CN114417921A - Method, device, equipment and storage medium for identifying geometric abnormal data of track - Google Patents
Method, device, equipment and storage medium for identifying geometric abnormal data of track Download PDFInfo
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Abstract
Provided are a method, a device, equipment and a storage medium for identifying track geometry abnormal data, wherein the method comprises the following steps: acquiring original detection data of the track geometry; processing the original detection data by combining an empirical mode decomposition method with a data segment sliding standard deviation, and identifying to obtain a first abnormal data segment; processing the original detection data by combining a local peak value search algorithm with a data segment sliding root mean square, and identifying to obtain a second abnormal data segment; processing the original detection data through a data segment sliding standard deviation, and identifying to obtain a third abnormal data segment; processing the original detection data by combining a data threshold value through a difference method, and identifying to obtain a fourth abnormal data segment; and merging the first abnormal data segment, the second abnormal data segment, the third abnormal data segment and the fourth abnormal data segment to obtain an abnormal data section. The efficiency and accuracy of abnormal data identification can be improved.
Description
Technical Field
The invention relates to the field of rail transit, in particular to a method, a device, equipment and a storage medium for identifying geometric abnormal data of a rail.
Background
At present, the evaluation of the rail irregularity state is obtained based on the evaluation standards of local peak values and section mean values of a plurality of rail geometric irregularity parameters in combination with the vibration acceleration of a vehicle body, and in addition, a rail maintenance scheme is also formulated mainly based on rail geometric irregularity data, wherein the rail geometric irregularity parameters mainly comprise height, rail direction, rail distance, level, triangular pits and the like of a rail.
In the actual detection process of the geometric irregularity data of the track, detection devices of the geometric irregularity data of the track comprise a laser assembly, an acceleration sensor, a string pulling displacement sensor, a gyroscope and the like, and the detection devices are influenced by various external conditions, so that some abnormal detection data of local and continuous sections inevitably occur.
For abnormal data of continuous sections, abnormal values in the data are generally identified and removed manually, or outlier judgment is directly performed on a calculation index by using a specific threshold range (such as a 3 sigma principle) by directly using the amplitude of track irregularity data or a section standard deviation. However, the manual identification has the problems of low efficiency and insufficient accuracy of abnormal data identification. For example, when a large number of track geometry inspection data calculations are required for research purposes, manual identification and culling of abnormal sections at this time is labor intensive and inefficient. In addition, for a line with a long mileage and complex equipment working condition, a condition of false judgment or missed judgment still exists when a specific threshold (such as a 3 sigma principle) is used for judging an abnormal section.
Therefore, a method for identifying track geometric abnormal data is needed to improve the efficiency and accuracy of identifying abnormal data.
Disclosure of Invention
An object of the embodiments herein is to provide a method, an apparatus, a device and a storage medium for identifying track geometry abnormal data, so as to improve the efficiency and accuracy of abnormal data identification.
To achieve the above object, in one aspect, an embodiment herein provides a method for identifying track geometry abnormal data, including:
acquiring original detection data of the track geometry;
processing the original detection data by combining an empirical mode decomposition method with a data segment sliding standard deviation, and identifying to obtain a first abnormal data segment;
processing the original detection data by combining a local peak value search algorithm with a data segment sliding root mean square, and identifying to obtain a second abnormal data segment;
processing the original detection data through a data segment sliding standard deviation, and identifying to obtain a third abnormal data segment;
processing the original detection data by combining a data threshold value through a difference method, and identifying to obtain a fourth abnormal data segment;
and merging the first abnormal data segment, the second abnormal data segment, the third abnormal data segment and the fourth abnormal data segment to obtain an abnormal data section.
Preferably, the raw detection data further includes:
laser signal data, string pulling signal data, gyro assembly signal data, and accelerometer signal data.
Preferably, the processing the original detection data by an empirical mode decomposition method in combination with a data segment sliding standard deviation, and identifying to obtain a first abnormal data segment further includes:
performing stabilization processing on the laser signal data by an empirical mode decomposition method to obtain N intrinsic mode components;
extracting the sum of the first M eigenmode components in the N eigenmode components;
performing sliding extraction on the sum of the first M intrinsic mode components according to a set time window length and a set step length to obtain a plurality of laser signal data sections;
calculating the standard deviation of each laser signal data segment to obtain a standard deviation sequence;
and screening the standard deviation sequence according to the standard deviation upper limit value and the standard deviation lower limit value to determine a first abnormal data segment.
Preferably, the processing the original detection data by the local peak search algorithm in combination with the data segment sliding root mean square, and the identifying to obtain the second abnormal data segment further includes:
sliding and extracting the string pulling signal data according to the set time window length and the set step length to obtain a plurality of string pulling signal data sections;
calculating the root mean square of each string pulling signal data segment to obtain a root mean square sequence;
judging whether an abnormal root mean square exists in the root mean square sequence or not;
if yes, circularly correcting the string pulling signal data to obtain a correction sequence;
performing sliding extraction on the correction sequence according to the set time window length and the set step length to obtain a plurality of correction data segments;
calculating the root mean square of each correction data segment to obtain a correction root mean square sequence;
and screening the corrected root-mean-square sequence according to a correction threshold value, and determining a second abnormal data segment.
Preferably, the cyclically modifying the string pulling signal data to obtain a modified sequence further includes:
the following steps are executed in a circulating mode for a plurality of times, and new string pulling signal data obtained after the circulation of the plurality of times are used as a correction sequence:
calculating the string pulling signal data according to a local peak value searching algorithm to obtain a local extreme value sequence;
carrying out interpolation resampling processing on the local extreme value sequence to obtain an expansion sequence; wherein the data volume of the capacity expansion sequence is the same as that of the string pulling signal data;
and correspondingly subtracting the string pulling signal data from the data in the expansion sequence to obtain new string pulling signal data.
Preferably, the determining whether an abnormal root mean square exists in the root mean square sequence further includes:
calculating any root mean square value in the root mean square sequence by the following formula:
|rms(r)-|G00||＜|αG00|；
wherein rms (r) is the r-th root mean square value in the root mean square sequence, G00The initial signal value of the string pulling signal data is alpha, and the fluctuation coefficient of the string pulling signal is alpha;
if any root mean square value satisfies the above formula, the root mean square value is an abnormal root mean square.
Preferably, the processing the original detection data by the data segment sliding standard deviation, and identifying a third difference data segment further includes:
calculating the standard deviation of each string pulling signal data segment to obtain a string pulling standard deviation sequence;
and screening the string pulling standard deviation sequence according to a string pulling threshold value, and determining a third different constant data segment.
Preferably, the processing the original detection data by combining a data threshold value through a difference method, and identifying to obtain a fourth abnormal data segment further includes:
calculating selected data by a difference method to obtain a difference value sequence, wherein the selected data is laser signal data, gyro component signal data or accelerometer signal data;
judging whether an abnormal value exists in the selected data, wherein the abnormal value is smaller than the data threshold, and the corresponding differential value of the abnormal value in the differential value sequence is smaller than the data threshold;
and if so, obtaining a fourth abnormal data segment according to the set of the abnormal values.
In another aspect, embodiments herein provide an apparatus for identifying track geometry anomaly data, the apparatus comprising:
the acquisition module is used for acquiring original detection data of the track geometry;
the first determining module is used for processing the original detection data by combining an empirical mode decomposition method with a data segment sliding standard deviation and identifying to obtain a first abnormal data segment;
the second determining module is used for processing the original detection data by combining a local peak value searching algorithm with a data segment sliding root mean square and identifying to obtain a second abnormal data segment;
the third determining module is used for processing the original detection data through the data segment sliding standard deviation and identifying to obtain a third irregular data segment;
the fourth determining module is used for processing the original detection data by combining a data threshold value through a difference method and identifying to obtain a fourth abnormal data segment;
and the merging module is used for merging the first abnormal data segment, the second abnormal data segment, the third abnormal data segment and the fourth abnormal data segment to obtain an abnormal data section.
In yet another aspect, embodiments herein also provide a computer device comprising a memory, a processor, and a computer program stored on the memory, the computer program, when executed by the processor, performing the instructions of any one of the methods described above.
In yet another aspect, embodiments herein also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer device, executes instructions according to any one of the methods described above.
According to the technical scheme provided by the embodiment, the original detection data is processed by an empirical mode decomposition method in combination with a data segment sliding standard deviation, a first abnormal data segment is obtained through identification, the original detection data is processed by a local peak search algorithm in combination with a data segment sliding root mean square, a second abnormal data segment is obtained through identification, the original detection data is processed by a data segment sliding standard deviation, a third abnormal data segment is obtained through identification, the original detection data is processed by a difference method in combination with a data threshold, a fourth abnormal data segment is obtained through identification, the first abnormal data segment, the second abnormal data segment, the third abnormal data segment and the fourth abnormal data segment are combined, and the abnormal data segment is obtained with high efficiency and high accuracy.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart illustrating a method for identifying track geometry anomaly data according to an embodiment of the present disclosure;
FIG. 2 illustrates a schematic flow chart provided in an embodiment herein for identifying a first anomalous data segment;
FIG. 3 illustrates a flow diagram for identifying a second anomalous data segment as provided by embodiments herein;
FIG. 4 illustrates a flow diagram for loop correction provided by embodiments herein;
FIG. 5 illustrates a flow diagram for identifying a third difference data segment provided by embodiments herein;
FIG. 6 illustrates a schematic flow chart provided in an embodiment herein for identifying a fourth anomalous data segment;
fig. 7 is a schematic block diagram illustrating an apparatus for identifying track geometry anomaly data according to an embodiment of the present disclosure;
fig. 8 shows a schematic structural diagram of a computer device provided in an embodiment herein.
Description of the symbols of the drawings:
100. an acquisition module;
200. a first determination module;
300. a second determination module;
400. a third determination module;
500. a fourth determination module;
600. a merging module;
802. a computer device;
804. a processor;
806. a memory;
808. a drive mechanism;
810. an input/output module;
812. an input device;
814. an output device;
816. a presentation device;
818. a graphical user interface;
820. a network interface;
822. a communication link;
824. a communication bus.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments herein without making any creative effort, shall fall within the scope of protection.
In the actual detection process of the geometric irregularity data of the track, detection devices of the geometric irregularity data of the track comprise a laser assembly, an acceleration sensor, a string pulling displacement sensor, a gyroscope and the like, and the detection devices are influenced by various external conditions, so that some abnormal detection data of local and continuous sections inevitably occur.
For abnormal data of continuous sections, abnormal values in the data are generally identified and removed manually, or outlier determination is directly performed on a calculation index by using a specific threshold range (such as a 3 sigma principle) by directly using the amplitude of track irregularity data or a section standard deviation. However, the manual identification has the problems of low efficiency and insufficient accuracy of abnormal data identification. For example, when a large number of track geometry inspection data calculations are required for research purposes, manual identification and culling of abnormal sections at this time is labor intensive and inefficient. In addition, for a line with a long mileage and complex equipment working condition, a condition of false judgment or missed judgment still exists when an abnormal section is judged by using a specific threshold range (such as a 3 sigma principle).
In order to solve the above problem, embodiments herein provide a method for identifying track geometry anomaly data. Fig. 1 is a schematic diagram of steps of a method for identifying track geometry anomaly data provided in an embodiment herein, and the present specification provides the method operation steps as described in the embodiment or a flowchart, but more or less operation steps can be included based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual system or apparatus product executes, it can execute sequentially or in parallel according to the method shown in the embodiment or the figures.
Referring to fig. 1, a method for identifying track geometry anomaly data includes:
s101: acquiring original detection data of the track geometry;
s102: processing the original detection data by combining an empirical mode decomposition method with a data segment sliding standard deviation, and identifying to obtain a first abnormal data segment;
s103: processing the original detection data by combining a local peak value search algorithm with a data segment sliding root mean square, and identifying to obtain a second abnormal data segment;
s104: processing the original detection data through a data segment sliding standard deviation, and identifying to obtain a third abnormal data segment;
s105: processing the original detection data by combining a data threshold value through a difference method, and identifying to obtain a fourth abnormal data segment;
s106: and merging the first abnormal data segment, the second abnormal data segment, the third abnormal data segment and the fourth abnormal data segment to obtain an abnormal data section.
Raw inspection data for the track geometry includes, but is not limited to: laser signal data, string pulling signal data, gyro assembly signal data, and accelerometer signal data. The laser signal data comprise left laser signal data and right laser signal data, the string pulling signal data comprise left string pulling signal data and right string pulling signal data, the gyro assembly signal data comprise three data channels of rolling, shaking and inclination angles, and the accelerometer signal data comprise two vertical and transverse data channels. The identification methods described herein are applicable to any of the left/right laser signal data, left/right string pull signal data, and gyro assembly signal data for any data channel and accelerometer signal data for any data channel described above.
The original detection data of the track geometry are used for detecting track geometric irregularity indexes such as track height, track direction, track gauge, horizontal pits and triangular pits.
What needs to be distinguished is that the abnormality caused by the influence of external environmental conditions such as sunlight, rainwater, rail grinding and the like on the laser signal includes various conditions: according to the conditions, the method can be divided into high-frequency abnormity and low-frequency abnormity, the high-frequency abnormity generates the high-frequency signal for the abnormal surge of the laser signal, the low-frequency abnormity is intermittent no-signal, the laser signal generates the no-fluctuation or the weak fluctuation along with the signal after the step-like mutation, and the first abnormal data section comprises a first high-frequency abnormal data section and a first low-frequency abnormal data section in the laser signal data.
Anomalies in the pull string signal data include a number of conditions: the string pulling signal abnormality caused by string pulling breakage and other reasons, the condition of step-shaped sharp increase, and the abnormal condition of step-shaped sharp increase, approximate stabilization and drop of the signal amplitude at the moment when the standby string pulling signal is switched on after the string pulling breakage and approximately keeps stable for a certain distance, the string pulling signal abnormality caused by string pulling aging and other reasons can be determined as the string pulling breakage abnormality in the first case, the string pulling breakage abnormality in the second case and the string pulling breakage abnormality in the third case, the second abnormal data segment is the data segment with string pulling breakage abnormality, and the third abnormal data segment is the data segment with string pulling abnormality.
The anomaly of the gyro component signal data and the accelerometer signal data only includes a case where the signal continuity is approximately 0, and the laser signal data also has a case where the signal continuity is approximately 0 in addition to the gyro component signal data and the accelerometer signal data, and the fourth anomaly data segment described herein is an anomaly data segment including the gyro component signal data, the accelerometer signal data and the laser signal data.
Compared with a method for directly identifying the abnormal section by the principle of direct 3 sigma of the standard deviation of the track irregularity parameters, the method has higher accuracy because the specific single track irregularity acquisition needs to be obtained by processing signals of a plurality of sensors by using a specific algorithm. When a fault occurs in an individual sensor, the standard deviation of the corresponding track irregularity data may still meet the 3 sigma principle, and an abnormal section cannot be identified; in addition, for a line with a long mileage and complex equipment working conditions, when the distribution characteristics of the track irregularity deviate from the gaussian distribution, the situation of erroneous judgment or missed judgment occurs. The method disclosed by the patent directly analyzes abnormal values from original signals of various sensors, and can improve the identification accuracy and applicability of abnormal sections compared with the abnormal section judgment method based on the 3 sigma principle or a specific threshold value.
Referring to fig. 2, in this embodiment, the processing the raw detection data by an empirical mode decomposition method in combination with a data segment sliding standard deviation, and the identifying a first abnormal data segment further includes:
s201: performing stabilization processing on the laser signal data by an empirical mode decomposition method to obtain N intrinsic mode components;
s202: extracting the sum of the first M eigenmode components in the N eigenmode components;
s203: performing sliding extraction on the sum of the first M intrinsic mode components according to a set time window length and a set step length to obtain a plurality of laser signal data sections;
s204: calculating the standard deviation of each laser signal data segment to obtain a standard deviation sequence;
s205: and screening the standard deviation sequence according to the standard deviation upper limit value and the standard deviation lower limit value to determine a first abnormal data segment.
Specifically, Empirical Mode Decomposition (EMD) can be used for stabilizing laser signal data, wherein the laser signal data comprise sampling mileage data set by a track detection vehicle during acquisition along a track, a plurality of acquisition points are arranged along the set sampling mileage, each acquisition point is provided with corresponding laser signal data, and the laser signal data obtained by the acquisition points are as follows: y ═ Y (i) }, i ═ 1, 2.
Obtaining N intrinsic mode components and residual amount of signals after the stabilization treatment by an empirical mode decomposition method, wherein the number N of the intrinsic mode components is determined by a cycle judgment criterion of the empirical mode decomposition method, N is a positive integer larger than 0, and the empirical mode decomposition method obtains N intrinsic mode components with main frequency components from high to lowComponent imf (j), j ═ 1, 2. Calculating the sum of the first M eigenmode components
M herein may take the value 1 or 2.
When the sum of the first M intrinsic mode components is extracted in a sliding mode according to a set time window length and a set step length, the set time window length refers to the length of any one laser signal data segment in a plurality of laser signal data segments obtained through sliding extraction, the set step length is slid after one laser signal data segment is extracted, and then the next laser signal data segment is extracted. It should be noted that the number of the laser signal data segments is an integer, and if the data amount of the end I is smaller than the set time window length after sliding to the end of I according to the set step length, the data amount of the end is not extracted as one laser signal data segment. The length of the set time window and the set step length can be determined according to actual working conditions.
For example, the first M eigenmode component sum data sequence includes 10000 data points with sampling mileage, the length of the time window is set to 800 data, the step length is set to 100 data, the first laser signal data segment is obtained according to the 1 st to 800 th data during the sliding extraction, the second laser signal data segment is obtained by extracting the 101 st and 900 th data after sliding 100 data, and a plurality of laser signal data segments are obtained sequentially.
And further calculating the standard deviation of each laser signal data segment to obtain a standard deviation sequence: std (r), r ═ 0,1,2, … s; wherein s is the number of laser signal data segments.
Before the standard deviation sequence is screened according to the standard deviation upper limit value and the standard deviation lower limit value, the standard deviation upper limit value and the standard deviation lower limit value are determined. Wherein the upper limit value of the standard deviation is Wu1＝max{Su,P99Get S immediatelyuAnd P99The maximum value of (1) is taken as the upper limit value of standard deviation, SuThe maximum standard deviation, P, in the standard deviations corresponding to the historical laser signal data section without abnormality99The cumulative percentage of the laser signal standard deviation sequence std (r) which needs to judge the abnormal state is 99 percent of the corresponding quantileThe value is obtained. Wherein the lower limit value of the standard deviation is Wl1＝min{Sl,P01Get S immediatelylAnd P01Minimum value of (5) as the lower limit of standard deviation, SlThe minimum standard deviation, P, in the standard deviations corresponding to the history laser signal data segment without abnormality01The cumulative percentage of the laser signal standard deviation sequence std (r) for judging the abnormal state is 1% corresponding to the quantile value.
According to the upper limit value and the lower limit value of the standard deviation, screening to obtain a standard deviation value which is greater than the upper limit value or less than the lower limit value of the standard deviation in the standard deviation sequence std (r), and according to a track mileage section corresponding to the standard deviation value, obtaining that a laser signal data section corresponding to the track mileage section is a first abnormal data section, specifically, if the standard deviation value is greater than the upper limit value of the standard, the corresponding laser signal data section is a first high-frequency abnormal data section, and if the standard deviation is less than the lower limit value of the standard, the corresponding laser signal data section is a first low-frequency abnormal data section.
The laser sensor is interfered by factors such as sunlight, rainwater and foreign matter shielding, abnormal conditions of abnormal fluctuation high-frequency component increase and approximate signal fluctuation (weak fluctuation) can occur, different from string pulling breakage of the string pulling sensor, the step-shaped fluctuation of the laser signal is continuous, standby replacement is not needed to solve the abnormality, the corresponding signal surge direction is unknown, and the processing method of the string pulling sensor abnormality is not applicable.
Referring to fig. 3, in this embodiment, the processing the original detection data by using a local peak search algorithm in combination with a data segment sliding root mean square, and identifying a second abnormal data segment further includes:
s301: sliding and extracting the string pulling signal data according to the set time window length and the set step length to obtain a plurality of string pulling signal data sections;
s302: calculating the root mean square of each string pulling signal data segment to obtain a root mean square sequence;
s303: judging whether an abnormal root mean square exists in the root mean square sequence or not;
s304: if yes, circularly correcting the string pulling signal data to obtain a correction sequence;
s305: performing sliding extraction on the correction sequence according to the set time window length and the set step length to obtain a plurality of correction data segments;
s306: calculating the root mean square of each correction data segment to obtain a correction root mean square sequence;
s307: screening the corrected root-mean-square sequence according to a correction threshold value, and determining a second abnormal data segment;
the string pulling signal data comprises sampling mileage data set when the string pulling signal data are collected along a track by a track detection vehicle, a plurality of collection points are arranged along the set sampling mileage, each collection point is provided with string pulling signal data corresponding to the collection point, and the string pulling signal data obtained by the collection points are as follows: x ═ 1, 2., n, n is the number of collection points.
And performing sliding extraction on the string pulling signal data according to the set time window length and the set step length to obtain a plurality of string pulling signal data sections, wherein each string pulling signal data section is provided with data of the set time window length, and the set step length is arranged between the starting points of any two adjacent string pulling signal data sections at intervals.
Further, the root mean square of each string signal data segment is calculated, and a root mean square sequence rms (r) is obtained, wherein r is 0,1,2 and … s, and s is the number of the string signal data segments.
And then judging whether the root mean square sequence has abnormal root mean square, if not, indicating that the second abnormal data segment does not exist in the string pulling signal data.
If yes, circularly correcting the string pulling signal data, and further obtaining a correction sequence: xk＝{xk(i) 1,2, n, where k represents the cyclic correction k times. And performing sliding extraction on the correction sequence according to the set time window length and the set step length to obtain a plurality of correction data segments, and calculating the root mean square of each correction data segment to obtain a corrected root mean square sequence rms' (r), wherein r is 0,1,2 and … s.
Before screening the corrected root-mean-square sequence according to the correction threshold, determining the correction threshold. Wherein the correction threshold is Wu2＝|βG00L, wherein G00The beta value is an initial signal value, and can be determined to be 0.5-0.9 according to the actual working condition. Specifically, the method for determining the initial signal value of the string pulling comprises the following steps: after the string pulling sensor is installed on the track detection vehicle, the corresponding signal value is an initial signal value, at the moment, a string pulling hook of the string pulling sensor is not installed, and the string pulling sensor does not work. The reason for the value of the beta is as follows: since the string pulling hook of the string pulling sensor is not installed when the initial signal value is obtained, the initial signal value can be regarded as a signal value corresponding to string pulling breakage, and if the root mean square value in the corrected root mean square sequence is close to the signal value, string pulling breakage is possibly caused correspondingly.
And screening to obtain a root mean square value which is greater than the correction threshold value in the corrected root mean square sequence rms' (r) according to the correction threshold value, and obtaining a string pulling signal data section corresponding to the track mileage section as a second abnormal data section according to the track mileage section corresponding to the root mean square value, specifically, if the root mean square value is greater than the correction threshold value, the corresponding string pulling signal data section is the second abnormal data section.
In this embodiment, the cyclically modifying the string pulling signal data to obtain a modified sequence further includes:
referring to fig. 4, the following steps are executed in a loop for several times, and new string pulling signal data obtained after several loops are used as a correction sequence:
s401: calculating the string pulling signal data according to a local peak value searching algorithm to obtain a local extreme value sequence;
s402: carrying out interpolation resampling processing on the local extreme value sequence to obtain an expansion sequence; wherein the data volume of the capacity expansion sequence is the same as that of the string pulling signal data;
s403: and correspondingly subtracting the string pulling signal data from the data in the expansion sequence to obtain new string pulling signal data.
It should be noted that the local extremum sequence is a local minimum sequence or a local maximum sequence, wherein if the initial signal value of the string is a positive sign, the local extremum sequence is taken as the local minimum sequence, and the local minimum sequence is subjected to interpolation resampling; if the initial signal value of the string is a negative sign, taking the local extreme value sequence as a local maximum value sequence, and carrying out interpolation resampling on the local maximum value sequence;
specifically, when the local peak search algorithm is applied, differential calculation may be performed on the string pulling signal data X ═ X (i), i ═ 1,2,.., n by using a difference method, so as to obtain a sequence after the difference, and if a certain difference value exists in the sequence after the difference, which is opposite to the sign of the previous adjacent difference value, the difference value is a local peak point, which is more specifically: if the difference value of a certain point is negative and the difference value of the previous adjacent point is positive, the point corresponds to a maximum value point, and conversely, the point corresponds to a minimum value point; after all local peaks are screened out, a local peak sequence Z is further determined1＝{z1(i) 1, 2.., a. The determination method of the local peak sequence may be: selecting local peak values larger than a set peak value absolute value lower limit according to a set interval to form a local peak value sequence, wherein the set interval can be determined according to actual working conditions, and the set peak value absolute value lower limit can be | beta G00|，G00And determining beta as an initial signal value to be 0.5-0.9 according to the actual working condition, starting from the initial position of the local peak value, judging whether the absolute value of the current local peak value is greater than the lower limit of the absolute value of the set peak value at set intervals, and if so, determining that the current local peak value belongs to the local peak value sequence, thereby obtaining the local peak value sequence formed by a local peak values.
Through the steps, a is less than n, so that interpolation resampling processing needs to be carried out on the local extremum sequence to obtain a capacity expansion sequence Z'1＝{z′1(i) 1, 2.., n. Since the data volume of the expansion sequence and the adjusted string signal data is the same, the data in the expansion sequence and the adjusted string signal data can be subtracted correspondingly, namely x (i) -z'1(i) (ii) a Obtaining new adjusted string pulling signal data, carrying out the steps again on the new adjusted string pulling signal data, circulating for k times, wherein k can be 3-5, and finally obtaining X after circulating for k timesk＝{xk(i)},i＝1,2,...,n。
In this embodiment, the determining whether an abnormal root mean square exists in the root mean square sequence further comprises:
calculating any root mean square value in the root mean square sequence by the following formula:
|rms(r)-|G00||＜|αG00|；
wherein rms (r) is the r-th root mean square value in the root mean square sequence, G00The initial signal value of the string pulling signal data is alpha, wherein alpha is a string pulling signal fluctuation coefficient, and can be 0.1-0.2;
if any root mean square value meets the formula, the root mean square value is an abnormal root mean square, namely, if only one root mean square value meets the formula, the root mean square value is an abnormal root mean square, and the second abnormal data section exists in the string pulling signal data.
Referring to fig. 5, in this embodiment, the processing the raw detection data by a data segment sliding standard deviation, and identifying a third irregular data segment further includes:
s501: sliding and extracting the string pulling signal data according to the set time window length and the set step length to obtain a plurality of string pulling signal data sections;
s502: calculating the standard deviation of each string pulling signal data segment to obtain a string pulling standard deviation sequence;
s503: and screening the string pulling standard deviation sequence according to a string pulling threshold value, and determining a third different constant data segment.
The string pulling signal data comprises sampling mileage data set when the string pulling signal data are collected along a track by a track detection vehicle, a plurality of collection points are arranged along the set sampling mileage, each collection point is provided with string pulling signal data corresponding to the collection point, and the string pulling signal data obtained by the collection points are as follows: x ═ 1, 2., n, n is the number of collection points.
And performing sliding extraction on the string pulling signal data according to the set time window length and the set step length to obtain a plurality of string pulling signal data sections, wherein each string pulling signal data section is provided with data of the set time window length, and the set step length is arranged between the starting points of any two adjacent string pulling signal data sections at intervals.
Further, calculating a standard deviation of each string pulling signal data segment to obtain a string pulling standard deviation sequence std' (r), wherein r is 0,1,2,. s; before screening the string pulling standard deviation sequence according to the string pulling threshold value, the string pulling threshold value W needs to be determined firstlyl2＝min{S1′,P′01Get S immediately1'and P'01Minimum value of (1) as a threshold value of string pulling, S1' is the smallest standard deviation, P ' in the standard deviations of the historical abnormal-free guyed signal data segments '01The cumulative percentage of the string standard deviation sequence std' (r) is 1% of the corresponding quantile value. If the standard deviation sequence does not have the standard deviation value smaller than the string pulling threshold, the string pulling signal data does not have a third abnormal data segment, and if the standard deviation sequence has the standard deviation value smaller than the string pulling threshold, the string pulling signal data segment corresponding to the track mileage segment can be obtained as the third abnormal data segment according to the track mileage segment corresponding to the standard deviation value.
The common fault of the string pulling signal is that the signal is suddenly increased due to string pulling breakage, the corresponding signal shows step-shaped sudden change and the signal is approximately maintained to be stable, at the moment that the standby string pulling is connected, the abnormal condition that the signal drops back step-shaped and returns to be normal after a certain distance occurs to the string pulling signal, besides, the abnormal condition that the string pulling signal is automatically recovered after the string pulling signal is suddenly increased step-shaped and approximately maintained to be stable for a certain distance also occurs due to the aging of the string pulling and the like besides the breakage condition. The local peak value searching algorithm is combined with a judging method of the sliding root mean square of the data section, so that a trend item of a string pulling signal caused by the curve section can be eliminated, step-shaped sudden change at a string pulling fracture position is kept, and the condition of an abnormal section is identified. And the situation that the signal is increased sharply and the position is stable can be directly judged by the standard deviation of the section sliding. The two are combined to judge whether the use state of the pull string has the conditions of fracture, aging and replacement.
Referring to fig. 6, in this embodiment, the processing the original detection data by using a difference method in combination with a data threshold, and identifying a fourth abnormal data segment further includes:
s601: calculating selected data by a difference method to obtain a difference value sequence, wherein the selected data is laser signal data, gyro component signal data or accelerometer signal data;
s602: judging whether an abnormal value exists in the selected data, wherein the abnormal value is smaller than the data threshold, and the corresponding differential value of the abnormal value in the differential value sequence is smaller than the data threshold;
s603: and if so, obtaining a fourth abnormal data segment according to the set of the abnormal values.
Specifically, the selected data may be Q ═ { Q (i) }, i ═ 1, 2., n, and the selected data is calculated by a difference method to obtain a sequence of difference values Δ Q ═ Δ Q (i) }, i ═ 1, 2., n-1. Those skilled in the art will recognize that the difference value is obtained by subtracting the previous data from the next data.
Before judging whether an abnormal value exists in the selected data, a data threshold needs to be determined, the data threshold can be determined according to actual working conditions, and since the fourth abnormal data segment is a signal with the persistence approximately equal to 0, the data threshold can be a value slightly larger than 0, such as 0.01, 0.02 and the like. If not, the fourth abnormal data segment does not exist.
If any data value exists in the selected data, the data value is smaller than the data threshold, and the difference value corresponding to the data value is also smaller than the data threshold, the data value is an abnormal value. Specifically, the differential value corresponding to the data value is a differential value obtained by a difference between the data value and a data value adjacent to the preamble of the data value.
And screening all abnormal values by the method to obtain a fourth abnormal data segment.
Finally, in the above step S106, the first abnormal data segment, the second abnormal data segment, the third abnormal data segment, and the fourth abnormal data segment are merged to obtain an abnormal data segment. Specifically, the abnormal data segment may include a plurality of abnormal data segments, and when merging is performed, if a data segment interval between any two adjacent abnormal data segments is smaller than a data segment interval threshold, it is determined that the two adjacent abnormal data segments are closer to each other, and the data segment between the two adjacent abnormal data segments is determined to be the abnormal data segment, where the size of the data segment interval threshold may be determined according to an actual working condition and a time window length in the text.
Based on the above method for identifying track geometry abnormal data, the embodiment herein further provides an apparatus for identifying track geometry abnormal data. The apparatus may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that employ the methods described herein in embodiments, in conjunction with any necessary apparatus to implement the hardware. Based on the same innovative concepts, embodiments herein provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific apparatus implementation in the embodiment of the present disclosure may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Specifically, fig. 7 is a schematic block structure diagram of an embodiment of an apparatus for identifying track geometry abnormal data provided in an embodiment of the present disclosure, and referring to fig. 7, the apparatus for identifying track geometry abnormal data provided in an embodiment of the present disclosure includes: the device comprises an acquisition module 100, a first determination module 200, a second determination module 300, a third determination module 400, a fourth determination module 500 and a merging module 600.
An obtaining module 100, configured to obtain original detection data of a track geometry;
the first determining module 200 is configured to process the original detection data by using an empirical mode decomposition method in combination with a data segment sliding standard deviation, and identify to obtain a first abnormal data segment;
the second determining module 300 is configured to process the original detection data by using a local peak search algorithm in combination with a data segment sliding root-mean-square, and identify to obtain a second abnormal data segment;
a third determining module 400, configured to process the original detection data according to a data segment sliding standard deviation, and identify to obtain a third exception data segment;
a fourth determining module 500, configured to process the original detection data by using a difference method in combination with a data threshold, and identify to obtain a fourth abnormal data segment;
a merging module 600, configured to merge the first abnormal data segment, the second abnormal data segment, the third abnormal data segment, and the fourth abnormal data segment to obtain an abnormal data segment.
Referring to fig. 8, based on the above-described method for identifying track geometry anomaly data, an embodiment herein further provides a computer device 802, wherein the above-described method runs on the computer device 802. Computer device 802 may include one or more processors 804, such as one or more Central Processing Units (CPUs) or Graphics Processors (GPUs), each of which may implement one or more hardware threads. The computer device 802 may also include any memory 806 for storing any kind of information, such as code, settings, data, etc., and in a particular embodiment a computer program on the memory 806 and executable on the processor 804, which computer program when executed by the processor 804 may perform instructions according to the above-described method. For example, and without limitation, memory 806 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent fixed or removable components of computer device 802. In one case, when the processor 804 executes the associated instructions, which are stored in any memory or combination of memories, the computer device 802 can perform any of the operations of the associated instructions. The computer device 802 also includes one or more drive mechanisms 808, such as a hard disk drive mechanism, an optical disk drive mechanism, etc., for interacting with any memory.
Corresponding to the methods in fig. 1-6, the embodiments herein also provide a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, performs the steps of the above-described method.
Embodiments herein also provide computer readable instructions, wherein when executed by a processor, a program thereof causes the processor to perform the method as shown in fig. 1-6.
It should be understood that, in various embodiments herein, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments herein.
It should also be understood that, in the embodiments herein, the term "and/or" is only one kind of association relation describing an associated object, meaning that three kinds of relations may exist. For example, a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided herein, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments herein.
In addition, functional units in the embodiments herein may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present invention may be implemented in a form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The principles and embodiments of this document are explained herein using specific examples, which are presented only to aid in understanding the methods and their core concepts; meanwhile, for the general technical personnel in the field, according to the idea of this document, there may be changes in the concrete implementation and the application scope, in summary, this description should not be understood as the limitation of this document.
Claims (11)
1. A method for identifying track geometric anomaly data is characterized by comprising the following steps:
acquiring original detection data of the track geometry;
processing the original detection data by combining an empirical mode decomposition method with a data segment sliding standard deviation, and identifying to obtain a first abnormal data segment;
processing the original detection data by combining a local peak value search algorithm with a data segment sliding root mean square, and identifying to obtain a second abnormal data segment;
processing the original detection data through a data segment sliding standard deviation, and identifying to obtain a third abnormal data segment;
processing the original detection data by combining a data threshold value through a difference method, and identifying to obtain a fourth abnormal data segment;
and merging the first abnormal data segment, the second abnormal data segment, the third abnormal data segment and the fourth abnormal data segment to obtain an abnormal data section.
2. The method for identifying track geometry anomaly data according to claim 1, wherein said raw detection data further comprises:
laser signal data, string pulling signal data, gyro assembly signal data, and accelerometer signal data.
3. The method for identifying geometric anomaly data of a track according to claim 2, wherein the processing the raw detection data by empirical mode decomposition in combination with a data segment sliding standard deviation, and the identifying a first anomaly data segment further comprises:
performing stabilization processing on the laser signal data by an empirical mode decomposition method to obtain N intrinsic mode components;
extracting the sum of the first M eigenmode components in the N eigenmode components;
performing sliding extraction on the sum of the first M intrinsic mode components according to a set time window length and a set step length to obtain a plurality of laser signal data sections;
calculating the standard deviation of each laser signal data segment to obtain a standard deviation sequence;
and screening the standard deviation sequence according to the standard deviation upper limit value and the standard deviation lower limit value to determine a first abnormal data segment.
4. The method for identifying track geometry abnormal data according to claim 2, wherein the processing the original detection data by combining a local peak search algorithm with a data segment sliding root mean square, and the identifying to obtain a second abnormal data segment further comprises:
sliding and extracting the string pulling signal data according to the set time window length and the set step length to obtain a plurality of string pulling signal data sections;
calculating the root mean square of each string pulling signal data segment to obtain a root mean square sequence;
judging whether an abnormal root mean square exists in the root mean square sequence or not;
if yes, circularly correcting the string pulling signal data to obtain a correction sequence;
performing sliding extraction on the correction sequence according to the set time window length and the set step length to obtain a plurality of correction data segments;
calculating the root mean square of each correction data segment to obtain a correction root mean square sequence;
and screening the corrected root-mean-square sequence according to a correction threshold value, and determining a second abnormal data segment.
5. The method for identifying track geometry anomaly data according to claim 4, wherein the cyclically modifying the string pulling signal data to obtain a modified sequence further comprises:
the following steps are executed in a circulating mode for a plurality of times, and new string pulling signal data obtained after the circulation of the plurality of times are used as a correction sequence:
calculating the string pulling signal data according to a local peak value searching algorithm to obtain a local extreme value sequence;
carrying out interpolation resampling processing on the local extreme value sequence to obtain an expansion sequence; wherein the data volume of the capacity expansion sequence is the same as that of the string pulling signal data;
and correspondingly subtracting the string pulling signal data from the data in the expansion sequence to obtain new string pulling signal data.
6. The method for identifying geometric anomaly data of a track according to claim 4, wherein said determining whether an anomalous root mean square exists in said root mean square sequence further comprises:
calculating any root mean square value in the root mean square sequence by the following formula:
|rms(r)-|G00||＜|αG00|；
wherein rms (r) is the r-th root mean square value in the root mean square sequence, G00The initial signal value of the string pulling signal data is alpha, and the fluctuation coefficient of the string pulling signal is alpha;
if any root mean square value satisfies the above formula, the root mean square value is an abnormal root mean square.
7. The method for identifying geometric anomaly data of a track according to claim 4, wherein the original detection data is processed through a data segment sliding standard deviation to identify a third anomaly data segment, and further comprising:
calculating the standard deviation of each string pulling signal data segment to obtain a string pulling standard deviation sequence;
and screening the string pulling standard deviation sequence according to a string pulling threshold value, and determining a third different constant data segment.
8. The method for identifying track geometry abnormal data according to claim 2, wherein the processing the original detection data by the difference method in combination with the data threshold value, and the identifying to obtain the fourth abnormal data segment further comprises:
calculating selected data by a difference method to obtain a difference value sequence, wherein the selected data is laser signal data, gyro component signal data or accelerometer signal data;
judging whether an abnormal value exists in the selected data, wherein the abnormal value is smaller than the data threshold, and the corresponding differential value of the abnormal value in the differential value sequence is smaller than the data threshold;
and if so, obtaining a fourth abnormal data segment according to the set of the abnormal values.
9. An apparatus for identifying geometric anomaly data of a track, the apparatus comprising:
the acquisition module is used for acquiring original detection data of the track geometry;
the first determining module is used for processing the original detection data by combining an empirical mode decomposition method with a data segment sliding standard deviation and identifying to obtain a first abnormal data segment;
the second determining module is used for processing the original detection data by combining a local peak value searching algorithm with a data segment sliding root mean square and identifying to obtain a second abnormal data segment;
the third determining module is used for processing the original detection data through the data segment sliding standard deviation and identifying to obtain a third irregular data segment;
the fourth determining module is used for processing the original detection data by combining a data threshold value through a difference method and identifying to obtain a fourth abnormal data segment;
and the merging module is used for merging the first abnormal data segment, the second abnormal data segment, the third abnormal data segment and the fourth abnormal data segment to obtain an abnormal data section.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, performs the instructions of the method of any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor of a computer device, is adapted to carry out the instructions of the method according to any one of claims 1-8.
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