Method and device for evaluating full-range orbit vertical irregularity state

Abstract

The application discloses a full-waveband track vertical unevenness state evaluation method and device, relates to the field of railway engineering, and comprises the following steps: obtaining axle box acceleration data of a high-speed train collected by a multi-section vehicle dynamic detection system, wherein the multi-section vehicle dynamic detection system is installed on the high-speed train; dividing the axle box acceleration data into uniform speed section data and variable speed section data, and performing segmented filtering processing on the uniform speed section data and the variable speed section data respectively; performing secondary integral transformation on the segmented filtering processed axle box acceleration data, and calculating track vertical unevenness data of different wavebands; converting the track vertical unevenness data of different wavebands into full-waveband track unevenness data subjected to spatial sampling processing; and comparing the full-waveband track unevenness data with preset evaluation criteria of the full-waveband track unevenness, so as to determine whether defects exist in the full-waveband track unevenness.

Description

Full-band orbit vertical irregularity assessment method and apparatus Technical Field

[0001] This invention relates to the field of railway engineering, and in particular to a method and apparatus for evaluating the vertical irregularity of tracks across all bands. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] The condition of the track directly determines the safety and comfort of the track-vehicle system. Currently, track geometry inspection plays a leading role in ensuring track condition. Ballastless tracks experience thermal expansion in summer and frost heave in winter, increasing the track geometry amplitude. This change is somewhat sudden, and long-term, low-frequency inspections cannot effectively capture sudden changes in track properties. High-speed trains use an inertial reference frame combined with an optical system for dynamic track geometry inspection, but this method has a long cycle and cannot detect track problems in a timely manner. Summary of the Invention

[0004] This invention provides a method for evaluating the vertical irregularity of a track across the entire frequency band, enabling timely and accurate evaluation of this irregularity. The method includes:

[0005] The axle box acceleration data of a high-speed train is obtained from a multi-section vehicle dynamic detection system, which is installed on the high-speed train.

[0006] The axle box acceleration data is divided into uniform speed segment data and variable speed segment data, and segmented filtering processing is performed on the uniform speed segment data and variable speed segment data respectively.

[0007] The axle box acceleration data after segmented filtering is subjected to a second integral transform to calculate the vertical track irregularity data for different bands.

[0008] The vertical irregularity data of the orbit in different bands is converted into full-band orbit irregularity data through spatial sampling. The full-band orbit irregularity data is then compared with the preset evaluation criteria for full-band orbit irregularity to determine whether there are any defects in the full-band orbit irregularity.

[0009] This invention also provides a full-band track vertical irregularity evaluation device to achieve timely and accurate evaluation of the track vertical irregularity. The device includes:

[0010] The data detection module is used to obtain axle box acceleration data of high-speed trains collected by the multi-section vehicle dynamic detection system, which is installed on the high-speed train.

[0011] The segmented filtering module is used to divide the axle box acceleration data into uniform speed segment data and variable speed segment data, and to perform segmented filtering processing on the uniform speed segment data and variable speed segment data respectively.

[0012] The data calculation module is used to perform a second integral transformation on the segmented filtered axle box acceleration data to calculate the vertical track irregularities in different bands.

[0013] The data judgment module is used to convert vertical track irregularity data of different bands into full-band track irregularity data of spatial sampling processing, compare the full-band track irregularity data with the preset judgment criteria of full-band track irregularity, and determine whether there are defects in the full-band track irregularity.

[0014] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for evaluating the vertical irregularity of the entire orbit.

[0015] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for evaluating the vertical irregularity of a full-band track.

[0016] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for evaluating the vertical irregularity of a full-band track.

[0017] In this embodiment of the invention, axle box acceleration data of a high-speed train is obtained from a multi-section vehicle dynamic detection system installed on the high-speed train. The axle box acceleration data is divided into uniform speed segment data and variable speed segment data, and segmented filtering is performed on both. The segmented filtered axle box acceleration data is then subjected to a second integral transform to calculate the vertical track irregularity data for different bands. The vertical track irregularity data for different bands is converted into full-band track irregularity data after spatial sampling. The full-band track irregularity data is compared with a preset evaluation standard for full-band track irregularity to determine whether there are defects in the full-band track irregularity. Compared with the existing technology of using an inertial reference frame combined with an optical system for dynamic track geometry detection in high-speed trains, this invention can shorten the detection cycle and obtain the vertical track irregularity status more timely and accurately. This invention can meet the requirements for online monitoring of track geometry using onboard equipment under high-speed conditions, as well as the condition monitoring and timely maintenance of railways, and helps to improve the efficiency of track inspection and maintenance. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0019] Figure 1 is a flowchart illustrating a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention.

[0020] Figure 2 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0021] Figure 3 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0022] Figure 4 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0023] Figure 5 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0024] Figure 6 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0025] Figure 7 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0026] Figure 8 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0027] Figure 9 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0028] Figure 10 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0029] Figure 11 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0030] Figure 12 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention;

[0031] Figure 13 is a specific example diagram of a full-band track vertical irregularity evaluation device in an embodiment of the present invention;

[0032] Figure 14 is a schematic diagram of a computer device used for evaluating the vertical irregularity of a track across the entire band in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0034] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0035] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0036] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0037] Figure 1 is a flowchart illustrating a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention. As shown in Figure 1, the method includes:

[0038] Step 101: Obtain axle box acceleration data of the high-speed train collected by the multi-section vehicle dynamic detection system, which is installed on the high-speed train.

[0039] Step 102: Divide the axle box acceleration data into uniform speed segment data and variable speed segment data, and perform segmented filtering processing on the uniform speed segment data and variable speed segment data respectively;

[0040] Step 103: Perform a second integral transform on the segmented filtered axle box acceleration data to calculate the vertical track irregularity data for different bands.

[0041] Step 104: Convert the vertical irregularity data of different bands into full-band track irregularity data of spatial sampling processing, compare the full-band track irregularity data with the preset evaluation criteria of full-band track irregularity, and determine whether there are defects in the full-band track irregularity.

[0042] In this embodiment of the invention, axle box acceleration data of a high-speed train is obtained from a multi-section vehicle dynamic detection system installed on the high-speed train. The axle box acceleration data is divided into uniform speed segment data and variable speed segment data, and segmented filtering is performed on both. The segmented filtered axle box acceleration data is then subjected to a second integral transform to calculate the vertical track irregularity data for different bands. The vertical track irregularity data for different bands is converted into full-band track irregularity data after spatial sampling. The full-band track irregularity data is compared with a preset evaluation standard for full-band track irregularity to determine whether there are defects in the full-band track irregularity. Compared with the existing technology of using an inertial reference frame combined with an optical system for dynamic track geometry detection in high-speed trains, this invention can shorten the detection cycle and obtain the vertical track irregularity status more timely and accurately. This invention can meet the requirements for online monitoring of track geometry using onboard equipment under high-speed conditions, as well as the condition monitoring and timely maintenance of railways, and helps to improve the efficiency of track inspection and maintenance.

[0043] In step 101, the axle box acceleration data of the high-speed train is obtained by the multi-section vehicle dynamic detection system, which is installed on the high-speed train. In one embodiment, the multi-section vehicle dynamic detection system uses multi-channel distributed networked testing technology to remotely control the testing equipment and collect the axle box acceleration of the high-speed train in real time.

[0044] An acceleration detection system was installed on the high-speed integrated inspection vehicle to measure the vertical axle box acceleration data. Figure 3 shows a specific example of a full-band track vertical irregularity evaluation method in this embodiment of the invention. The multi-section vehicle dynamic detection system, as shown in Figure 3, can collect the acceleration of the car body, frame, and axle boxes in real time to assist in the analysis of track irregularities. This detection system adopts multi-channel distributed networked testing technology, using a computer to remotely control test equipment distributed in different locations to work synchronously, and transmits data and synchronization information through the network. It features large measurement data volume, geographical dispersion, high real-time performance and reliability, and long-distance collaborative operation. The multi-section vehicle dynamic detection system has functions such as online acquisition and processing of raw signals, storage of intermediate data and final results, online display of waveforms, data transmission through the network, output of over-limit reports, mileage correction, post-event playback of stored data, and output of waveform data and corresponding locations and speeds. It realizes data acquisition, raw data storage, data validity judgment, and waveform display.

[0045] In step 102, the axle box acceleration data is divided into uniform speed segment data and variable speed segment data, and segmented filtering processing is performed on the uniform speed segment data and variable speed segment data respectively.

[0046] In one embodiment, the axle box acceleration data is divided into uniform speed segment data and variable speed segment data, and segmented filtering processing is performed on the uniform speed segment data and the variable speed segment data respectively, including: for the uniform speed segment data, filtering processing is performed using fast Fourier transform and inverse transform; for the variable speed segment data, filtering processing is performed using fractional Fourier transform and inverse transform.

[0047] In one embodiment, for data in a uniform velocity segment, filtering can be performed according to the following steps:

[0048] The filtering range is [F L ,F H ], where F L and F H These are the upper and lower limits of the filter, respectively, and their values ​​are determined based on the corresponding wavelength [L]. L ,L H ] and velocity V are used for calculation:

[0049]

[0050] Obtain the axle box acceleration signal a(t) from the axle box acceleration data, where t is time;

[0051] Perform a fast Fourier transform on a(t),

[0052] A(ω)=F[a(t)]

[0053] Where A(ω) represents the result of the fast Fourier transform of a(t), ω represents the angular frequency, and F represents the Fourier transform;

[0054] In the frequency domain, set the filter range [F] L ,F H Values ​​outside of [ ] are set to 0, and the result of the entire frequency domain transformation is denoted as A. F (ω);

[0055] For A F (ω) performs the inverse fast Fourier transform.

[0056] a F (t)=F -1 [A F (ω)]

[0057] This allows us to obtain the filtered data under constant speed conditions.

[0058] In one embodiment, the data for the variable speed section can be filtered according to the following steps:

[0059] Obtain the axle box acceleration signal a(t) from the axle box acceleration data, where t is time;

[0060] Perform a p-order fractional Fourier transform on a(t) (a(t) at angle α).

[0061]

[0062] A p (u) represents the p-th order fractional Fourier transform result of a(t), u represents the angular frequency in the fractional domain, and F p It is a fractional Fourier transform operator, K p (t,u) is the kernel function, and its expression is:

[0063]

[0064] in The rotation angle representing the time-frequency plane shows that the decomposition basis function of the fractional Fourier transform is extended from a single-frequency sinusoidal signal to a linear frequency-modulated signal.

[0065] Within the fractional domain, set the filter range [F]. α L ,F α H Values ​​other than those specified are set to 0, and the result of the frequency domain transformation is denoted as A. F (u), F α L and F α H The value is determined by the wavelength [L] L ,L H The velocity V and α determine the speed.

[0066]

[0067] For A F (u) Perform inverse fractional Fourier transform.

[0068] a F,i =F -p [A F (u)]

[0069] This yields the filtered data under variable speed conditions.

[0070] The p-th order fractional Fourier transform can be understood as the time-frequency plane of a signal rotating counterclockwise around the origin. The linear canonical transformation matrix of the p-th order fractional Fourier transform is:

[0071]

[0072] When p=1 The above matrix is At this point, the fractional Fourier transform becomes the traditional Fourier transform. Based on the above transformation, the time-frequency plane of the p-th order fractional Fourier transform of the signal is equivalent to the time-frequency plane of the traditional Fourier transform rotated counterclockwise. Angle. In this way, the time-frequency plane of the original linear frequency modulated signal is transformed into a form similar to the time-frequency plane of an impulse or single-cycle signal, which will make subsequent filtering processing much easier.

[0073] For variable speed sections, the dynamic response signal is generally a type of signal with very strong nonlinear vibration. Although some sections belong to nonlinear frequency modulation signals, overall, the speed increase or decrease sections can be approximated as linear frequency modulation signals. Thus, we calculate the rate of change of speed to obtain the angle α of the linear frequency modulation signal, and then obtain the order of the fractional Fourier transform through the angle α, and then realize the filtering of the axle box acceleration data. Figure 4 is a specific example of a full-band track vertical irregularity state evaluation method in an embodiment of the present invention. As shown in Figure 4, the axle box acceleration data before and after filtering for a certain section are shown.

[0074] In step 103, the axle box acceleration data after segmented filtering is subjected to a second integral transform to calculate the track vertical irregularity data for different bands; Figure 5 is a specific example of a full-band track vertical irregularity state evaluation method in an embodiment of the present invention, involving a method for calculating track irregularities, including the following steps:

[0075] Step 501: Calculate the vertical displacement based on the axle box acceleration data after segmented filtering.

[0076] Step 502: Perform Fourier transform and inverse transform on the vertical displacement to obtain orbital vertical irregularity data for different wavebands.

[0077] In one embodiment, the calculation method for track irregularities can be performed according to the following steps:

[0078] Using the piecewise filtered axle box acceleration data A(ω) as input, the Fourier transform of the vertical displacement can be calculated by the following formula:

[0079]

[0080] Where A(ω) and X(ω) are the Fourier transforms of the axle box acceleration a(t) and vertical displacement x(t), respectively, and x0 and v0 are the initial displacement and initial velocity, respectively;

[0081] When both x0 and v0 are 0, the vertical irregularity data x of the full-band track can be calculated using the inverse Fourier transform of the above formula:

[0082] x = F -1 [X(ω)]

[0083] Figure 6 is a specific example of a method for evaluating the vertical irregularity of a track across the entire band according to an embodiment of the present invention. It shows the vertical irregularity of a medium-to-long-wave track obtained by integration calculation in a uniform speed section. The horizontal axis represents the travel distance of the high-speed train, i.e., the distance between the high-speed train and the starting point, expressed in kilometers. The vertical axis represents the displacement of the high-speed train in the vertical direction, which can also be considered as the length of the wheelset from the ground in the vertical direction.

[0084] Figure 7 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention. It shows the power spectrum of the vertical irregularity of a medium- and long-wave track obtained by integral calculation in the case of a uniform velocity section. The horizontal axis represents the frequency after the displacement is Fourier transformed, that is, the number of sine waves contained within a one-meter distance, and the vertical axis represents the energy density.

[0085] Figure 8 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention. It shows the vertical irregularity of a medium- and long-wave track obtained by integration calculation in the case of variable speed sections. The horizontal axis represents the travel distance of the high-speed train, i.e., the distance between the high-speed train and the starting point, expressed in kilometers. The vertical axis represents the displacement of the high-speed train in the vertical direction, which can also be considered as the length of the wheelset from the ground in the vertical direction.

[0086] Figure 9 is a specific example of a method for evaluating the vertical irregularity of a full-band track in an embodiment of the present invention. It shows the power spectrum of the vertical irregularity of a medium- and long-wave track obtained by integral calculation in the variable speed section. The horizontal axis represents the frequency after the displacement is Fourier transformed, that is, the number of sine waves contained within a one-meter distance, and the vertical axis represents the energy density.

[0087] Figure 10 is a specific example of a method for evaluating the vertical irregularity of a shortwave track according to an embodiment of the present invention, showing the vertical irregularity of a shortwave track and its spatial spectrum. Sub-Figure 2 shows the spectrum corresponding to the data after spatial sampling processing.

[0088] In step 104, the vertical irregularity data of the track in different bands are converted into full-band track irregularity data through spatial sampling processing. The full-band track irregularity data is then compared with the preset evaluation criteria for full-band track irregularity to determine whether there are any defects in the full-band track irregularity.

[0089] In one embodiment, vertical orbital irregularity data from different bands are converted into full-band orbital irregularity data through spatial sampling processing. The full-band orbital irregularity data is then compared with a preset evaluation standard for full-band orbital irregularity to determine whether defects exist in the full-band orbital irregularity, including:

[0090] Based on the mileage data of high-speed trains, the vertical track irregularities of different bands are grouped in order, with single-digit meters as the unit.

[0091] The vertical irregularity data of the track within the same group are then sampled again, divided equally by meter.

[0092] The maximum displacement value corresponding to the sampling point within the same group is determined as the track irregularity data of that group. The maximum displacement values ​​corresponding to the sampling points of different groups constitute the full-band track irregularity data of spatial sampling processing.

[0093] Determine whether the track irregularity data across the entire band exceeds the value given in the preset evaluation criteria; the preset evaluation criteria include dynamic detection and evaluation rules for track geometry and maintenance rules for ballastless track lines;

[0094] If so, it confirms that the entire band track is not smooth and has defects.

[0095] The original axle box acceleration data is time-sampled data, and the amount of sampled data per meter of distance is huge, especially the medium and long wave data. The data storage, data viewing and correspondence with field data are quite cumbersome. It is necessary to convert the time-sampled data into spatial-sampled data to become sparse data in order to meet the requirements.

[0096] In this example, the process of converting temporal sampled data into spatial sampled data can be described as follows:

[0097] Set the number of digits in meters in the mileage data of the time sampling data to a single digit;

[0098] Take out the data with the same integer part in several batches, and record its length as N;

[0099] Divide the retrieved data into c groups in order;

[0100] Set the decimal part of the mileage data for each group of data to...

[0101] Remove duplicate mileage data;

[0102] The final spatial sampling of track irregularity data is selected from the data corresponding to the maximum displacement in each group of data.

[0103] After obtaining the spatially sampled track irregularity data, the track irregularity defects across the entire band are assessed according to the dynamic detection and evaluation of the geometric state of high-speed railway tracks and the maintenance rules for high-speed railway ballastless track lines. If the sampled data exceeds the standard given value, a defect is determined to exist on site. Figure 11 is a specific example diagram of a method for evaluating the vertical irregularity state of a track across the entire band in an embodiment of the present invention, showing the comparison results between the calculation results and the geometric system and static data; Figure 12 is a specific example diagram of a method for evaluating the vertical irregularity state of a track across the entire band in an embodiment of the present invention, showing the defects found on a certain line due to elevation irregularities detected based on the calculation results.

[0104] This invention also provides a full-band track vertical irregularity evaluation device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to the full-band track vertical irregularity evaluation method, the implementation of this device can refer to the implementation of the full-band track vertical irregularity evaluation method, and repeated details will not be elaborated further. Figure 13 is a specific example diagram of a full-band track vertical irregularity evaluation device according to an embodiment of this invention. As shown in Figure 13, the device includes:

[0105] The data detection module 1301 is used to obtain axle box acceleration data of high-speed trains collected by the multi-section vehicle dynamic detection system, which is installed on the high-speed train.

[0106] The segmented filtering processing module 1302 is used to divide the axle box acceleration data into uniform speed segment data and variable speed segment data, and to perform segmented filtering processing on the uniform speed segment data and variable speed segment data respectively.

[0107] The data calculation module 1303 is used to perform a second integral transformation on the axle box acceleration data after segmented filtering to calculate the vertical irregularity data of the track in different bands.

[0108] The data judgment module 1304 is used to convert the vertical irregularity data of the track in different bands into the full-band track irregularity data of spatial sampling processing, compare the full-band track irregularity data with the preset judgment standard of full-band track irregularity, and determine whether there are defects in the full-band track irregularity.

[0109] In one embodiment, the multi-section vehicle dynamic detection system uses multi-channel distributed networked testing technology to remotely control the testing equipment and collect the axle box acceleration of the high-speed train in real time.

[0110] Furthermore, the segmented filtering module 1302 is specifically used for:

[0111] For data in uniform speed sections, fast Fourier transform and inverse transform are used for filtering.

[0112] For data in the variable speed range, fractional Fourier transform and inverse transform are used for filtering.

[0113] Furthermore, the data calculation module 1303 is specifically used for:

[0114] Calculate the vertical displacement based on the axle box acceleration data after segmented filtering;

[0115] The vertical displacement was subjected to Fourier transform and inverse transform to obtain orbital vertical irregularity data for different wavebands.

[0116] Furthermore, the data judgment module 1304 is specifically used for:

[0117] Based on the mileage data of high-speed trains, the vertical track irregularities of different bands are grouped in order, with single-digit meters as the unit.

[0118] The vertical irregularity data of the track within the same group are then sampled again, divided equally by meter.

[0119] The maximum displacement value corresponding to the sampling point within the same group is determined as the track irregularity data of that group. The maximum displacement values ​​corresponding to the sampling points of different groups constitute the full-band track irregularity data of spatial sampling processing.

[0120] Determine whether the track irregularity data across the entire band exceeds the value given in the preset evaluation criteria; the preset evaluation criteria include dynamic detection and evaluation rules for track geometry and maintenance rules for ballastless track lines;

[0121] If so, it confirms that the entire band track is not smooth and has defects.

[0122] This invention provides an embodiment of a computer device for implementing all or part of the above-described full-band orbital vertical irregularity evaluation method. The computer device specifically includes the following components:

[0123] The computer device comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between related devices; the computer device can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the computer device can be implemented with reference to the embodiments for implementing the method for evaluating the vertical irregularity of a full-band track and the embodiments for implementing the device for evaluating the vertical irregularity of a full-band track, the contents of which are incorporated herein, and repeated parts will not be described again.

[0124] Figure 14 is a schematic block diagram of the system configuration of a computer device 1400 according to an embodiment of this application. As shown in Figure 14, the computer device 1400 may include a central processing unit 1401 and a memory 1402; the memory 1402 is coupled to the central processing unit 1401. It is worth noting that Figure 14 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunications functions or other functions.

[0125] In one embodiment, the full-band orbital vertical irregularity assessment function can be integrated into the central processing unit 1401. The central processing unit 1401 can be configured to perform the following control:

[0126] The axle box acceleration data of a high-speed train is obtained from a multi-section vehicle dynamic detection system, which is installed on the high-speed train.

[0127] The axle box acceleration data is divided into uniform speed segment data and variable speed segment data, and segmented filtering processing is performed on the uniform speed segment data and variable speed segment data respectively.

[0128] The axle box acceleration data after segmented filtering is subjected to a second integral transform to calculate the vertical track irregularity data for different bands.

[0129] The vertical irregularity data of the orbit in different bands is converted into full-band orbit irregularity data through spatial sampling. The full-band orbit irregularity data is then compared with the preset evaluation criteria for full-band orbit irregularity to determine whether there are any defects in the full-band orbit irregularity.

[0130] In another embodiment, the full-band track vertical irregularity status evaluation device can be configured separately from the central processing unit 1401. For example, the full-band track vertical irregularity status evaluation device can be configured as a chip connected to the central processing unit 1401, and the full-band track vertical irregularity status evaluation function can be realized through the control of the central processing unit.

[0131] As shown in Figure 14, the computer device 1400 may further include: a communication module 1403, an input unit 1404, an audio processor 1405, a display 1406, and a power supply 1407. It is worth noting that the computer device 1400 does not necessarily include all the components shown in Figure 14; furthermore, the computer device 1400 may also include components not shown in Figure 14, as can be found in existing technologies.

[0132] As shown in Figure 14, the central processing unit 1401, sometimes also referred to as a controller or operation control, may include a microprocessor or other processor device and / or logic device. The central processing unit 1401 receives input and controls the operation of various components of the computer device 1000.

[0133] The memory 1402 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 1401 may execute the program stored in the memory 1402 to perform information storage or processing, etc.

[0134] Input unit 1404 provides input to central processing unit 1401. This input unit 1404 may be, for example, a keypad or touch input device. Power supply 1407 provides power to computer device 1400. Display 1406 displays images and text. This display may be, for example, an LCD display, but is not limited thereto.

[0135] The memory 1402 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 1402 can also be some other type of device. The memory 1402 includes a buffer memory 1421 (sometimes referred to as a buffer). The memory 1402 may include an application / function storage unit 1422 for storing application programs and function programs or processes for executing the operation of the computer device 1400 via the central processing unit 1401.

[0136] The memory 1402 may also include a data storage unit 1423 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 1424 of the memory 1402 may include various drivers for the computer device for communication functions and / or for performing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0137] The communication module 1403 is a transmitter / receiver 1403 that transmits and receives signals via the antenna 1408. The communication module (transmitter / receiver) 1403 is coupled to the central processing unit 1401 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0138] Based on different communication technologies, multiple communication modules 1403 can be configured in the same computer device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 1403 is also coupled to a speaker 1409 and a microphone 1410 via an audio processor 1405 to provide audio output via the speaker 1409 and receive audio input from the microphone 1410, thereby realizing typical telecommunications functions. The audio processor 1405 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 1405 is also coupled to a central processing unit 1401, enabling on-device recording via the microphone 1410 and on-device playback of stored sound via the speaker 1409.

[0139] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for evaluating the vertical irregularity of a full-band track.

[0140] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for evaluating the vertical irregularity of a full-band track.

[0141] In this embodiment of the invention, axle box acceleration data of a high-speed train is obtained from a multi-section vehicle dynamic detection system installed on the high-speed train. The axle box acceleration data is divided into uniform speed segment data and variable speed segment data, and segmented filtering is performed on both. The segmented filtered axle box acceleration data is then subjected to a second integral transform to calculate the vertical track irregularity data for different bands. The vertical track irregularity data for different bands is converted into full-band track irregularity data after spatial sampling. The full-band track irregularity data is compared with a preset evaluation standard for full-band track irregularity to determine whether there are defects in the full-band track irregularity. Compared with the existing technology of using an inertial reference frame combined with an optical system for dynamic track geometry detection in high-speed trains, this invention can shorten the detection cycle and obtain the vertical track irregularity status more timely and accurately. This invention can meet the requirements for online monitoring of track geometry using onboard equipment under high-speed conditions, as well as the condition monitoring and timely maintenance of railways, and helps to improve the efficiency of track inspection and maintenance.

[0142] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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, etc.) containing computer-usable program code.

[0143] This 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 block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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 one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0144] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0145] 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 one or more flowcharts and / or one or more block diagrams.

[0146] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the vertical irregularities of a full-band orbit, characterized in that, include: The axle box acceleration data of a high-speed train is obtained from a multi-section vehicle dynamic detection system, which is installed on the high-speed train. The axle box acceleration data is divided into uniform speed segment data and variable speed segment data, and segmented filtering is performed on the uniform speed segment data and variable speed segment data respectively. The segmented filtered axle box acceleration data is then subjected to a second integral transform to calculate the track vertical irregularity data of different bands. The track vertical irregularity data of different bands is converted into spatially sampled full-band track irregularity data, and the full-band track irregularity data is compared with the preset evaluation criteria for full-band track irregularity to determine whether there are defects in the full-band track irregularity. The process of converting the track vertical irregularity data of different bands into spatially sampled full-band track irregularity data and comparing the full-band track irregularity data with the preset evaluation criteria for full-band track irregularity to determine whether there are defects in the full-band track irregularity includes: grouping the track vertical irregularity data of different bands into single-digit meters in sequence according to the mileage data of high-speed trains. The vertical track irregularity data within the same group are then sampled evenly by meter. The maximum displacement value corresponding to the sampling point within the same group is determined as the track irregularity data for that group. The maximum displacement values ​​corresponding to the sampling points in different groups constitute the full-band track irregularity data for spatial sampling processing. It is then determined whether the full-band track irregularity data exceeds the value given in the preset evaluation criteria. The preset evaluation criteria include the dynamic detection and evaluation rules for track geometry and the maintenance rules for ballastless track lines. If so, it is determined that there is a defect in the full-band track irregularity.

2. The method as described in claim 1, characterized in that, The multi-section vehicle dynamic detection system uses multi-channel distributed networked testing technology to remotely control the testing equipment and collect the axle box acceleration of high-speed trains in real time.

3. The method as described in claim 1, characterized in that, The data in the uniform speed section and the data in the variable speed section are processed by segmented filtering, including: for the data in the uniform speed section, fast Fourier transform and inverse transform are used for filtering; for the data in the variable speed section, fractional Fourier transform and inverse transform are used for filtering.

4. The method as described in claim 1, characterized in that, The axle box acceleration data after segmented filtering is subjected to a second integral transform to calculate the vertical track irregularity data for different bands, including: calculating the vertical displacement based on the segmented filtered axle box acceleration data; and performing Fourier transform and inverse transform on the vertical displacement to obtain the vertical track irregularity data for different bands.

5. A device for evaluating the vertical irregularities of a track across all wavelengths, characterized in that, include: The data detection module is used to obtain axle box acceleration data of high-speed trains collected by the multi-section vehicle dynamic detection system, which is installed on the high-speed train. The segmented filtering module is used to divide the axle box acceleration data into uniform speed segment data and variable speed segment data, and to perform segmented filtering processing on the uniform speed segment data and variable speed segment data respectively. The data calculation module is used to perform a second integral transformation on the segmented filtered axle box acceleration data to calculate the vertical track irregularities in different bands. The data judgment module is used to convert vertical track irregularity data from different bands into spatially sampled full-band track irregularity data, compare the full-band track irregularity data with a preset evaluation standard for full-band track irregularity, and determine whether there are defects in the full-band track irregularity. Specifically, the data judgment module is used to: group the vertical track irregularity data from different bands into single-digit meters based on the mileage data of high-speed trains; further sample the vertical track irregularity data within the same group by dividing it into evenly distributed units of meters; determine the maximum displacement value corresponding to the sampling point within the same group as the track irregularity data of that group, and the maximum displacement values ​​corresponding to the sampling points of different groups constitute the spatially sampled full-band track irregularity data; determine whether the full-band track irregularity data exceeds the value given in the preset evaluation standard; the preset evaluation standard includes dynamic detection and evaluation rules for track geometry and maintenance rules for ballastless track lines; if so, it is determined that there are defects in the full-band track irregularity.

6. The apparatus as claimed in claim 5, characterized in that, The multi-section vehicle dynamic detection system uses multi-channel distributed networked testing technology to remotely control the testing equipment and collect the axle box acceleration of high-speed trains in real time.

7. The apparatus as claimed in claim 5, characterized in that, The segmented filtering module is specifically used for: filtering data in uniform speed segments using fast Fourier transform and inverse transform; and filtering data in variable speed segments using fractional Fourier transform and inverse transform.

8. The apparatus as claimed in claim 5, characterized in that, The data calculation module is specifically used to: calculate the vertical displacement based on the axle box acceleration data after segmented filtering; and perform Fourier transform and inverse transform on the vertical displacement to obtain track vertical irregularity data for different bands.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 4.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 4.

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