Segment track state evaluation method and device

Abstract

The application discloses a kind of section track state evaluation method and device, it is related to high-speed railway engineering technical field, wherein the method includes: to the frame lateral displacement signal of motor train unit collected in detecting track section, the lateral acceleration signal of motor train unit is carried out EEMD processing, obtains the multiple components of frame lateral displacement signal, the multiple components of car body lateral acceleration signal;According to the first proportion value of the energy of each component of frame lateral displacement signal and the total energy of frame lateral displacement signal, determine the first vibration main frequency of frame lateral displacement signal, according to the second proportion value of the energy of each component of car body lateral acceleration signal and the total energy of car body lateral acceleration signal, determine the second vibration main frequency of car body lateral acceleration signal;According to first vibration main frequency, second vibration main frequency, preset frequency distribution range, determine the track state of detection track section.The application can improve the accuracy of section track state evaluation.

Description

Technical Field

[0001] This invention relates to the field of high-speed railway engineering technology, and in particular to a method and apparatus for evaluating the condition of track sections. 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 track system provides support and guidance for high-speed trains, but with increasing service time, its condition inevitably deteriorates, affecting the motion performance of the vehicle-track coupling system. Currently, my country's high-speed lines handle a massive volume of passenger transport services. Under continuous operation, wheels and rails come into contact and wear on each other, leading to changes in the shape of the wheel treads and rail profiles, affecting wheel-rail matching. When wheel-rail matching is poor, such as when the equivalent cone between the wheel and rail is too small or too large, the train will experience lateral instability, manifesting as low-frequency "swaying" or medium-to-high-frequency "shaking."

[0004] Because low-frequency periodic swaying directly affects passenger comfort, it has gradually become a research hotspot in recent years. A survey of domestic and international research reveals that current diagnostic methods for low-frequency periodic swaying focus on the lateral acceleration of the train body, determining whether its amplitude exceeds a certain limit and whether the signal exhibits strong periodicity. These diagnostic methods are designed based on the fact that the lateral acceleration of the train body exhibits periodic characteristics when low-frequency periodic swaying occurs, while it exhibits random vibration characteristics during normal operation, showing a significant difference. Further research has revealed that the frequency of low-frequency periodic swaying is specific, identical to the inherent upper center rolling frequency of the train body. Some scholars believe this characteristic is another key factor in evaluating low-frequency periodic swaying; therefore, some studies have added the judgment of the dominant vibration frequency to the quantitative description of signal periodicity. Currently, the description and evaluation of this characteristic are based on the theoretical calculation formula of the inherent upper center rolling frequency of the train body derived from the Newton-Euler equations. However, the theoretical formula for calculating the inherent roll-slip frequency of the car body is derived under undamped conditions, while damping exists during actual operation. In real-world operating conditions, the train's state changes, and some technical parameters differ from the original design parameters. Therefore, the results obtained using the theoretical formula will differ from the actual roll-slip frequency under real-world conditions. Furthermore, the theoretical formula involves a large number of technical parameters—seven in total. This requires mastering these parameters to obtain reliable results, but currently, these parameters are relatively confidential, making numerical acquisition difficult. Moreover, differences in technical parameters between different car models prevent cross-estimation, thus making it difficult to meet the needs of practical engineering applications.

[0005] In summary, it can be found that there are two problems with the theoretical value of the inherent upper center rolling frequency of the car body calculated by the theoretical value calculation formula: (1) The theoretical value calculation formula is derived under the assumption that there is no damping during the operation of the EMU, which is different from the actual operation; and some technical parameters will change with operation, so the theoretical calculation value obtained is different from the actual value, which reduces the accuracy of the evaluation process; (2) It is difficult to obtain the technical parameters involved in the theoretical value calculation formula, and there are differences in the technical parameters between different models. Therefore, it is technically difficult to evaluate the current EMU car body position vibration main frequency by using the theoretical inherent upper center rolling frequency of the car body in the actual application process. Summary of the Invention

[0006] This invention provides a method for evaluating the condition of a track section, which improves the accuracy of track section condition evaluation and allows for evaluation with less data. The method includes:

[0007] Ensemble empirical mode decomposition (EEMD) was performed on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section to obtain multiple components of the lateral displacement signal of the frame and the lateral acceleration signal of the train body.

[0008] Calculate the first ratio of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame, and the second ratio of the energy of each component of the lateral acceleration signal of the vehicle body to the total energy of the lateral acceleration signal of the vehicle body;

[0009] The first proportional value of each component of the lateral displacement signal of the frame is sorted, and the second proportional value of each component of the lateral acceleration signal of the vehicle body is sorted.

[0010] The first ratio value within the preset sorting range is summed to obtain the third ratio value, and the second ratio value within the preset sorting range is summed to obtain the fourth ratio value.

[0011] When both the third and fourth proportional values ​​are greater than the preset proportional values, the detection track section is marked;

[0012] When the track section is marked, the components corresponding to the first proportional value in the frame lateral displacement signal within the preset sorting range are added together to obtain the first intermediate component, and the components corresponding to the second proportional value in the vehicle body lateral acceleration signal within the preset sorting range are added together to obtain the second intermediate component.

[0013] The first dominant vibration frequency of the lateral displacement signal of the frame is determined based on the first intermediate component, and the second dominant vibration frequency of the lateral acceleration signal of the vehicle body is determined based on the second intermediate component.

[0014] The track condition of the detection track section is determined based on the first dominant vibration frequency, the second dominant vibration frequency, and the preset frequency distribution range.

[0015] This invention also provides a section track condition evaluation device to reduce the evaluation error of section track condition, enabling the evaluation of section track condition with less data. The device includes:

[0016] The first processing module is used to perform ensemble empirical mode decomposition (EEMD) on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section, so as to obtain multiple components of the lateral displacement signal of the frame and multiple components of the lateral acceleration signal of the train body.

[0017] The proportional value determination module is used to calculate the first proportional value of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame, and the second proportional value of the energy of each component of the lateral acceleration signal of the vehicle body to the total energy of the lateral acceleration signal of the vehicle body.

[0018] The sorting module is used to sort the first proportional value of each component of the frame lateral displacement signal and the second proportional value of each component of the vehicle body lateral acceleration signal.

[0019] The second processing module is used to sum the first ratio values ​​within the preset sorting range to obtain the third ratio value, and to sum the second ratio values ​​within the preset sorting range to obtain the fourth ratio value.

[0020] The marking module is used to mark the detected track section when both the third and fourth proportional values ​​are greater than the preset proportional values;

[0021] The third processing module is used to add the components corresponding to the first proportional value in the frame lateral displacement signal within the preset sorting range to obtain the first intermediate component when the track section is marked, and to add the components corresponding to the second proportional value in the vehicle body lateral acceleration signal within the preset sorting range to obtain the second intermediate component.

[0022] The vibration main frequency determination module is used to determine the first vibration main frequency of the frame lateral displacement signal based on the first intermediate component, and to determine the second vibration main frequency of the vehicle body lateral acceleration signal based on the second intermediate component.

[0023] The fourth processing module is used to determine the track status of the detection track section based on the first vibration main frequency, the second vibration main frequency, and the preset frequency distribution range.

[0024] 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 section track state evaluation method.

[0025] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described segment track state evaluation method.

[0026] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described section track state evaluation method.

[0027] In this embodiment of the invention, ensemble empirical mode decomposition (EEMD) is performed on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section to obtain multiple components of the lateral displacement signal of the train frame and multiple components of the lateral acceleration signal of the train body. A first ratio of the energy of each component of the lateral displacement signal of the train frame to the total energy of the lateral displacement signal of the train frame, and a second ratio of the energy of each component of the lateral acceleration signal of the train body to the total energy of the lateral acceleration signal of the train body are calculated. The first ratio values ​​of each component of the lateral displacement signal of the train frame are sorted, and the second ratio values ​​of each component of the lateral acceleration signal of the train body are sorted. The first ratio values ​​within a preset sorting range are summed to obtain a third ratio value, which is then used to calculate a third ratio value. The second proportional value within the sorting range is summed to obtain the fourth proportional value. When both the third and fourth proportional values ​​are greater than the preset proportional value, the detected track section is marked. When the detected track section is marked, the components of the frame lateral displacement signal corresponding to the first proportional value within the preset sorting range are summed to obtain the first intermediate component. The components of the car body lateral acceleration signal corresponding to the second proportional value within the preset sorting range are summed to obtain the second intermediate component. The first vibration dominant frequency of the frame lateral displacement signal is determined based on the first intermediate component, and the second vibration dominant frequency of the car body lateral acceleration signal is determined based on the second intermediate component. Based on the first vibration dominant frequency, the second vibration dominant frequency, and the preset frequency distribution range, it is determined whether the detected track section is a track defect section. This invention does not require the calculation of theoretical values. It only needs to obtain the frame lateral displacement signal and the car body lateral acceleration signal of the EMU for data analysis and judgment to achieve the evaluation of the track condition of the section. It requires less data than the prior art and improves the accuracy of the evaluation of the track condition of the section. Attached Figure Description

[0028] 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:

[0029] Figure 1 This is a flowchart of a section track condition evaluation method provided in an embodiment of the present invention;

[0030] Figure 2 This is a flowchart of a method for calculating a first proportional value and a second proportional value provided in an embodiment of the present invention;

[0031] Figure 3This is an example diagram of the original waveform of the lateral displacement signal of a low-frequency periodic swaying track section structure provided in an embodiment of the present invention;

[0032] Figure 4 This is an example diagram of the original waveform of the lateral acceleration signal of the vehicle body corresponding to a low-frequency periodic swaying track section provided in an embodiment of the present invention;

[0033] Figure 5 This is an example diagram showing the power spectral density analysis results of a lateral displacement signal of a structure provided in an embodiment of the present invention;

[0034] Figure 6 This is an example diagram showing the power spectral density analysis results of a vehicle body lateral acceleration signal provided in an embodiment of the present invention;

[0035] Figure 7 This is an example diagram of the EEMD results of a low-frequency periodic swaying track section of the present invention.

[0036] Figure 8 This is an example diagram of the EEMD results of a low-frequency periodic swaying track section frame structure provided in an embodiment of the present invention;

[0037] Figure 9 This is an example diagram illustrating the ratio of the energy of each component of the lateral acceleration signal EEMD obtained from the vehicle body to the total signal energy in an embodiment of the present invention.

[0038] Figure 10 This is an example diagram illustrating the ratio of the energy of each component of the lateral displacement signal EEMD obtained from the structure to the total signal energy in an embodiment of the present invention.

[0039] Figure 11 This is a schematic diagram of a section track condition evaluation device provided in an embodiment of the present invention;

[0040] Figure 12 This is a schematic diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0041] 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.

[0042] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0043] 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.

[0044] 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.

[0045] Research has revealed that current diagnostic methods for low-frequency periodic train swaying focus on the lateral acceleration of the train body, assessing whether its amplitude exceeds a certain limit and whether the signal exhibits strong periodicity. These diagnostic methods are designed based on the fact that the lateral acceleration of the train body exhibits periodic characteristics during low-frequency periodic swaying, while it exhibits random vibration characteristics during normal operation, showing a significant difference. Further research has revealed that the frequency of motion during low-frequency periodic swaying is specific, identical to the inherent upper center rolling frequency of the train body. Some scholars believe this characteristic is another key factor in evaluating low-frequency periodic swaying; therefore, some studies have added the judgment of the dominant vibration frequency to the quantitative description of signal periodicity. Currently, the description and evaluation of this characteristic rely on the theoretical calculation formula for the inherent upper center rolling frequency of the train body derived from the Newton-Euler equations. However, the theoretically derived formula for the inherent upper center rolling frequency is obtained under undamped conditions, while damping exists during actual operation. Under real-world operating conditions, the state of the EMU (Electric Multiple Unit) changes, and some technical parameters differ from the original design parameters. Therefore, the results obtained using theoretical calculation formulas will differ from the actual roll frequency of the car body under real-world conditions. Furthermore, the theoretical calculation formulas involve a large number of technical parameters—seven in total. This requires mastering these parameters to obtain reliable results, but currently, these parameters are relatively confidential, making numerical acquisition difficult. Moreover, differences in technical parameters between different EMU models prevent cross-estimation, thus making it difficult to meet the needs of practical engineering applications.

[0046] In summary, it can be found that there are two problems with the theoretical value of the inherent upper center rolling frequency of the car body calculated by the theoretical value calculation formula: (1) The theoretical value calculation formula is derived under the assumption that there is no damping during the operation of the EMU, which is different from the actual operation; and some technical parameters will change with operation, so the theoretical calculation value obtained is different from the actual value, which reduces the accuracy of the evaluation process; (2) It is difficult to obtain the technical parameters involved in the theoretical value calculation formula, and there are differences in the technical parameters between different models. Therefore, it is technically difficult to evaluate the current EMU car body position vibration main frequency by using the theoretical inherent upper center rolling frequency of the car body in the actual application process.

[0047] In response to the above research, embodiments of the present invention provide a method for evaluating the track condition of a section, such as... Figure 1 As shown, it includes:

[0048] S101: Perform ensemble empirical mode decomposition (EEMD) on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section to obtain multiple components of the lateral displacement signal of the frame and multiple components of the lateral acceleration signal of the train body.

[0049] S102: Calculate the first ratio of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame, and the second ratio of the energy of each component of the lateral acceleration signal of the vehicle body to the total energy of the lateral acceleration signal of the vehicle body;

[0050] S103: Sort the first proportional value of each component of the frame lateral displacement signal and sort the second proportional value of each component of the vehicle body lateral acceleration signal.

[0051] S104: Summing the first ratio values ​​within the preset sorting range to obtain the third ratio value, and summing the second ratio values ​​within the preset sorting range to obtain the fourth ratio value;

[0052] S105: When both the third and fourth proportional values ​​are greater than the preset proportional values, the detection track section is marked;

[0053] S106: When the track section is marked, the components corresponding to the first proportional value in the frame lateral displacement signal within the preset sorting range are added together to obtain the first intermediate component, and the components corresponding to the second proportional value in the vehicle body lateral acceleration signal within the preset sorting range are added together to obtain the second intermediate component.

[0054] S107: Determine the first dominant vibration frequency of the lateral displacement signal of the frame based on the first intermediate component, and determine the second dominant vibration frequency of the lateral acceleration signal of the vehicle body based on the second intermediate component.

[0055] S108: Determine the track status of the detection track section based on the first dominant vibration frequency, the second dominant vibration frequency, and the preset frequency distribution range.

[0056] In this embodiment of the invention, ensemble empirical mode decomposition (EEMD) is performed on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section to obtain multiple components of the lateral displacement signal of the train frame and multiple components of the lateral acceleration signal of the train body. A first ratio of the energy of each component of the lateral displacement signal of the train frame to the total energy of the lateral displacement signal of the train frame, and a second ratio of the energy of each component of the lateral acceleration signal of the train body to the total energy of the lateral acceleration signal of the train body are calculated. The first ratio values ​​of each component of the lateral displacement signal of the train frame are sorted, and the second ratio values ​​of each component of the lateral acceleration signal of the train body are sorted. The first ratio values ​​within a preset sorting range are summed to obtain a third ratio value, which is then used to calculate a third ratio value. The second proportional value within the sorting range is summed to obtain the fourth proportional value. When both the third and fourth proportional values ​​are greater than the preset proportional value, the detected track section is marked. When the detected track section is marked, the components of the frame lateral displacement signal corresponding to the first proportional value within the preset sorting range are summed to obtain the first intermediate component. The components of the car body lateral acceleration signal corresponding to the second proportional value within the preset sorting range are summed to obtain the second intermediate component. The first vibration dominant frequency of the frame lateral displacement signal is determined based on the first intermediate component, and the second vibration dominant frequency of the car body lateral acceleration signal is determined based on the second intermediate component. Based on the first vibration dominant frequency, the second vibration dominant frequency, and the preset frequency distribution range, it is determined whether the detected track section is a track defect section. This invention does not require the calculation of theoretical values. It only needs to obtain the frame lateral displacement signal and the car body lateral acceleration signal of the EMU for data analysis and judgment to achieve the evaluation of the track condition of the section. It requires less data than the prior art and improves the accuracy of the evaluation of the track condition of the section.

[0057] The method for evaluating the track condition of the above-mentioned sections will be explained in detail below.

[0058] Regarding the above S101, considering that both the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body are non-steady-state signals with a high degree of nonlinearity, in order to improve the accuracy of data processing and extraction of the vibration main frequency, for example, time-frequency analysis methods can be used: Empirical Mode Decomposition (EEMD) can be used to obtain the vibration main frequency of the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body.

[0059] In addition to EEMD, other time-frequency analysis methods that can adaptively decompose signals according to frequency can also be used here.

[0060] In addition, in order to further improve the accuracy and efficiency of the track condition evaluation results of the section, in one embodiment of the present invention, before performing EEMD on the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body, the method further includes: performing a filtering operation on the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body collected in the detection track section to filter out the low-frequency trend term and high-frequency elastic vibration information generated by passing through the curve in the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body.

[0061] In one embodiment of the present invention, the filtering operation includes, for example, any of the following: a filtering operation based on Fourier transform, or a filtering operation based on inverse Fourier transform.

[0062] Specifically, for example, the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body can be filtered by 0.1 to 20 Hz to remove the low-frequency trend terms and high-frequency elastic vibration information caused by the curve.

[0063] Regarding the above S102, such as Figure 2 The diagram shown is a flowchart of a method for calculating a first proportional value and a second proportional value according to an embodiment of the present invention, including:

[0064] S201: Based on the standard deviation of each component of the lateral displacement signal of the frame and the standard deviation of the lateral displacement signal of the frame, the energy of each component of the lateral displacement signal of the frame and the total energy of the lateral displacement signal of the frame are obtained.

[0065] S202: Based on the standard deviation of each component of the vehicle body lateral acceleration signal and the standard deviation of the vehicle body lateral acceleration signal, obtain the energy of each component of the vehicle body lateral acceleration signal and the total energy of the vehicle body lateral acceleration signal.

[0066] S203: Calculate the first ratio of the energy of each component of the lateral displacement signal to the total energy of the lateral displacement signal based on the energy of each component of the lateral displacement signal and the total energy of the lateral displacement signal.

[0067] For example, if the energy of each component of the lateral displacement signal of the frame is p1, p2, p3, p4, and the total energy P of the lateral displacement signal of the frame is calculated, then the first proportional value is calculated. First proportional value First proportional value and the first ratio value

[0068] S204: Based on the energy of each component of the vehicle's lateral acceleration signal and the total energy of the vehicle's lateral acceleration signal, calculate the second ratio of the energy of each component of the vehicle's lateral acceleration signal to the total energy of the vehicle's lateral acceleration signal.

[0069] For example, the energy of each component of the vehicle body lateral acceleration signal is q1, q2, q3, q4, and the total energy Q of the frame lateral displacement signal is calculated as follows: Second proportional value Second proportional value Second proportional value and the second ratio value

[0070] For the above S103 to S104, when sorting the first ratio value, it can be sorted in descending order or ascending order, and the preset sorting range is set according to the sorting method.

[0071] For example, if the first ratio value is sorted in descending order, the preset sorting range can be the first two digits; if the first ratio value is sorted in ascending order, the sorting range can be the last two digits.

[0072] Furthermore, the method for sorting the second ratio value and determining the preset sorting range is similar to that for the first ratio value, and the repetitive parts will not be repeated.

[0073] Regarding S105 above, when both the third and fourth proportional values ​​are greater than the preset proportional values, the detection track segment may be a problem segment. Therefore, it is necessary to mark the detection track segment for further judgment.

[0074] In addition, the range of the preset ratio value can be set according to the actual application scenario. In one embodiment of the present invention, the preset ratio value can be, for example, seventy percent.

[0075] Regarding the above S106, it is necessary to add the two components with the larger energy proportion obtained from the EEMD of potentially problematic sections and then perform power spectrum analysis. Therefore, when the track section is marked, the components corresponding to the first proportion value in the frame lateral displacement signal within the preset sorting range are added to obtain the first intermediate component, and the components corresponding to the second proportion value in the vehicle body lateral acceleration signal within the preset sorting range are added to obtain the second intermediate component.

[0076] Regarding S017 above, determining the first dominant vibration frequency of the lateral displacement signal of the frame based on the first intermediate component, and determining the second dominant vibration frequency of the lateral acceleration signal of the vehicle body based on the second intermediate component, for example includes: performing power spectrum analysis on the first intermediate component and determining the frequency corresponding to the position of the maximum power spectral density value of the first intermediate component as the first dominant vibration frequency of the lateral displacement signal of the frame; performing power spectrum analysis on the second intermediate component and determining the frequency corresponding to the position of the maximum power spectral density value of the second intermediate component as the second dominant vibration frequency of the lateral acceleration signal of the vehicle body.

[0077] Regarding S108 above, in one embodiment of the present invention, determining the track state of the detection track segment based on the first dominant vibration frequency, the second dominant vibration frequency, and a preset frequency distribution range includes, for example:

[0078] A: When the first dominant vibration frequency exceeds the preset frequency distribution range or the second dominant vibration frequency exceeds the preset frequency distribution range, determine that the detected track section is not a defective track section.

[0079] B: When the first dominant vibration frequency does not exceed the preset frequency distribution range and the second dominant vibration frequency also does not exceed the preset frequency distribution range, the larger of the first dominant vibration frequency and the second dominant vibration frequency is taken as the first value, and the smaller one is taken as the second value. For example: Where F1 is the first value, F2 is the second value, and F... Pc F is the first dominant vibration frequency. Pb The second dominant vibration frequency. When the first and second values ​​satisfy the following formula, the track section to be tested is determined to be a track defect section; when the first and second values ​​do not satisfy the following formula, the track section to be tested is determined not to be a track defect section: F1-F2≤2%F1, where F1 is the first value and F2 is the second value.

[0080] Here, the preset frequency distribution range is determined by the following method before performing ensemble empirical mode decomposition (EEMD) on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected from the detection track section: based on the relationship between the forward bogie swaying frequency, the rear bogie swaying frequency, the frequency at which the front and rear bogies of the train body are forced to perform upper-center rolling swaying motion during low-frequency periodic swaying, the theoretical inherent upper-center rolling swaying frequency of the train body, and the vibration frequency distribution range during historical low-frequency periodic swaying, the vibration frequency distribution range during historical low-frequency periodic swaying is determined as the preset distribution range.

[0081] Specifically, to better realize the engineering application of low-frequency periodic sway diagnosis, the causes of motion during low-frequency periodic sway are analyzed before designing the track condition evaluation method for a section: Due to the small equivalent cone of the wheel-rail matching system formed by the matching of rails and wheels, the bogies exhibit continuous and large-amplitude periodic motion. Under certain special conditions, when the two bogies supporting the same car simultaneously exhibit periodic motion (the two bogies have basically the same motion state at the same moment), this motion state will be transmitted to the car body through the secondary suspension springs. The car body will then undergo forced upper-center rolling oscillation under excitation, and the frequency of this motion is consistent with the periodic motion frequency of the bogies. Since the car body is a complex mechanical system with multiple suspension modes, one of which is the car body's inherent upper-center rolling oscillation mode. When the forced upper-center rolling oscillation of the car body has the same natural frequency as the inherent upper-center rolling oscillation mode, structural resonance will be induced, the amplitude of the upper-center rolling oscillation will be amplified, and the train will exhibit significant lateral sway. Motion analysis reveals the following frequency relationship between the bogie and car body positions during low-frequency periodic swaying:

[0082] f Hb1 =f Hb2 =f Hc =f csway

[0083] In the formula, f Hb1 f is the frequency of the forward bogie's homing motion on the high-speed train. Hb2 f is the frequency of the hunting motion of the rear bogie of the EMU. Hc The frequency at which the front and rear bogies of the EMU are forced to perform upward rolling motion by the train body during in-phase serpentine movement; f csway The theoretically inherent top-center rolling frequency of the EMU body.

[0084] As can be seen from the frequency relationship, when low-frequency periodic swaying occurs, the train body and bogie have the same motion state, that is, periodic motion with the same frequency occurs at both positions. Considering that the vibration frequency during low-frequency periodic swaying is generally distributed within the 2Hz range (i.e., the vibration frequency distribution range during historical low-frequency periodic swaying is within the 2Hz range), the original method of determining the dominant vibration frequency by "calculating the theoretical value of the car body's inherent upper center rolling frequency" can be transformed into a method that does not require theoretical value calculation but only data analysis and judgment: "whether the dominant lateral vibration frequency of the frame is consistent with the dominant lateral vibration frequency of the car body and is distributed within the 2Hz range."

[0085] Considering that the data collected by the current high-speed integrated inspection EMU track geometry detection system to describe the lateral motion of the frame is the frame lateral displacement signal, and the data to describe the lateral motion of the car body is the car body lateral acceleration signal, these two data points, one a displacement signal and the other an acceleration signal, are not from the same source. Therefore, before using them for data characteristic verification, it is necessary to analyze the characteristics between the two signals.

[0086] From a physics perspective, both acceleration and displacement are physical quantities used to describe the motion characteristics of an object within space. By definition, displacement can be obtained by performing a double integral on acceleration. Here, we derive the process of obtaining displacement through the double integral of acceleration:

[0087] Suppose there exists an acceleration signal sequence 'a', which, after Fourier transform, can be represented as A(ω).

[0088] The displacement information s(t) in the time domain can be obtained by performing a quadratic integration of the acceleration in the time domain.

[0089]

[0090] Where a is the acceleration signal and t is time;

[0091] Based on the integral property of the Fourier transform

[0092]

[0093] in, [] represents the Fourier transform; t represents time; f(t) represents the signal sequence in the time domain; ω represents the frequency; and F(w) represents the Fourier expression of the signal f(t).

[0094] The Fourier expression for the displacement can be written in the following form:

[0095]

[0096] As can be seen from formula (1-2), during the process of obtaining displacement by double integration of acceleration, the components of the signal remain unchanged, and the only difference lies in the specific values.

[0097] The above analysis reveals that the lateral displacement signal and the lateral acceleration signal of the frame have the same signal composition. Therefore, when low-frequency periodic swaying occurs, the lateral displacement of the frame can also well characterize the lateral motion of the frame, and the signal will also have obvious periodicity.

[0098] This section analyzes the lateral displacement signals of the frame and the lateral acceleration signals of the car body corresponding to the sections where significant low-frequency periodic swaying occurs during EMU operation. The results are as follows: Figure 3 , Figure 4 As shown.

[0099] from Figure 3 , Figure 4 As can be seen, during low-frequency periodic vehicle swaying, both the frame lateral displacement signal and the vehicle body lateral acceleration signal exhibit significant harmonic characteristics, and their waveforms are extremely similar, with a phase difference. This is due to the presence of the secondary suspension springs, which creates a certain time interval between their movements. Power spectral density analysis was performed on the frame lateral displacement signal and the vehicle body lateral acceleration signal, and the results are as follows: Figure 5 , Figure 6 As shown. From Figure 5 , Figure 6 As can be seen from the power spectrum analysis results of the lateral displacement signal of the frame and the lateral acceleration signal of the car body in this section, there is a local peak with a frequency of 1.241Hz, indicating a high concentration of energy. This suggests that when the train passed through this section, both the car body and the bogie experienced lateral periodic motion, with a dominant vibration frequency of 1.241Hz.

[0100] The above analysis verifies that the lateral displacement signal of the frame and the lateral acceleration signal of the car body collected during low-frequency periodic swaying both exhibit strong periodicity and have the same frequency. This verifies the feasibility of the section track condition evaluation method of this embodiment.

[0101] The effects and functions of this invention will be analyzed and illustrated below with an example:

[0102] Passengers riding in the inspection train reported a noticeable swaying motion when passing through the section of track between K1568+250 and K1568+650 on the northbound line. On-site measurements of the rail surface and profile at the corresponding location revealed that the rail surface band was significantly wider than normal, generally exceeding 40mm, indicating a noticeable wheel-rail mismatch when the train passed through.

[0103] Based on the measured rail profile data, the deviation range of the effective side of the profile was found to be -0.4mm to +0.6mm, which is slightly large. There are relatively obvious profile defects on site.

[0104] The following analysis applies the track condition evaluation method proposed in this invention to the lateral acceleration signal of the vehicle body and the lateral displacement signal of the frame collected when the inspection vehicle passes through this track section. For example... Figure 7 , Figure 8 The results of EEMD are for the lateral acceleration signal of the car body and the lateral displacement signal of the frame in this track section. EEMD of the lateral displacement signal of the frame yields multiple components (see...). Figure 8 The lateral acceleration signal of the vehicle body (imf1~imf9) and a residual term, when subjected to EEMD, will yield multiple components (see [link to EEMD]). Figure 7 (imf1~imf9) and a residual term.

[0105] The energy of each component of the vehicle body lateral acceleration signal and the frame lateral displacement signal EEMD was calculated to be the ratio of the energy of each component to the total energy of the original signal. The results are as follows: Figure 9 , Figure 10 As shown.

[0106] from Figure 9 As can be seen, the fourth component of the vehicle's lateral acceleration signal in this section contains the highest energy, accounting for 0.42% of the total energy; the fifth component has the second highest energy, accounting for 0.35% of the total energy. The combined proportion of the fourth and fifth components to the total energy exceeds the reference value of 0.7%. Figure 10 As can be seen, the fifth component of the lateral displacement signal of this section contains the highest energy, accounting for 0.49 of the total energy; the fourth component has the second highest energy, accounting for 0.30 of the total energy. The sum of the proportions of the fourth and fifth components to the total energy exceeds the reference value (i.e. the preset proportion value) of 0.7.

[0107] In summary, this section can be identified as a potentially problematic section. The dominant vibration frequencies of this track section were calculated, with the second dominant vibration frequency of the car body lateral acceleration signal being 1.46 Hz and the first dominant vibration frequency of the frame lateral displacement signal being 1.44 Hz. The difference between the second dominant vibration frequency of the car body lateral acceleration signal and the first dominant vibration frequency of the frame lateral displacement signal meets the criteria for determining poor track conditions in this section. Therefore, this section is determined to have poor passing performance when the train passed, indicating a poor track condition in this track section.

[0108] This example demonstrates that the proposed method for evaluating track conditions can effectively identify sections where low-frequency periodic swaying occurs due to poor track conditions.

[0109] This invention also provides a section track condition evaluation device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the section track condition evaluation method, the implementation of this device can refer to the implementation of the section track condition evaluation method; repeated details will not be elaborated further.

[0110] like Figure 11 The diagram shown is a schematic representation of a section track condition evaluation device provided in an embodiment of the present invention, comprising:

[0111] The first processing module 1101 is used to perform ensemble empirical mode decomposition (EEMD) on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section to obtain multiple components of the lateral displacement signal of the frame and multiple components of the lateral acceleration signal of the train body.

[0112] The proportional value determination module 1102 is used to calculate a first proportional value of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame, and a second proportional value of the energy of each component of the lateral acceleration signal of the vehicle body to the total energy of the lateral acceleration signal of the vehicle body.

[0113] The sorting module 1103 is used to sort the first proportional value of each component of the frame lateral displacement signal and sort the second proportional value of each component of the vehicle body lateral acceleration signal.

[0114] The second processing module 1104 is used to sum the first ratio values ​​within a preset sorting range to obtain a third ratio value, and to sum the second ratio values ​​within a preset sorting range to obtain a fourth ratio value.

[0115] The marking module 1105 is used to mark the detection track section when both the third and fourth proportional values ​​are greater than the preset proportional values;

[0116] The third processing module 1106 is used to add the components corresponding to the first proportional value in the frame lateral displacement signal within the preset sorting range to obtain the first intermediate component when the track section is marked, and to add the components corresponding to the second proportional value in the vehicle body lateral acceleration signal within the preset sorting range to obtain the second intermediate component.

[0117] The vibration main frequency determination module 1107 is used to determine the first vibration main frequency of the frame lateral displacement signal based on the first intermediate component, and to determine the second vibration main frequency of the vehicle body lateral acceleration signal based on the second intermediate component.

[0118] The fourth processing module 1108 is used to determine the track status of the detection track section based on the first vibration main frequency, the second vibration main frequency, and the preset frequency distribution range.

[0119] In one possible implementation, it further includes: a filtering module, used to filter the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body collected in the detection track section, and to filter out the low-frequency trend term and high-frequency elastic vibration information generated by the curve in the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body.

[0120] In one possible implementation, the filtering operation includes any of the following: a Fourier transform-based filtering operation, or an inverse Fourier transform-based filtering operation.

[0121] In one possible implementation, the ratio determination module is specifically configured to: obtain the energy of each component of the lateral displacement signal of the frame and the total energy of the lateral displacement signal of the frame based on the standard deviation of each component of the calculated lateral displacement signal of the frame and the standard deviation of the lateral displacement signal of the frame; obtain the energy of each component of the lateral acceleration signal of the vehicle body and the total energy of the lateral acceleration signal of the vehicle body based on the standard deviation of each component of the calculated lateral acceleration signal of the vehicle body and the standard deviation of the lateral acceleration signal of the vehicle body; calculate a first ratio of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame based on the energy of each component of the lateral displacement signal of the frame and the total energy of the lateral displacement signal of the frame; and calculate a second ratio of the energy of each component of the lateral acceleration signal of the vehicle body to the total energy of the lateral acceleration signal of the vehicle body based on the energy of each component of the lateral acceleration signal of the vehicle body and the total energy of the lateral acceleration signal of the vehicle body.

[0122] In one possible implementation, the vibration dominant frequency determination module is specifically used to perform power spectrum analysis on the first intermediate component and determine the frequency corresponding to the position of the maximum power spectral density value of the first intermediate component as the first vibration dominant frequency of the frame lateral displacement signal; and to perform power spectrum analysis on the second intermediate component and determine the frequency corresponding to the position of the maximum power spectral density value of the second intermediate component as the second vibration dominant frequency of the vehicle body lateral acceleration signal.

[0123] In one possible implementation, the preset frequency distribution range is determined by the following method before performing ensemble empirical mode decomposition (EEMD) on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected from the detection track section: based on the relationship between the forward bogie swaying frequency, the rear bogie swaying frequency, the frequency at which the front and rear bogies of the train body are forced to perform upper-center rolling swaying motion during low-frequency periodic swaying, the theoretical inherent upper-center rolling swaying frequency of the train body, and the vibration frequency distribution range during historical low-frequency periodic swaying, the vibration frequency distribution range during historical low-frequency periodic swaying is determined as the preset distribution range.

[0124] In one possible implementation, the fourth processing module is specifically used to determine that the detected track segment is not a track defect segment when the first dominant vibration frequency exceeds the preset frequency distribution range or the second dominant vibration frequency exceeds the preset frequency distribution range; when the first dominant vibration frequency does not exceed the preset frequency distribution range and the second dominant vibration frequency does not exceed the preset frequency distribution range, the larger of the first dominant vibration frequency and the second dominant vibration frequency is taken as the first value and the smaller of the two dominant vibration frequencies is taken as the second value. When the first value and the second value satisfy the following formula, the detected track segment is determined to be a track defect segment; when the first value and the second value do not satisfy the following formula, the detected track segment is determined not to be a track defect segment: F1-F2≤2%F1, where F1 is the first value and F2 is the second value.

[0125] Based on the aforementioned inventive concept, such as Figure 12 As shown, the present invention also proposes a computer device 1200, including a memory 1210, a processor 1220, and a computer program 1230 stored in the memory 1210 and executable on the processor 1220. When the processor 1220 executes the computer program 1230, it implements the aforementioned section track state evaluation method.

[0126] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described segment track state evaluation method.

[0127] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described section track state evaluation method.

[0128] In this embodiment of the invention, ensemble empirical mode decomposition (EEMD) is performed on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section to obtain multiple components of the lateral displacement signal of the train frame and multiple components of the lateral acceleration signal of the train body. A first ratio of the energy of each component of the lateral displacement signal of the train frame to the total energy of the lateral displacement signal of the train frame, and a second ratio of the energy of each component of the lateral acceleration signal of the train body to the total energy of the lateral acceleration signal of the train body are calculated. The first ratio values ​​of each component of the lateral displacement signal of the train frame are sorted, and the second ratio values ​​of each component of the lateral acceleration signal of the train body are sorted. The first ratio values ​​within a preset sorting range are summed to obtain a third ratio value, which is then used to calculate a third ratio value. The second proportional value within the sorting range is summed to obtain the fourth proportional value. When both the third and fourth proportional values ​​are greater than the preset proportional value, the detected track section is marked. When the detected track section is marked, the components of the frame lateral displacement signal corresponding to the first proportional value within the preset sorting range are summed to obtain the first intermediate component. The components of the car body lateral acceleration signal corresponding to the second proportional value within the preset sorting range are summed to obtain the second intermediate component. The first vibration dominant frequency of the frame lateral displacement signal is determined based on the first intermediate component, and the second vibration dominant frequency of the car body lateral acceleration signal is determined based on the second intermediate component. Based on the first vibration dominant frequency, the second vibration dominant frequency, and the preset frequency distribution range, it is determined whether the detected track section is a track defect section. This invention does not require the calculation of theoretical values. It only needs to obtain the frame lateral displacement signal and the car body lateral acceleration signal of the EMU for data analysis and judgment to achieve the evaluation of the track condition of the section. It requires less data than the prior art and improves the accuracy of the evaluation of the track condition of the section.

[0129] 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.

[0130] 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0131] 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, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] 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 condition of a track section, characterized in that, include: Ensemble empirical mode decomposition (EEMD) was performed on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section to obtain multiple components of the lateral displacement signal of the frame and the lateral acceleration signal of the train body. Calculate the first ratio of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame, and the second ratio of the energy of each component of the lateral acceleration signal of the vehicle body to the total energy of the lateral acceleration signal of the vehicle body; The first proportional value of each component of the lateral displacement signal of the frame is sorted, and the second proportional value of each component of the lateral acceleration signal of the vehicle body is sorted. The first ratio value within the preset sorting range is summed to obtain the third ratio value, and the second ratio value within the preset sorting range is summed to obtain the fourth ratio value. When both the third and fourth proportional values ​​are greater than the preset proportional values, the detection track section is marked; When the track section is marked, the components corresponding to the first proportional value in the frame lateral displacement signal within the preset sorting range are added together to obtain the first intermediate component, and the components corresponding to the second proportional value in the vehicle body lateral acceleration signal within the preset sorting range are added together to obtain the second intermediate component. The first dominant vibration frequency of the lateral displacement signal of the frame is determined based on the first intermediate component, and the second dominant vibration frequency of the lateral acceleration signal of the vehicle body is determined based on the second intermediate component. The track condition of the detection track section is determined based on the first dominant vibration frequency, the second dominant vibration frequency, and the preset frequency distribution range.

2. The method for evaluating the track condition of a section as described in claim 1, characterized in that, Before performing EEMD on the lateral displacement signal of the frame and the lateral acceleration signal of the vehicle body, the following steps are also included: The lateral displacement signal of the frame and the lateral acceleration signal of the car body collected in the detection track section are filtered to remove the low-frequency trend term and high-frequency elastic vibration information caused by passing through the curve in the lateral displacement signal of the frame and the lateral acceleration signal of the car body.

3. The method for evaluating the track condition of a section as described in claim 2, characterized in that, Filtering operations include any of the following: Filtering operations based on Fourier transform and filtering operations based on inverse Fourier transform.

4. The method for evaluating the track condition of a section as described in claim 1, characterized in that, The calculation includes: a first ratio of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame, and a second ratio of the energy of each component of the lateral acceleration signal of the vehicle body to the total energy of the lateral acceleration signal of the vehicle body, including: Based on the standard deviation of each component of the lateral displacement signal of the frame, and the standard deviation of the lateral displacement signal of the frame, the energy of each component of the lateral displacement signal of the frame, and the total energy of the lateral displacement signal of the frame are obtained. Based on the standard deviation of each component of the vehicle's lateral acceleration signal and the standard deviation of the vehicle's lateral acceleration signal, the energy of each component of the vehicle's lateral acceleration signal and the total energy of the vehicle's lateral acceleration signal are obtained. Based on the energy of each component of the lateral displacement signal of the frame and the total energy of the lateral displacement signal of the frame, calculate the first ratio of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame. Based on the energy of each component of the vehicle's lateral acceleration signal and the total energy of the vehicle's lateral acceleration signal, a second ratio value is calculated between the energy of each component of the vehicle's lateral acceleration signal and the total energy of the vehicle's lateral acceleration signal.

5. The method for evaluating the condition of a track section as described in claim 1, characterized in that, The first dominant vibration frequency of the lateral displacement signal of the frame is determined based on the first intermediate component, and the second dominant vibration frequency of the lateral acceleration signal of the vehicle body is determined based on the second intermediate component, including: Power spectrum analysis is performed on the first intermediate component, and the frequency corresponding to the position of the maximum power spectral density value of the first intermediate component is determined as the first vibration main frequency of the lateral displacement signal of the frame. Power spectrum analysis was performed on the second intermediate component, and the frequency corresponding to the position of the maximum power spectral density value of the second intermediate component was determined as the second dominant vibration frequency of the vehicle body lateral acceleration signal.

6. The method for evaluating the track condition of a section as described in claim 1, characterized in that, The preset frequency distribution range is determined using the following method before performing ensemble empirical mode decomposition (EEMD) on the lateral displacement signals of the train frame and the lateral acceleration signals of the train body collected from the detection track section: Based on the relationship between the forward bogie serpentine motion frequency, the rear bogie serpentine motion frequency, the frequency at which the front and rear bogies of the EMU are forced to perform upper-center rolling motion due to in-phase serpentine motion, the theoretical inherent upper-center rolling frequency of the EMU car body, and the vibration frequency distribution range during historical low-frequency periodic swaying, the vibration frequency distribution range during historical low-frequency periodic swaying is determined as the preset distribution range.

7. The method for evaluating the condition of a track section as described in claim 6, characterized in that, Based on the first dominant vibration frequency, the second dominant vibration frequency, and the preset frequency distribution range, the track state of the detection track section is determined, including: When the first dominant vibration frequency exceeds the preset frequency distribution range or the second dominant vibration frequency exceeds the preset frequency distribution range, it is determined that the detected track section is not a track defect section. When the first dominant vibration frequency does not exceed the preset frequency distribution range and the second dominant vibration frequency also does not exceed the preset frequency distribution range, the larger of the first and second dominant vibration frequencies is taken as the first value, and the smaller one is taken as the second value. If the first and second values ​​satisfy the following formula, the detected track section is determined to be a track defect section; if the first and second values ​​do not satisfy the following formula, the detected track section is determined not to be a track defect section. F1-F2≤2%F1, where F1 is the first value and F2 is the second value.

8. A section track condition evaluation device, characterized in that, include: The first processing module is used to perform ensemble empirical mode decomposition (EEMD) on the lateral displacement signal of the train frame and the lateral acceleration signal of the train body collected in the detection track section, so as to obtain multiple components of the lateral displacement signal of the frame and multiple components of the lateral acceleration signal of the train body. The proportional value determination module is used to calculate the first proportional value of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame, and the second proportional value of the energy of each component of the lateral acceleration signal of the vehicle body to the total energy of the lateral acceleration signal of the vehicle body. The sorting module is used to sort the first proportional value of each component of the frame lateral displacement signal and the second proportional value of each component of the vehicle body lateral acceleration signal. The second processing module is used to sum the first ratio values ​​within the preset sorting range to obtain the third ratio value, and to sum the second ratio values ​​within the preset sorting range to obtain the fourth ratio value. The marking module is used to mark the detected track section when both the third and fourth proportional values ​​are greater than the preset proportional values; The third processing module is used to add the components corresponding to the first proportional value in the frame lateral displacement signal within the preset sorting range to obtain the first intermediate component when the track section is marked, and to add the components corresponding to the second proportional value in the vehicle body lateral acceleration signal within the preset sorting range to obtain the second intermediate component. The vibration main frequency determination module is used to determine the first vibration main frequency of the frame lateral displacement signal based on the first intermediate component, and to determine the second vibration main frequency of the vehicle body lateral acceleration signal based on the second intermediate component. The fourth processing module is used to determine the track status of the detection track section based on the first vibration main frequency, the second vibration main frequency, and the preset frequency distribution range.

9. The section track condition evaluation device as described in claim 8, characterized in that, Also includes: The filtering module is used to filter the lateral displacement signal of the frame and the lateral acceleration signal of the car body collected in the detection track section, and to filter out the low-frequency trend term and high-frequency elastic vibration information caused by passing through the curve in the lateral displacement signal of the frame and the lateral acceleration signal of the car body.

10. The section track condition evaluation device as described in claim 9, characterized in that, Filtering operations include any of the following: Filtering operations based on Fourier transform and filtering operations based on inverse Fourier transform.

11. The section track condition evaluation device as described in claim 8, characterized in that, The proportional value determination module is specifically used to obtain the energy of each component of the lateral displacement signal of the frame and the total energy of the lateral displacement signal of the frame based on the standard deviation of each component of the lateral displacement signal of the frame and the standard deviation of the lateral displacement signal of the frame. Based on the standard deviation of each component of the vehicle's lateral acceleration signal and the standard deviation of the vehicle's lateral acceleration signal, the energy of each component of the vehicle's lateral acceleration signal and the total energy of the vehicle's lateral acceleration signal are obtained. Based on the energy of each component of the lateral displacement signal of the frame and the total energy of the lateral displacement signal of the frame, calculate the first ratio of the energy of each component of the lateral displacement signal of the frame to the total energy of the lateral displacement signal of the frame. Based on the energy of each component of the vehicle's lateral acceleration signal and the total energy of the vehicle's lateral acceleration signal, a second ratio value is calculated between the energy of each component of the vehicle's lateral acceleration signal and the total energy of the vehicle's lateral acceleration signal.

12. The section track condition evaluation device as described in claim 8, characterized in that, The vibration main frequency determination module is specifically used to perform power spectrum analysis on the first intermediate component and determine the frequency corresponding to the position of the maximum power spectral density value of the first intermediate component as the first vibration main frequency of the frame lateral displacement signal. Power spectrum analysis was performed on the second intermediate component, and the frequency corresponding to the position of the maximum power spectral density value of the second intermediate component was determined as the second dominant vibration frequency of the vehicle body lateral acceleration signal.

13. The section track condition evaluation device as described in claim 8, characterized in that, The preset frequency distribution range is determined using the following method before performing ensemble empirical mode decomposition (EEMD) on the lateral displacement signals of the train frame and the lateral acceleration signals of the train body collected from the detection track section: Based on the relationship between the forward bogie serpentine motion frequency, the rear bogie serpentine motion frequency, the frequency at which the front and rear bogies of the EMU are forced to perform upper-center rolling motion due to in-phase serpentine motion, the theoretical inherent upper-center rolling frequency of the EMU car body, and the vibration frequency distribution range during historical low-frequency periodic swaying, the vibration frequency distribution range during historical low-frequency periodic swaying is determined as the preset distribution range.

14. The section track condition evaluation device as described in claim 8, characterized in that, The fourth processing module is specifically used to determine that the detected track section is not a defective track section when the first vibration main frequency exceeds the preset frequency distribution range or the second vibration main frequency exceeds the preset frequency distribution range. When the first dominant vibration frequency does not exceed the preset frequency distribution range and the second dominant vibration frequency also does not exceed the preset frequency distribution range, the larger of the first and second dominant vibration frequencies is taken as the first value, and the smaller one is taken as the second value. If the first and second values ​​satisfy the following formula, the detected track section is determined to be a track defect section; if the first and second values ​​do not satisfy the following formula, the detected track section is determined not to be a track defect section. F1-F2≤2%F1, where F1 is the first value and F2 is the second value.

15. 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 7.

16. 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 7.

17. 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 7.

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