Rail smoothness adjustment method and device and computer readable storage medium

Abstract

This specification provides a method, apparatus, and computer-readable storage medium for adjusting track smoothness. Based on this method, after acquiring dynamic detection data of a target track with mileage errors, the mileage in the dynamic detection data can first be corrected according to a preset correlation rule, using easily obtainable reference detection data of the target track, based on the principle of optimal correlation. This results in corrected dynamic detection data with satisfactory mileage positioning accuracy. Then, based on a preset multi-chord control optimization algorithm, the adjustment amounts for each measuring point are determined using the corrected dynamic detection data to ensure high track smoothness. Finally, the target track's smoothness can be adjusted based on the adjustment amounts for each measuring point. Thus, by acquiring and effectively utilizing dynamic detection data with high detection density that better reflects the true state of the track, the smoothness of the target track can be adjusted efficiently and accurately.

Description

Technical Field

[0001] This manual pertains to the field of railway track technology, and particularly relates to methods, devices, and computer-readable storage media for adjusting track smoothness. Background Technology

[0002] As the service life increases, the actual geometry of the track will deviate from its design geometry, resulting in noticeable irregularities. These irregularities exacerbate wheel-rail interactions, thus affecting the safety and comfort of train operation. Therefore, track adjustments are necessary to maintain high track smoothness.

[0003] Based on existing methods, it is usually necessary to first use absolute measurement to perform static detection to obtain static detection data with high mileage positioning accuracy; then, based on the above static detection data, the track smoothness is adjusted to achieve the whole track.

[0004] However, the above methods require acquiring and utilizing static detection data for the entire process, which often results in low efficiency and high cost.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This specification provides a method, apparatus, and computer-readable storage medium for adjusting track smoothness. By acquiring and effectively utilizing dynamic detection data with high detection density that better reflects the actual state of the track under load, the smoothness of the target track can be adjusted efficiently and accurately. This reduces the overall track cost and improves overall track efficiency. At the same time, it can also better control the track smoothness of the target track across the entire band, achieving better overall track performance.

[0007] This specification provides an embodiment of a method for adjusting track smoothness, including:

[0008] Acquire reference detection data and dynamic detection data of the target orbit; wherein, the reference detection data includes historical static detection data of the target orbit, or static detection data obtained by relative measurement of the target orbit;

[0009] According to the preset correlation rules, the mileage in the dynamic detection data is corrected using the reference detection data to obtain the corrected dynamic detection data.

[0010] Based on the preset multi-string control optimization algorithm, the adjustment amount of each measuring point on the target track is determined using the corrected dynamic detection data;

[0011] Based on the adjustment amount of each measuring point of the target track, the smoothness of the target track is adjusted.

[0012] In some embodiments, the reference detection data includes at least: static gauge irregularity data of the target track.

[0013] In some embodiments, the mileage in the dynamic detection data is corrected using the reference detection data according to a preset correlation rule to obtain corrected dynamic detection data, including:

[0014] The static gauge irregularity data of the target track is divided into multiple calibration units; each calibration unit contains multiple data points; the length of the calibration unit is a preset first distance.

[0015] Based on the preset mileage error threshold and the dynamic gauge irregularity data of the target track in the dynamic detection data, target correction data is constructed.

[0016] According to the preset correlation rules, multiple calibration units are used to sequentially determine and correct each correction unit in the target correction data, resulting in multiple corrected units.

[0017] The multiple corrected units are resampled to obtain corrected dynamic detection data.

[0018] In some embodiments, according to a preset correlation rule, multiple calibration units are used to sequentially determine and correct each correction unit in the target correction data, resulting in multiple corrected units, including:

[0019] The current correction unit in the target correction data shall be determined and corrected in the following manner:

[0020] Based on the previously corrected unit, determine the current correction unit in the target correction data;

[0021] Multiple correlation coefficients are calculated using multiple data points in the current calibration unit and multiple data points in the current correction unit.

[0022] Select the correlation coefficient with the highest value from multiple correlation coefficients and use it as the current correlation coefficient;

[0023] Based on the current correlation coefficient and the mileage in the current calibration unit, the current correction unit is corrected to obtain the currently corrected unit.

[0024] In some embodiments, multiple correlation coefficients are calculated using multiple data points in the current calibration unit and multiple data points contained in the current correction unit, including:

[0025] The correlation coefficient k, numbered among multiple correlation coefficients, is calculated using the following formula:

[0026]

[0027] Where, ρ j (k) represents the correlation coefficient with index k in the current correction unit, j represents the index of the current correction unit, k represents the index of the correlation coefficient in the current correction unit, and y represents the correlation coefficient with index k in the current correction unit. j (i) represents the static gauge irregularity data of data point i in the current calibration unit, q j (i+k-1) represents the dynamic gauge irregularity data of the data point numbered i+k-1 in the current correction unit.

[0028] In some embodiments, based on a preset multi-string control optimization algorithm, the adjustment amount of each measuring point on the target track is determined using the corrected dynamic detection data, including:

[0029] Construct an objective function for the sum of adjustments at multiple measurement points on the target orbit;

[0030] A first type of constraint is constructed using multiple preset chord lengths to control the linearity and smoothness of the track; wherein the combination of effective detection bands corresponding to the multiple preset chord lengths covers a preset band range;

[0031] Based on the limiting effect of the target object on the orbital adjustment amount, a second type of constraint condition is constructed;

[0032] Based on the first and second types of constraints, the adjustment amount of each measuring point on the target track is determined by solving the objective function using the dynamic long-wave elevation irregularity data and track alignment irregularity data in the corrected dynamic detection data.

[0033] In some embodiments, the plurality of preset chord lengths include: a first preset chord length of 5 meters, a second preset chord length of 10 meters, a third preset chord length of 30 meters, and a fourth preset chord length of 60 meters.

[0034] In some embodiments, after determining the adjustment amount for each measuring point on the target orbit, the method further includes:

[0035] Based on the adjustment amount of each measuring point on the target track, the dynamic long-wave elevation irregularity data and track alignment irregularity data of each measuring point after adjustment are calculated.

[0036] Using a preset filter, the adjusted dynamic long-wave elevation and trajectory irregularities of each measuring point are smoothed and filtered to obtain the smoothed dynamic long-wave elevation and trajectory irregularities of each measuring point.

[0037] Based on the smoothed and filtered dynamic long-wave elevation and directional irregularities at each measuring point, the adjustment amount after smoothing and filtering at each measuring point is determined.

[0038] This specification provides an embodiment of a track smoothness adjustment device, comprising:

[0039] The acquisition module is used to acquire reference detection data and dynamic detection data of the target orbit; wherein, the reference detection data includes historical static detection data of the target orbit, or static detection data obtained by relative measurement of the target orbit;

[0040] The correction module is used to correct the mileage in the dynamic detection data according to the preset correlation rules and the reference detection data, so as to obtain the corrected dynamic detection data.

[0041] The determination module is used to determine the adjustment amount of each measuring point on the target track based on the preset multi-string control optimization algorithm and the corrected dynamic detection data;

[0042] The adjustment module is used to adjust the smoothness of the target track according to the adjustment amount of each measuring point of the target track.

[0043] This specification also provides a computer-readable storage medium storing computer instructions that, when executed, implement the relevant steps of the method for adjusting track smoothness.

[0044] The track smoothness adjustment method, apparatus, and computer-readable storage medium provided in this manual, after obtaining dynamic detection data of the target track through a highly efficient and low-cost dynamic detection method, can first correct the mileage in the dynamic detection data based on the principle of optimal correlation using readily available reference detection data of the target track, according to preset correlation rules, to obtain corrected dynamic detection data with mileage positioning accuracy meeting requirements. Then, based on a preset multi-chord control optimization algorithm, the adjustment amount of each measuring point that can make the adjusted track have high smoothness is determined using the above-mentioned corrected dynamic detection data. Subsequently, the smoothness of the target track can be adjusted according to the adjustment amount of each measuring point. Thus, by acquiring and effectively utilizing dynamic detection data with high detection density that can better reflect the true state of the line, the smoothness of the target track can be adjusted efficiently and accurately, which can effectively reduce the overall track cost, improve the overall track efficiency, and at the same time, better control the track smoothness of the target track across the entire band, achieving a better overall track performance. Attached Figure Description

[0045] To more clearly illustrate the embodiments of this specification, the accompanying drawings used in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a method for adjusting track smoothness provided in one embodiment of this specification;

[0047] Figure 2 This is a schematic diagram of the structural composition of an electronic device provided in one embodiment of this specification;

[0048] Figure 3 This is a schematic diagram of the structural composition of a track smoothness adjustment device provided in one embodiment of this specification;

[0049] Figure 4 This is a schematic diagram illustrating one embodiment of the track smoothness adjustment method provided in this specification, applied in a scenario example.

[0050] Figure 5 This is a schematic diagram illustrating one embodiment of the track smoothness adjustment method provided in this specification, applied in a scenario example.

[0051] Figure 6 This is a schematic diagram illustrating one embodiment of the track smoothness adjustment method provided in this specification, applied in a scenario example.

[0052] Figure 7 This is a schematic diagram illustrating one embodiment of the track smoothness adjustment method provided in this specification, applied in a scenario example.

[0053] Figure 8 This is a schematic diagram illustrating one embodiment of the track smoothness adjustment method provided in this specification, applied in a scenario example.

[0054] Figure 9 This is a schematic diagram illustrating one embodiment of the track smoothness adjustment method provided in this specification, applied in a scenario example.

[0055] Figure 10 This is a schematic diagram illustrating one embodiment of the track smoothness adjustment method provided in this specification, applied in a scenario example.

[0056] Figure 11 This is a schematic diagram illustrating one embodiment of the track smoothness adjustment method provided in this specification, applied in a scenario example.

[0057] Figure 12 This is a schematic diagram of an embodiment of applying the method for adjusting track smoothness provided in the embodiments of this specification in a scenario example. Specific implementation manners

[0058] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without making creative efforts shall fall within the scope of protection of this specification.

[0059] Considering that based on the existing methods, it is usually necessary to first perform static detection by using the absolute measurement method to obtain static detection data with high mileage positioning accuracy; then adjust the track smoothness based on the above static detection data to achieve track lining.

[0060] First of all, based on the above method, when performing static detection by using the absolute measurement method, it is necessary to use the CPⅢ control points on both sides of the line as the coordinate reference points, which means that the measurement efficiency will become relatively low, generally not higher than 100 meters per hour. Moreover, if the CPⅢ control points are damaged, it will also have an adverse impact on the measured data. In addition, based on the above static detection, although high measurement accuracy can be obtained by using corresponding precise measurement equipment, relatively high precise measurement costs and high labor costs need to be invested.

[0061] Secondly, when adjusting the track smoothness based on the above method by using the obtained static detection data, it is necessary to rely on the in-house processing software supporting the precise measurement equipment and use the maximum adjustable amount as the constraint. And, the above adjustment process often highly depends on the design experience of technicians. Although it can reduce the deviation of the track plane and elevation to a certain extent, due to the "memory" of the track geometry, the smoothness of the adjusted track cannot be well controlled, which will further restrict the track lining effect.

[0062] Although obtaining dynamic detection data through dynamic detection has high efficiency and low cost, there will be a large error in the mileage of the directly obtained dynamic detection data, and the mileage positioning accuracy of the dynamic detection data usually cannot reach the level of static detection, resulting in that the dynamic detection data often cannot be directly used to guide the precise adjustment or precise tamping operation (track lining) of the track.

[0063] In reality, dynamic inspection, unlike static inspection, obtains data by detecting the track under wheel loads. Compared to static inspection, dynamic inspection better reflects the true state characteristics of the track under train load conditions, and it can also collect inspection data with a relatively higher density.

[0064] Noting the aforementioned characteristics of dynamic and static detection, this application considers using dynamic detection data—which is relatively easy to obtain, has a higher detection density, and better reflects the true condition of the track—instead of static detection data, which is costly and inefficient, for track re-alignment. Before using dynamic detection data for track re-alignment, considering the potential errors in mileage within the data, reference detection data of the target track (e.g., historical static detection data of the target track, or static detection data obtained through relative measurement of the target track) can be used to specifically correct the mileage in the dynamic detection data based on the principle of optimal correlation. This results in corrected dynamic detection data with satisfactory mileage positioning accuracy. Then, based on a pre-set multi-chord control optimization algorithm, without relying on supporting processing software or the design experience of technical personnel, the corrected dynamic detection data can be fully utilized to accurately determine the adjustment amounts at various measuring points that result in a smoother track and better track re-alignment. Finally, based on the adjustment amounts at each measuring point, targeted smoothness adjustments can be made to the track at the corresponding positions on the target track. By acquiring and effectively utilizing dynamic detection data with high detection density that better reflects the true state of the track, the smoothness of the target track can be adjusted efficiently and accurately. This reduces track maintenance costs and improves track maintenance efficiency while also effectively controlling the track smoothness across the entire band, resulting in better track maintenance performance.

[0065] See Figure 1 As shown in the embodiments of this specification, a method for adjusting track smoothness is provided. Specifically, this method may include the following:

[0066] S101: Obtain reference detection data and dynamic detection data of the target orbit; wherein, the reference detection data includes historical static detection data of the target orbit, or static detection data obtained by relative measurement of the target orbit;

[0067] S102: According to the preset correlation rules, the mileage in the dynamic detection data is corrected using the reference detection data to obtain the corrected dynamic detection data;

[0068] S103: Based on the preset multi-string control optimization algorithm, the adjustment amount of each measuring point on the target track is determined using the corrected dynamic detection data;

[0069] S104: Adjust the smoothness of the target track according to the adjustment amount of each measuring point of the target track.

[0070] In some embodiments, the target track can be specifically understood as the track section of the line to be rectified.

[0071] In some embodiments, the aforementioned reference detection data can be specifically understood as detection data used to specifically correct the mileage of dynamic detection data.

[0072] In some embodiments, the aforementioned reference detection data may specifically include historical static detection data of the target orbit. Typically, the target orbit undergoes periodic static detection at preset time intervals, and the detection data collected during these periodic static detections can be recorded and saved as historical static detection data. In specific implementations, existing historical static detection data can be directly obtained and used as reference detection data, eliminating the need for further static detection, thereby improving detection efficiency and reducing detection costs.

[0073] In some embodiments, the aforementioned reference detection data may specifically be static detection data obtained using a relative measurement method. Static detection using a relative measurement method differs from static detection using an absolute measurement method in that it does not require coordinate alignment every few detection points. Therefore, using static detection data obtained by performing static detection on the target track using a relative measurement method, as reference detection data, is more cost-effective and efficient than static detection using an absolute measurement method.

[0074] In some embodiments, the aforementioned dynamic detection data can be specifically understood as detection data collected by dynamically detecting the target orbit.

[0075] Specifically, the detection data may include one or more of the following: elevation irregularity data, track alignment irregularity data, track gauge irregularity data, triangular pit irregularity data, horizontal irregularity data, and mileage.

[0076] In some embodiments, the target track can be dynamically detected by a detection train traveling on the target track to obtain dynamic detection data of the target track.

[0077] In this embodiment, the mileage in the dynamic detection data is obtained by accumulating the pulse count of the encoder at the axle box end of the detection train. Typically, the mileage is corrected using GPS coordinates or RFID tags at known distances after the detection train has traveled a certain distance. However, factors such as wheel wear, changes in the rolling radius caused by lateral movement or yaw motion, and GPS positioning errors can easily lead to errors in the mileage in the dynamic detection data. Static detection, on the other hand, uses ground markings as the initial mileage and calculates the mileage along the line by measuring the wheel speed of the track inspection trolley. Therefore, the mileage error is relatively small and almost negligible for maintenance.

[0078] In some embodiments, this application compares a large amount of dynamic and static detection data and finds that the similarity between the track gauge irregularity data in the dynamic detection data and the track gauge irregularity data in the static detection data is the highest. Therefore, the correlation coefficient between the track gauge irregularity data in the two types of detection data can be used to establish a matching relationship between the track gauge irregularities in the dynamic and static detection data. Then, based on this matching relationship, the mileage in the dynamic detection data is corrected to obtain corrected dynamic detection data with mileage accuracy reaching the level of static detection data. Subsequently, the corrected dynamic detection data can be used to replace the static detection data to realize the entire track.

[0079] In some embodiments, the reference detection data includes at least: static gauge irregularity data of the target track. Correspondingly, the dynamic detection data includes at least: dynamic gauge irregularity data of the target track.

[0080] In some embodiments, the mileage in the dynamic detection data is corrected using the reference detection data according to a preset correlation rule (also known as the correlation optimal principle) to obtain corrected dynamic detection data. In specific implementation, this may include the following steps:

[0081] S1: Divide the static gauge irregularity data of the target track into multiple calibration units; wherein each calibration unit contains multiple data points; the length of the calibration unit is a preset first distance;

[0082] S2: Based on the preset mileage error threshold and the dynamic gauge irregularity data of the target track in the dynamic detection data, construct the target correction data;

[0083] S3: Based on the preset correlation rules, multiple calibration units are used to sequentially determine and correct each correction unit in the target correction data to obtain multiple corrected units.

[0084] S4: Resample the multiple corrected units to obtain corrected dynamic detection data.

[0085] In some embodiments, during implementation, the static gauge irregularity data of the target track can be divided into multiple sequentially connected segments along the target track according to a preset first distance, serving as multiple calibration units. The length of each calibration unit is the preset first distance; each calibration unit contains multiple data points, corresponding to gauge irregularity data at multiple locations on the track; the center data points of two adjacent calibration units are separated by the preset first distance. The preset first distance can be greater than or equal to 20 meters and less than or equal to 50 meters.

[0086] Specifically, for example, the complete static gauge irregularity data of the target track can be divided into M calibration units at equal intervals, which can be denoted as: [Y1, Y2, ... Y t ,…Y M ]. Among them, Y t Let t be the calibration unit numbered t, where t is an integer greater than or equal to 1 and less than or equal to M.

[0087] Each calibration unit can contain multiple data points. The multiple data points contained in a calibration unit can be represented as a sequence of gauge irregularities corresponding to that calibration unit. For example, corresponding to any Y... t The track gauge irregularity data sequence can be denoted as: Y t =[y t (i)|i=1,2,…,N]. Among them, y t (i) represents the gauge irregularity data (static gauge irregularity data) of data point i in calibration unit t, where i is the number of the data point in calibration unit t, and N is the total number of data points in calibration unit t.

[0088] Furthermore, the interval between two adjacent data points in each calibration unit is a preset first sampling distance. For static detection data, the preset first sampling distance can specifically be 0.25 meters. Correspondingly, the total number of data points N contained in each calibration unit can be the ratio of the preset first distance (e.g., 30 meters) to the preset first sampling distance (e.g., 0.25 meters) (e.g., 30 / 0.25 = 120).

[0089] In this embodiment, since the aforementioned reference detection data is essentially static detection data, the mileage in the aforementioned reference data has high accuracy and can be used as a benchmark to correct dynamic detection data.

[0090] In practice, the target correction data can be constructed by first determining the preset mileage error threshold (also known as the maximum mileage error value, denoted as l) and combining it with the dynamic gauge irregularity data of the target track in the dynamic detection data. For example, the target correction data can be constructed by first finding the data that corresponds to the calibration unit Y in the dynamic detection data. t A corresponding data segment is used; then, a preset number of data points are added before and after this data segment to obtain a correction unit in the corresponding target correction data. The specific value of the preset number can be a preset mileage error threshold and a preset first sampling distance quotient.

[0091] Specifically, for example, if the preset mileage error threshold is 5 meters and the preset first sampling distance is 0.25 meters, then the preset number of samples is 5 / 0.25 = 20. Correspondingly, with Y... t The correction unit in the corresponding target correction data can be specifically represented in the following form: Q t =[q t (i)|i=1,2,…,N+1,…,N+20]. Among them, q t (i) Correct the data related to Y in the target data t The track gauge irregularity data (dynamic track gauge irregularity data) of data point i in the corresponding correction unit numbered t.

[0092] Furthermore, based on preset correlation rules, the aforementioned multiple calibration units can be used to sequentially determine and correct each correction unit in the target correction data from front to back, thereby improving the mileage accuracy of dynamic detection data.

[0093] In some embodiments, the above-mentioned method, based on a preset correlation rule, uses multiple calibration units to sequentially determine and correct each correction unit in the target correction data, resulting in multiple corrected units. In specific implementations, this may include the following: determining and correcting the current correction unit in the target correction data in the following manner:

[0094] S1: Based on the previously corrected unit, determine the current correction unit in the target correction data;

[0095] S2: Calculate multiple correlation coefficients using multiple data points in the current calibration unit and multiple data points in the current correction unit;

[0096] S3: Select the correlation coefficient with the largest value from multiple correlation coefficients and use it as the current correlation coefficient;

[0097] S4: Based on the current correlation coefficient and the mileage in the current calibration unit, correct the current correction unit to obtain the current corrected unit.

[0098] In some embodiments, the above-mentioned calculation of multiple correlation coefficients using multiple data points in the current calibration unit and multiple data points contained in the current correction unit may specifically include the following:

[0099] The correlation coefficient k, numbered among multiple correlation coefficients, is calculated using the following formula:

[0100]

[0101] Where, ρ j (k) represents the correlation coefficient with index k in the current correction unit, j represents the index of the current correction unit, k represents the index of the correlation coefficient in the current correction unit, and y represents the correlation coefficient with index k in the current correction unit. j (i) represents the gauge irregularity data of data point i in the current calibration unit, q j (i+k-1) represents the track gauge irregularity data of the data point numbered i+k-1 in the current correction unit.

[0102] Where k = 1, 2, ..., 2l / d1. L is the preset mileage error threshold, and d1 is the preset first sampling distance.

[0103] In some embodiments, when using calibration unit Y t-1 The correction unit Q, numbered t-1, has been repaired. t-1 The corrected cell Q with the number t-1 is obtained. t-1 After that, the current correction unit to be corrected is correction unit Q with the number t. t .

[0104] Specifically, in the target correction data, immediately following the previous corrected unit Q... t-1 At the location of ′, the current correction unit Q is determined. t And using the calibration unit Y t The multiple data points included, and the multiple data points included in the current correction unit, are used to calculate 2l / d1 correlation coefficients.

[0105] Next, compare the multiple correlation coefficients mentioned above, find the one with the largest value, and record it as the current correlation coefficient. Then, the mileage in the current calibration unit corresponding to the current correlation coefficient can be assigned to the current correction unit Q. t In the process, the current correction unit is modified to obtain the current corrected unit Q with the required mileage accuracy. t ′.

[0106] Then, based on the corrected cell Q t Then, in the same manner as above, the next correction unit is identified and corrected in the target correction data until all correction units in the target correction data have been corrected.

[0107] By using the above correction method, each correction can utilize the previously corrected unit to determine and correct the current correction unit, thereby reducing the search range, improving correction efficiency, and efficiently and accurately completing the correction of the target data, resulting in corrected dynamic detection data that meets the required mileage accuracy and reaches the mileage accuracy level of static detection data.

[0108] In some embodiments, after the correction is performed as described above, it is also considered that the sampling interval used for dynamic detection data (denoted as the preset second sampling distance) is often different from the preset first sampling distance used for static detection data. For example, the preset first sampling distance can be 0.625 meters, and the preset second sampling distance can be 0.25 meters.

[0109] Therefore, after completing the correction of multiple correction units and obtaining the corresponding multiple corrected units, the multiple corrected units can be further resampled to match the data in the corrected units with the preset second sampling distance, so as to obtain the corrected dynamic detection data that meets the requirements.

[0110] In some embodiments, the above-mentioned correction of the current correction unit based on the current correlation coefficient and the mileage in the current calibration unit to obtain the current corrected unit may specifically include: determining the current offset distance corresponding to the current correlation coefficient; subtracting the current offset distance from the mileage of each data point in the current calibration unit to obtain the current corrected unit.

[0111] In some embodiments, the above-described resampling process of the plurality of corrected units to obtain corrected dynamic detection data may specifically include: mapping the dynamic long-wave elevation irregularity data and track alignment irregularity data in the plurality of corrected units to the corresponding sleepers (as measuring points) according to the sleeper interval (a preset second sampling distance, for example, 0.625 meters) to obtain corrected dynamic detection data.

[0112] In some embodiments, the above-mentioned adjustment amount of each measuring point on the target track is determined using the modified dynamic detection data based on the preset multi-string control optimization algorithm. In specific implementation, this may include the following:

[0113] S1: Construct an objective function for the sum of adjustments at multiple measurement points on the target trajectory;

[0114] S2: Using multiple preset chord lengths, construct a first type of constraint condition for controlling the linear smoothness of the track; wherein, the combination of effective detection bands corresponding to the multiple preset chord lengths covers a preset band range;

[0115] S3: Construct the second type of constraint conditions based on the limiting effect of the target object on the track;

[0116] S4: Based on the first type of constraint and the second type of constraint, using the dynamic long-wave elevation irregularity data and trajectory irregularity data in the corrected dynamic detection data, the adjustment amount of each measuring point on the target track is determined by solving the objective function.

[0117] Specifically, the preset waveband range can be greater than or equal to 1.5 meters and less than or equal to 120 meters, where 120 meters is the cutoff wavelength for dynamic long waves. The target objects can specifically include structures such as bridges, tunnels, and overhead contact lines, as well as track fasteners. The adjustment amounts can specifically include adjustments to the elevation and / or plane of the track measuring point.

[0118] In some embodiments, the plurality of preset chord lengths may specifically include: a first preset chord length of 5 meters, a second preset chord length of 10 meters, a third preset chord length of 30 meters, and a fourth preset chord length of 60 meters. The combination of the effective detection bands corresponding to the above four preset chord lengths can better cover the required preset band range.

[0119] Specifically, based on the amplitude-frequency gain characteristics between the input and output of the midpoint chord measurement method, the effective detection bands for the four preset chord lengths can be obtained as follows: 3-10 meters, 7-20 meters, 20-60 meters, and 40-120 meters. After combination, the multi-chord constraint can control the bands of 1.5-120 meters contained in the dynamic long-wave irregularity data, that is, it can cover the preset band range.

[0120] In some embodiments, during specific implementation, based on the dynamic long-wave elevation irregularity data and / or track alignment irregularity data in the corrected dynamic detection data, isostatic resampling can be performed at sleeper intervals of 0.625 meters to locate the corresponding irregularity data onto the corresponding sleepers. The dynamic long-wave elevation irregularity data and / or track alignment irregularity data of the i-th sleeper (corresponding to the measuring point position numbered i) can be represented as p(i), and the adjustment amount at each measuring point position can be represented as t(i), where i = 1, 2, ..., M, and M is the number of sleepers. The adjusted dynamic long-wave elevation irregularity data and / or track alignment irregularity data p′(i) can be calculated according to the following formula: p′(i) = p(i) + t(i).

[0121] In some embodiments, the objective function for constructing the sum of adjustments at multiple measuring points on the target orbit described above may, in specific implementation, include constructing the objective function according to the following formula:

[0122]

[0123] Where f(i) is the objective function, and t(i) is the adjustment amount of the measuring point numbered i. N can specifically be the number of measuring points included in the fourth preset chord length of 60 meters.

[0124] In practice, the above objective function can be used to find the optimal solution that minimizes the sum of the absolute values ​​of the track adjustment, thereby reducing the disturbance to the track alignment and weakening the adverse effects of track "memory" on the overall track performance.

[0125] In some embodiments, the above-described method uses multiple preset chord lengths to construct a first type of constraint for controlling the linear smoothness of the track. In specific implementation, this may include constructing the first type of constraint according to the following formula:

[0126]

[0127] Where, δ L For the chord sine limit corresponding to the preset chord length of L meters, p′(i) and p′ are the allowable limits. L,1 (i), p′ L,2 (i) are the dynamic long-wave elevation and trajectory irregularities after adjusting the start point, midpoint, and end point of a preset chord length of L meters. The specific values ​​of L can include 5, 10, 30, and 60, corresponding to the first, second, third, and fourth preset chord lengths, respectively.

[0128] In practice, the first type of constraint conditions mentioned above can be used to control the midpoint chord distance of the four preset chord lengths to ensure the smoothness of the adjusted alignment and optimize the track smoothness.

[0129] In some embodiments, the second type of constraint condition is constructed based on the limiting effect of the target object on the track. In specific implementation, this may include the following: Constructing the second type of constraint condition according to the following formula:

[0130] α(i)≤t(i)≤β(i)

[0131] Wherein, β(i) is the upper limit of the restrictive effect of the target object on the measuring point with track number i, and α(i) is the lower limit of the restrictive effect of the target object on the measuring point with track number i.

[0132] In practice, the second type of constraint conditions mentioned above can be used to introduce the effects of structures such as bridges, tunnels, and overhead contact lines, as well as track fasteners, on the track, in order to further constrain the adjustment amount.

[0133] In some embodiments, specifically, the first type of constraints and the second type of constraints described above can be combined to obtain the following overall constraints:

[0134]

[0135] Based on the above overall constraints and objective function, the dynamic long-wave elevation irregularity data and trajectory irregularity data of the adjusted measuring point i can be calculated by adjusting the amount t(i). Then, let i = i + 1 and repeat the above optimization process until the adjustment amount of all N measuring points is calculated, so as to obtain the adjustment amount of each measuring point on the target track.

[0136] In some embodiments, after determining the adjustment amount for each measuring point on the target orbit, the method may further include the following:

[0137] S1: Based on the adjustment amount of each measuring point on the target track, calculate the dynamic long-wave elevation irregularity data and track alignment irregularity data of each measuring point after adjustment (which can be denoted as p′(i));

[0138] S2: Using a preset filter, the adjusted dynamic long-wave height and trajectory irregularity data and track irregularity data of each measuring point are smoothed and filtered to obtain the smoothed and filtered dynamic long-wave height and trajectory irregularity data and track irregularity data of each measuring point (which can be denoted as p″(i)).

[0139] S3: Based on the smoothed and filtered dynamic long-wave elevation and directional irregularities at each measuring point, determine the smoothed and filtered adjustment amount for each measuring point. For example, t′(i) = p″(i) - p(i).

[0140] Specifically, the aforementioned preset filters may include: Savitzky-Golay filters, etc.

[0141] Using the smoothed and filtered adjustment values ​​obtained from each measuring point in the above method for track straightening can make the final track smoother, without any abrupt changes such as burrs or steps, resulting in a relatively better track straightening effect.

[0142] In some embodiments, the smoothness adjustment of the target track based on the adjustment amount of each measuring point of the target track can specifically include the following: based on the adjustment amount of each measuring point, targeted adjustments and maintenance are performed on the track at the positions corresponding to the measuring points on the target track to ensure that the smoothness of the target track meets the requirements. This allows for better completion of the overall maintenance of the target track.

[0143] As can be seen from the above, the track smoothness adjustment method provided in the embodiments of this specification, after obtaining dynamic detection data of the target track with mileage errors in a highly efficient and low-cost manner, can first correct the mileage in the dynamic detection data based on the principle of optimal correlation using easily obtainable reference detection data of the target track according to preset correlation rules, thus obtaining corrected dynamic detection data with mileage positioning accuracy meeting the requirements; then, based on a preset multi-chord control optimization algorithm, using the above-mentioned corrected dynamic detection data, the adjustment amount of each measuring point that can make the adjusted track have high smoothness is determined; and then, the smoothness of the target track can be adjusted according to the adjustment amount of each measuring point. Therefore, by acquiring and effectively utilizing dynamic detection data with high detection density that better reflects the true state of the line, the smoothness of the target track can be adjusted efficiently and accurately, effectively reducing the overall track cost and improving the overall track efficiency. At the same time, it can also better control the track smoothness of the target track across the entire band, achieving a better overall track performance.

[0144] This specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions. Specifically, the processor can perform the following steps according to the instructions: acquiring reference detection data and dynamic detection data of a target track; wherein the reference detection data includes historical static detection data of the target track, or static detection data obtained by relative measurement of the target track; correcting the mileage in the dynamic detection data using the reference detection data according to a preset correlation rule, to obtain corrected dynamic detection data; determining the adjustment amount of each measuring point on the target track using the corrected dynamic detection data based on a preset multi-string control optimization algorithm; and adjusting the smoothness of the target track according to the adjustment amounts of each measuring point on the target track.

[0145] To execute the above instructions more accurately, please refer to... Figure 2 As shown in the embodiments of this specification, another specific electronic device is also provided, wherein the electronic device includes a network communication port 201, a processor 202 and a memory 203, and the above structures are connected by internal cables so that the various structures can perform specific data interaction.

[0146] Specifically, the network communication port 201 can be used to acquire reference detection data and dynamic detection data of the target orbit; wherein, the reference detection data includes historical static detection data of the target orbit, or static detection data obtained by relative measurement of the target orbit.

[0147] The processor 202 can be specifically used to correct the mileage in the dynamic detection data according to the reference detection data based on the preset correlation rules, so as to obtain the corrected dynamic detection data; to determine the adjustment amount of each measuring point on the target track based on the preset multi-string control optimization algorithm and the corrected dynamic detection data; and to adjust the smoothness of the target track according to the adjustment amount of each measuring point on the target track.

[0148] The memory 203 can be used to store the corresponding instruction program.

[0149] In this embodiment, the network communication port 201 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0150] In this embodiment, the processor 202 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0151] In this embodiment, the memory 203 may include multiple layers. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0152] This specification also provides a computer-readable storage medium based on the above-described track smoothness adjustment method. The computer-readable storage medium stores computer program instructions that, when executed, implement the following: acquiring reference detection data and dynamic detection data of a target track; wherein the reference detection data includes historical static detection data of the target track, or static detection data obtained by relative measurement of the target track; correcting the mileage in the dynamic detection data using the reference detection data according to a preset correlation rule, to obtain corrected dynamic detection data; determining the adjustment amount of each measuring point on the target track using the corrected dynamic detection data based on a preset multi-chord control optimization algorithm; and adjusting the smoothness of the target track according to the adjustment amounts of each measuring point on the target track.

[0153] In this embodiment, the storage medium includes, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured according to standards specified in the communication protocol for network connection communication.

[0154] In this embodiment, the specific functions and effects implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other embodiments, and will not be repeated here.

[0155] See Figure 3 As shown, at the software level, this embodiment of the specification also provides a track smoothness adjustment device, which may specifically include the following structural modules:

[0156] The acquisition module 301 can be specifically used to acquire reference detection data and dynamic detection data of the target orbit; wherein, the reference detection data includes historical static detection data of the target orbit, or static detection data obtained by relative measurement of the target orbit;

[0157] The correction module 302 can be used to correct the mileage in the dynamic detection data according to the preset correlation rules and the reference detection data to obtain the corrected dynamic detection data.

[0158] The determination module 303 can be specifically used to determine the adjustment amount of each measuring point on the target track based on the preset multi-string control optimization algorithm and the corrected dynamic detection data;

[0159] The adjustment module 304 can be used to adjust the smoothness of the target track according to the adjustment amount of each measuring point of the target track.

[0160] It should be noted that the units, devices, or modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described by dividing them into various modules according to their functions. Of course, in implementing this specification, the functions of each module can be implemented in one or more software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection between the devices or units shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0161] As can be seen from the above, the track smoothness adjustment device provided in the embodiments of this specification can efficiently and accurately adjust the smoothness of the target track by acquiring and effectively utilizing dynamic detection data with high detection density that can better reflect the true state of the line. This can effectively reduce the overall track cost and improve the overall track efficiency. At the same time, it can also better control the track smoothness of the target track across the entire band, thus achieving better overall track performance.

[0162] In a specific scenario example, the track smoothness adjustment method provided in the embodiments of this specification can be applied to adjust and maintain a certain section of track.

[0163] In this scenario example, static gauge irregularity data (e.g., reference detection data) can be used as a benchmark, and the mileage error of the dynamic detection data can be corrected based on the principle of optimal correlation (resulting in corrected dynamic detection data). Then, track adjustment amounts are determined based on a multi-chord control optimization algorithm, and the track is adjusted accordingly. This effectively utilizes dynamic detection data, which has a higher detection density and better reflects the true condition of the line, to guide the maintenance of high-speed railways. On the one hand, it resolves the contradiction between the precision measurement efficiency and the frequent maintenance of the line in existing track fine-tuning and tamping technologies; on the other hand, it makes the overall track scheme compatible with track smoothness management.

[0164] In practice, the following steps can be followed.

[0165] Step 1: Correct the mileage error of the dynamic detection data based on the principle of optimal correlation (corresponding to the correction of the mileage in the dynamic detection data by using the reference detection data according to the preset correlation rules, so as to obtain the corrected dynamic detection data).

[0166] Specifically, for example, if the maximum mileage error (e.g., a preset mileage error threshold) of the acquired dynamic detection data is 60 meters, and the length of a calibration unit is 50 meters, the method provided in the application is used to first correct the mileage error of the first calibration unit. The variation of the correlation coefficient with the unit's movement distance can be found in [reference needed]. Figure 4 As shown, the correlation reaches its optimal (maximum value) at a unit movement distance of -7.5 meters, with a correlation coefficient of 0.94. Therefore, the dynamic detection data mileage of this calibration unit can be corrected by adding 7.5 meters to each unit. The same method can be used to correct other units one by one.

[0167] After correcting the mileage deviation of all calibration units' dynamic test data, resampling can be performed at each sampling point of the static test data to obtain the mileage correction result, as shown below. Figure 5 As shown in the diagram (corresponding to K16+000~K16+800), it can be seen that after the mileage deviation correction, the dynamic and static gauge irregularity waveforms are aligned. Further details can be found in the following documentation. Figure 6 (Corresponding to K16+050~K16+063.2), it shows a detailed diagram of the section from K16+060.8 to K16+074. Before the correction, the mileage deviation of the dynamic inspection data reached 3 meters. After the correction, the mileage error was controlled within one sleeper interval of 0.6 meters.

[0168] Step 2: Determine the track adjustment amount based on the multi-string control optimization algorithm (corresponding to the adjustment amount of each measuring point on the target track determined by using the corrected dynamic detection data based on the preset multi-string control optimization algorithm).

[0169] Specifically, for example, taking the dynamic long-wave elevation irregularities after mileage correction (cutoff wavelength 120 meters) as the optimization object, the track smoothness constraints are set as follows: 1 mm for 60-meter chord, 0.5 mm for 30-meter chord, 0.3 mm for 10-meter chord, and 0.1 mm for 5-meter chord. The optimization results and adjustment amounts obtained using the algorithm of this patent can be found in the following document. Figure 7 (Comparison of 60-meter string before and after adjustment) Figure 8 (Comparison of 30-meter string before and after adjustment) Figure 9 (Comparison of 10-meter string before and after adjustment) Figure 10 (Comparison of 5-meter string before and after adjustment) Figure 11 (Comparison of uneven 120-meter long-wave height before and after adjustment) and Figure 12 (Elevation adjustment amount)

[0170] Among them, the absolute value of the maximum value of the 60-meter chord before adjustment was 9.2 mm, and after optimization, it was all controlled within 1 mm; the absolute value of the maximum value of the 30-meter chord before adjustment was 8.3 mm, and after optimization, it was all controlled within 0.5 mm; the absolute value of the maximum value of the 10-meter chord before adjustment was 4.0 mm, and after optimization, it was all controlled within 0.3 mm; the absolute value of the maximum value of the 5-meter chord before adjustment was 3.2 mm, and after optimization, it was all controlled within 0.1 mm. All four smoothness indicators were strictly controlled. The maximum value of dynamic long-wave elevation unevenness before adjustment was 6.4 mm, and the minimum value was -7.0 mm, and after optimization, it was controlled at 2.9 mm and -2.3 mm respectively. The maximum adjustment amount was +5.3 mm, and the minimum value was -5.0 mm.

[0171] The above scenario examples verify that the track smoothness adjustment method provided in the embodiments of this specification can indeed efficiently and accurately adjust the smoothness of the target track by acquiring and effectively utilizing dynamic detection data with high detection density that can better reflect the true state of the line.

[0172] While this specification provides the steps of operation for the methods described in the embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. The terms "first," "second," etc., are used to denote names and do not indicate any particular order.

[0173] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0174] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer-readable storage media, including storage devices.

[0175] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, electronic device, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of this specification.

[0176] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. This specification can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, electronic computer, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0177] Although this specification has been described by way of examples, those skilled in the art will recognize that many variations and modifications are possible without departing from the spirit of this specification, and it is intended that the appended claims cover such variations and modifications without departing from the spirit of this specification.

Claims

1. A method of adjusting track regularity, characterized in that The method comprises the following steps: acquiring reference detection data and dynamic detection data of a target track; wherein the reference detection data comprises historical static detection data of the target track, or static detection data obtained by using a relative measurement method on the target track; correcting the mileage in the dynamic detection data by using the reference detection data according to a preset correlation rule, to obtain corrected dynamic detection data; determining adjustment amounts of each measuring point of the target track by using the corrected dynamic detection data based on a preset multi-string control optimization algorithm; comprising: constructing a target function about the adjustment amounts of a plurality of measuring points on the target track; constructing a first type of constraint condition for controlling track linear smoothness by using a plurality of preset string lengths; wherein the combination of effective detection wave bands corresponding to the plurality of preset string lengths covers a preset wave band range; constructing a second type of constraint condition according to the limiting effect of the target object on the track; based on the first type of constraint condition and the second type of constraint condition, determining the adjustment amounts of each measuring point of the target track by solving the target function by using dynamic long-wave unevenness data and alignment unevenness data in the corrected dynamic detection data; adjusting the smoothness of the target track according to the adjustment amounts of each measuring point of the target track. The target function comprises: The target function comprises: f(i) is the target function, t(i) is the adjustment amount of the measuring point numbered i, and N is the number of measuring points contained in the fourth preset chord length with a chord length of 60 meters. The first type of constraint includes: Where, δ L For the chord sine limit corresponding to the preset chord length of L meters, p′(i) and p′ are the allowable limits. L,1 (i), p′ L,2 (i) are the dynamic long wave elevation irregularity data and trajectory irregularity data after adjusting the start point, midpoint and end point of the preset chord length of L meters, respectively. The values ​​of L include 5, 10, 30 and 60, which correspond to the first preset chord length, the second preset chord length, the third preset chord length and the fourth preset chord length, respectively. The second type of constraint condition comprises: α(i)≤t(i)≤β(i), wherein β(i) is an upper limit value of the limiting effect of the target object on the measuring point numbered i of the track, and α(i) is a lower limit value of the limiting effect of the target object on the measuring point numbered i of the track.

2. The method of claim 1, wherein, The reference detection data at least comprises static track gauge unevenness data of the target track.

3. The method of claim 2, wherein, According to a preset correlation rule, the mileage in the dynamic detection data is corrected by using the reference detection data, to obtain corrected dynamic detection data, comprising: dividing the static track gauge unevenness data of the target track into a plurality of calibration units; wherein each calibration unit in the plurality of calibration units contains a plurality of data points; the length of the calibration unit is a preset first distance; constructing target correction data according to a preset mileage error threshold and dynamic track gauge unevenness data of the target track in the dynamic detection data; determining and correcting each correction unit in the target correction data in sequence by using the plurality of calibration units according to a preset correlation rule, to obtain a plurality of corrected units; performing resampling processing on the plurality of corrected units to obtain the corrected dynamic detection data.

4. The method of claim 3, wherein, According to a preset correlation rule, the mileage in the dynamic detection data is corrected by using the reference detection data, to obtain corrected dynamic detection data, comprising: determining and correcting the current correction unit in the target correction data in the following manner: determining the current correction unit in the target correction data according to the last corrected unit; calculating a plurality of correlation coefficients by using a plurality of data points in the current calibration unit and a plurality of data points contained in the current correction unit; selecting the correlation coefficient with the largest value from the plurality of correlation coefficients as the current correlation coefficient; According to the current correlation coefficient and the mileage in the current calibration unit, a current correction unit is corrected to obtain a current corrected unit.

5. The method of claim 4, wherein, A plurality of correlation coefficients are calculated using a plurality of data points in the current calibration unit and a plurality of data points contained in the current correction unit, including: The kth correlation coefficient in the plurality of correlation coefficients is calculated according to the following formula: wherein p j (k) is the correlation coefficient for the kth current correction unit, j is the number of the current correction unit, k is the number of the correlation coefficient of the current correction unit, y j (i) is the track gauge irregularity data for the ith data point in the current calibration unit, q j (i+k-1) is the track gauge irregularity data for the ith+k-1 data point in the current correction unit.

6. The method of claim 1, wherein, The plurality of preset chord lengths include: a first preset chord length with a chord length of 5 meters, a second preset chord length with a chord length of 10 meters, a third preset chord length with a chord length of 30 meters, and a fourth preset chord length with a chord length of 60 meters.

7. The method of claim 1, wherein, After determining the adjustment amount of each measuring point on the target track, the method further includes: According to the adjustment amount of each measuring point on the target track, the adjusted dynamic long-wave high-low irregularity data and the track irregularity data of each measuring point are calculated; The adjusted dynamic long-wave high-low irregularity data and the track irregularity data of each measuring point are smoothed and filtered using a preset filter to obtain the smoothed and filtered dynamic long-wave high-low irregularity data and the track irregularity data of each measuring point; According to the smoothed and filtered dynamic long-wave high-low irregularity data and the track irregularity data of each measuring point, the smoothed and filtered adjustment amount of each measuring point is determined.

8. A device for adjusting the track regularity, characterized in that It includes: The acquisition module is configured to acquire reference detection data and dynamic detection data of a target track; the reference detection data includes historical static detection data of the target track or static detection data obtained by using a relative measurement method on the target track; The correction module is configured to correct the mileage in the dynamic detection data using the reference detection data according to a preset correlation rule to obtain corrected dynamic detection data; The determination module is configured to determine the adjustment amount of each measuring point on the target track based on a preset multi-chord control optimization algorithm using the corrected dynamic detection data; the determination module is specifically configured to: construct a target function about the adjustment amount of a plurality of measuring points on the target track; use a plurality of preset chord lengths to construct a first type of constraint condition for controlling track linear smoothness; the combination of effective detection wave bands corresponding to the plurality of preset chord lengths covers a preset wave band range; construct a second type of constraint condition according to the limiting effect of the target object on the track; based on the first type of constraint condition and the second type of constraint condition, the dynamic long-wave high-low irregularity data and the track irregularity data in the corrected dynamic detection data are used to solve the target function to determine the adjustment amount of each measuring point of the target track; The adjustment module is configured to adjust the smoothness of the target track according to the adjustment amount of each measuring point of the target track. The target function comprises: The target function comprises: f(i) is the target function, t(i) is the adjustment amount of the measuring point numbered i, and N is the number of measuring points contained in the fourth preset chord length with a chord length of 60 meters. The first type of constraint condition includes: Wherein, δ L is a chord secant allowable limit value corresponding to a preset chord length of L meters, p'(i), p'(i) L,1 (i), p'(i) L,2 (i) are respectively the dynamic long-wave high-low irregularity data and the alignment irregularity data of the starting point, the midpoint and the ending point of the preset chord length of L meters, and the value of L includes: 5, 10, 30 and 60, which respectively correspond to the first preset chord length, the second preset chord length, the third preset chord length and the fourth preset chord length. The second type of constraint condition includes: α(i)≤t(i)≤β(i), where β(i) is an upper limit value of the limiting effect of the target object on the i th measuring point of the track, and α(i) is a lower limit value of the limiting effect of the target object on the i th measuring point of the track.

9. A computer-readable storage medium, characterized in that, The computer instructions are stored thereon, and the instructions are executed to implement the steps of the method of any one of claims 1 to 7. The computer instructions are stored thereon, and the instructions are executed to implement the steps of the method of any one of claims 1 to 7.

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