High-speed railway multi-source detection data high-precision mileage alignment method and device

Abstract

The invention discloses a high-precision mileage alignment method and device for multi-source detection data of a high-speed railway, which can be used in the field of track engineering detection, and the method comprises the following steps: correcting vehicle body acceleration data, axle box acceleration data and wheel-rail force data through interpolation; calculating the speed and mileage of the train at any moment in the running process according to the longitudinal acceleration; calculating the mileage deviation when the train passes through the curve road section by adopting linear interpolation to obtain the running mileage deviation; according to the waveform matching result and the detection interval, calculating the time delay amount among the multiple pieces of multi-source detection data, and correcting the train speed; and correcting the train speed at any moment according to the running mileage deviation, determining real-time running mileage data of the train, and carrying out mileage alignment on the mileage at any moment in the running process of the train. According to the method, the track disease detection accuracy and timeliness can be improved, and the high-speed railway mileage alignment accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of track engineering detection, and in particular to a method and device for high-precision mileage alignment of multi-source detection data for high-speed railways. Background Art

[0002] This section is intended to provide a background or context for embodiments of the present invention. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] As the carrier of high-speed train operation, the high-speed railway track structure shows different degrees of deterioration under long-term and complex service environment, such as track slab arching, bridge creep, tunnel arch, rail corrugation and other track structural diseases, which form track unevenness with multi-wavelength components at the wheel-rail contact interface, thereby aggravating the wheel-rail force, wheel-rail noise and broadband vibration of key components of the vehicle-track coupling system, affecting the safety, stability and ride comfort of high-speed train operation.

[0004] Rapid detection of track defects is an important prerequisite for guiding the maintenance and repair of track structures in service. A lot of work has been done on identifying track defects based on track unevenness and vehicle vibration and noise data. For example, track unevenness is used to identify track slab arching, creep of simply supported beams, arching of inverts in tunnels and other sub-track infrastructure structural defects. The vibration response of vehicle components and the time-frequency characteristics of wheel-rail forces are used to identify rail corrugation, uneven rail wear and other short-wave track surface unevenness defects. However, existing studies have mostly used single-source data for track defect identification, which has the disadvantages of low accuracy, insufficient timeliness, and failure to reflect the degree of defects. The comprehensive inspection train not only contains a track geometry inspection system, but is also equipped with axle boxes, frames, car body acceleration inspection systems, wheel-rail force inspection systems, and car interior and exterior noise inspection systems. Due to the frequency limit of comprehensive inspection train inspections, rail engineering departments often use portable ride-adding instruments to detect car body vibration and car noise, and diagnose track conditions based on car body vibration frequency, ride comfort indicators, noise levels and main frequency, filling the gap in comprehensive inspection train inspections. At the same time, the railway engineering department reviewed the track defects on site and measured a large amount of static data, including static unevenness data measured by the track inspection instrument, track damage inspection record text and image data, rail corrugation measuring instrument data, rail profile data, etc. Because the mileage positioning of each detection system is not synchronized, the evaluation of each detection data is relatively single, and there is a deviation between the mileage information of track structure defects and the actual mileage, which limits the application of multi-source data fusion in track defect diagnosis and restricts the application of multi-source data in full-band control of track defects. It is urgent to solve the key problem of mileage synchronization of multi-source data. Summary of the invention

[0005] The embodiment of the present invention provides a high-precision mileage alignment method for multi-source detection data of high-speed railways, which is used to improve the accuracy and timeliness of track disease detection and improve the accuracy of high-speed railway mileage alignment. The method includes:

[0006] Collecting multi-source detection data of high-speed railways, the multi-source detection data including track inventory data, vehicle body acceleration data, axle box acceleration data and wheel-rail force data;

[0007] According to the track record data, the vehicle body acceleration data, axle box acceleration data and wheel-rail force data are corrected by interpolation to obtain the corrected multi-source detection data;

[0008] The longitudinal acceleration, rolling angular velocity and yaw angular velocity of the vehicle body are extracted from the corrected multi-source detection data, and the speed and mileage of the train at any time during operation are calculated based on the longitudinal acceleration;

[0009] The mileage data corresponding to each angular velocity of the train when it passes through the curved section is collected, and the mileage deviation of the train when it passes through the curved section is calculated by linear interpolation to obtain the running mileage deviation. The mileage correction is performed on the train speed at any time according to the running mileage deviation;

[0010] Waveform matching is performed on the rolling angular velocity and the shaking angular velocity to obtain the detection interval between multiple multi-source detection data, and the time delay between the multiple multi-source detection data is calculated according to the waveform matching result and the detection interval, wherein the multiple multi-source detection data are obtained by detecting the same train using multiple train-mounted instruments arranged at different positions;

[0011] Determine the speed deviation of the corrected train speed at any time according to the time delay, and perform speed correction on the train speed at any time according to the speed deviation;

[0012] The real-time mileage data of the vehicle is determined based on the speed of the train after speed correction, and the mileage of the train at any time during its operation is aligned based on the real-time mileage data of the vehicle.

[0013] The embodiment of the present invention also provides a high-precision mileage alignment device for high-speed railway multi-source detection data, which is used to improve the accuracy and timeliness of track disease detection and improve the accuracy of high-speed railway mileage alignment. The device includes:

[0014] A data acquisition module is used to collect multi-source detection data of high-speed railways, wherein the multi-source detection data includes track ledger data, vehicle body acceleration data, axle box acceleration data and wheel-rail force data;

[0015] The multi-source data correction module is used to correct the vehicle acceleration data, axle box acceleration data and wheel-rail force data through interpolation according to the track ledger data to obtain the corrected multi-source detection data;

[0016] The data extraction module is used to extract the longitudinal acceleration, rolling angular velocity, and yaw angular velocity of the vehicle body from the corrected multi-source detection data, and calculate the speed and mileage of the train at any time during operation based on the longitudinal acceleration;

[0017] The mileage correction module is used to collect the mileage data corresponding to each angular velocity of the train when it passes through the curved section, use linear interpolation to calculate the mileage deviation of the train when it passes through the curved section, obtain the running mileage deviation, and perform mileage correction on the train speed at any time according to the running mileage deviation;

[0018] A time delay calculation module is used to perform waveform matching on the rolling angular velocity and the shaking angular velocity, obtain the detection interval between multiple multi-source detection data, and calculate the time delay between multiple multi-source detection data according to the waveform matching result and the detection interval, wherein the multiple multi-source detection data are obtained by detecting the same train using multiple train-mounted instruments arranged at different positions;

[0019] A speed correction module, used to determine a speed deviation of the corrected train speed at any time according to the time delay, and to correct the train speed at any time according to the speed deviation;

[0020] The mileage alignment module is used to determine the real-time mileage data of the vehicle according to the speed-corrected train speed, and to align the mileage of the train at any time during its operation according to the real-time mileage data of the vehicle.

[0021] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned high-speed railway multi-source detection data high-precision mileage alignment method when executing the computer program.

[0022] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned high-precision mileage alignment method for multi-source detection data of high-speed railways is implemented.

[0023] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned high-speed railway multi-source detection data high-precision mileage alignment method is implemented.

[0024] In an embodiment of the present invention, multi-source detection data of a high-speed railway is collected, and the multi-source detection data includes track inventory data, car body acceleration data, axle box acceleration data and wheel-rail force data; according to the track inventory data, the car body acceleration data, axle box acceleration data and wheel-rail force data are corrected by interpolation to obtain corrected multi-source detection data; the longitudinal acceleration, roll angular velocity and yaw angular velocity of the car body are extracted from the corrected multi-source detection data, and the speed and mileage of the train at any time during operation are calculated according to the longitudinal acceleration; the mileage data corresponding to each yaw angular velocity when the train passes through a curved section are collected, and the mileage deviation when the train passes through the curved section is calculated by linear interpolation to obtain the running mileage deviation, and the running mileage deviation is obtained according to the running mileage deviation. The mileage deviation corrects the mileage of the train speed at any time; waveform matching is performed on the roll angular velocity and the angular velocity of the sway, and the detection interval between multiple multi-source detection data is obtained. According to the waveform matching results and the detection interval, the time delay between multiple multi-source detection data is calculated, and the multiple multi-source detection data are obtained by detecting the same train by multiple train-adding instruments set at different positions; the speed deviation of the corrected train speed at any time is determined according to the time delay, and the speed of the train speed at any time is corrected according to the speed deviation; according to the speed of the train after the speed correction, the real-time mileage data of the vehicle is determined, and the mileage of the train at any time during the operation is aligned according to the real-time mileage data of the vehicle. In this way, the speed and mileage of the train at any running time are obtained through the longitudinal acceleration of the vehicle body, the mileage information corresponding to the angular velocity of the sway, the running mileage deviation is obtained, the train speed is corrected, and the mileage synchronization of the detection data and the dynamic inspection vehicle detection data is achieved, providing technical support for multi-scale fusion diagnosis of track service status, and realizing high-frequency tracking measurement of track service status. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0026] Figure 1 It is a flow chart of a high-precision mileage alignment method for multi-source detection data of high-speed railway provided in an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of multi-source detection data provided in an embodiment of the present invention;

[0028] Figure 3 Another schematic diagram of multi-source detection data provided in an embodiment of the present invention;

[0029] Figure 4 It is a schematic diagram of the mileage alignment effect of the dynamic inspection vehicle track geometry, vehicle body acceleration, wheel-rail force, and axle box acceleration data provided in an embodiment of the present invention;

[0030] Figure 5 A schematic diagram of train running speed estimation and mileage positioning provided in an embodiment of the present invention;

[0031] Figure 6 A schematic diagram of detection data of two ride-sharing instruments provided in an embodiment of the present invention;

[0032] Figure 7 A schematic diagram of mileage deviation at a straight-slow point and a slow-straight point of a curve provided in an embodiment of the present invention;

[0033] Figure 8 Figures ad in the figure are schematic diagrams of comparison between the angular velocity of the train head and the superelevation data in the mileage dimension before and after the correction of the train initial speed estimation provided in an embodiment of the present invention;

[0034] Fig. 9 A schematic diagram of the dynamic inspection vehicle detection data after mileage synchronization provided in an embodiment of the present invention;

[0035] Fig.10 A schematic diagram of data visualization after synchronization of dynamic vehicle inspection data and passenger instrument data mileage provided in an embodiment of the present invention;

[0036] Fig.11 A schematic diagram of a high-precision mileage alignment device for multi-source detection data of a high-speed railway provided in an embodiment of the present invention;

[0037] Fig.12 It is a structural block diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0039] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0040] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps is not limited and can be appropriately adjusted as needed.

[0041] The embodiment of the present invention provides a high-precision mileage alignment method for multi-source detection data of high-speed railways, such as Figure 1 As shown, including:

[0042] Step 101: Collect multi-source detection data of the high-speed railway, wherein the multi-source detection data includes track inventory data, vehicle body acceleration data, axle box acceleration data, and wheel-rail force data;

[0043] Step 102: According to the track record data, the vehicle body acceleration data, the axle box acceleration data and the wheel-rail force data are corrected by interpolation to obtain corrected multi-source detection data;

[0044] Step 103: extracting the longitudinal acceleration, rolling angular velocity, and yaw angular velocity of the vehicle body from the corrected multi-source detection data, and calculating the speed and mileage of the train at any time during the running process according to the longitudinal acceleration;

[0045] Step 104: collecting mileage data corresponding to each angular velocity of the train when passing through the curved section, using linear interpolation to calculate the mileage deviation of the train when passing through the curved section, obtaining the running mileage deviation, and performing mileage correction on the train speed at any time according to the running mileage deviation;

[0046] Step 105: waveform matching is performed on the rolling angular velocity and the shaking angular velocity to obtain a detection interval between a plurality of multi-source detection data, and a time delay between the plurality of multi-source detection data is calculated according to the waveform matching result and the detection interval, wherein the plurality of multi-source detection data is obtained by detecting the same train using a plurality of train-mounted instruments arranged at different positions;

[0047] Step 106: determining a speed deviation of the corrected train speed at any time according to the time delay, and performing speed correction on the train speed at any time according to the speed deviation;

[0048] Step 107: Determine the real-time mileage data of the vehicle according to the speed-corrected train speed, and align the mileage of the train at any time during its operation according to the real-time mileage data of the vehicle.

[0049] The high-precision mileage alignment method for multi-source detection data of high-speed railways proposed in the embodiment of the present invention further improves the function of integrating multi-source data to diagnose the track service status and improves the accuracy of identifying track structure defects. It can synchronize the high-frequency passenger data of operating trains with the mileage of comprehensive detection train data to achieve high-frequency tracking and measurement of track service status. It provides technical support for the subsequent multi-source data fusion diagnosis of track structure defects, further reveals the correlation between typical track defects of high-speed railways and multi-source data features, constructs a more comprehensive and efficient track defect diagnosis and safety assessment method, and establishes a prediction and early warning method for track service performance degradation, providing technical support for scientifically guiding the maintenance and repair work of track engineering departments and ensuring the long-term safety and stable operation of high-speed trains.

[0050] During the specific implementation, the multi-source detection data to be aligned with high-precision mileage include track inventory data, dynamic inspection vehicle data, passenger instrument data, track inspection instrument data, rail corrugation measuring instrument data, manual inspection images and text data.

[0051] Track inventory data used for mileage alignment of multi-source inspection data include curve superelevation, turnouts, and slopes; dynamic inspection vehicle data include track geometry (height, track direction, level, triangular pits, superelevation), vehicle acceleration (vertical acceleration, transverse acceleration), wheel-rail force, axle box acceleration; add-on instrument data (vehicle longitudinal acceleration, transverse acceleration, vertical acceleration, roll angular velocity, nod angular velocity, shake angular velocity, vehicle interior noise); track inspection instrument data (height, track gauge); rail corrugation measuring instrument data (short-wave irregularities in the wavelength range of 0.01 to 3 m); manual inspection images and text data (track disease type, mileage, image), such as Figure 2 shown.

[0052] Figure 2 The above multi-source detection data types are illustrated. Each data source has an independent mileage positioning system, and there are differences in the positioning accuracy of different data, resulting in mileage errors in the multi-source detection data. The above data all need to use the track ledger as the absolute mileage benchmark to correct the mileage errors of the multi-source detection data and provide data support for the fusion of multi-source detection data to diagnose track diseases.

[0053] In one embodiment, the vehicle acceleration data is corrected by interpolation based on the track record data, including:

[0054] Extract track geometric irregularity waveform data based on multi-source detection data;

[0055] Perform waveform matching between the superelevation data of the track geometry irregularity waveform data and the superelevation data of the track ledger data;

[0056] According to the waveform matching results, the superelevation data of the track geometry irregularity waveform data is interpolated to calculate the mileage correction amount;

[0057] The vehicle acceleration data is corrected according to the mileage correction amount.

[0058] In one embodiment, the axle box acceleration data and the wheel-rail force data are corrected by interpolation according to the track record data, including:

[0059] Solve the envelope curve for the axle box acceleration data and wheel-rail force data;

[0060] Extract the peak points of the axle box acceleration envelope and the wheel-rail force envelope, and determine the mileage information corresponding to the peak points of the axle box acceleration envelope and the mileage information corresponding to the peak points of the wheel-rail force envelope;

[0061] The difference of the mileage information between adjacent peak points is calculated, and it is determined whether the difference of the mileage information between adjacent peak points is within a preset range, and the mileage information between adjacent peak points whose difference exceeds the preset range is interpolated.

[0062] During the specific implementation, the track structure mileage is used as a reference benchmark, including actual mileage information such as curves, switches, rail joints, and rail welds. The mileage deviations of data such as the dynamic inspection vehicle track geometry, wheel-rail force, axle box acceleration, and vehicle body acceleration are corrected based on the waveform correlation coefficient and feature recognition.

[0063] Affected by factors such as changes in wheel radius, wheel idling or sliding, there are mileage errors in multi-source inspection data such as the unevenness of the dynamic inspection vehicle track geometry (height, track direction, track gauge, triangular pit, level, superelevation), vehicle body acceleration, axle box acceleration, wheel-rail force, etc.

[0064] Taking the mileage information of the superelevation of the track record curve as the absolute reference benchmark, the mileage information of the key points of the curve (straight-slow point, slow-round point, round-slow point, slow-straight point) of the detected section is expressed as {x i , i = 1, 2, ..., N}, where N is the number of curve key points in the detected section. Since the mileage labels corresponding to the geometric irregularity of the dynamic inspection vehicle track and the vehicle acceleration are consistent, the superelevation data of the geometric irregularity of the dynamic inspection vehicle track {(s j ,y j ), j = 1, 2, ..., M} and the superelevation data of the track inventory curve {(x i ,h i ), i=1, 2, ..., N} for waveform matching. The mileage interval of the dynamic inspection vehicle track geometric irregularity is 0.25m, and the length of the dynamic inspection vehicle superelevation data is not equal to the length of the track ledger curve superelevation data, that is, M≠N, and M>N. The track ledger curve superelevation data is interpolated at 0.25m intervals to make the data lengths of the two consistent.

[0065] h′ j =interp(x i ,h i ,s j ),j=1,2,…,M; i=1,2,…,N

[0066] In the formula, h′ j is the superelevation data of the track inventory curve after interpolation, and interp(·) is the linear interpolation operator.

[0067] Calculate the correlation coefficient between the two, output the position point m corresponding to the maximum correlation coefficient, and calculate the mileage correction value based on the deviation point position m.

[0068]

[0069] In the formula, ρ y，h′ is the mileage correction.

[0070] Figure 3 This is the alignment effect of superelevation data and track record curve mileage under different inspection times of the dynamic inspection vehicle. k , k=1,2,…,K} and wheel-rail force {F q , q=1,2,…,Q} data, belongs to high frequency vibration data, respectively for a k and F q To solve the envelope, the specific formula is as follows:

[0071]

[0072] In the formula, a up , a lo are the upper and lower envelopes of the axle box acceleration respectively; F up , F lo are the upper and lower envelopes of the wheel-rail force, respectively; envelope(·) is the operator for calculating the outer envelope of the signal data.

[0073] Extract the axle box acceleration envelope a up , a lo and wheel-rail force envelope F up , F lo The peak point of the axle box acceleration envelope is obtained by calculating the mileage information corresponding to the peak point of the axle box acceleration envelope. a，j} and the mileage information corresponding to the peak point of the wheel-rail force envelope {p F，j}, j = 1, 2, ..., N, N is the number of rail joints. The difference between the mileage information of adjacent peak points is calculated as follows:

[0074]

[0075] In the formula, diff(·) is the difference operator; Δp a，j Δp is the distance between adjacent peak points of the axle box acceleration envelope; F，j is the distance between adjacent peak points of the wheel-rail force envelope.

[0076] Determine Δp a，j and Δp F，j Whether the condition of 100±5m is met, if it is not between [95, 105], the mileage from point j to point j+1 is interpolated to correct the mileage error of the axle box acceleration and wheel-rail force data. Figure 4 To dynamically check the track geometry, vehicle acceleration, wheel-rail force, axle box acceleration mileage alignment effect.

[0077] In one embodiment, the train operation data further includes: lateral acceleration, vertical acceleration, nodal angular velocity and carriage noise;

[0078] Also includes:

[0079] Spatial mileage information is added to each item of train operation data, and each item of the train operation data is time series data. The spatial mileage information corresponds to the mileage of the train at any time during the operation process, and is used to represent the mileage information of the train on the high-speed railway.

[0080] In the specific implementation, the mileage correction of the vehicle-mounted instrument detection data is carried out in the absence of GPS. The train speed and mileage position are preliminarily estimated based on the longitudinal acceleration of the vehicle body. The angular velocity data of the instrument's shaking head and the main point data of the line curve are integrated to output the mileage deviation of the main point of the curve position, and then the mileage deviation of the instrument detection data is corrected.

[0081] The vehicle-mounted instrument has built-in acceleration sensors, gyroscopes, and microphone sensors, which are used for three-dimensional acceleration measurement (longitudinal acceleration, lateral acceleration, vertical acceleration), body attitude measurement (roll acceleration, nod angular velocity, shake angular velocity) and car noise measurement. The same acquisition card collects and stores sensor data in different channels synchronously, and each data is time series data. Because the instrument does not have a mileage positioning function, it is necessary to add spatial mileage information to the original time series data collected and correspond it to the track site mileage information to facilitate the later on-site review of track structure defects.

[0082] In one embodiment, the speed and mileage of the train at any time during operation are calculated based on the longitudinal acceleration, including:

[0083] The speed and mileage of the train at any time during operation are calculated according to the following formula:

[0084]

[0085] Among them, v(t) is the train speed at any time t, s(t) is the mileage data at any time t, and a x (t) is the longitudinal acceleration of the vehicle body detected by the vehicle acceleration meter, unit: m / s 2 , t∈[t0,t N ], t0, t N are the start and end time of the measurement of the instrument respectively; dt is the sampling interval of the accelerometer of the instrument.

[0086] In one embodiment, the mileage data corresponding to each angular velocity of the train when passing through the curved section is collected, and the mileage deviation of the train when passing through the curved section is calculated by linear interpolation to obtain the running mileage deviation, including:

[0087] Collect the mileage data corresponding to each angular velocity of the train when it passes through the straight-slow point and the slow-straight point on the curve;

[0088] Obtain the curve superelevation data of the ledger curves at the straight-slow point and the slow-straight point of the train track curve, and compare the curve superelevation data with the mileage data corresponding to each shaking angle speed;

[0089] According to the comparison results, the mileage deviation at each moment is calculated by linear interpolation as the running mileage deviation.

[0090] In one embodiment, according to the comparison result, the mileage deviation at each moment is calculated by linear interpolation, including:

[0091] The mileage deviation at each moment is calculated according to the following formula:

[0092] Δs j =interp(τ q , Δs q , t j )

[0093] Where, Δs j For any time t j ∈[t0, t N ] is the mileage deviation, interp(·) is the interpolation operator, the total number of curve straight-slow points and slow-straight points is n, and the angular velocity of the curve straight-slow points and slow-straight points and the mileage deviation of the curve superelevation account data are Δs in order according to the train travel direction. q , q=1,…,n, the corresponding time is τ q , q=1,…,n;

[0094] Starting time Δv1=0, then any time t j ∈[t0, t N ] speed correction Δv j for:

[0095]

[0096] Δt is the accelerometer sampling interval, unit is s; F acc is the sampling frequency of the accelerometer, in Hz; t0 is the start time of the multiplier measurement, t N Δv is the end time of the multiplication instrument measurement; k It is the speed correction value at the previous sampling moment.

[0097] For example, a passenger instrument without GPS positioning function estimates the train running speed and mileage. Figure 5 Taking an 8-carriage train as an example, two additional instruments are placed in the 1# and 8# carriages respectively. The detection data are as follows: Figure 6 As shown, it includes longitudinal acceleration, lateral acceleration, vertical acceleration, roll angular velocity, nod angular velocity, and sway angular velocity. The test data of the two train-mounted instruments have good repeatability. Due to the difference in placement, there is a delay in the waveform. For example, the sway angular velocity and roll angular velocity waveforms are consistent, with only a delay. The data delay can be used to estimate the train running speed.

[0098] In the specific implementation, in the first step, v(t) and s(t) are the train speed and mileage information at any time t, a 1，x (t),a 2，x (t) are the longitudinal acceleration of the vehicle body detected by the instrument 1 and 2 (unit: m / s 2 ), t∈[t0,t N Taking the multiplier 1 as an example, the speed v(t) and mileage s(t) at any time are calculated by performing the first and second integration, namely:

[0099]

[0100] Where: t0, t N are the start and end time of the measurement of the instrument 1; dt is the sampling interval of the accelerometer of the instrument.

[0101] In the second step, when the vehicle passes through the curve, the angular velocity of the vehicle body measured by the gyroscopes carried by the two passenger instruments is consistent with the curve superelevation information. The angular velocity mileage information of the train passing through the straight-slow point and slow-straight point of the curve is extracted, and the mileage deviation {Δs i，j =1, 2; j = 1, 2, ..., N} for speed correction. The calculation principle is shown in Figure 7 .

[0102] The total number of curve straight-slow points and slow-straight points is n. According to the train running direction, the angular velocity of the curve straight-slow points and slow-straight points and the mileage deviation of the curve superelevation account data are Δs in order. i，q, i = 1, 2, q = 1, ..., n, the corresponding time is τ i，q , i = 1, 2, q = 1, ..., n, calculate any time t by interpolation j ∈[t0,t N ] mileage deviation Δs i，j for:

[0103] Δs i，j =interp(τ i，q , Δs i，q , t j )

[0104] Where: interp(·) is the interpolation operator, and linear interpolation is used here.

[0105] Starting time Δv1=0, then any time t j ∈[t0,t N ] speed correction Δv i，j for:

[0106]

[0107]

[0108] Where: Δt is the accelerometer sampling interval, unit is s; F acc is the accelerometer sampling frequency, in Hz; Δv k is the speed correction Δv at the previous moment i，j-1 .

[0109] Correct the train speed at any time, and the corrected train speed v′ i (t j )for:

[0110] v′ i (t j )=v i (t j )+Δv i，j

[0111] The third step is to Figure 6 It can be seen that according to the rolling angular velocity and the angular velocity γ i The time delay Δt of the detection data of the multiplier 1 and the multiplier 2 is calculated by waveform matching (t) g , g = 1, 2, ..., G, it is known that the placement distance between the multiplier 1 and the multiplier 2 is L, then the speed at the corresponding time is calculated as follows:

[0112]

[0113] Step 4: Extract t gThe speed v′ at the moment i (t g ), and with V i，g Compare and calculate the speed deviation ΔV i，g , calculate at any time t by interpolation j ∈[t0,t N ] speed deviation The calculation is as follows:

[0114]

[0115] The train speed at any time is estimated to be V′ i (t j ), calculated as follows:

[0116]

[0117] By integrating the corrected train speed curve, the real-time vehicle mileage information with higher accuracy can be obtained.

[0118]

[0119] The mileage position of the shaking angle velocity at any time is calculated by integrating the initial speed and compared with the mileage of the curve superelevation account to verify the accuracy of the initial speed estimation.

[0120] Comparison of the train's initial speed estimate and the superelevation data in the mileage dimension before and after correction is shown in Figure 8 , Figure 8 The black line in a is the result of the moving average filtering of the shaking angular velocity; the straight-slow point and the slow-straight point are automatically identified and marked as black dots, such as Figure 8 As shown in b; calculate the deviation Δs from the curve superelevation ledger i , calculate the corrected train speed curve, such as Figure 8 As shown in c; integrating the corrected speed curve, the angular velocity curve after mileage correction is consistent with the curve superelevation account, as shown in Figure 8 As shown in (d), it is verified that the mileage synchronization accuracy of the passenger instrument detection data is good.

[0121] During the specific implementation, the track inventory data is used to correct the mileage error of the track inspection instrument through the curve mileage information; the mileage error of the rail corrugation measuring instrument data is corrected through the rail welding joint spacing; the manual inspection images and text data are digitally encoded as the mileage information of the track disease location, and associated with other detection data features.

[0122] The data of the track inspection instrument and the rail corrugation measuring instrument are both static detection data, with slow detection data and short measurement distance. The detection data mileage accuracy is high. The correlation coefficient between the super-high speed of the track inspection instrument and the super-high data of the track account curve is calculated to align the track inspection instrument data with the absolute reference datum. The account spacing of the high-speed railway rail welding head is 100m. The outer envelope of the rail corrugation measuring instrument detection data can be calculated. By identifying the peak position of the outer envelope, calculating the spacing between adjacent peak points, and comparing it with 100m, the mileage deviation of the rail corrugation measuring instrument detection data can be corrected.

[0123] In one embodiment, it further includes:

[0124] Establish analysis criteria for track structure data and train operation data;

[0125] Periodically obtain the mileage alignment data of high-speed railways and analyze the mileage alignment data according to the analysis standards of track structure data and train operation data;

[0126] When the analysis results do not meet the preset standards, a reminder will be issued.

[0127] For example, the dynamic inspection data of a high-speed railway line is as follows: Fig. 9 As shown in the figure, after mileage alignment, the dynamic vehicle inspection data and the data of the vehicle loading instrument are mapped to the data visualization under the synchronized mileage information as shown in the figure. Fig.10 shown.

[0128] The embodiment of the present invention also provides a high-speed railway multi-source detection data high-precision mileage alignment device, as described in the following embodiment. Since the principle of solving the problem by the device is similar to the high-speed railway multi-source detection data high-precision mileage alignment method, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0129] Fig.11 Schematic diagram of a marketing activity information push device provided in an embodiment of the present invention. Fig.11 As shown, the device comprises:

[0130] The data acquisition module 1101 is used to collect multi-source detection data of the high-speed railway, wherein the multi-source detection data includes track record data, vehicle body acceleration data, axle box acceleration data and wheel-rail force data;

[0131] The multi-source data correction module 1102 is used to correct the vehicle acceleration data, the axle box acceleration data and the wheel-rail force data by interpolation according to the track record data to obtain the corrected multi-source detection data;

[0132] The data extraction module 1103 is used to extract the longitudinal acceleration, rolling angular velocity, and yaw angular velocity of the vehicle body from the corrected multi-source detection data, and calculate the speed and mileage of the train at any time during the operation according to the longitudinal acceleration;

[0133] The mileage correction module 1104 is used to collect the mileage data corresponding to each angular velocity of the train when passing through the curved section, use linear interpolation to calculate the mileage deviation of the train when passing through the curved section, obtain the running mileage deviation, and perform mileage correction on the train speed at any time according to the running mileage deviation;

[0134] The time delay calculation module 1105 is used to perform waveform matching on the rolling angular velocity and the shaking angular velocity, obtain the detection interval between multiple multi-source detection data, and calculate the time delay between multiple multi-source detection data according to the waveform matching result and the detection interval, wherein the multiple multi-source detection data are obtained by detecting the same train using multiple train-mounted instruments arranged at different positions;

[0135] A speed correction module 1106 is used to determine a speed deviation of the corrected train speed at any time according to the time delay, and to correct the train speed at any time according to the speed deviation;

[0136] The mileage alignment module 1107 is used to determine the real-time running mileage data of the vehicle according to the speed-corrected train speed, and to align the mileage of the train at any time during its operation according to the real-time running mileage data of the vehicle.

[0137] In one embodiment, the multi-source data correction module 1102 is specifically used for:

[0138] Extract track geometric irregularity waveform data based on multi-source detection data;

[0139] Perform waveform matching between the superelevation data of the track geometry irregularity waveform data and the superelevation data of the track ledger data;

[0140] According to the waveform matching results, the superelevation data of the track geometry irregularity waveform data is interpolated to calculate the mileage correction amount;

[0141] The vehicle acceleration data is corrected according to the mileage correction amount.

[0142] In one embodiment, the multi-source data correction module 1102 is specifically used for:

[0143] Solve the envelope curve for the axle box acceleration data and wheel-rail force data;

[0144] Extract the peak points of the axle box acceleration envelope and the wheel-rail force envelope, and determine the mileage information corresponding to the peak points of the axle box acceleration envelope and the mileage information corresponding to the peak points of the wheel-rail force envelope;

[0145] The difference of the mileage information between adjacent peak points is calculated, and it is determined whether the difference of the mileage information between adjacent peak points is within a preset range, and the mileage information between adjacent peak points whose difference exceeds the preset range is interpolated.

[0146] In one embodiment, the train operation data further includes: lateral acceleration, vertical acceleration, nodal angular velocity and carriage noise;

[0147] Also includes:

[0148] Spatial mileage information is added to each item of train operation data, and each item of the train operation data is time series data. The spatial mileage information corresponds to the mileage of the train at any time during the operation process, and is used to represent the mileage information of the train on the high-speed railway.

[0149] In one embodiment, the data extraction module 1103 is specifically used for:

[0150] The speed and mileage of the train at any time during operation are calculated according to the following formula:

[0151]

[0152] Among them, v(t) is the train speed at any time t, s(t) is the mileage data at any time t, and a x (t) is the longitudinal acceleration of the vehicle body detected by the vehicle acceleration meter, unit: m / s 2 , t∈[t0,t N ], t0, t N are the start and end time of the measurement of the instrument respectively; dt is the sampling interval of the accelerometer of the instrument.

[0153] In one embodiment, the mileage correction module 1104 is specifically used for:

[0154] Collect the mileage data corresponding to each angular velocity of the train when it passes through the straight-slow point and the slow-straight point on the curve;

[0155] Obtain the curve superelevation data of the ledger curves at the straight-slow point and the slow-straight point of the train track curve, and compare the curve superelevation data with the mileage data corresponding to each shaking angle speed;

[0156] According to the comparison results, the mileage deviation at each moment is calculated by linear interpolation as the running mileage deviation.

[0157] In one embodiment, the mileage correction module 1104 is specifically used for:

[0158] The mileage deviation at each moment is calculated according to the following formula:

[0159] Δs j=interp(τ q , Δs q , t j )

[0160] Where, Δs j For any time t j ∈[t0, t N ] is the mileage deviation, interp(·) is the interpolation operator, the total number of curve straight-slow points and slow-straight points is n, and the angular velocity of the curve straight-slow points and slow-straight points and the mileage deviation of the curve superelevation account data are Δs in order according to the train travel direction. q , q=1,…,n, the corresponding time is τ q , q=1,…,n;

[0161] Starting time Δv1=0, then any time t j ∈[t0, t N ] speed correction Δv j for:

[0162]

[0163] Δt is the accelerometer sampling interval, unit is s; F acc is the accelerometer sampling frequency, in Hz; t0, t N are the start and end time of the measurement by the instrument; Δv k It is the speed correction value at the previous sampling moment.

[0164] In one embodiment, an analysis and reminder module is also included, which is specifically used to:

[0165] Establish analysis criteria for track structure data and train operation data;

[0166] Periodically obtain the mileage alignment data of high-speed railways and analyze the mileage alignment data according to the analysis standards of track structure data and train operation data;

[0167] When the analysis results do not meet the preset standards, a reminder will be issued.

[0168] Based on the above invention concept, Fig.12 As shown, the present invention also proposes a computer device 1200, including a memory 1210, a processor 1220, and a computer program 1230 stored in the memory 1210 and executable on the processor 1220, wherein the processor 1220 implements the aforementioned high-precision mileage alignment method for multi-source detection data of high-speed railways when executing the computer program 1230.

[0169] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned high-precision mileage alignment method for multi-source detection data of high-speed railways is implemented.

[0170] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned high-speed railway multi-source detection data high-precision mileage alignment method is implemented.

[0171] To summarize, in an embodiment of the present invention, multi-source detection data of a high-speed railway is collected, and the multi-source detection data includes track inventory data, car body acceleration data, axle box acceleration data and wheel-rail force data; according to the track inventory data, the car body acceleration data, axle box acceleration data and wheel-rail force data are corrected by interpolation to obtain corrected multi-source detection data; the longitudinal acceleration, roll angular velocity and yaw angular velocity of the car body are extracted from the corrected multi-source detection data, and the speed and mileage of the train at any time during operation are calculated according to the longitudinal acceleration; the mileage data corresponding to each yaw angular velocity when the train passes through a curved section is collected, and the mileage deviation when the train passes through the curved section is calculated by linear interpolation to obtain the running mileage deviation, and the running mileage deviation is calculated based on the longitudinal acceleration. According to the running mileage deviation, the train speed at any time is corrected for mileage; the roll angular velocity and the angular velocity of the swaying are waveform matched to obtain the detection interval between multiple multi-source detection data, and the time delay between multiple multi-source detection data is calculated according to the waveform matching result and the detection interval, and the multiple multi-source detection data are obtained by detecting the same train by multiple train-adding instruments set at different positions; the speed deviation of the corrected train speed at any time is determined according to the time delay, and the speed of the train at any time is corrected according to the speed deviation; according to the speed of the train after the speed correction, the real-time running mileage data of the vehicle is determined, and the mileage of the train at any time during the operation is aligned according to the real-time running mileage data of the vehicle. In this way, the speed and mileage of the train at any running time are obtained through the longitudinal acceleration of the vehicle body, the mileage information corresponding to the angular velocity of the swaying is obtained, the running mileage deviation is obtained, the train speed is corrected, and the mileage synchronization of the detection data and the dynamic inspection vehicle detection data is achieved, providing technical support for multi-scale fusion diagnosis of track service status, and realizing high-frequency tracking measurement of track service status.

[0172] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0174] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0176] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-precision mileage alignment method for multi-source detection data of high-speed railway, characterized in that: include: Collecting multi-source detection data of high-speed railways, the multi-source detection data including track inventory data, vehicle body acceleration data, axle box acceleration data and wheel-rail force data; According to the track record data, the vehicle body acceleration data, axle box acceleration data and wheel-rail force data are corrected by interpolation to obtain the corrected multi-source detection data; The longitudinal acceleration, rolling angular velocity and yaw angular velocity of the vehicle body are extracted from the corrected multi-source detection data, and the speed and mileage of the train at any time during operation are calculated based on the longitudinal acceleration; The mileage data corresponding to each angular velocity of the train when it passes through the curved section is collected, and the mileage deviation of the train when it passes through the curved section is calculated by linear interpolation to obtain the running mileage deviation. The mileage correction is performed on the train speed at any time according to the running mileage deviation; Waveform matching is performed on the rolling angular velocity and the shaking angular velocity to obtain the detection interval between multiple multi-source detection data, and the time delay between the multiple multi-source detection data is calculated according to the waveform matching result and the detection interval, wherein the multiple multi-source detection data are obtained by detecting the same train using multiple train-mounted instruments arranged at different positions; Determine the speed deviation of the corrected train speed at any time according to the time delay, and perform speed correction on the train speed at any time according to the speed deviation; The real-time mileage data of the vehicle is determined based on the speed of the train after speed correction, and the mileage of the train at any time during its operation is aligned based on the real-time mileage data of the vehicle.

2. The method according to claim 1, characterized in that According to the track record data, the vehicle acceleration data is corrected by interpolation, including: Extract track geometric irregularity waveform data based on multi-source detection data; Perform waveform matching between the superelevation data of the track geometry irregularity waveform data and the superelevation data of the track ledger data; According to the waveform matching results, the superelevation data of the track geometry irregularity waveform data is interpolated to calculate the mileage correction amount; The vehicle acceleration data is corrected according to the mileage correction amount.

3. The method according to claim 1, characterized in that According to the track record data, the axle box acceleration data and wheel-rail force data are corrected by interpolation, including: Solve the envelope curve for the axle box acceleration data and wheel-rail force data; Extract the peak points of the axle box acceleration envelope and the wheel-rail force envelope, and determine the mileage information corresponding to the peak points of the axle box acceleration envelope and the mileage information corresponding to the peak points of the wheel-rail force envelope; The difference of the mileage information between adjacent peak points is calculated, and it is determined whether the difference of the mileage information between adjacent peak points is within a preset range, and the mileage information between adjacent peak points whose difference exceeds the preset range is interpolated.

4. The method according to claim 1, characterized in that Train operation data also includes: lateral acceleration, vertical acceleration, nodal angular velocity and carriage noise; Also includes: Spatial mileage information is added to each item of train operation data, and each item of the train operation data is time series data. The spatial mileage information corresponds to the mileage of the train at any time during the operation process, and is used to represent the mileage information of the train on the high-speed railway.

5. The method according to claim 1, characterized in that The speed and mileage of the train at any time during operation are calculated based on the longitudinal acceleration, including: The speed and mileage of the train at any time during operation are calculated according to the following formula: Among them, v(t) is the train speed at any time t, s(t) is the mileage data at any time t, and a x (t) is the longitudinal acceleration of the vehicle body detected by the vehicle acceleration meter, unit: m / s 2 , t∈[t0,t N ], t0, t N are the start and end time of the measurement of the instrument respectively; dt is the sampling interval of the accelerometer of the instrument.

6. The method according to claim 1, characterized in that The mileage data corresponding to each angular velocity of the train when it passes through the curved section is collected, and the mileage deviation of the train when it passes through the curved section is calculated by linear interpolation to obtain the running mileage deviation, including: Collect the mileage data corresponding to each angular velocity of the train when it passes through the straight-slow point and the slow-straight point on the curve; Obtain the curve superelevation data of the ledger curves at the straight-slow point and the slow-straight point of the train track curve, and compare the curve superelevation data with the mileage data corresponding to each shaking angle speed; According to the comparison results, the mileage deviation at each moment is calculated by linear interpolation as the running mileage deviation.

7. The method according to claim 6, characterized in that According to the comparison results, the mileage deviation at each moment is calculated by linear interpolation, including: The mileage deviation at each moment is calculated according to the following formula: Δs j =interp(τ q ,Δs q ,t j ) Where, Δs j For any time t j ∈[t0,t N ] is the mileage deviation, interp(·) is the interpolation operator, the total number of curve straight-slow points and slow-straight points is n, and the angular velocity of the curve straight-slow points and slow-straight points and the mileage deviation of the curve superelevation account data are Δs in order according to the train travel direction. q , q=1,…,n, the corresponding time is τ q , q=1,…,n; Starting time Δv1=0, then at any time t j ∈[t0,t N ] speed correction Δv j for: Δt is the accelerometer sampling interval, unit is s; F acc is the accelerometer sampling frequency, in Hz; t0, t N are the start and end time of the measurement by the instrument; Δv k It is the speed correction value at the previous sampling moment.

8. The method according to claim 1, characterized in that Also includes: Establish analysis criteria for track structure data and train operation data; Periodically obtain the mileage alignment data of high-speed railways and analyze the mileage alignment data according to the analysis standards of track structure data and train operation data; When the analysis results do not meet the preset standards, a reminder will be issued.

9. A high-precision mileage alignment device for multi-source detection data of high-speed railway, characterized in that: include: A data acquisition module is used to collect multi-source detection data of high-speed railways, wherein the multi-source detection data includes track ledger data, vehicle body acceleration data, axle box acceleration data and wheel-rail force data; The multi-source data correction module is used to correct the vehicle acceleration data, axle box acceleration data and wheel-rail force data through interpolation according to the track ledger data to obtain the corrected multi-source detection data; The data extraction module is used to extract the longitudinal acceleration, rolling angular velocity, and yaw angular velocity of the vehicle body from the corrected multi-source detection data, and calculate the speed and mileage of the train at any time during operation based on the longitudinal acceleration; The mileage correction module is used to collect the mileage data corresponding to each angular velocity of the train when it passes through the curved section, use linear interpolation to calculate the mileage deviation of the train when it passes through the curved section, obtain the running mileage deviation, and perform mileage correction on the train speed at any time according to the running mileage deviation; A time delay calculation module is used to perform waveform matching on the rolling angular velocity and the shaking angular velocity, obtain the detection interval between multiple multi-source detection data, and calculate the time delay between multiple multi-source detection data according to the waveform matching result and the detection interval, wherein the multiple multi-source detection data are obtained by detecting the same train using multiple train-mounted instruments arranged at different positions; A speed correction module, used to determine a speed deviation of the corrected train speed at any time according to the time delay, and to correct the train speed at any time according to the speed deviation; The mileage alignment module is used to determine the real-time mileage data of the vehicle according to the speed-corrected train speed, and to align the mileage of the train at any time during its operation according to the real-time mileage data of the vehicle.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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