CN117029766A - Track local settlement identification method and device - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000004062 sedimentation Methods 0.000 claims abstract description 70
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- 238000000691 measurement method Methods 0.000 claims description 21
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4007—Interpolation-based scaling, e.g. bilinear interpolation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
Abstract
The invention discloses a method and a device for identifying local settlement of a track, wherein the method comprises the following steps: acquiring three-dimensional attitude data of a measurement carrier; the ground is used as a reference system, and relative three-dimensional attitude data of the measuring carrier and the track are obtained; determining track short chord vector distance data according to the three-dimensional attitude data and the relative three-dimensional attitude data of the measured carrier, wherein the track short chord vector distance data is track angular displacement change data; determining geometric parameters of the track according to the short chord vector distance data of the track; identifying a track local sedimentation area according to the track geometric parameters; and calculating the relative elevation change and the corresponding maximum sedimentation value in the sedimentation region range by utilizing the gradient angle in the geometric parameters of the track in the identified track local sedimentation region range. The method can improve the accuracy of identifying the local settlement of the track and realize the dynamic monitoring of the local settlement of the track.
Description
Technical Field
The invention relates to the technical field of railway track geometric measurement, in particular to a track local settlement identification method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, railways generally face the problem of foundation settlement, the foundation settlement inevitably leads to track deformation, and especially local settlement can bring potential safety hazards to driving. The existing static settlement monitoring method is difficult to be effectively implemented in practical application. The method realizes the high-efficiency detection of the rail surface local settlement deformation, and has important significance for researching the development excitation and the characteristics of the line full-line settlement deformation. The dynamic monitoring of roadbed settlement is realized on the comprehensive detection train, and is a precondition for researching a foundation engineering settlement deformation evaluation system based on vehicle-mounted detection data. There is a need for a high-precision track local settlement recognition method that solves the above problems.
Disclosure of Invention
The embodiment of the invention provides a method for identifying track local subsidence, which is used for improving the accuracy of track local subsidence identification and realizing dynamic monitoring of track local subsidence, and comprises the following steps:
acquiring three-dimensional attitude data of a measurement carrier;
the ground is used as a reference system, and relative three-dimensional attitude data of the measuring carrier and the track are obtained;
determining track short chord vector distance data according to the three-dimensional attitude data and the relative three-dimensional attitude data of the measured carrier, wherein the track short chord vector distance data is track angular displacement change data;
determining geometric parameters of the track according to the short chord vector distance data of the track;
identifying a track local sedimentation area according to the track geometric parameters;
and calculating the relative elevation change and the corresponding maximum sedimentation value in the sedimentation region range by utilizing the gradient angle in the geometric parameters of the track in the identified track local sedimentation region range.
The embodiment of the invention also provides a track local settlement recognition device, which is used for improving the accuracy of track local settlement recognition and realizing dynamic monitoring of track local settlement, and comprises the following steps:
the three-dimensional attitude data acquisition module is used for acquiring three-dimensional attitude data of the measurement carrier;
the relative three-dimensional attitude data acquisition module is used for acquiring relative three-dimensional attitude data of the measurement carrier and the track by taking the ground as a reference system;
the track short chord vector distance data determining module is used for determining track short chord vector distance data according to the three-dimensional attitude data and the relative three-dimensional attitude data of the measuring carrier, wherein the track short chord vector distance data is track angular displacement change data;
the track geometric parameter determining module is used for determining track geometric parameters according to the track short chord vector distance data;
the track local sedimentation area identification module is used for identifying a track local sedimentation area according to the track geometric parameters;
and the relative elevation change calculation module is used for calculating the relative elevation change and the corresponding maximum sedimentation value in the sedimentation area range by utilizing the gradient angle in the geometric parameters of the track in the identified track local sedimentation area range.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the track local settlement identification method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the track local settlement identification method when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the track local settlement identification method when being executed by a processor.
In the embodiment of the invention, the three-dimensional attitude data of the measuring carrier and the relative three-dimensional attitude data of the measuring carrier and the track are obtained; and acquiring track short chord vector distance data, determining track geometric parameters according to the track short chord vector distance data, identifying a track local sedimentation region according to the track geometric parameters, and calculating relative elevation change and corresponding maximum sedimentation value in the sedimentation region range by using gradient angles in the track geometric parameters. Thereby improving the accuracy of the track local sedimentation identification and realizing the dynamic monitoring of the track local sedimentation.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a method for identifying track local settlement in an embodiment of the invention;
FIG. 2 is a block diagram of a track local sedimentation system in an embodiment of the invention;
FIG. 3 is a schematic diagram of acquiring three-dimensional pose data of a measurement carrier according to an embodiment of the present invention;
FIG. 4 is a graph of the result of fusion filtering according to an embodiment of the present invention;
FIG. 5 is a graph showing the result of designing elevation values according to an embodiment of the present invention;
FIG. 6 is a graph showing the contrast of the cut-off wavelength irregularity in the embodiment of the present invention;
FIG. 7 is a graph showing the repeatability of multiple measurements of the same segment relative to elevation change in an embodiment of the present invention;
FIG. 8 is a schematic diagram of a track local settlement identifying device according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a specific track local settlement identifying device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
Fig. 1 is a flowchart of a method for identifying local sedimentation of a track according to an embodiment of the present invention, where the method includes:
step 101, acquiring three-dimensional attitude data of a measurement carrier;
102, obtaining relative three-dimensional attitude data of a measurement carrier and a track by taking the ground as a reference system;
step 103, determining track short chord vector distance data according to the three-dimensional attitude data and the relative three-dimensional attitude data of the measured carrier, wherein the track short chord vector distance data is track angular displacement change data;
104, determining geometric parameters of the track according to the short chord vector distance data of the track;
step 105, identifying a track local sedimentation area according to the track geometric parameters;
and 106, calculating the relative elevation change and the corresponding maximum sedimentation value in the sedimentation region range by using the gradient angle in the geometric parameters of the track in the identified track local sedimentation region range.
Each step is specifically described below.
In step 101, measurement carrier three-dimensional pose data is acquired.
As shown in fig. 2, the measurement carrier in a particular embodiment includes an IMU, an odometer, a visual ranging unit, and a detection beam. The visual ranging unit comprises a CCD high-speed industrial camera and a laser, and measures the displacement value of the rail plane relative to the optical center of the camera; the odometer is arranged on the wheel and provides speed and mileage information for the system; the IMU and the visual ranging unit are both arranged on a detection beam, and the detection beam is hung on the front side and the rear side 2 of the bogie through a suspension arm; the test beam and the truck are not designed with shock absorbing structures and can be considered as being rigidly connected. The mechanical platform formed by the framework and the detection beam is used as a measuring carrier of the system.
And the data acquisition unit is used for triggering and sampling the IMU and the visual ranging unit at fixed space intervals according to the output distance value of the odometer so as to realize synchronous acquisition of multi-sensor data. The data processing unit is used for analyzing the original measured values online or offline to obtain the geometric parameters of the orbit space. The system calculates real-time mileage by accumulating the pulse number of the encoder, and the mileage positioning system uses the traditional GNSS module and the transponder to determine the mileage and correct the real-time mileage
In a specific embodiment, as shown in fig. 3, the process of acquiring three-dimensional attitude data of a measurement carrier includes:
(1) Initial alignment: and acquiring IMU data in a stationary state of the train, and determining an initial attitude angle of the IMU by using a traditional self-alignment method.
(2) Data preprocessing: and (3) carrying out linear interpolation on the angular speed and the acceleration output by the IMU module, converting the original data of the time sequence into a space sequence, and realizing synchronization with the odometer and the visual ranging unit.
(3) The rotation matrix and the speed state are calculated and updated; and selecting error state quantity as rotation vector, speed under horizontal coordinate system and zero offset 9 dimensions of gyroscope.
(4) Based on the non-integrity constraint, the three-dimensional speed in the carrier coordinate system is approximately measured by an odometer and used as a state quantity observation value. The non-integrity constraint means that when the train moves on the track along the track, the track has stronger constraint on the movement state of the framework, the curvature of the track is smaller, the transverse and vertical speeds of the measuring carrier are almost zero, and the longitudinal speed can be measured by an odometer or is called dynamic zero-speed correction.
The horizontal coordinate system is set as an n-system, the IMU body coordinate system as a b-system and the carrier coordinate system where the odometer is located as a v-system. According to the constraint condition of dynamic zero speed correction, the following relation exists:
in the formula, v b For the speed of the system under the b-seriesIs a rotation matrix from a body coordinate system b system to a carrier coordinate system v system,a rotation matrix from a body coordinate system b system to a navigation coordinate system n system, v n Is the speed of the system under the n series; t is the transpose of the matrix,is->Transpose of matrix.
Wherein, the observation residual is expressed as:
the observation matrix H is expressed as:
in the middle ofV is n Is an antisymmetric matrix of r i Is the i-th observation residual.
(5) And updating the Kalman filtering, and carrying out feedback correction on the error state quantity to obtain the three-dimensional attitude data of the measuring carrier. The pitch angle of the IMU is most important for a track situation sedimentation system, and under the condition of incomplete constraint, the precision depends on 3 aspects of the relative pitch angle of a measuring carrier and a track surface, the zero offset of an accelerometer and the speed measurement precision of an odometer.
In step 102, relative three-dimensional pose data of the measurement carrier and the track is obtained using the ground as a reference system.
In step 103, track short chord vector distance data is determined according to the three-dimensional attitude data and the relative three-dimensional attitude data of the measured carrier, wherein the track short chord vector distance data is track angular displacement change data.
As shown in fig. 4, in an embodiment, the method further includes:
and carrying out fusion filtering processing on the short chord vector distance data of the track.
In one embodiment, the fusion filtering processing is performed on the track short chord vector distance data, including:
acquiring first short chord vector distance data according to an acceleration measurement method, wherein the acceleration measurement method acquires the first short chord vector distance data through an accelerometer sensor and a visual ranging unit;
the method comprises the steps of obtaining second short chord vector distance data according to an angular velocity measurement method, wherein the angular velocity measurement method obtains the second short chord vector distance data according to three-dimensional attitude data and relative three-dimensional attitude data of a measurement carrier;
and carrying out fusion filtering processing on the first short chord vector distance data and the second short chord vector distance data to obtain track short chord vector distance data after the fusion filtering processing.
In an embodiment, performing fusion filtering processing on the first short chord vector distance data and the second short chord vector distance data includes:
the first short string vector distance data is subjected to a high-pass filter, and the second short string vector distance data is subjected to a band-pass filter, so that filtered first short string vector distance data and second short string vector distance data are respectively obtained;
and carrying out fusion processing on the filtered first short chord vector distance data and the filtered second short chord vector distance data.
In step 104, track geometry parameters are determined from the track short chord vector distance data.
In one embodiment, the track geometry parameters include space curve, chord measurements, vector distance differences, and slope angles.
In a specific embodiment, the step of obtaining the geometric parameters of the track includes:
(1) Measuring the three-dimensional attitude of the IMU under a horizontal coordinate system;
(2) The front-rear vision distance measuring unit compensates the relative angle between the rail plane and the measuring carrier so as to measure the angular displacement of a reference string formed by 2 points at fixed step length intervals of the rail plane; the deviation between the actual sagittal height differences of two mileage points at the selected step interval is the sagittal distance difference, a reference chord is formed by the 2 points of the orbit longitudinal section where the front and rear vision ranging units are positioned, and the angular displacement in the space is changed into a short chord sagittal distance value; the space displacement of the track in the transverse direction and the vertical direction can be obtained by integrating the short chord vector distance value in the space domain.
(3) The traditional track dynamic measurement method takes an accelerometer sensor and a visual ranging unit as cores, and can also measure the space displacement of the track in the transverse direction and the vertical direction.
(4) And processing detection results of the acceleration measurement method and the angular velocity measurement method by using a fusion filtering method, passing the measured value of the angular velocity measurement method through a band-pass filter, passing the measured value of the acceleration measurement method through a high-pass filter, and adding to obtain the measured value of the track irregularity. The 2 filter frequency responses add to form a high pass filter with a cut-off wavelength.
(5) And calculating to obtain a chord measurement value and a vector distance difference value.
For the visual ranging unit in (2) to measure the transverse and vertical displacement of the orbit vertical section relative to the camera, the following detailed steps are provided: the laser emitted by the laser irradiates on the section of the steel rail, and the camera shoots the section image, and the center line of the light bar under the image coordinate system is obtained after filtering, binarization and refinement treatment; according to the rail section image, a global coordinate system is established, and the relation among the global coordinate system, the camera coordinate system and the image coordinate system is converted; and selecting the position 16mm below the tread of the head of the steel rail as a characteristic point of the track gauge of the steel rail, and extracting unilateral transverse displacement components and unilateral vertical displacement components of the steel rail at the left side and the right side according to the displacement change of the point under a global coordinate system.
In step 105, the track local sedimentation zone is identified from the track geometry parameters.
In one embodiment, identifying the track local settlement region according to the track geometry parameters comprises:
acquiring a design gradient angle and a design mileage value recorded in a gradient ledger, and acquiring an actual gradient angle and an actual mileage value obtained by actual measurement;
generating a design space sequence according to the gradient standing book, and obtaining a design curve according to the generated design space sequence;
matching the designed mileage value with the actual mileage value according to the correlation between the designed gradient angle and the actual gradient angle, and correspondingly eliminating the designed curve in the space curve according to the matching result;
and identifying the track local sedimentation area according to the removed space curve, chord measurement value and vector distance difference value.
In a specific embodiment, the correlation between the designed gradient angle and the actual gradient angle is r (n), where r (n) has the expression:
wherein ρ is a correlation coefficient, x 1 Representing the design slope angle, x 2 Representing the actual gradient angle x 1,k For the k value of the design gradient angle, x 2,n-k Is the actual gradient anglen-k values, std (x 2 ) Is x 2 Standard deviation of (2). And when r (n) reaches the maximum value, the data correlation is highest, when ρ is larger than 10, the sequence contains track irregularity of the same section, and the mileage deviation of the actual measurement gradient angle relative to the ledger is determined according to the offset value k.
In step 106, the relative elevation change and corresponding maximum sedimentation value within the sedimentation zone range are calculated using the slope angle in the track geometry parameters within the identified track local sedimentation zone range.
In one embodiment, calculating the relative elevation change and corresponding maximum sedimentation value over the sedimentation zone using the slope angle comprises:
obtaining a design elevation value recorded in a gradient ledger and an actual elevation value;
subtracting the actual elevation value from the design elevation value to obtain a relative elevation change;
and carrying out adjustment treatment on the relative elevation change to obtain the maximum sedimentation value.
As shown in fig. 5, in one embodiment, calculating the relative elevation change and corresponding maximum sedimentation value over the sedimentation zone using the slope angle includes:
the design elevation value is calculated according to the following formula:
h design ＝∑ψΔx；
wherein Deltax is a space sampling interval, psi is a pitch angle of a measured carrier, the pitch angle of the measured carrier is approximately a slope angle of a track, h design To design elevation values;
the actual elevation value is calculated according to the following formula:
h k ＝h k-1 -Δxsinψ；
wherein h is k 、h k-1 The actual elevation values of the k-1 sampling moments are respectively k, deltax is a space sampling interval, psi is a pitch angle of a measured carrier, and the pitch angle of the measured carrier is approximate to a slope angle of a track;
the relative elevation change is calculated according to the following formula:
Δh k ＝h k -h design ；
wherein Δh k Is the relative elevation change at the kth sampling instant.
In a specific embodiment, the relative elevation change is subjected to adjustment treatment to obtain a maximum sedimentation value.
In a specific embodiment, as shown in fig. 6, in order to evaluate the internal coincidence accuracy of the measurement result of the present invention, in the test, 3 dynamic measurements are performed on a section of the ballastless track of a high-speed line, including 1 round trip measurement and 1 repetition measurement, the distance is up to 60km, and the detection speed is 300km/h. Multiple repeatability tests show that the irregularity of the cut-off wavelength of 1000m has better repeatability precision. After the structural irregularity of the vertical curve section is removed, the track local uneven settlement change can be identified according to the irregularity of 1000m, and no obvious track local settlement section exists in the line.
In addition, in a certain ballasted track section, the detection train carries out the same-direction repeated dynamic measurement, the distance is 8.5km, the detection speed is within 100km/h, and the detection result is shown in fig. 7. As can be seen from fig. 7, the local sedimentation in the line can be identified from the 1000m height irregularity, then the section is selected to calculate the relative elevation change, the relative height Cheng Zui small value is-400 mm, and the 4 measurements are almost consistent, and the repeatability deviation is within 10 mm. The system is less influenced by the running speed, the repeatability of the relative elevation measured value is slightly lower than 1000m, the irregularity is low, and the high precision can be maintained at medium and low speeds.
The embodiment of the invention also provides a track local settlement recognition device, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to that of the track local settlement recognition method, the implementation of the device can be referred to the implementation of the method, and the repetition is omitted. As shown in fig. 8, the apparatus includes:
a three-dimensional posture data acquisition module 801, configured to acquire three-dimensional posture data of a measurement carrier;
a relative three-dimensional attitude data acquisition module 802, configured to acquire relative three-dimensional attitude data of the measurement carrier and the track with the ground as a reference system;
the track short chord vector distance data determining module 803 is configured to determine track short chord vector distance data according to the three-dimensional attitude data and the relative three-dimensional attitude data of the measurement carrier, where the track short chord vector distance data is track angular displacement change data;
the track geometric parameter determining module 804 is configured to determine a track geometric parameter according to the track short chord vector distance data;
the track local sedimentation area identification module 805 is configured to identify a track local sedimentation area according to track geometric parameters;
a relative elevation change calculation module 806 for calculating a relative elevation change and a corresponding maximum sedimentation value within the sedimentation zone range using the slope angle in the track geometry parameters within the identified track local sedimentation zone range.
In one embodiment, the track geometry parameters include space curve, chord measurements, vector distance differences, and slope angles.
As shown in fig. 9, in an embodiment, the method further includes a fusion filtering processing module 901, configured to:
and carrying out fusion filtering processing on the short chord vector distance data of the track.
In an embodiment, the fusion filtering processing module 901 is specifically configured to:
acquiring first short chord vector distance data according to an acceleration measurement method, wherein the acceleration measurement method acquires the first short chord vector distance data through an accelerometer sensor and a visual ranging unit;
the method comprises the steps of obtaining second short chord vector distance data according to an angular velocity measurement method, wherein the angular velocity measurement method obtains the second short chord vector distance data according to three-dimensional attitude data and relative three-dimensional attitude data of a measurement carrier;
and carrying out fusion filtering processing on the first short chord vector distance data and the second short chord vector distance data to obtain track short chord vector distance data after the fusion filtering processing.
In an embodiment, the fusion filtering processing module 901 is further configured to:
the first short string vector distance data is subjected to a high-pass filter, and the second short string vector distance data is subjected to a band-pass filter, so that filtered first short string vector distance data and second short string vector distance data are respectively obtained;
and carrying out fusion processing on the filtered first short chord vector distance data and the filtered second short chord vector distance data.
In one embodiment, the track local sedimentation zone identification module 805 comprises:
acquiring a design gradient angle recorded in a gradient standing book;
generating a design space sequence according to the gradient standing book, and obtaining a design curve according to the generated design space sequence;
matching the designed mileage value with the actual mileage value according to the correlation of the gradient angle of the designed gradient and the gradient angle of the actual gradient, and correspondingly eliminating the designed curve in the space curve according to the matching result;
and identifying the track local sedimentation area according to the removed space curve, chord measurement value and vector distance difference value.
In one embodiment, the relative elevation change calculation module 806 is specifically configured to:
obtaining a design elevation value recorded in a gradient ledger and an actual elevation value;
subtracting the actual elevation value from the design elevation value to obtain a relative elevation change;
and carrying out adjustment treatment on the relative elevation change to obtain the maximum sedimentation value.
In one embodiment, the relative elevation change calculation module 806 is further configured to:
the design elevation value is calculated according to the following formula:
h design ＝∑ψΔx；
wherein Deltax is a space sampling interval, psi is a pitch angle of a measured carrier, the pitch angle of the measured carrier is approximately a slope angle of a track, h design To design elevation values;
the actual elevation value is calculated according to the following formula:
h k ＝h k-1 -Δxsinψ；
wherein h is k 、h k-1 The actual elevation values of the k-1 sampling moments are respectively, deltax is the space sampling interval, psi is the pitch angle of the measured carrier, and the carrier is measuredThe pitch angle of (a) is approximately the slope angle of the track;
the relative elevation change is calculated according to the following formula:
Δh k ＝h k -h design ；
wherein Δh k Is the relative elevation change at the kth sampling instant.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
(1) The high-precision and long-term stability of the inertial measurement unit and the odometer are utilized, and the inertial measurement unit and the odometer are not dependent on any track control point and other auxiliary reference points, so that the effective, continuous and high-precision dynamic monitoring of the track local settlement can be realized; the repeatability of the measurement result is less influenced by the running state and speed of the train, and the repeatability precision is within 5 mm.
(2) Based on a high-speed comprehensive detection train platform, the elevation change of the rail surface along the line is dynamically obtained in the running process of the detection train, the periodic and comprehensive detection and identification of the local settlement of the high-speed line rail can be realized according to the running plan of the detection train, the professional requirements on measuring staff are lower, and the efficiency is far higher than that of a static measuring means.
(3) The measurement results comprise a space curve with a certain cut-off wavelength, vector distance difference, chord measurement value and relative elevation change. The method is consistent with the existing track detection system in the track irregularity evaluation mode, can also analyze corresponding to the static absolute measurement result, and provides new possibility for making a maintenance plan and reducing the railway maintenance investment cost.
(4) The method can realize the rapid screening of the differential settlement of bridges, roadbeds, ballast beds and the like.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the track local settlement identification method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the track local settlement identification method when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the track local settlement identification method when being executed by a processor.
In the embodiment of the invention, the three-dimensional attitude data of the measuring carrier and the relative three-dimensional attitude data of the measuring carrier and the track are obtained; and acquiring track short chord vector distance data, determining track geometric parameters according to the track short chord vector distance data, identifying a track local sedimentation region according to the track geometric parameters, and calculating relative elevation change and corresponding maximum sedimentation value in the sedimentation region range by using gradient angles in the track geometric parameters. Thereby improving the accuracy of the track local sedimentation identification and realizing the dynamic monitoring of the track local sedimentation.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (19)
1. A method for identifying local sedimentation of a track, comprising:
acquiring three-dimensional attitude data of a measurement carrier;
the ground is used as a reference system, and relative three-dimensional attitude data of the measuring carrier and the track are obtained;
determining track short chord vector distance data according to the three-dimensional attitude data and the relative three-dimensional attitude data of the measured carrier, wherein the track short chord vector distance data is track angular displacement change data;
determining geometric parameters of the track according to the short chord vector distance data of the track;
identifying a track local sedimentation area according to the track geometric parameters;
and calculating the relative elevation change and the corresponding maximum sedimentation value in the sedimentation region range by utilizing the gradient angle in the geometric parameters of the track in the identified track local sedimentation region range.
2. The method of claim 1, wherein the track geometry parameters include space curves, chord measurements, vector distance differences, and grade angles.
3. The method as recited in claim 1, further comprising:
and carrying out fusion filtering processing on the short chord vector distance data of the track.
4. A method as claimed in claim 3, wherein the fusion filtering of the short chord vector data of the track comprises:
acquiring first short chord vector distance data according to an acceleration measurement method, wherein the acceleration measurement method acquires the first short chord vector distance data through an accelerometer sensor and a visual ranging unit;
the method comprises the steps of obtaining second short chord vector distance data according to an angular velocity measurement method, wherein the angular velocity measurement method obtains the second short chord vector distance data according to three-dimensional attitude data and relative three-dimensional attitude data of a measurement carrier;
and carrying out fusion filtering processing on the first short chord vector distance data and the second short chord vector distance data to obtain track short chord vector distance data after the fusion filtering processing.
5. The method of claim 4, wherein performing fusion filtering on the first short chordal vector data and the second short chordal vector data comprises:
the first short string vector distance data is subjected to a high-pass filter, and the second short string vector distance data is subjected to a band-pass filter, so that filtered first short string vector distance data and second short string vector distance data are respectively obtained;
and carrying out fusion processing on the filtered first short chord vector distance data and the filtered second short chord vector distance data.
6. The method of claim 2, wherein identifying the localized sedimentation region of the track based on the track geometry comprises:
obtaining an actual measured gradient angle and an actual mileage value;
obtaining a designed gradient angle and a designed mileage value recorded in a gradient ledger, and obtaining a track design elevation;
matching the designed mileage value with the actual mileage value according to the correlation between the designed gradient angle and the actual gradient angle, and correspondingly eliminating the designed curve in the space curve according to the matching result;
and identifying the track local sedimentation area according to the removed space curve, chord measurement value and vector distance difference value.
7. The method of claim 1, wherein calculating the relative elevation change and corresponding maximum sedimentation value over the sedimentation zone using the slope angle comprises:
obtaining a design elevation value recorded in a gradient ledger and an actual elevation value;
subtracting the actual elevation value from the design elevation value to obtain a relative elevation change;
and carrying out adjustment treatment on the relative elevation change to obtain the maximum sedimentation value.
8. The method of claim 7, wherein calculating the relative elevation change and corresponding maximum sedimentation value over the sedimentation zone using the slope angle comprises:
the design elevation value is calculated according to the following formula:
h design ＝∑ψΔx；
wherein Deltax is a space sampling interval, psi is a pitch angle of a measured carrier, the pitch angle of the measured carrier is approximately a slope angle of a track, h design To design elevation values;
the actual elevation value is calculated according to the following formula:
h k ＝h k-1 -Δxsinψ；
wherein h is k 、h k-1 Actual elevation values, delta, at the kth and k-1 sampling moments, respectivelyx is a space sampling interval, psi is a pitch angle of a measured carrier, and the pitch angle of the measured carrier is approximately a slope angle of a track;
calculating the elevation deviation according to the following formula
Δh k ＝h k -h design ；
Wherein Δh k Is the elevation deviation of the kth sampling moment.
9. A track local settlement recognition device, comprising:
the three-dimensional attitude data acquisition module is used for acquiring three-dimensional attitude data of the measurement carrier;
the relative three-dimensional attitude data acquisition module is used for acquiring relative three-dimensional attitude data of the measurement carrier and the track by taking the ground as a reference system;
the track short chord vector distance data determining module is used for determining track short chord vector distance data according to the three-dimensional attitude data and the relative three-dimensional attitude data of the measuring carrier, wherein the track short chord vector distance data is track angular displacement change data;
the track geometric parameter determining module is used for determining track geometric parameters according to the track short chord vector distance data;
the track local sedimentation area identification module is used for identifying a track local sedimentation area according to the track geometric parameters;
and the relative elevation change calculation module is used for calculating the relative elevation change and the corresponding maximum sedimentation value in the sedimentation area range by utilizing the gradient angle in the geometric parameters of the track in the identified track local sedimentation area range.
10. The apparatus of claim 9, wherein the track geometry parameters include space curves, chord measurements, vector distance differences, and grade angles.
11. The apparatus of claim 9, further comprising a fusion filter processing module to:
and carrying out fusion filtering processing on the short chord vector distance data of the track.
12. The apparatus of claim 11, wherein the fusion filtering processing module is specifically configured to:
acquiring first short chord vector distance data according to an acceleration measurement method, wherein the acceleration measurement method acquires the first short chord vector distance data through an accelerometer sensor and a visual ranging unit;
the method comprises the steps of obtaining second short chord vector distance data according to an angular velocity measurement method, wherein the angular velocity measurement method obtains the second short chord vector distance data according to three-dimensional attitude data and relative three-dimensional attitude data of a measurement carrier;
and carrying out fusion filtering processing on the first short chord vector distance data and the second short chord vector distance data to obtain track short chord vector distance data after the fusion filtering processing.
13. The apparatus of claim 12, wherein the fused filter processing module is further configured to:
the first short string vector distance data is subjected to a high-pass filter, and the second short string vector distance data is subjected to a band-pass filter, so that filtered first short string vector distance data and second short string vector distance data are respectively obtained;
and carrying out fusion processing on the filtered first short chord vector distance data and the filtered second short chord vector distance data.
14. The apparatus of claim 10, wherein the track local sedimentation zone identification module is configured to:
acquiring a design gradient angle and a design mileage value recorded in a gradient ledger, and acquiring an actual gradient angle and an actual mileage value obtained by actual measurement;
generating a design space sequence according to the gradient standing book, and obtaining a design curve according to the generated design space sequence;
matching the designed mileage value with the actual mileage value according to the correlation between the designed gradient angle and the actual gradient angle, and correspondingly eliminating the designed curve in the space curve according to the matching result;
and identifying the track local sedimentation area according to the removed space curve, chord measurement value and vector distance difference value.
15. The apparatus of claim 9, wherein the relative elevation change calculation module is configured to:
obtaining a design elevation value recorded in a gradient ledger and an actual elevation value;
subtracting the actual elevation value from the design elevation value to obtain a relative elevation change;
and carrying out adjustment treatment on the relative elevation change to obtain the maximum sedimentation value.
16. The apparatus of claim 15, wherein the relative elevation calculation module is further configured to:
the design elevation value is calculated according to the following formula:
h design ＝∑ψΔx；
wherein Deltax is a space sampling interval, psi is a pitch angle of a measured carrier, the pitch angle of the measured carrier is approximately a slope angle of a track, h design To design elevation values;
the actual elevation value is calculated according to the following formula:
h k ＝h k-1 -Δxsinψ；
wherein h is k 、h k-1 The actual elevation values of the k-1 sampling moments are respectively k, deltax is a space sampling interval, psi is a pitch angle of a measured carrier, and the pitch angle of the measured carrier is approximate to a slope angle of a track;
the relative elevation change is calculated according to the following formula:
Δh k ＝h k -h design ；
wherein Δh k Is the relative elevation change at the kth sampling instant.
17. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 8 when executing the computer program.
18. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 8.
19. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the method of any of claims 1 to 8.
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