further comprises a weak area determining module, which specifically comprises:
dividing a railway track into a plurality of sections of equal length;
determining total deduction of the diseases of the section according to the disease grade determined by the peak value of each effective shaking disease point in the section and the number of the effective shaking disease points;
the first n sections where the total knot of disease is high are determined to be weak sections.
Optionally, the historical vehicle shaking waveform data acquisition module specifically includes:
the total number of the vehicle shaking disease points to be corrected is I, and the ith disease point alpha in the vehicle shaking disease points to be corrected i The corresponding shaking waveform data sequence is Represents the disease point alpha i A vehicle shaking waveform data sequence set in a front-back C_FD mileage range; set->The total number of elements n=2×c_fd×f, f representing the sampling frequency of the line quality inspection instrument, +.>Representing a train wave data sequence +.>Peak point in>The mileage position of (a) represents the disease point alpha i Is to correct the position of the ith disease point alpha in the vehicle shaking disease points to be corrected i Corresponding sloshing waveform data +.>Is +.>
Optionally, the correlation coefficient calculating module specifically includes:
obtaining shaking waveform data corresponding to shaking disease points to be rectified in historical P-th (P is more than or equal to 1 and less than or equal to P) detection, and integrating the shaking waveform data points for useA representation;
using the formulaCalculating a correlation coefficient between the shaking vehicle waveform data and the shaking vehicle disease waveform data obtained by the p-th detection of the history;
using the formulaAcquiring a minimum correlation coefficient;
wherein P represents a vehicle shaking disease point to be correctedThe total number of historical detections within the time constraint C _ FT,representing the shaking waveform data obtained by historical p-th detection and the ith disease point alpha i Corresponding correlation coefficients between the vehicle-shaking disease waveform data cov (M 0 i ,M p i ) Representing variable M 0 i And M is as follows p i Covariance of->Representing variable M 0 i 、M p i In (d) represents the minimum value in the variables (. Cndot.),. Cndot.,>and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained through each historical detection.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a vehicle shaking disease data deviation correcting method and a system based on a waveform correlation, wherein the method calculates a correlation coefficient between a vehicle shaking data waveform and a vehicle shaking disease data waveform, which are obtained by each historical detection in a set mileage range, by acquiring a vehicle shaking disease data waveform corresponding to a vehicle shaking disease point to be corrected and a vehicle shaking data waveform corresponding to a mileage range in multiple historical detection, and judging whether the minimum correlation coefficient in the correlation coefficient is smaller than a set correlation coefficient threshold; if so, judging that the vehicle shaking disease point to be rectified is invalid; otherwise, judging that the vehicle shaking disease point to be rectified is effective. The method can identify effective vehicle shaking disease points, eliminates ineffective vehicle shaking disease points, improves the reliability of detection data, and meanwhile, the method does not need to recheck the diseases on site again through an instrument, reduces the workload of site personnel and improves the working efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for correcting deviation of vehicle shaking disease data based on waveform correlation;
FIG. 2 shows the disease point alpha i Is an overrun assessment schematic of (1);
FIG. 3 is a schematic diagram of an algorithm of the method for correcting deviation of vehicle-shaking disease data based on waveform correlation in the invention;
fig. 4 is a schematic diagram of a system for correcting shaking car disease data deviation based on waveform correlation in the invention.
Symbol description:
the system comprises a 1-vehicle shaking disease waveform data acquisition module, a 2-historical vehicle shaking waveform data acquisition module, a 3-correlation coefficient calculation module and a 4-vehicle shaking disease point correction module.
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.
The invention aims to provide a vehicle shaking disease data deviation correcting method and system based on a waveform correlation, which can improve the reliability of detection data and improve the working efficiency.
The correction of the data deviation of the invention means the identification of effective vehicle shaking disease points, and the false alarm vehicle shaking disease data with measurement deviation caused by the reasons of the vehicle body, abnormal operation and the like are removed from the original vehicle shaking disease data.
The "correlation" means: and the correlation degree of the vehicle shaking disease data waveform corresponding to the vehicle shaking disease point to be corrected in the repeated historical detection within the set mileage range and the given time range and the vehicle shaking data waveform corresponding to the vehicle shaking disease point to be corrected.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the vehicle shaking disease data deviation correcting method based on waveform correlation of the invention comprises the following steps:
step 101: obtaining shaking disease waveform data corresponding to a shaking disease point to be corrected, wherein the shaking disease waveform data is mileage-acceleration value waveform data, the mileage range constraint in the shaking disease waveform data is within a mileage range C_FD set before and after the shaking disease point to be corrected, and the acceleration value in the shaking disease waveform data is the acceleration measurement value of each measurement point within the mileage range constraint. The vehicle shaking disease data refer to vehicle body horizontal acceleration and vertical acceleration data which exceed a certain management threshold and are detected by a line quality inspection instrument.
Step 102: obtaining vehicle shaking waveform data corresponding to a vehicle shaking disease point to be corrected in multiple historical detection, wherein the vehicle shaking waveform data are mileage-acceleration value waveform data, the mileage range constraint in the vehicle shaking waveform data is within a mileage range C_FD set before and after the vehicle shaking disease point to be corrected, the acceleration value in the vehicle shaking waveform data is an acceleration measurement value of each measurement point in the mileage range constraint, and the time constraint C_FT of the historical detection is within a set time before the measurement time of the vehicle shaking disease point to be corrected. In general, the time constraint c_ft is 1 to 3 days before the latest vehicle-shaking disease detection, so as to ensure that the corresponding vehicle-shaking disease is not remedied in the time interval.
Step 103: and calculating a correlation coefficient between the shaking vehicle waveform data and the shaking vehicle disease waveform data obtained through each historical detection. Because the line quality detector has short detection period, the track irregularity disease at the same position can be repeatedly detected for a plurality of times before being cured, and the corresponding original shaking waveform data detected for a plurality of times has higher similarity.
Step 104: judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value gamma base Can be obtained according to expert experience and statistical historical data.
Step 105: if so, judging that the vehicle shaking disease point to be corrected is invalid, wherein the disease point is not caused by track irregularity disease, and then eliminating the invalid vehicle shaking disease point.
Step 106: if not, judging that the vehicle shaking disease point to be rectified is effective.
Further, the vehicle shaking disease data deviation correcting method based on the waveform correlation relationship further comprises the following steps:
step 107: the railway track is divided into a plurality of sections with equal length, and the sections can be divided according to actual conditions, wherein the length of each section is 200 meters.
And determining total withholding of the diseases of the section according to the disease grade determined by the peak value of each effective vehicle shaking disease point in the section and the number of the effective vehicle shaking disease points.
The first n sections with higher total deduction of the diseases are determined as weak sections, and the staff can schedule corresponding maintenance activities for the first n weak sections in time.
Specifically, the sloshing disease is generally classified into 4 disease grades, and the higher the disease grade value is, the more serious the sloshing disease is. Assuming that the single 4-level disease score is 10, the single 3-level disease score is 5, the single 2-level disease score is 3, and the single 1-level disease score is 1. If 3 effective vehicle-shaking disease points exist in 1 level of a certain specific section and 2 effective vehicle-shaking disease points exist in 3 levels, the total deduction of the section disease is divided into the number of the effective vehicle-shaking disease points and the weighted average value of the disease grades determined by the peak value of each effective vehicle-shaking disease point: 3×1+2×5=13.
Further, the total number of the vehicle shaking disease points to be rectified is I, and the ith disease point alpha in the vehicle shaking disease points to be rectified is i The corresponding shaking waveform data sequence is Represents the disease point alpha i A vehicle shaking waveform data sequence set in a front-back C_FD mileage range; set->The total number of elements n=2×c_fd×f, f representing the sampling frequency of the line quality inspection instrument, +.>Representing a train wave data sequence +.>Peak point in>The mileage position of (a) represents the disease point alpha i Is to correct the position of the ith disease point alpha in the vehicle shaking disease points to be corrected i Corresponding shaking waveform dataIs +.>Disease point alpha i A schematic of overrun assessment of (2) is shown in figure 2.
Preferably, the calculating a correlation coefficient between the vehicle-shaking waveform data and the vehicle-shaking disease waveform data obtained by each historical detection specifically includes:
obtaining shaking waveform data corresponding to shaking disease points to be rectified in historical P-th (P is more than or equal to 1 and less than or equal to P) detection, and integrating the shaking waveform data points for useA representation;
using the formulaCalculating a correlation coefficient between the shaking vehicle waveform data and the shaking vehicle disease waveform data obtained by the p-th detection of the history;
using the formulaAcquiring a minimum correlation coefficient;
wherein P represents the historical detection total times of the vehicle shaking disease point to be rectified within the time constraint C_FT,representing the shaking waveform data obtained by historical p-th detection and the ith disease point alpha i Corresponding correlation coefficients between the vehicle-shaking disease waveform data cov (M 0 i ,M p i ) Representing variable M 0 i And M is as follows p i Covariance of->Representing variable M 0 i 、M p i In (d) represents the minimum value in the variables (. Cndot.),. Cndot.,>and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained through each historical detection.
Similarly, the calculation steps 101-107 are sequentially performed on the I vehicle-shaking disease points in the vehicle-shaking disease point set to be corrected, and finally an effective vehicle-shaking disease point set B= [ B ] which is detected at the latest time in a period of mileage can be obtained 1 ,b 2 ,…,b S ]S represents the total number of effective vehicle shaking disease points detected by the latest primary line quality inspection instrument.
In order to achieve the above object, as shown in fig. 4, the present invention further provides a vehicle-shaking disease data deviation correcting system based on a waveform correlation, the vehicle-shaking disease data deviation correcting system based on a waveform correlation comprising: the system comprises a vehicle shaking disease waveform data acquisition module 1, a historical vehicle shaking waveform data acquisition module 2, a correlation coefficient calculation module 3 and a vehicle shaking disease point correction module 4.
The vehicle shaking disease waveform data acquisition module 1 is used for acquiring vehicle shaking disease waveform data corresponding to a vehicle shaking disease point to be corrected, wherein the vehicle shaking disease waveform data is mileage-acceleration value waveform data, the mileage range constraint in the vehicle shaking disease waveform data is within a mileage range C_FD set before and after the vehicle shaking disease point to be corrected, and the acceleration value in the vehicle shaking disease waveform data is an acceleration measurement value of each measurement point within the mileage range constraint.
The historical shaking waveform data acquisition module 2 is used for acquiring shaking waveform data corresponding to the shaking disease point to be corrected in multiple historical detection, the shaking waveform data is mileage-acceleration value waveform data, the mileage range constraint in the shaking waveform data is within a preset mileage range C_FD before and after the shaking disease point to be corrected, the acceleration value in the shaking waveform data is an acceleration measurement value of each measurement point in the mileage range constraint, and the time constraint C_FT of the historical detection is within a preset time before the measurement time of the shaking disease point to be corrected.
The correlation coefficient calculating module 3 is configured to calculate a correlation coefficient between the sloshing waveform data and the sloshing disease waveform data obtained through each history detection.
The vehicle shaking disease point correction module 4 is used for judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value; if so, judging that the vehicle shaking disease point to be rectified is invalid; if not, judging that the vehicle shaking disease point to be rectified is effective.
Further, the shaking car disease data deviation correcting system based on the waveform correlation relationship further comprises: a weak section determining module (not shown in the figure) for determining a weak section in a railway track section, specifically comprising:
the railway track is divided into a plurality of sections of equal length.
And determining total withholding of the diseases of the section according to the disease grade determined by the peak value of each effective vehicle shaking disease point in the section and the number of the effective vehicle shaking disease points.
The first n sections where the total knot of disease is high are determined to be weak sections.
Further, the historical vehicle-shaking waveform data acquisition module 2 specifically includes:
the total number of the disease points of the vehicle to be corrected is I, and the ith disease point alpha in the disease points of the vehicle to be corrected is the ith disease point alpha in the disease points of the vehicle to be corrected i The corresponding shaking waveform data sequence is Represents the disease point alpha i A vehicle shaking waveform data sequence set in a front-back C_FD mileage range; set->The total number of elements n=2×c_fd×f, f representing the sampling frequency of the line quality inspection instrument, +.>Representing a train wave data sequence +.>Peak point in>The mileage position of (a) represents the disease point alpha i Is to correct the position of the ith disease point alpha in the vehicle shaking disease points to be corrected i Corresponding sloshing waveform data +.>Is within the range of
Preferably, the correlation coefficient calculation module 3 specifically includes:
obtaining shaking waveform data corresponding to shaking disease points to be rectified in historical P-th (P is more than or equal to 1 and less than or equal to P) detection, and integrating the shaking waveform data points for useA representation;
using the formulaCalculating a correlation coefficient between the shaking vehicle waveform data and the shaking vehicle disease waveform data obtained by the p-th detection of the history;
using the formulaAcquiring a minimum correlation coefficient;
wherein P represents the historical detection total times of the vehicle shaking disease point to be rectified within the time constraint C_FT,representing the shaking waveform data obtained by historical p-th detection and the ith disease point alpha i Corresponding correlation coefficients between the vehicle-shaking disease waveform data cov (M 0 i ,M p i ) Representing variable M 0 i And M is as follows p i Covariance of->Representing variable M 0 i 、M p i In (d) represents the minimum value in the variables (. Cndot.),. Cndot.,>and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained through each historical detection.
The vehicle shaking disease data deviation correcting method and system based on the waveform correlation can be used for correcting deviation of vehicle shaking disease data which is detected at the latest time, realizing real-time detection of effective vehicle shaking disease points, and also can be used for correcting deviation of vehicle shaking disease data in the past days.
According to the invention, the corresponding program is integrated on the line quality detector, so that the integration of data acquisition and deviation correction is realized. The protection scope of the invention is not limited to a line quality inspection instrument, a car shaking instrument, a car adding instrument, a subway running service detection device and the like, and any instrument device for measuring the vertical acceleration and the horizontal acceleration of the locomotive body based on an acceleration sensor is applicable.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. The vehicle shaking disease data deviation correcting method based on the waveform correlation is characterized by comprising the following steps of:
obtaining shaking disease waveform data corresponding to a shaking disease point to be corrected, wherein the shaking disease waveform data is mileage-acceleration value waveform data, mileage range constraint in the shaking disease waveform data is within a mileage range C_FD set before and after the shaking disease point to be corrected, and acceleration values in the shaking disease waveform data are acceleration measurement values of measurement points in the mileage range constraint;
obtaining vehicle shaking waveform data corresponding to a vehicle shaking disease point to be corrected in multiple historical detection, wherein the vehicle shaking waveform data are mileage-acceleration value waveform data, the mileage range constraint in the vehicle shaking waveform data is within a mileage range C_FD set before and after the vehicle shaking disease point to be corrected, the acceleration value in the vehicle shaking waveform data is an acceleration measurement value of each measurement point in the mileage range constraint, and the time constraint C_FT of the historical detection is within a set time before the measurement time of the vehicle shaking disease point to be corrected;
calculating a correlation coefficient between the shaking vehicle waveform data and the shaking vehicle disease waveform data obtained through each historical detection;
judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not;
if so, judging that the vehicle shaking disease point to be rectified is invalid;
if not, judging that the vehicle shaking disease point to be rectified is effective.
2. The method for correcting the shaking car disease data deviation based on the waveform correlation as claimed in claim 1, wherein the method for correcting the shaking car disease data deviation based on the waveform correlation further comprises:
dividing a railway track into a plurality of sections of equal length;
determining total deduction of the diseases of the section according to the disease grade determined by the peak value of each effective shaking disease point in the section and the number of the effective shaking disease points;
the first n sections where the total knot of disease is high are determined to be weak sections.
3. The method for correcting vehicle-shaking disease data deviation based on waveform correlation as claimed in claim 1, wherein the total number of the vehicle-shaking disease points to be corrected is I, and an I-th disease point α among the vehicle-shaking disease points to be corrected is i The corresponding shaking waveform data sequence is Represents the disease point alpha i A vehicle shaking waveform data sequence set in a front-back C_FD mileage range; set->The total number of elements n=2×c_fd×f, f representing the sampling frequency of the line quality inspection instrument, +.>Representing a train wave data sequence +.>Peak point in>The mileage position of (a) represents the disease point alpha i Is to correct the position of the ith disease point alpha in the vehicle shaking disease points to be corrected i Corresponding sloshing waveform data +.>Is within the range of
4. The method for correcting deviation of vehicle-shaking disease data based on waveform correlation as claimed in claim 3, wherein said calculating correlation coefficients between said vehicle-shaking waveform data and said vehicle-shaking disease waveform data obtained by each history detection specifically comprises:
obtaining shaking waveform data corresponding to shaking disease points to be rectified in historical P-th (P is more than or equal to 1 and less than or equal to P) detection, and integrating the shaking waveform data points for useA representation;
using the formulaCalculating a correlation coefficient between the shaking vehicle waveform data and the shaking vehicle disease waveform data obtained by the p-th detection of the history;
using the formulaAcquiring a minimum correlation coefficient;
wherein P represents the historical detection total times of the vehicle shaking disease point to be rectified within the time constraint C_FT,representing the shaking waveform data obtained by historical p-th detection and the ith disease point alpha i Correlation coefficient between corresponding vehicle shaking disease waveform data, < >>Representing variable M 0 i And M is as follows p i Covariance of->Representing variable M 0 i 、M p i In (d) represents the minimum value in the variables (. Cndot.),. Cndot.,>and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained through each historical detection.
5. The system for correcting the shaking car disease data deviation based on the waveform correlation is characterized by comprising the following components:
the vehicle shaking disease waveform data acquisition module is used for acquiring vehicle shaking disease waveform data corresponding to a vehicle shaking disease point to be corrected, wherein the vehicle shaking disease waveform data is mileage-acceleration value waveform data, the mileage range constraint in the vehicle shaking disease waveform data is within a mileage range C_FD set before and after the vehicle shaking disease point to be corrected, and the acceleration value in the vehicle shaking disease waveform data is an acceleration measurement value of each measurement point within the mileage range constraint;
the historical shaking waveform data acquisition module is used for acquiring shaking waveform data corresponding to the shaking disease point to be corrected in multiple historical detection, wherein the shaking waveform data is mileage-acceleration value waveform data, the mileage range constraint in the shaking waveform data is within a mileage range C_FD set before and after the shaking disease point to be corrected, the acceleration value in the shaking waveform data is an acceleration measurement value of each measurement point in the mileage range constraint, and the time constraint C_FT of the historical detection is within a set time before the measurement time of the shaking disease point to be corrected;
the correlation coefficient calculation module is used for calculating the correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained through each historical detection;
the vehicle shaking disease point correction module is used for judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not; if so, judging that the vehicle shaking disease point to be rectified is invalid; if not, judging that the vehicle shaking disease point to be rectified is effective.
6. The system for correcting vehicle-shaking disease data deviation based on waveform correlation as claimed in claim 5, wherein the system for correcting vehicle-shaking disease data deviation based on waveform correlation further comprises a weak area determining module, specifically comprising:
dividing a railway track into a plurality of sections of equal length;
determining total deduction of the diseases of the section according to the disease grade determined by the peak value of each effective shaking disease point in the section and the number of the effective shaking disease points;
the first n sections where the total knot of disease is high are determined to be weak sections.
7. The system for correcting vehicle-shaking disease data deviation based on waveform correlation as claimed in claim 5, wherein the historical vehicle-shaking waveform data acquisition module specifically comprises:
the total number of the vehicle shaking disease points to be corrected is I, and the ith disease point alpha in the vehicle shaking disease points to be corrected i The corresponding shaking waveform data sequence is Represents the disease point alpha i A vehicle shaking waveform data sequence set in a front-back C_FD mileage range; set->The total number of elements n=2×c_fd×f, f representing the sampling frequency of the line quality inspection instrument, +.>Representing a train wave data sequence +.>Peak point in>The mileage position of (a) represents the disease point alpha i Is to correct the position of the ith disease point alpha in the vehicle shaking disease points to be corrected i Corresponding sloshing waveform data +.>Is within the range of
8. The system for correcting vehicle sloshing disease data deviation based on waveform correlation as claimed in claim 7, wherein the correlation coefficient calculating module specifically includes:
obtaining shaking waveform data corresponding to shaking disease points to be rectified in historical P-th (P is more than or equal to 1 and less than or equal to P) detection, and integrating the shaking waveform data points for useA representation;
using the formulaCalculating a correlation coefficient between the shaking vehicle waveform data and the shaking vehicle disease waveform data obtained by the p-th detection of the history;
using the formulaAcquiring a minimum correlation coefficient;
wherein P represents the historical detection total times of the vehicle shaking disease point to be rectified within the time constraint C_FT,representing the shaking waveform data obtained by historical p-th detection and the ith disease point alpha i Corresponding correlation coefficients between the vehicle-shaking disease waveform data cov (M 0 i ,M p i ) Representing variable M 0 i And M is as follows p i Covariance of->Representing variable M 0 i 、M p i The standard deviation, min (. Cndot.) represents the taking variableMinimum in (-),>and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained through each historical detection.
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