A method, device, computing device and readable storage medium for detecting track irregularities

Abstract

The present application provides a method, apparatus, computing device, and readable storage medium for detecting track irregularities. Detection is performed by measuring the movement of a trolley on the track, and the true track irregularities are calculated using the geometric relationships between displacement sensors and the front wheels, rear wheels, and vehicle body, thereby solving the problems of inaccurate measurement and long measurement period in existing irregularity measurement methods. Specifically, the following steps are included: obtaining initial parameters; collecting, by a displacement sensor, the distance from the displacement sensor to the track in a direction perpendicular to the traveling direction of the measurement trolley; calculating and recording the deviation of the current front wheel from the track reference function; and calculating the irregularity function of the track based on the historical data sequence of the deviation of the front wheel from the track reference function. The present application can provide a detection method that is not restricted by speed, consumes less computing resources, has high computing accuracy, and can detect track irregularities online in real time, so as to more efficiently complete the track irregularity detection work.

Description

Technical Field

[0001] This application relates to the technical field of track detection, and in particular, to a method and device for detecting track irregularities, a computing device, and a readable storage medium. Background Art

[0002] Track irregularity diseases exist in various railway track lines, with a wavelength distribution of 20 - 1200 mm, a wave crest spacing mostly between 200 - 700 mm, and a wave depth that can reach 1.5 mm in severe cases. Track irregularity is the main cause of severe vibration in the wheel-rail system, directly affecting riding comfort, reducing the service life of tracks, structural components, and vehicles, and seriously endangering train operation safety.

[0003] Currently, there are mainly two methods for detecting track irregularities at home and abroad: the inertial reference method and the chord measurement method. The inertial reference method obtains track irregularity information by performing double integration on the signals collected by accelerometers. Usually, the measurement data of the inertial reference method needs to be filtered using a high-pass filter, but the high-pass filter will cause large errors when the vehicle is running at low speeds. Therefore, the inertial reference method is not suitable for working at low speeds. The chord measurement method means pulling a string of a fixed length along the track surface and arranging one or more displacement sensors on the string. By measuring the distance between the string and the track surface, the irregularity of the track surface is judged. The chord measurement method has the advantage of being independent of the speed. However, since the transfer function of the chord measurement formula is not always 1, an inverse filter needs to be designed in combination with the wavelength for inverse second processing to restore the corrugation condition of the track. After the string is fixed, some waveforms with a transfer function of zero cannot be inverted. In addition, for the offset chord method in the chord measurement method, its phase-frequency response is not 0, there is a phase non-linearity problem, and it is difficult to design the inverse filter function. In addition, the chord measurement method usually needs to store all the original measurement data and can only start the analysis and evaluation process after the measurement is completed to obtain the true track irregularity data. In this process, large-scale matrix operations are required, and the data often needs to be copied to a desktop computer for offline processing after the skylight time.

[0004] In recent years, with the rapid development of artificial intelligence technology, some machine vision methods have been used for track irregularity detection. After learning and training a large number of track irregularity image samples, this type of method can achieve online real-time detection of track irregularities. However, this type of method is greatly affected by factors such as image quality, lighting, and dirt on the track surface, with low system stability and poor track irregularity detection accuracy.

[0005] Therefore, in this context, how to provide a track irregularity detection method that is not limited by speed, consumes less computing resources, has high computing accuracy, and can perform online real-time detection of track irregularities to more efficiently complete the track irregularity detection work is a technical problem to be solved. Summary of the Invention

[0006] In view of the above problems of the prior art, the present application provides a method for detecting track irregularities, which can provide a detection method that is not limited by speed, consumes less computing resources, has high computing accuracy, and can detect track irregularities online in real time, so as to more efficiently complete the track irregularity detection work.

[0007] To achieve the above object, the first aspect of the present application provides a method for detecting track irregularities, which is detected during the driving of a measuring trolley with a front wheel and a rear wheel on the track. The method includes the following steps:

[0008] Obtain initial parameters, where the initial parameters include the reference function of the track;

[0009] Collect the distance from the displacement sensor to the track along the direction perpendicular to the traveling direction of the measuring trolley through the displacement sensor arranged on the measuring trolley;

[0010] Calculate and record the deviation of the current front wheel from the track reference function according to the initial parameters, the distance from the displacement sensor to the track, and the deviation of the front wheel from the track in the previous time;

[0011] Calculate the irregularity function of the track according to the historical data sequence of the deviations of the front wheel from the track reference function recorded multiple times.

[0012] Thus, by using the geometric relationship between the measuring trolley and the track for derivation, it is possible to collect only a small amount of data including the distance from the displacement sensor to the track, and quickly calculate the deviation of the front wheel from the track reference function; and based on this, the irregularity function of the track can be calculated more accurately in real time. In addition, due to the low calculation cost and fast calculation speed, online real-time monitoring can be realized, avoiding the problem of the long measurement and calculation feedback cycle brought by the existing methods such as the chord measurement method.

[0013] As a possible implementation manner of the first aspect, the collection of the distance from the displacement sensor to the track along the direction perpendicular to the traveling direction of the measuring trolley is performed every certain period of time or distance.

[0014] Thus, by using a displacement sensor for data collection, the usage requirements of equipment are reduced. Collecting every certain period of time or distance can update the distance from the displacement sensor to the track multiple times.

[0015] As a possible implementation manner of the first aspect, the initial parameters include: the length of the measuring trolley, the distance from the displacement sensor to the axis of the rear wheel along the longitudinal axis of the vehicle body of the measuring trolley, and the position of the displacement sensor in the traveling direction of the trolley.

[0016] As described above, by including the above-mentioned small amount of data collection and performing calculations, the calculation cost is relatively small, and it can run on a relatively small computing memory and system, and real-time calculations can be performed. Among them, some data can be obtained through pre-collection, further reducing the calculation cost.

[0017] As a possible implementation of the first aspect, the deviation of the front wheel from the track reference function and the unevenness function are calculated according to the following formula:

[0018]

[0019] Wherein, is the position of the displacement sensor in the traveling direction of the trolley; is the length of the measuring trolley; is the distance from the displacement sensor along the longitudinal axis of the body of the measuring trolley to the axis of the rear wheel; is the position deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the front wheel is located in the uneven track section and the standard value; is the position deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the rear wheel is located in the uneven track section and the standard value; is the deviation of the front wheel from the track reference function; is the average measurement value of the displacement sensor on the initial reference track; g(x) is the distance from the displacement sensor to the track; is the track reference function; is the unevenness function of the track.

[0020] As described above, through the above calculation method, the deviation of the front wheel from the track reference function is calculated multiple times. Compared with the signal calculation methods that require filtering or inversion, the intermediate operations are reduced. The track unevenness function can be deduced from the measured values, with high calculation accuracy and fewer measurement parameters used. Since there are no operations such as iteration, integration, and solution in the calculation, the requirements for the calculation system are relatively low.

[0021] As a possible implementation of the first aspect, the displacement sensor is located at the middle position of the measuring trolley.

[0022] As described above, by setting the displacement sensor in the center of the measuring trolley, the position of the displacement sensor on the measuring trolley is not obvious relative to the calculation formula, which can eliminate one variable and further save computing resources.

[0023] As a possible implementation of the first aspect, the deviation of the front wheel from the track reference function and the unevenness function are calculated according to the following formula:

[0024]

[0025] Among them, is the position of the displacement sensor in the traveling direction of the trolley; is the length of the measurement trolley; is the distance from the displacement sensor along the longitudinal axis of the body of the measurement trolley to the axis of the rear wheel; is the position deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the front wheel is located in the uneven track section and the standard value; is the position deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the rear wheel is located in the uneven track section and the standard value; is the deviation of the front wheel from the track reference function; is the moving average value of the sequence of the distance from the displacement sensor to the track; is the distance from the displacement sensor to the track; is the track reference function; is the unevenness function of the track.

[0026] The distance from the displacement sensor to the track, the moving average distance from the displacement sensor to the track is calculated according to the following formula:

[0027]

[0028]

[0029] Among them, is the weighting coefficient, is the arithmetic mean of the N nearest measured values of the displacement sensor at x, where N > 0.

[0030] Thus, by recalculating the measured mean value of the displacement sensor to the reference track after a certain distance, it can be ensured that in the measurement over a long distance, the cumulative error caused by the large difference from the track condition in the initial section will not occur.

[0031] As a possible implementation manner of the first aspect, a fixed-length FIFO queue is used to record the deviation of the front wheel from the track reference function; wherein, the fixed length is the ratio of the length of the measurement trolley to the distance interval of each acquisition.

[0032] Thus, by using the storage method of FIFO, the system memory occupancy can be further reduced. When the FIFO length is the above ratio, it can ensure the correct basic operation of the measurement method.

[0033] The second aspect of the present application provides a device for detecting track unevenness, including:

[0034] The acquisition module is used to obtain initial parameters, including the reference function of the track; and collect the distance from the displacement sensor to the track in the direction perpendicular to the traveling direction of the measurement trolley through the displacement sensor arranged on the measurement trolley.

[0035] The calculation module is used to calculate the deviation of the current front wheel from the track reference function according to the distance from the displacement sensor to the track and the deviation of the front wheel from the track in the previous time; and calculate the track unevenness function according to the historical data sequence of the deviations of the front wheel from the track reference function recorded multiple times.

[0036] The storage module is used to record the deviation of the current front wheel from the track reference function.

[0037] The third aspect of the present application provides a computing device, including: a processor, and a memory, on which program instructions are stored, and when the program instructions are executed by the processor, the processor is caused to execute the track unevenness detection method according to any one of the first aspect.

[0038] The fourth aspect of the present application provides a computer-readable storage medium, on which program instructions are stored, and when the program instructions are executed by a computer, the computer is caused to execute the track unevenness detection method according to any one of the first aspect. Description of the Drawings

[0039] Figure 1 is a flowchart of the track unevenness detection method provided by the first embodiment of the present application;

[0040] Figure 2a is a flowchart of the track unevenness detection method provided by the second embodiment of the present application;

[0041] Figure 2b is a working schematic diagram of the track unevenness measurement trolley provided by the second embodiment of the present application;

[0042] Figure 2c is a schematic diagram of the influence of a single-wave valley track unevenness on the measurement trolley provided by the second embodiment of the present application;

[0043] Figure 2d is a waveform schematic diagram of the measurement data when the measurement trolley passes through a single-wave valley ("pit") provided by the second embodiment of the present application;

[0044] Figure 2e is a schematic diagram of the geometric relationship when the front wheel of the measurement trolley enters the unevenness wave valley provided by the second embodiment of the present application;

[0045] Figure 2f is a schematic diagram of the geometric relationship when the measurement trolley passes through a corrugated track provided by the second embodiment of the present application;

[0046] Figure 2g It is a flowchart of the real-time online detection algorithm for track irregularity provided by the second embodiment of the present application;

[0047] Figure 2h It is a schematic diagram of the main memory overhead allocation in the algorithm provided by the second embodiment of the present application;

[0048] Figure 3 It is a schematic diagram of the detection device for track irregularity provided by the embodiment of the present application;

[0049] Figure 4 It is a structural schematic diagram of a computing device provided by the embodiment of the present application.

[0050] It should be understood that in the above structural schematic diagrams, the sizes and shapes of each block diagram are for reference only and should not constitute an exclusive interpretation of the embodiments of the present invention. The relative positions and inclusion relationships between the block diagrams presented in the structural schematic diagrams only schematically represent the structural associations between the block diagrams, rather than limiting the physical connection manners of the embodiments of the present invention. Detailed Embodiments

[0051] The following will further illustrate the technical solutions provided by the present application by way of examples in conjunction with the accompanying drawings. It should be understood that the system structures and business scenarios provided in the embodiments of the present application are mainly for illustrating possible implementation manners of the technical solutions of the present application and should not be construed as the only limitation to the technical solutions of the present application. Those of ordinary skill in the art know that with the evolution of the system structure and the emergence of new business scenarios, the technical solutions provided by the present application are equally applicable to similar technical problems.

[0052] It should be understood that the detection solutions for track irregularity provided by the embodiments of the present application include a detection method, device, computing device, and computer-readable storage medium for track irregularity. Since the principles of these technical solutions for solving problems are the same or similar, in the following introduction of specific embodiments, some repeated parts may not be elaborated again, but it should be regarded that there are mutual references between these specific embodiments and they can be combined with each other.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. In case of inconsistency, it shall be subject to the meaning stated in this specification or the meaning obtained according to the content recorded in this specification. In addition, the terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application. In order to accurately describe the technical content in this application and to accurately understand the present invention, the following explanations or definitions are given to the terms used in this specification before the description of the specific embodiments:

[0054] 1) First In First Out (FIFO): It is a principle or method for processing and organizing data, which stipulates that the element that enters the system earliest should also be the one that is processed or removed earliest. This principle is widely applied in the fields of computer science and engineering. In an operating system, the FIFO algorithm is used to manage the allocation of various resources, such as memory management, etc., to ensure that the data that enters the memory earliest is replaced last.

[0055] The track irregularity detection solution provided by the embodiments of the present application can be detected by a measuring trolley with a front wheel and a rear wheel during driving. The distance from the displacement sensor to the track is collected by the displacement sensor arranged on the measuring trolley; and the deviation between the current front wheel and the track reference function is calculated and recorded according to the distance from the displacement sensor to the track and the deviation of the previous front wheel from the track reference function; finally, the irregularity function of the track is calculated based on the historical data sequence of the deviation values recorded multiple times. This method provides a detection method that is not limited by speed, consumes less computing resources, has high computing accuracy, and can detect track irregularities online in real time, so as to complete the track irregularity detection work more efficiently. The embodiments of the present application can be applied to track irregularity detection in fields such as track detection and design. The following will introduce each embodiment of the present application in detail with reference to the accompanying drawings.

[0056] The first embodiment of the present application provides a method for detecting track irregularities. The following will be combined with Figure 1 , and specifically illustrate the implementation manners of each step of this method, including steps S10 - S40.

[0057] S10: Obtain initial parameters, where the initial parameters include the reference function of the track.

[0058] In some embodiments, it is necessary to obtain the length of the measuring trolley, the distance from the displacement sensor to the axis of the rear wheel along the longitudinal axis of the vehicle body of the measuring trolley, the position of the displacement sensor in the traveling direction of the trolley, and the reference function of the track (track initial function). Among them, the reference function of the track is actually a section of relatively smooth or smoothness-compliant track before the formal measurement of the measuring trolley. For simplicity, it can be recorded as f0(x) = 0. At this time, for the track with the same line smoothness as this section, f(x) = 0. If driving on a roughly horizontal track, this value can also be taken as the standard track height value. The initial function of the track can also be obtained by directly measuring a section of track with a fixed slope or a roughly horizontal track, and then adjusting according to the measurement results after running for a period of time.

[0059] S20: Collect the distance from the displacement sensor to the track along the direction perpendicular to the traveling direction of the measuring trolley through the displacement sensor arranged on the measuring trolley.

[0060] In some embodiments, the measuring direction of the displacement sensor is perpendicular to the traveling direction of the measuring trolley; the distance from the displacement sensor to the track is the distance from the displacement sensor to the track along the measuring direction; the acquisition is performed at regular intervals of time or distance. Among them, the displacement sensor can be a laser rangefinder, an ultrasonic sensor, an optical sensor, etc., and can also work together with an accelerometer, an angle sensor, etc. The displacement sensor should be calibrated and tested before use, with a range of less than 50 mm and a detection accuracy of 1 or less. The sampling interval distance can be 1 mm.

[0061] In some embodiments, the detection method may further include a commissioning phase, during which instrument calibration, acquisition of initial detection values, equipment debugging, etc. can be carried out.

[0062] In some embodiments, the collected data can be preprocessed, which may include: outlier screening, missing value interpolation, smoothing processing, etc. Among them, multiple displacement sensors can be installed, the distance from the displacement sensor to the track is measured multiple times, and operations such as taking the average value and screening outliers are performed according to the measurement results.

[0063] In some embodiments, the speed of the measuring trolley should not be too fast, and the wheels of the measuring trolley should be in full contact with the track. In necessary cases, a stable structure should be installed for the displacement sensor to prevent the measurement results from being affected by the mechanical properties of the measuring trolley itself.

[0064] In some embodiments, a temperature sensor and a humidity sensor can also be used to correct the detection results according to the measurement data of the temperature sensor and the humidity sensor.

[0065] S30: Calculate and record the deviation of the current front wheel from the track reference function according to the distance from the displacement sensor to the track and the deviation of the previous front wheel from the track reference function.

[0066] In some embodiments, the initial deviation value of the front wheel from the track reference function can be set to 0 or obtained through actual measurement.

[0067] In some embodiments, the deviation of the current front wheel from the track reference function and the unevenness function are calculated according to the following formula:

[0068]

[0069] where, is the position of the displacement sensor in the traveling direction of the trolley; is the length of the measuring trolley; is the distance from the displacement sensor to the axis of the rear wheel along the longitudinal axis of the body of the measuring trolley; is the position deviation between the distance from the displacement sensor to the track in the vertical direction of the track and the standard value when the front wheel is located on the uneven track section; is the position deviation between the distance from the displacement sensor to the track in the vertical direction of the track and the standard value when the rear wheel is located on the uneven track section; is the deviation of the current front wheel from the track reference function; is the measured average value of the displacement sensor on the initial reference track; is the distance from the displacement sensor to the track; is the track reference function; is the unevenness function of the track.

[0070] In some embodiments, the displacement sensor may be located at the middle position of the measuring carriage. The deviation of the current front wheel from the track reference function is calculated according to the following formula:

[0071]

[0072] In some embodiments, the measured average value of the displacement sensor to the reference track can be obtained by measurement, or the moving average value of the sequence of the distance from the displacement sensor to the track can be used to replace the measured average value of the displacement sensor on the initial reference track , the is calculated according to the following formula:

[0073]

[0074]

[0075] wherein, is the weighting coefficient, is the arithmetic mean of the N nearest measured values of the displacement sensor at x, where N>0. The measured average value of the displacement sensor to the reference track can also be continuously updated by manually measuring again at intervals.

[0076] In some embodiments, the displacement sensor of the measuring carriage can also combine acceleration and angular velocity information, and eliminate the additional displacement component of the measured value caused by the acceleration or deceleration of the carriage by calculating acceleration and angular velocity compensation.

[0077] S40: Calculate the unevenness function of the track according to the historical data sequence of the deviation of the front wheel from the track reference function recorded multiple times.

[0078] In some embodiments, a fixed-length FIFO queue is used to record the deviation between the front wheel and the track reference function; wherein, the fixed length is the ratio of the length of the measurement trolley to the distance interval collected each time. Sliding windows, priority queues, distributed hash tables, etc. can also be used to manage the stored data. Among them, the calculated data can be stored on edge devices or local caches, combined with cloud storage technology to reduce local memory occupancy.

[0079] The second embodiment of the present application provides a method for detecting track irregularities. The following will be described with reference to Figure 2a the flowchart shown. The method provided by this second embodiment includes the following steps S200 - S230.

[0080] S200: Derivation of the calculation formula for track irregularity detection.

[0081] In the embodiments of the present application, according to the parameters of the measurement trolley, by solving the geometric position relationship among the track, the wheels, and the displacement sensor during the operation of the measurement trolley, the calculation formula for the deviation between the bottom of the front wheel and the track reference function is obtained, thereby deriving the true function of track irregularity. The following will derive the calculation formula for track irregularity detection. During the actual detection process, the detection can directly start from S210.

[0082] As Figures 2b - 2d shown, when the measurement trolley passes through the uneven track at a speed , the wheels rise and fall with the uneven track, driving the attitude of the vehicle body to change. It can be seen from the figure that:

[0083] (1) Any section of unevenness on the track (such as a trough or a peak) is first "perceived" by the front wheel of the measurement trolley, and then successively taken over by the on-vehicle displacement sensor and the rear wheel of the measurement trolley, as shown in Figure 2c (a)-(d) in

[0084] (2) On the uneven track, the position state of the front wheel or the rear wheel (such as the position in the trough) directly affects the change of the measurement data of the displacement sensor in the vertical direction of the track, as shown in Figure 2c (b)(d) in Figure 2d ;

[0085] (3) On the uneven track, the data collected by the displacement sensor (i.e., the measured value) not only contains the information of the unevenness of the track at the measurement point, but also contains the information of the unevenness of the track at the positions of the front and rear wheels, as shown in Figure 2c and Figure 2d .

[0086] According to the observation results, the geometric relationship of the front wheel of the measurement trolley entering the uneven trough is placed in a coordinate system, as shown in Figure 2e shown. Among them, is the track irregularity function, is the track reference function. is a function of x, representing the vertical deviation value between the lowest point of the front wheel and the track reference function in the vertical direction.

[0087] The measured value of the displacement sensor at x is , and its projection on the track in the vertical direction is .

[0088] is the average measured value of the displacement sensor on the initial reference track. At this time, the standard value .

[0089] is the deviation between the measured value of the displacement sensor on the track in the vertical direction and the standard value.

[0090] Then, according to the geometric relationship, we have:

[0091]

[0092] As Figure 2e shown, the measurement positions of the rear wheel of the measurement trolley and the displacement sensor are both on the reference track. At this time, the distance of the change of the displacement sensor in the vertical direction of the track caused by the front wheel entering the irregularity trough is equal to the deviation between the measured value of the displacement sensor and the standard value, that is:

[0093]

[0094] Similarly, when the rear wheel enters the irregularity trough and the measurement positions of the front wheel and the displacement sensor are both on the reference track, the distance of the change of the displacement sensor in the vertical direction of the track or the deviation between the measured value of the displacement sensor and the standard value is:

[0095]

[0096] In comparison, the depth of the track irregularity is much smaller than the length of the vehicle body, that is . If L = 1000mm, l = 500mm, = 1.5mm, then = 0.0015. At this time, cos(θ) 0.9999989. At this time, the deviation between the position of the front wheel and the position on the x-axis, and the deviation between the position of the rear wheel and are both .

[0097] It can be seen that, relative to the sampling interval of the displacement sensor on the x-axis (e.g., Δx = 1 mm), this deviation value can be ignored.

[0098] Using a similar analysis, for a displacement sensor with a range of less than 50 mm, the maximum deviation between the measured value g(x) and g(x)cos(θ) at this time is . Usually, the detection accuracy of the track irregularity displacement sensor is above 1 μm, so this deviation can also be ignored.

[0099] Therefore, can be used to replace , to replace , to replace , then the above formula can be approximated as:

[0100]

[0101] As mentioned before, during the detection operation of the measurement trolley, any section of the track irregularity (such as a wave crest or a wave trough) is successively "sensed and detected" by the front wheel, the sensor, and the rear wheel. Since the wheel diameters of the front and rear wheels of the measurement trolley are the same, for the same section of the track irregularity, the deviation of the wheel bottom of the front wheel and the track reference function in the vertical direction is the same, that is .

[0102] In addition, during the operation, both the front and rear wheels of the measurement trolley may be located at the wave crest or wave trough of the irregular track, and respectively cause changes in the measurement position and measurement data of the displacement sensor in the vertical direction of the track, as Figure 2f shown. At this time, there is:

[0103]

[0104] Furthermore, the calculation formula for track irregularity detection is obtained:

[0105]

[0106] In special cases, such as taking , at this time, the displacement sensor is deployed at the midpoint of the measurement trolley body, then the calculation formula for track irregularity detection can be rewritten as:

[0107]

[0108] It can be seen from the calculation formula for track irregularity detection that:

[0109] (1) The deviation of the wheel bottom of the front wheel from the track reference function can be obtained from the deviation between the measured value of the displacement sensor and the measured value of the standard track and the deviation old value of the front wheel at position x and are jointly determined; and jointly determine;

[0110] (2) When the initial value of the track reference function is given (such as taking ), the true unevenness f(x) of the track at the position x of the displacement sensor is determined by the deviation old value of the front wheel at position x determined.

[0111] When the detected track is relatively long, the track conditions in different sections may change. At this time, the formula can be further optimized to improve the adaptability of the detection method. For example, the moving average of the sequence of the distance from the displacement sensor to the track is used to replace the measurement mean value of the displacement sensor on the initial reference track , and the is calculated according to the following formula:

[0112]

[0113]

[0114] where is the weighting coefficient, is the arithmetic mean of the N nearest measurement values of the displacement sensor at x, where N > 0.

[0115] At this time, the calculation formula for track unevenness detection is:

[0116]

[0117] S210: Obtain the initial parameters, and collect the distance from the displacement sensor to the track through the displacement sensor.

[0118] The above calculation method can be implemented through a real-time monitoring algorithm, and the algorithm steps are as Figure 2g shown. First, obtain the length L of the measurement trolley, the distance l from the displacement sensor to the axis of the rear wheel along the longitudinal axis of the trolley body, the track reference function , the weighting coefficient , etc. as the initial data of the parameters such as L, l, , , (parameter initialization).

[0119] During the algorithm implementation process, to improve the memory utilization efficiency and reduce the data storage resource overhead, a FIFO queue ΔH[] with a fixed length (L / Δx) can be applied to update and store the deviation ΔH of the front wheel bottom in real time. The memory allocation relationship is as Figure 2h shown.

[0120] Place the measurement trolley on a relatively standard track to be measured and start running, obtain and store the average value of the measurement readings of the displacement sensors set on the measurement trolley as the initial benchmark for sensor unevenness monitoring.

[0121] S220: Calculate and record the deviation of the current front wheel from the track reference function according to the distance from the displacement sensor to the track and the deviation of the previous front wheel from the track reference function.

[0122] According to the change of the sensor measurement value calculate , update .

[0123] Calculate the current using the track unevenness detection calculation formula, and record the calculation result in the data queue as the latest data. If the queue length is exceeded, the oldest data is removed.

[0124] S230: Calculate the track unevenness function based on the historical data sequence of the deviation of the front wheel from the track reference function recorded multiple times.

[0125] According to calculate the unevenness of the track at and finally obtain the detection result.

[0126] In summary, the algorithm of the present invention has the following beneficial effects:

[0127] (1) The data volume involved in the calculation process of this algorithm is small, the memory overhead is low, the computing resource requirements are low, and the algorithm can be easily deployed and run in real time online;

[0128] (2) Directly calculate the true unevenness of the track from the sensor measurement data without the need for inverse transformation in the frequency transformation domain, and no data information is lost;

[0129] (3) This algorithm is applicable to all displacement sensors;

[0130] (4) This algorithm can be applied to track vertical unevenness and alignment unevenness;

[0131] (5) This algorithm can be used for real-time online detection of independent damages such as high joints, welds, abrasions, missing blocks, and gouges.

[0132] The third embodiment of the present application provides a detection device for track irregularity, which can be used to implement the track irregularity detection method in the above embodiments, such as Figure 3 As shown, the detection device for track irregularity includes:

[0133] An acquisition module, configured to obtain initial parameters; collect the distance from the displacement sensor to the track in a direction perpendicular to the traveling direction of the measurement trolley through the displacement sensor arranged on the measurement trolley; specifically, the acquisition module can be used to implement steps S10 - S20 and their optional embodiments in the first embodiment.

[0134] A calculation module, configured to calculate the deviation of the current front wheel from the track reference function according to the distance from the displacement sensor to the track and the deviation of the previous front wheel from the track; and calculate the irregularity function of the track according to the deviations of the front wheel from the track reference function recorded multiple times; specifically, the calculation module can be used to implement steps S30 - S40 and their optional embodiments in the first embodiment.

[0135] A storage module, configured to record the deviation of the current front wheel from the track reference function. Specifically, the storage module can be used to implement step S40 and its optional embodiments in the first embodiment.

[0136] Figure 4 It is a structural schematic diagram of a computing device 900 provided by an embodiment of the present application. The computing device can execute various optional embodiments in the above method. The computing device can be a terminal, or a chip or chip system inside the terminal. Such as Figure 4 As shown, the computing device 900 includes: a processor 910, a memory 920, and a communication interface 930.

[0137] It should be understood that Figure 4 The communication interface 930 in the computing device 900 shown can be used for communication with other devices, and specifically can include one or more transceiver circuits or interface circuits.

[0138] Among them, the processor 910 can be connected to the memory 920. The memory 920 can be used to store the program code and data. Therefore, the memory 920 can be an internal storage unit of the processor 910, or an external storage unit independent of the processor 910, or a component including an internal storage unit of the processor 910 and an external storage unit independent of the processor 910.

[0139] Optionally, the computing device 900 may further include a bus. Among them, the memory 920 and the communication interface 930 may be connected to the processor 910 through the bus. The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 In Figure 4 , a line without an arrow is used to represent it, but it does not mean that there is only one bus or one type of bus.

[0140] It should be understood that in the embodiments of the present application, the processor 910 may adopt a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. Or the processor 910 adopts one or more integrated circuits to execute relevant programs to implement the technical solutions provided by the embodiments of the present application.

[0141] The memory 920 may include a read-only memory and a random access memory, and provide instructions and data to the processor 910. A part of the processor 910 may also include a non-volatile random access memory. For example, the processor 910 may also store information about the device type.

[0142] When the computing device 900 is running, the processor 910 executes the computer-executable instructions in the memory 920 to perform any operation step of the above method and any optional embodiment thereof.

[0143] It should be understood that the computing device 900 according to the embodiments of the present application may correspond to the corresponding main body for executing the methods according to the embodiments of the present application, and the above and other operations and / or functions of each module in the computing device 900 respectively correspond to the corresponding processes of the methods in the present embodiments. For the sake of brevity, they will not be described in detail here.

[0144] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or in combination with computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0145] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be electrical, mechanical, or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0149] When the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0150] The embodiments of this application also provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it is used to execute the above-mentioned method, and the method includes at least one of the solutions described in the above-mentioned various embodiments.

[0151] The computer storage medium of the embodiments of this application can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component.

[0152] The computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component.

[0153] The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0154] The computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or, it can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0155] In addition, the terms "first, second, third, etc." or terms such as module A, module B, module C, etc. in the specification and claims are only used to distinguish similar objects and do not represent a specific order for the objects. Understandably, the specific order or sequence can be interchanged under allowable circumstances so that the embodiments of this application described here can be implemented in an order other than that illustrated or described here.

[0156] In the above description, the reference numerals representing steps, such as S110, S120, etc., do not necessarily mean that the steps will be executed in this order. Under allowable circumstances, the order of the front and back steps can be interchanged, or they can be executed simultaneously.

[0157] The term "comprising" used in the specification and claims should not be construed as being limited to the content listed thereafter; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the presence of the mentioned features, wholes, steps, or components, but does not exclude the presence or addition of one or more other features, wholes, steps, or components and their groups. Therefore, the expression "a device comprising device A and B" should not be limited to a device consisting only of components A and B.

[0158] As used herein, the term "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places in this specification are not necessarily all referring to the same embodiment, but may refer to the same embodiment. Additionally, in one or more embodiments, the various specific features, structures, or characteristics can be combined in any suitable manner, as will be apparent to those of ordinary skill in the art from the present disclosure.

[0159] Note that the above is only a preferred embodiment of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in relatively detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, more other equivalent embodiments can be included, all of which fall within the protection scope of the present application.

Claims

1. A method for detecting track irregularities, characterized in that, Detecting is performed during the running of a measuring trolley with front wheels and rear wheels on a track, and the method includes the following steps: Obtain initial parameters, including the reference function of the track; wherein, the reference function of the track is the function of a relatively smooth or standard-compliant track section before the formal measurement of the measuring trolley, with a value of 0 or the height value of the standard track; Collect the distance from the displacement sensor to the track in the direction perpendicular to the traveling direction of the measuring trolley by arranging the displacement sensor on the measuring trolley; Calculate and record the deviation of the current front wheel from the track reference function according to the initial parameters, the distance from the displacement sensor to the track, and the deviation of the front wheel bottom from the track reference function in the previous measurement; Calculate the track irregularity function based on the historical data sequence of the deviations of the front wheel from the track reference function recorded multiple times; Wherein, the deviation of the front wheel bottom from the track reference function and the track irregularity function are calculated according to the following formula: ； Wherein, is the position of the displacement sensor in the traveling direction of the trolley; is the length of the measurement trolley; is the distance from the displacement sensor along the longitudinal axis of the trolley body of the measurement trolley to the axis of the rear wheel; is the deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the front wheel is located in the uneven track section and the standard value; wherein, the standard value is the average measurement of the displacement sensor on the standard track; is the deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the rear wheel is located in the uneven track section and the standard value; is the deviation of the front wheel from the track reference function; is the average measurement of the displacement sensor on the standard track; is the distance from the displacement sensor to the track; is the track reference function; is the unevenness function of the track.

2. The method according to claim 1, characterized in that, The collection of the distance from the displacement sensor to the track in the direction perpendicular to the traveling direction of the measuring trolley is performed at regular time intervals or at regular distance intervals.

3. The method according to claim 2, characterized in that The initial parameters include: The length of the measuring trolley, the distance from the displacement sensor to the axis of the rear wheel along the longitudinal axis of the trolley body, and the position of the displacement sensor in the traveling direction of the trolley.

4. The method according to claim 3, characterized in that, The displacement sensor is located at the middle position of the measuring trolley.

5. The method according to claim 4, characterized in that Moving average of a sequence of distances from a displacement sensor to a track Instead of the measured mean value of the displacement sensor on a standard track , the deviation of the bottom of the front wheel from the track reference function and the roughness function are calculated according to the following formula: ； wherein, is the position of the displacement sensor in the traveling direction of the trolley; is the length of the measurement trolley; is the distance from the displacement sensor along the longitudinal axis of the body of the measurement trolley to the axis of the rear wheel; is the deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the front wheel is located in the uneven track section and the standard value; wherein, the standard value is the average measurement of the displacement sensor on the standard track; is the deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the rear wheel is located in the uneven track section and the standard value; is the deviation of the front wheel from the track reference function; is the moving average of the distance from the displacement sensor to the track; is the distance from the displacement sensor to the track; is the track reference function; is the unevenness function of the track; The moving average of the distance from the displacement sensor to the track Calculated according to the following formula: ； ； Among them, is the weighting coefficient, is the arithmetic mean of the N nearest measured values of the displacement sensor at x, where N >

0.

6. The method according to claim 1, wherein Record the deviation of the front wheel from the track reference function using a fixed-length FIFO queue; wherein, the fixed length is the ratio of the length of the measuring trolley to the distance interval for each collection.

7. A detection device for track irregularities, characterized in that, Include: A collection module for obtaining initial parameters, including the reference function of the track; wherein, the reference function of the track is the function of a relatively smooth or standard-compliant track section before the formal measurement of the measuring trolley, with a value of 0 or the height value of the standard track; collect the distance from the displacement sensor to the track in the direction perpendicular to the traveling direction of the measuring trolley by arranging the displacement sensor on the measuring trolley; A calculation module for calculating the deviation of the current front wheel from the track reference function according to the distance from the displacement sensor to the track and the deviation of the front wheel bottom from the track in the previous measurement; The calculation module is further configured to calculate the track irregularity function based on the historical data sequence of the deviations of the front wheel from the track reference function recorded multiple times; wherein, the deviation of the front wheel bottom from the track reference function and the track irregularity function are calculated according to the following formula: ； Wherein, is the position of the displacement sensor in the traveling direction of the trolley; is the length of the measuring trolley; is the distance from the displacement sensor along the longitudinal axis of the body of the measuring trolley to the axis of the rear wheel; is the deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the front wheel is located in the uneven track section and the standard value; wherein, the standard value is the average measurement of the displacement sensor on the standard track; is the deviation between the distance from the displacement sensor to the track in the vertical direction of the track when the rear wheel is located in the uneven track section and the standard value; is the deviation of the front wheel from the track reference function; is the average measurement of the displacement sensor on the standard track; is the distance from the displacement sensor to the track; is the track reference function; is the unevenness function of the track; A storage module for recording the deviation of the current front wheel from the track reference function.

8. A computing device, characterized in that, Include: A processor, and A memory on which program instructions are stored, and when the program instructions are executed by the processor, the processor executes the method for detecting track irregularity according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, On which program instructions are stored, and when the program instructions are executed by a computer, the computer executes the method for detecting track irregularity according to any one of claims 1 to 6.

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