Method for eliminating high-speed train passing condition marks based on train vibration characteristics

Abstract

The present invention discloses a method for eliminating high-speed train meeting condition marks based on train vibration characteristics, and relates to the technical field of automatic identification of high-speed railway locomotives meeting. The method comprises: obtaining a train lateral vibration acceleration signal collected by an on-board vibration sensor, and screening it: setting positive and negative peak point thresholds and a screening range to screen target vibration peak points; based on the fixed interval between the two-direction vibration peak point pairs in a single vibration in the meeting state, the relatively stable interval between the first and second lateral vibrations of the vehicle body in the meeting state, and the feature that the middle two peak points of adjacent peak point pairs in the meeting state have the same vibration direction, screening the positive and negative peak point pairs that meet the conditions, and returning the position of the positive and negative peak point pairs as meeting identification points. The present invention introduces the inherent vibration characteristics of the vehicle body to analyze the meeting state, which can effectively identify the meeting state.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic identification of high-speed railway locomotives meeting each other, and in particular to a method for eliminating high-speed train meeting condition marks based on train vibration characteristics. Background Art

[0002] Current high-speed trains are equipped with sensors that collect vertical and lateral vibration acceleration data. These sensors monitor train vibration at various speeds and track sections. The collected vibration data can be used to assist in evaluating track conditions. When the sensors detect significant lateral or vertical vibration fluctuations, the system triggers an alarm and records the relevant data, facilitating subsequent track maintenance and repairs, thereby ensuring the safe operation of the EMU.

[0003] During high-speed travel, a train (EMU) may cross paths with another EMU traveling in the same or opposite direction. This phenomenon is known as a crossover. Due to changes in the EMU's aerodynamic characteristics during a crossover, the train's lateral vibration acceleration increases dramatically, generating alarm signals similar to those of track defects. These signals can interfere with maintenance personnel's assessment. Failure to promptly identify and address this effect can reduce the accuracy and reliability of track condition assessments. Therefore, effective identification of crossover conditions that occur during EMU operation is essential.

[0004] After searching, the existing patent "CN117565930 A high-speed train meeting identification method for a vehicle-mounted line inspection instrument" provides a railway meeting identification method. First, the raw data of the on-board sensors of a certain train for a period of time is selected, the meeting waveforms are screened out to form a meeting data set, and the average speed of the trains at each meeting is calculated; the raw data is filtered to obtain a filtered meeting data set; the speed range is divided, and the meeting waveform identification parameters for different speed ranges are trained separately; the speed data is used to train the meeting correlation threshold in the meeting identification algorithm to analyze the train lateral vibration acceleration data and perform meeting identification.

[0005] The technical method of the above patent application divides the train speed range into different ranges to train different passing waveform recognition parameters. This method requires first determining the train speed, but when measuring the actual train running status, it will be found that the train speed is not constant. In addition, the patent application locates the passing peak point based on the train mileage data obtained by monitoring, which requires that the format of the monitoring data conform to the specifications of the patent application.

[0006] Based on this, the present invention proposes a method for eliminating high-speed train passing condition marks based on train vibration characteristics, which is no longer limited by vehicle speed and standard mileage record data and is more universal. Summary of the Invention

[0007] The present invention provides a method for eliminating high-speed train meeting condition marks based on train vibration characteristics, comprising:

[0008] Acquire the train lateral vibration acceleration signal collected by the on-board vibration sensor and pre-process the train lateral vibration acceleration signal;

[0009] The following peak screening is performed on the pre-processed train lateral vibration acceleration signal:

[0010] Setting positive and negative peak point thresholds and a screening range as global screening conditions, and screening target vibration peak points that meet the global screening conditions;

[0011] Based on the characteristic that a single vibration in the meeting state always presents a pair of positive and negative peak points vibrating in both positive and negative directions, and the fixed interval between the positive and negative peak point pairs, a first local screening is performed on the screened target vibration peak points to obtain the first positive and negative peak point pairs that meet the first local screening conditions;

[0012] Based on the relatively stable interval between the first and second vehicle body lateral vibrations in the meeting state, a local screening window is set between peak point pairs. The first positive and negative peak point pairs are subjected to a second local screening based on the local screening window to select the second positive and negative peak point pairs that meet the second local screening conditions.

[0013] According to the characteristic that the vibration directions of the middle two peak points of the adjacent peak point pairs in the meeting state are the same, the second positive and negative peak point pairs are subjected to a third local screening to select the third positive and negative peak point pairs that meet the third local screening conditions;

[0014] According to the position information of the third positive and negative peak point pair, the positive and negative peak point pairs that meet the continuous peak screening conditions are screened out, and the positions of the positive and negative peak point pairs are returned as the oncoming vehicle identification points.

[0015] As described above, a method for eliminating high-speed train passing condition marks based on train vibration characteristics is described, wherein, after obtaining the train lateral vibration acceleration signal, a time domain waveform diagram of the original data of the train lateral vibration acceleration during the monitoring period is drawn, the time data corresponding to the acceleration is converted into a time interpolation array with linear equal spacing in milliseconds, and the train lateral vibration acceleration signal is bandpass filtered.

[0016] As described above, a method for eliminating high-speed train meeting condition marks based on train vibration characteristics is provided, wherein positive and negative peak point thresholds and screening ranges are set as global screening conditions, and target vibration peak points that meet the global screening conditions are screened, specifically: the minimum peak threshold of positive and negative peak points and the peak point screening range are set, all positive and negative peak points that are greater than the minimum peak threshold of peak points are screened out, and their corresponding position sequences are found.

[0017] In the method for eliminating high-speed train passing condition marks based on train vibration characteristics, the fixed interval between pairs of vibration peak points in two directions in a single vibration is determined by analysis as follows:

[0018] Statistical analysis of the intervals between positive and negative peak points of a single vibration in the meeting state based on measured data;

[0019] Based on the analysis of the inherent vibration characteristics of the vehicle body;

[0020] The interval between positive and negative peak points of a single vibration in the meeting state is determined based on the vibration characteristics of the train.

[0021] As described above, a method for eliminating high-speed train meeting condition marks based on train vibration characteristics is used, wherein the second positive and negative peak point pairs are screened, specifically: a peak point search threshold is defined, a positive peak point that meets the peak point search threshold is searched, and a first local screening window is formulated according to the relatively stable interval between the first train lateral vibration and the second train lateral vibration in the meeting state; and a judgment is made as to whether the number of negative peak points in the first local screening window is greater than two. If so, the peak points that meet the conditions are retained; otherwise, the peak point pairs including the positive peak point are eliminated.

[0022] As described above, a method for eliminating high-speed train meeting condition marks based on train vibration characteristics, wherein the third positive and negative peak point pairs are screened, specifically: based on the characteristic that the middle two peak points in the adjacent peak point pairs in the meeting state have the same vibration direction, a second local screening window is formulated; the directions of the latest peak point and the negative peak point found through the second local screening window are judged, and if the directions are the same, the latest peak point is used as the target peak point, that is, the third positive and negative peak point pair; otherwise, the latest peak point is eliminated until there are no peak points to be judged in the local screening window.

[0023] As described above, a method for eliminating high-speed train meeting condition marks based on train vibration characteristics is used, wherein positive and negative peak point pairs that meet the continuous peak screening conditions are screened out, specifically: based on the position information of the third positive and negative peak point pair, a screening window is further introduced, and if there are more than three consecutive peak points in the local screening window, they are eliminated; if the peak point found does not satisfy the requirement that the first vibration amplitude is greater than the second vibration peak value, it is eliminated; and the positions of the positive and negative peak point pairs remaining after elimination are used as meeting identification points.

[0024] The present invention also provides a computer storage medium, comprising: at least one memory and at least one processor;

[0025] The memory is used to store one or more program instructions;

[0026] The processor is used to run one or more program instructions to execute any of the above-mentioned methods for eliminating high-speed train meeting condition marks based on train vibration characteristics.

[0027] The beneficial effects achieved by the present invention are as follows: Based on the train lateral vibration acceleration data collected by onboard vibration monitoring equipment, the present invention develops a method for eliminating the marking of a passing condition suitable for high-speed trains, based on the waveform and spectrum characteristics of the train lateral vibration during the passing state. This method differs from existing research in that it no longer relies on the train's running speed to identify key peak points of the passing state. Instead, it introduces the inherent vibration characteristics of the train body to analyze the train's lateral vibration characteristics during the passing state, locates key peak points based on the sampling frequency of the monitoring data, and can effectively identify the passing state based on the vibration characteristics of the train body. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0029] Figure 1 This is a flow chart of a method for eliminating high-speed train meeting condition marks based on train vibration characteristics provided by the first embodiment of the present invention;

[0030] Figure 2 The diagram shows the time domain waveform of the train's lateral vibration acceleration and the comparison of the screening results after the first peak screening.

[0031] Figure 3 This is the measured lateral vibration curve of the train set under the state of meeting each other;

[0032] Figure 4 It is the cumulative distribution diagram of the vehicle body lateral acceleration amplitude at different peak points;

[0033] Figure 5 This is a comparison chart of local peak screening results;

[0034] Figure 6 This is the final result diagram of the vehicle-meeting state identification. DETAILED DESCRIPTION

[0035] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] like Figure 1 As shown, the first embodiment of the present invention provides a method for eliminating a high-speed train meeting condition mark based on train vibration characteristics, comprising:

[0038] Step 110: Acquire a train lateral vibration acceleration signal collected by an onboard vibration sensor, and preprocess the train lateral vibration acceleration signal;

[0039] Specifically, after obtaining the train's lateral vibration acceleration signal, the time domain waveform of the train's lateral vibration acceleration raw data during the monitoring period is drawn, the time data corresponding to the acceleration is converted into a time interpolation array with linear equal spacing in milliseconds, and the train's lateral vibration acceleration signal is bandpass filtered with a passband range of 2Hz~20Hz.

[0040] The following peak filtering operation is performed on the pre-processed train lateral vibration acceleration signal:

[0041] Step 120: setting positive and negative peak point thresholds and a screening range as global screening conditions, screening target vibration peak points that meet the global screening conditions, and removing invalid points that do not meet the global screening conditions;

[0042] In the embodiment of the present application, the first group of peak point pairs is screened by setting a minimum peak value threshold for positive and negative peak points and a peak point screening range, screening out all positive and negative peak points that are greater than the minimum peak value threshold for peak points, and finding their corresponding position sequences;

[0043] By default, the positive peak point is in the positive direction and the negative peak point is in the negative direction; set the minimum peak threshold of the positive and negative peak points and the peak point screening range, where the minimum amplitude threshold of the positive and negative peak points is set to 0.013g; filter out all positive peak points that are greater than the minimum peak threshold of the peak point and find their corresponding position sequence ,but: in, is the corresponding position information of the i-th positive peak point; is the lateral acceleration amplitude of the train corresponding to the i-th positive peak point; g is the acceleration due to gravity; M is the total length of the positive peak point sequence.

[0044] Figure 2The diagram shows the time domain waveform of the train's lateral vibration acceleration and the comparison of the screening results after the first peak screening.

[0045] Step 130: Based on the characteristic that a single vibration in the meeting state always presents positive and negative peak point pairs vibrating in both positive and negative directions, and the fixed interval between the positive and negative peak point pairs, the screened target vibration peak points are subjected to a first local screening to obtain first positive and negative peak point pairs that meet the first local screening conditions, and invalid points that do not meet the first local screening conditions are removed;

[0046] Define a peak point search threshold, find positive peak points that meet the peak point search threshold, and establish a first local screening window based on the relatively stable interval between the first and second train lateral vibrations in the meeting state. Determine whether the number of negative peak points in the first local screening window is greater than two. If so, retain the peak points that meet the conditions; otherwise, remove the peak point pair including the positive peak point.

[0047] Specifically, according to the meeting state, the interval between the first train lateral vibration and the second train lateral vibration is The relatively stable characteristics (the time interval between two lateral vibrations of the oncoming vehicle is calculated and analyzed by taking the vehicle speed of 243 km / h as an example, and the interval between the two vibrations is about 100 m, which is about half the vehicle length). The local screening window is established, and the window length is ,Right now: Where, the window length Measured by the number of data points; coef is the compensation coefficient, which is 1.1 here; and are the minimum and maximum vehicle speeds, which are 100km / h and 380km / h respectively.

[0048] For example, the peak point search threshold is defined as 0.007g, and the selection of this value is determined through data statistical analysis.

[0049] Return to see Figure 1 Step 140: Based on the relatively stable interval between the first and second vehicle body lateral vibrations in the meeting state, a local screening window is set between peak point pairs. The first positive and negative peak point pairs are subjected to a second local screening according to the local screening window to select the second positive and negative peak point pairs that meet the second local screening condition, and invalid points that do not meet the local screening condition are removed.

[0050] Figure 3 The figure shows the lateral vibration curve of the train set under the actual passing state. The position marked with the dotted box in the figure is the passing section.

[0051] As can be seen from the figure, prior to the oncoming train collision, the train's lateral vibration was relatively stable, with the train's lateral acceleration amplitude ranging from 0 to 0.003g. The maximum value was well below the 0.06g standard for routine maintenance of Level I high-speed railway maintenance management, indicating that the EMU train's lateral vibration was stable. At 14:57:06, the train on the adjacent line encountered an oncoming train, at which point the train's lateral vibration acceleration amplitude rapidly increased, reaching 0.045g.

[0052] Afterward, the train body continued to vibrate, and the train's lateral vibration continued to increase, reaching an amplitude of 0.048g, still significantly greater than the lateral vibration in the non-passing section (amplitude less than 0.003g). During this vibration, the amplitude of the train's lateral vibration acceleration at the latter peak was slightly smaller than that at the former peak. Afterward, the train body vibration quickly returned to a stable state. A second lateral vibration then occurred, approximately 1.5 seconds after the first, and its peak value was smaller than the first.

[0053] from Figure 3 It can be seen from the figure that when a meeting occurs, the body of the oncoming train will experience two lateral vibrations in opposite directions. There are four obvious peak points within a certain length range, and the first and fourth peak points have the same sign. Through the above research on the peak points of the meeting, the characteristics of the four peak points of the meeting condition are determined.

[0054] Analyze the cumulative distribution of the train lateral vibration acceleration amplitude at different peak points and create a threshold array thresholds from 0.005 to 0.1 with a step size of 0.001. This array is used to subsequently calculate the percentage of acceleration data less than each threshold. Extract the train lateral vibration acceleration amplitude at all the 1st, 2nd, 3rd, and 4th peak points in the vehicle body vibration monitoring data and draw the corresponding cumulative distribution curve. The results are as follows: Figure 4 shown.

[0055] from Figure 4 It can be seen that among the four peak points of vibration under the oncoming vehicle condition, almost 90% of the peak points have amplitudes higher than 0.013g, and there are a few third and fourth peak points with amplitudes between 0.007g and 0.013g. Therefore, the global screening threshold is set to 0.013g and the local screening threshold is set to 0.007g in the present invention.

[0056] Specifically, according to the fixed interval between the vibration peak point pairs in two directions in a single vibration, based on the position of any positive peak point, the position of the negative peak point in its corresponding peak point pair is found and determined;

[0057] The fixed interval between pairs of vibration peak points in two directions in a single vibration is determined by the following analysis:

[0058] ① Statistical analysis of the intervals between positive and negative peak points of a single vibration in the meeting state based on measured data

[0059] The time intervals between the positive and negative peak points of a single vibration in the monitoring data were statistically analyzed, and the results are shown in Table 1. The statistical results show that for the monitoring data, 56.25% of the data showed a single vibration peak interval of 0.101s, 18.75% of the data showed a single vibration peak interval of 0.102s, 21.88% of the data showed a single vibration peak interval of 0.106s, and the remaining 3.12% of the data showed a single vibration peak interval of 0.121s. Therefore, it can be assumed that approximately 97% of the data showed that the single vibration peak interval during the meeting state was 0.1s. Considering the simple harmonic characteristics of the car body vibration during the meeting state, it can be assumed that when the running train and the adjacent train meet, the car body of the meeting train is affected by the impact force of the meeting airflow and produces a periodic vibration response with a vibration period of about 0.1s. Further conversion results in a corresponding vibration frequency f of 10Hz.

[0060] Table 1 Statistical distribution of the time interval between single vibration peak points in the oncoming vehicle state

[0061]

[0062] ②Based on the analysis of the inherent vibration characteristics of the vehicle body

[0063] For example, a finite element analysis of the vibration characteristics of a high-speed train car body was introduced. The research results showed that the seventh-order natural vibration frequency of this car model was approximately 9.7052 Hz. Therefore, it can be considered that when the monitored high-speed train met an adjacent train during operation, the impact airflow between the two trains just stimulated the seventh-order natural vibration of the car body, which in turn caused significantly increased lateral vibration of the car body during the meeting.

[0064] ③ Determination of positive and negative peak point pairs of single vibration in the meeting state based on train vibration characteristics

[0065] Based on the above-mentioned natural vibration characteristics of the vehicle body, the present invention proposes that the interval between the positive and negative peak points in a single vibration can be converted from the sampling frequency of the monitoring data, which is no longer limited by the train speed and the monitoring data format, and introduces the natural resonance frequency of the vehicle body. and monitoring data sampling frequency , a screening range is formulated based on finding the corresponding peak point in the opposite direction based on a peak point, that is, Where, To find the interval range of negative peak points before and after any positive peak point, is the lower limit of the range, is the upper limit of the range; is the sampling frequency of the monitoring data, which is 200Hz here; is the inherent resonance frequency of the vehicle body caused by the oncoming train. After statistical analysis, Take 9.7Hz; ceil() is the rounding up function.

[0066] Step 150: Based on the characteristic that the middle two peak points of the adjacent peak point pairs in the meeting state have the same vibration direction, perform a third local screening on the second positive and negative peak point pairs to select the third positive and negative peak point pairs that meet the third local screening condition, and remove the peak points that do not meet the third local screening condition;

[0067] Based on the characteristic that the middle two peak points in the adjacent peak point pairs in the oncoming state have the same vibration direction, a second local screening window is established; the directions of the latest peak point and the negative peak point found through the second local screening window are judged; if the directions are the same, the latest peak point is used as the third positive-negative peak point pair; otherwise, the latest peak point is eliminated until there are no peak points to be judged in the local screening window.

[0068] Step 160 : Filter out the positive and negative peak point pairs that meet the continuous peak screening condition based on the position information of the third positive and negative peak point pairs, and return the positions of the positive and negative peak point pairs as the meeting identification points.

[0069] Based on the above steps, all peak points that meet the peak threshold, the interval between single vibration peaks, and the interval between two vibrations can be found. However, these peak points are still mixed with interference from single-point vibration peaks and multiple continuous vibration peaks caused by crossing the turnout. Therefore, based on the above screening, the present invention introduces local screening conditions based on the vibration characteristics of the passing train, namely:

[0070] Condition 1: If there are more than three consecutive peak points in the local screening window, they will be eliminated;

[0071] Condition 2: If the peak point found does not satisfy the requirement that the first vibration amplitude is greater than the second vibration peak value, it will be eliminated.

[0072] For example, based on the position and amplitude information of the four peak points in the third group of peak point pairs, according to the characteristic that the amplitudes of the first and second peak points are greater than those of the third and fourth peak points under the meeting condition, the above four peak point information is finally determined and named as the first, second, third, and fourth peak points respectively, and the position of the first peak point is returned as the meeting identification point.

[0073] The following are specific implementation examples of the present invention:

[0074] Taking the operation of a train on a certain line under the jurisdiction of a railway bureau as an example, this study uses the original lateral vibration acceleration data of the on-board sensor and MATLAB software to obtain the time domain waveform of its lateral vibration acceleration. The monitoring data of the train's lateral vibration acceleration curve can be used to obtain the number of times the train passes the adjacent line train.

[0075] On a certain day, approximately 1.8 million data points of onboard vibration signals were collected (sampling frequency was 200 Hz and sampling time was 150 minutes) for analysis. The train's lateral vibration curve from 13:38 to 16:07 was monitored. According to the monitoring data, the train had a total of 16 encounters with adjacent trains during this period.

[0076] The first group of meeting peak point pairs that meet the conditions are obtained after the initial screening of the filtered train lateral vibration acceleration. Figure 2 It can be seen that there are 24 pairs of peak points that meet the conditions after the first peak point screening, which is significantly more than the number of trains meeting each other. Therefore, further screening of peak points is required. Figure 5 This is a comparison chart of the local peak screening results. It can be seen from the figure that there are 18 pairs of peak points that meet the conditions after screening. Compared with the first screening results, the number of peak point pairs that meet the conditions after this screening is significantly reduced. Figure 6 The final result of the meeting state identification diagram is shown. The proposed method accurately captured 14 of the 16 meeting records in the data, with an accuracy rate of 87.5%. This method essentially meets the requirements for automatic identification of meeting locomotives on high-speed railways. Future plans are to optimize and adjust the screening parameters in the invention based on big data from train operation monitoring to improve the accuracy of meeting identification. The present invention is simple to implement, has a wide range of applications, and offers high accuracy.

[0077] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, comprising: at least one memory and at least one processor;

[0078] The memory is used to store one or more program instructions;

[0079] The processor is used for running one or more program instructions to execute a method for eliminating high-speed train meeting condition marks based on train vibration characteristics.

[0080] Corresponding to the above embodiment, an embodiment of the present invention provides a computer-readable storage medium, which contains one or more program instructions, and the one or more program instructions are used by a processor to execute a method for eliminating high-speed train meeting condition marks based on train vibration characteristics.

[0081] The embodiment disclosed in the present invention provides a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned method for eliminating high-speed train meeting condition marks based on train vibration characteristics.

[0082] In the embodiments of the present invention, the processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0083] The methods, steps, and logic diagrams disclosed in the embodiments of the present invention can be implemented or executed. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The processor reads the information from the storage medium and, in conjunction with its hardware, completes the steps of the aforementioned methods.

[0084] The storage medium may be a memory and may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0085] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0086] Volatile memory may be random access memory (RAM), which is used as an external cache memory. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM).

[0087] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0088] Those skilled in the art will appreciate that in one or more of the above examples, the functions described herein can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media includes any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0089] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for eliminating high-speed train passing condition marks based on train vibration characteristics, characterized in that: include: Acquire the train lateral vibration acceleration signal collected by the on-board vibration sensor and pre-process the train lateral vibration acceleration signal; The following peak screening is performed on the pre-processed train lateral vibration acceleration signal: Setting positive and negative peak point thresholds and a screening range as global screening conditions, and screening target vibration peak points that meet the global screening conditions; Based on the characteristic that a single vibration in the meeting state always presents a pair of positive and negative peak points vibrating in both positive and negative directions, and the fixed interval between the positive and negative peak point pairs, a first local screening is performed on the screened target vibration peak points to obtain the first positive and negative peak point pairs that meet the first local screening conditions; Based on the relatively stable interval between the first and second vehicle body lateral vibrations in the meeting state, a local screening window is set between peak point pairs. The first positive and negative peak point pairs are subjected to a second local screening based on the local screening window to select the second positive and negative peak point pairs that meet the second local screening conditions. Based on the fixed interval between the vibration peak point pairs in two directions in a single vibration, the position of the negative peak point in the corresponding peak point pair is found based on the position of any positive peak point. The fixed interval between the vibration peak point pairs in two directions in a single vibration is specifically determined by the following analysis: Statistical analysis of the intervals between positive and negative peak points of a single vibration in the meeting state based on measured data; Introduce finite element analysis of the vehicle body's natural vibration characteristics to determine the vehicle body's natural vibration frequency; Based on the inherent vibration characteristics of the car body, the positive and negative peak intervals in a single vibration are converted from the sampling frequency of the monitoring data, which is no longer limited by the train speed and monitoring data format. The inherent resonance frequency of the car body is introduced. and monitoring data sampling frequency , a screening range is formulated based on finding the corresponding peak point in the opposite direction based on a peak point, that is, Where, To find the interval range of negative peak points before and after any positive peak point, is the lower limit of the range, is the upper limit of the range; is the sampling frequency of this monitoring data; is the inherent resonance frequency of the vehicle body caused by the oncoming train; ceil() is the upward rounding function; According to the characteristic that the vibration directions of the middle two peak points of the adjacent peak point pairs in the meeting state are the same, the second positive and negative peak point pairs are subjected to a third local screening to select the third positive and negative peak point pairs that meet the third local screening conditions; According to the position information of the third positive and negative peak point pair, the positive and negative peak point pairs that meet the continuous peak screening conditions are screened out, and the positions of the positive and negative peak point pairs are returned as the oncoming vehicle identification points.

2. A method for eliminating high-speed train meeting condition marks based on train vibration characteristics according to claim 1, characterized in that: After obtaining the train's lateral vibration acceleration signal, the time domain waveform of the train's lateral vibration acceleration raw data during the monitoring period is drawn, the time data corresponding to the acceleration is converted into a time interpolation array with linear equal spacing in milliseconds, and the train's lateral vibration acceleration signal is band-pass filtered.

3. The method for eliminating high-speed train meeting condition marks based on train vibration characteristics according to claim 1, characterized in that: Set the positive and negative peak point thresholds and the screening range as global screening conditions, and screen the target vibration peak points that meet the global screening conditions. Specifically, set the minimum peak threshold of the positive and negative peak points and the peak point screening range, screen out all positive and negative peak points that are greater than the minimum peak threshold of the peak points, and find their corresponding position sequence.

4. A method for eliminating high-speed train meeting condition marks based on train vibration characteristics according to claim 3, characterized in that: The fixed interval between the peak points of vibration in two directions in a single vibration is determined by analysis as follows: Statistical analysis of the intervals between positive and negative peak points of a single vibration in the meeting state based on measured data; Based on the analysis of the inherent vibration characteristics of the vehicle body; The interval between positive and negative peak points of a single vibration in the meeting state is determined based on the vibration characteristics of the train.

5. The method for eliminating high-speed train meeting condition marks based on train vibration characteristics according to claim 4, characterized in that: The second positive and negative peak point pairs are screened, specifically: defining a peak point search threshold, searching for positive peak points that meet the peak point search threshold, and formulating a first local screening window based on the relatively stable interval between the first train lateral vibration and the second train lateral vibration in the meeting state; determining whether the number of negative peak points in the first local screening window is greater than two, and if so, retaining the peak points that meet the conditions; otherwise, removing the peak point pairs including the positive peak point.

6. A method for eliminating high-speed train meeting condition marks based on train vibration characteristics according to claim 5, characterized in that: The third positive and negative peak point pair is screened, specifically: based on the characteristic that the middle two peak points in the adjacent peak point pairs in the meeting state have the same vibration direction, a second local screening window is formulated; the directions of the latest peak point and the negative peak point found through the second local screening window are judged, and if the directions are the same, the latest peak point is used as the target peak point, that is, the third positive and negative peak point pair; otherwise, the latest peak point is eliminated until there is no peak point to be judged in the local screening window.

7. A method for eliminating high-speed train meeting condition marks based on train vibration characteristics according to claim 6, characterized in that: Filter out the positive and negative peak point pairs that meet the continuous peak screening conditions. Specifically: according to the position information of the third positive and negative peak point pair, continue to introduce the screening window. If there are more than three consecutive peak points in the local screening window, they will be eliminated; if the peak point found does not meet the requirement that the first vibration amplitude is greater than the second vibration peak value, it will be eliminated; the positions of the positive and negative peak point pairs remaining after elimination are used as oncoming vehicle identification points.

8. A computer storage medium, characterized in that include: at least one memory and at least one processor; The memory is used to store one or more program instructions; A processor is used to run one or more program instructions to execute the method for eliminating high-speed train meeting condition marks based on train vibration characteristics as described in any one of claims 1 to 7.

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