CN113987819A - Track smoothness adjustment method, device and computer readable storage medium - Google Patents
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Abstract
本说明书提供了轨道平顺性的调整方法、装置和计算机可读存储介质。基于该方法，在获取得到里程存在误差的目标轨道的动态检测数据之后，可以先根据预设的相关性规则，利用较易获取的目标轨道的参照检测数据，基于相关性最优原则对动态检测数据中的里程进行修正，得到里程定位精度满足要求的修正后的动态检测数据；再基于预设的多弦控制优化算法，利用上述修正后的动态检测数据，确定出能够使得调整后的轨道具有较高的平顺性的各个测点的调整量；进而可以根据各个测点的调整量，对目标轨道进行平顺性调整。从而可以通过获取并有效利用检测密度较高、较能反映线路真实状态的动态检测数据，高效、精准地对目标轨道进行平顺性调整。
The present specification provides a method, apparatus, and computer-readable storage medium for adjusting track smoothness. Based on this method, after the dynamic detection data of the target track with mileage error is obtained, the dynamic detection data of the target track which is easier to obtain can be used according to the preset correlation rule, and the dynamic detection data can be detected based on the principle of optimal correlation. The mileage in the data is corrected to obtain the corrected dynamic detection data whose mileage positioning accuracy meets the requirements; and then based on the preset multi-chord control optimization algorithm, using the above corrected dynamic detection data, it is determined that the adjusted track has Adjustment of each measuring point with higher smoothness; further, the smoothness of the target track can be adjusted according to the adjustment of each measuring point. Therefore, the smoothness of the target track can be adjusted efficiently and accurately by acquiring and effectively utilizing the dynamic detection data with higher detection density and better reflecting the true state of the line.
Description
Technical Field
The present disclosure relates to railway track technologies, and in particular, to a method and an apparatus for adjusting track smoothness, and a computer-readable storage medium.
Background
With the increase of the service life, the actual geometric form and the design geometric form of the track have deviation, and obvious irregularity exists. This kind of track irregularity can aggravate the interact between the wheel rail, and then influences the operation safety and the travelling comfort of train on the track. Therefore, the track needs to be adjusted to maintain high smoothness of the track.
Based on the existing method, static detection is usually carried out by adopting an absolute measurement mode to obtain static detection data with higher mileage positioning accuracy; and then, based on the static detection data, the smoothness of the track is adjusted so as to realize the whole track.
However, the above method needs to acquire and utilize static detection data to perform the lane trimming, and often has the problems of low efficiency, high cost, and the like.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the specification provides a method and a device for adjusting track smoothness and a computer-readable storage medium, which can efficiently and accurately adjust the smoothness of a target track by acquiring and effectively utilizing dynamic detection data which has higher detection density and can better reflect the real state of a line under a load condition, thereby reducing the track-leveling cost, improving the track-leveling efficiency, better controlling the track smoothness of all bands of the target track and obtaining better track-leveling effect.
An embodiment of the present specification provides a method for adjusting track smoothness, including:
acquiring reference detection data and dynamic detection data of a target track; the reference detection data comprise historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track;
according to a preset correlation rule, the mileage in the dynamic detection data is corrected by using the reference detection data, and corrected dynamic detection data is obtained;
based on a preset multi-chord control optimization algorithm, determining the adjustment quantity of each measuring point on the target track by using the modified dynamic detection data;
and carrying out smoothness adjustment on the target track according to the adjustment quantity of each measuring point of the target track.
In some embodiments, the reference detection data includes at least: static gauge irregularity data for the target track.
In some embodiments, modifying the mileage in the dynamic detection data by using the reference detection data according to a preset correlation rule to obtain modified dynamic detection data includes:
dividing static track gauge irregularity data of the target track into a plurality of calibration units; wherein each calibration unit of the plurality of calibration units comprises 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 value and dynamic track gauge irregularity data of a target track in the dynamic detection data;
according to a preset correlation rule, sequentially determining and correcting each correction unit in the target correction data by using a plurality of calibration units according to a sequence to obtain a plurality of corrected units;
and performing resampling processing on the plurality of corrected units to obtain corrected dynamic detection data.
In some embodiments, sequentially determining and correcting each correction unit in the target correction data in sequence by using a plurality of calibration units according to a preset correlation rule to obtain a plurality of corrected units, including:
determining and correcting a current correction unit in the target correction data in the following manner:
determining a 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;
screening out a correlation coefficient with the largest value from the plurality of correlation coefficients to serve as a current correlation coefficient;
and correcting the current correction unit according to the current correlation coefficient and the mileage in the current calibration unit to obtain the current corrected unit.
In some embodiments, calculating a plurality of correlation coefficients using a plurality of data points in the current calibration unit and a plurality of data points included in the current correction unit includes:
and calculating a number k correlation coefficient in the plurality of correlation coefficients according to the following formula:
where ρ isj(k) The number of the current correction unit is k, j is the number of the current correction unit, k is the number of the correlation coefficient in the current correction unit, yj(i) Static track-gage irregularity data for data points numbered i in the current calibration unit, qjAnd (i + k-1) is dynamic track gauge irregularity data of data points numbered as i + k-1 in the current correction unit.
In some embodiments, determining the adjustment amount of each measuring point on the target track by using the modified dynamic detection data based on a preset multi-chord control optimization algorithm includes:
constructing an objective function about the adjustment quantity sum of a plurality of measuring points on the target track;
constructing a first class of constraint conditions for controlling the linear smoothness of the track by using a plurality of preset chord lengths; the combination of the effective detection wave bands corresponding to the preset chord lengths covers a preset wave band range;
constructing a second class of constraint conditions according to the limiting effect of the target object on the track adjustment amount;
and based on the first class constraint condition and the second class constraint condition, determining the adjustment quantity of each measuring point on the target track by solving the target function by utilizing the dynamic long-wavelength height irregularity data and the rail irregularity data in the modified dynamic detection data.
In some embodiments, the plurality of preset chord lengths comprises: the chord length is 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.
In some embodiments, after determining the adjustment amount for each of the stations on the target track, the method further comprises:
calculating the adjusted dynamic long wave height irregularity data and the adjusted rail irregularity data of each measuring point according to the adjustment quantity of each measuring point on the target track;
performing smooth filtering processing on the adjusted dynamic long-wave height irregularity data and the adjusted rail direction irregularity data of each measuring point by using a preset filter to obtain the smooth filtered dynamic long-wave height irregularity data and the rail direction irregularity data of each measuring point;
and determining the adjustment quantity of each measuring point after smooth filtering according to the dynamic long wave height irregularity data and the rail irregularity data of each measuring point after smooth filtering.
An embodiment of the present specification provides an adjusting device for track smoothness, including:
the acquisition module is used for acquiring reference detection data and dynamic detection data of the target track; the reference detection data comprise historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track;
the correction module is used for 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;
the determining module is used for determining the adjustment quantity of each measuring point on the target track by using the modified dynamic detection data based on a preset multi-chord control optimization algorithm;
and the adjusting module is used for performing smooth adjustment on the target track according to the adjustment quantity of each measuring point of the target track.
The present specification also provides a computer readable storage medium, on which computer instructions are stored, and when executed, the instructions implement the relevant steps of the method for adjusting track smoothness.
After the dynamic detection data of the target track is obtained through a dynamic detection mode with high efficiency and low cost, the mileage in the dynamic detection data can be corrected based on the optimal principle of the relevance according to the preset relevance rule by utilizing the reference detection data of the target track which is easy to obtain, so that the corrected dynamic detection data with the mileage positioning accuracy meeting the requirement is obtained; determining the adjustment amount of each measuring point which can enable the adjusted track to have higher smoothness by using the corrected dynamic detection data based on a preset multi-chord control optimization algorithm; and then the smoothness of the target track can be adjusted according to the adjustment amount of each measuring point. Therefore, the dynamic detection data which is high in detection density and can reflect the real state of the line can be obtained and effectively utilized, smoothness adjustment can be efficiently and accurately carried out on the target track, the track arrangement cost can be effectively reduced, track arrangement efficiency is improved, meanwhile, the track smoothness of the target track full wave band can be well controlled, and a good track arrangement effect is obtained.
Drawings
In order to more clearly illustrate the embodiments of the present specification, the drawings needed to be used in the embodiments will be briefly described below, and the drawings in the following description are only some of the embodiments described in the specification, and it is obvious to those skilled in the art that other drawings can be obtained based on the drawings without any inventive work.
Fig. 1 is a schematic flow chart of an adjusting method for track smoothness according to an embodiment of the present disclosure;
fig. 2 is a schematic structural component diagram of an electronic device provided in an embodiment of the present specification;
fig. 3 is a schematic structural diagram of an adjusting device for track smoothness according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness, which is provided by the embodiments of the present disclosure, in a scene example;
fig. 5 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness, which is provided by the embodiments of the present disclosure, in a scene example;
fig. 6 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness, which is provided by the embodiments of the present disclosure, in a scene example;
fig. 7 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness according to an embodiment of the present disclosure;
fig. 8 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness according to an embodiment of the present disclosure;
fig. 9 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness according to an embodiment of the present disclosure;
fig. 10 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness according to an embodiment of the present disclosure;
fig. 11 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness according to an embodiment of the present disclosure;
fig. 12 is a schematic diagram illustrating an embodiment of an adjusting method for track smoothness, which is provided by the embodiments of the present specification, in a scene example.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
Considering that static detection is usually carried out in an absolute measurement mode based on the existing method, static detection data with high mileage positioning accuracy are obtained; and then, based on the static detection data, the smoothness of the track is adjusted so as to realize the whole track.
First, based on the above method, when static detection is performed by using absolute measurement, CP iii control points on both sides of a line are required to be used as coordinate reference points, which means that the measurement efficiency will become low, and generally will not be higher than 100 meters per hour. Also, if the CP iii control point is damaged, the measured data may be adversely affected. In addition, based on the static detection, the corresponding precision measurement equipment is used for measurement, and although higher measurement precision can be obtained, more expensive precision measurement expense and higher labor cost are required to be invested.
Secondly, when the smoothness of the track is adjusted by using the obtained static detection data based on the method, the maximum adjustable quantity is used as constraint by depending on the internal processing software matched with the precision measurement equipment. Moreover, the adjustment process often depends heavily on the design experience of the technician. Although the deviation between the plane and the elevation of the track can be reduced to a certain extent, the smoothness of the adjusted track cannot be well controlled due to the 'memory' of the geometric position of the track, and the track adjusting effect is restricted.
Although the efficiency of obtaining dynamic detection data through dynamic detection is high and the cost is low, the mileage of the directly obtained dynamic detection data has a large error, and the mileage positioning accuracy of the dynamic detection data usually cannot reach the level of static detection, so that the dynamic detection data cannot be directly used for guiding fine adjustment or fine tamping operation (track adjustment) of a track.
In fact, dynamic detection is distinguished from static detection in that the track is detected under the action of wheel loads. Compared with static detection, dynamic detection can better reflect the real state characteristics of the track under the condition of train load, and meanwhile, detection data with relatively higher detection density can be acquired.
By just paying attention to the characteristics of dynamic detection and static detection, the method and the device consider that the static detection data which is high in detection cost and low in detection efficiency is not used for track finishing, but the dynamic detection data which is relatively easy to detect and obtain, high in detection density and capable of reflecting the real state of a line is used for track finishing instead of the static detection data. Before the dynamic detection data is used for executing track completion, considering that the mileage in the dynamic detection data has errors, the reference detection data of the target track (for example, historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode for the target track) which is easy to obtain can be utilized firstly, and the mileage in the dynamic detection data is corrected in a targeted manner based on the principle of optimal correlation, so as to obtain the corrected dynamic detection data with the mileage positioning accuracy meeting the requirement; based on a preset multi-chord control optimization algorithm, the corrected dynamic detection data can be fully utilized without depending on matched processing software and design experience of technicians, and the adjustment quantity of each measuring point, which can enable the adjusted track to have higher smoothness and better track-adjusting effect, can be accurately determined; and further, the track pertinence at the corresponding position of the target track can be adjusted smoothly according to the adjustment amount of each measuring point. Therefore, the smoothness of the target track can be efficiently and accurately adjusted by acquiring and effectively utilizing the dynamic detection data which is high in detection density and can better reflect the real state of the line, the track smoothness of the target track can be better controlled while the track shaping cost is reduced and the track shaping efficiency is improved, and a better track shaping effect is obtained.
Referring to fig. 1, an embodiment of the present disclosure provides a method for adjusting track smoothness. When the method is implemented, the following contents can be included:
s101: acquiring reference detection data and dynamic detection data of a target track; the reference detection data comprise historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track;
s102: according to a preset correlation rule, the mileage in the dynamic detection data is corrected by using the reference detection data, and corrected dynamic detection data is obtained;
s103: based on a preset multi-chord control optimization algorithm, determining the adjustment quantity of each measuring point on the target track by using the modified dynamic detection data;
s104: and carrying out smoothness adjustment on the target track according to the adjustment quantity of each measuring point of the target track.
In some embodiments, the target track may be understood as a track of a section of a track to be completed.
In some embodiments, the above-mentioned reference detection data may be specifically understood as a detection data for performing targeted correction on the mileage of the dynamic detection data.
In some embodiments, the reference detection data may specifically be historical static detection data including the target track. Usually, the target track is periodically detected at preset time intervals, and the historical static detection data collected by the periodic static detection may be recorded and saved as the historical static detection data. During specific implementation, the existing historical static detection data can be directly acquired and used as reference detection data, and static detection is not needed, so that the effects of improving the detection efficiency and reducing the detection cost can be achieved.
In some embodiments, the reference detection data may be static detection data obtained by using a relative measurement method. The static detection performed by adopting a relative measurement mode is different from the static detection performed by adopting an absolute measurement mode, and the coordinate alignment is not required to be performed at intervals of a plurality of detection points. Therefore, the static detection is carried out on the target track in a relative measurement mode, and the obtained corresponding static detection data is used as reference detection data, so that the cost is lower and the efficiency is higher compared with the static detection in an absolute measurement mode.
In some embodiments, the dynamic detection data may be specifically understood as detection data acquired by performing dynamic detection on the target track.
The detection data may specifically include one or more of the following multiple data: height irregularity data, rail direction irregularity data, rail gauge irregularity data, triangular pit irregularity data, horizontal irregularity data, and mileage.
In some embodiments, when implemented, the target track may be dynamically detected by a detection train running on the target track to obtain dynamic detection data of the target track.
In the present embodiment, the mileage in the dynamic detection data is obtained by integrating the number of pulses of the axle box end encoder of the detected train. It is common to detect that the mileage is corrected by GPS coordinate points or radio frequency tags of known mileage each time the train travels a certain distance. However, the mileage in the train detection data is prone to error due to factors such as rolling circle radius change, GPS positioning error and the like caused by wheel abrasion, traversing or shaking motion of the train. The static detection is that the ground identification is used as the initial mileage, the mileage along the line is calculated through the wheel rotating speed of the rail inspection trolley, the mileage error is relatively small, and the mileage error can be almost ignored for maintenance and repair.
In some embodiments, the present application finds that by comparing a large amount of dynamic detection data with static detection data: the track gauge irregularity data in the dynamic detection data and the track gauge irregularity data in the static detection data have relatively maximum similarity, so that the track gauge irregularity data in the two detection data can be used for establishing a matching relation between the dynamic detection data and the track gauge irregularity data in the static detection data; then, the mileage in the dynamic detection data is corrected based on the matching relation, and the corrected dynamic detection data with the mileage precision reaching the level of the static detection data is obtained; further, the modified dynamic detection data can be used to replace the static detection data to realize the whole track.
In some embodiments, the reference detection data includes at least: static gauge irregularity data for the target track. Correspondingly, the dynamic detection data at least comprises: dynamic gauge irregularity data for the target track.
In some embodiments, the above modifying, according to a preset correlation rule (which may also be referred to as a correlation optimization principle), the mileage in the dynamic detection data by using the reference detection data to obtain modified dynamic detection data may include the following steps:
s1: dividing static track gauge irregularity data of the target track into a plurality of calibration units; wherein each calibration unit of the plurality of calibration units comprises a plurality of data points; the length of the calibration unit is a preset first distance;
s2: constructing target correction data according to a preset mileage error threshold value and dynamic track gauge irregularity data of a target track in the dynamic detection data;
s3: according to a preset correlation rule, sequentially determining and correcting each correction unit in the target correction data by using a plurality of calibration units according to a sequence to obtain a plurality of corrected units;
s4: and performing resampling processing on the plurality of corrected units to obtain corrected dynamic detection data.
In some embodiments, in specific implementation, the static track gauge irregularity data of the target track may be divided into multiple segments sequentially connected in sequence along the target track according to the preset first distance, and the multiple segments are used as the multiple calibration units. The length of each calibration unit is a preset first distance; each calibration unit comprises a plurality of data points which respectively correspond to track gauge irregularity data of a plurality of position points on the track; the central data points of two adjacent calibration units are separated by a preset first distance. The preset first distance may be a value range of 20 meters or more and 50 meters or less.
Specifically, for example, the complete static track gauge irregularity data of the target track may be divided into M calibration units at equal intervals, which may be written as: [ Y ]1,Y2,…Yt,…YM]. Wherein, YtThe calibration unit is numbered t, and t is an integer greater than or equal to 1 and less than or equal to M.
Each calibration cell may contain a plurality of data points. The data points contained by a calibration cell may be represented as a track-non-uniform data sequence corresponding to the calibration cell. For example, corresponding to any one YtThe track-irregularity data sequence of (a) can be written as: y ist＝[yt(i)|i＝1,2,…,N]. Wherein, yt(i) Track gauge inequality for data points numbered i in calibration cell numbered tSmoothing data (static track-pitch irregularity data), i is the number of data points in the calibration unit numbered t, and N is the total number of data points contained in the calibration unit numbered t.
Further, two adjacent data points in each calibration unit are separated by a preset first sampling distance. For static detection data, the preset first sampling distance may be 0.25 m. Accordingly, the total number of data points N included in each calibration unit may be a ratio (e.g., 30/0.25 ═ 120) of a preset first distance (e.g., 30 meters) to a preset first sampling distance (e.g., 0.25 meters).
In this embodiment, since the reference detection data is also static detection data, the mileage in the reference data has high accuracy, and can be used as a reference for correcting the dynamic detection data.
In specific implementation, corresponding target correction data can be constructed according to a preset mileage error threshold (also called a mileage maximum error value, marked as l) and by combining dynamic track gauge irregularity data of a target track in dynamic detection data. For example, the unit Y may be found and calibrated from the dynamic detection datatA corresponding segment of data; and respectively adding a preset number of data points before and after the section of data to obtain a correction unit in the corresponding target correction data. The specific numerical value of the preset number may be a preset mileage error threshold value and a preset first sampling distance quotient value.
Specifically, for example, the preset mileage error threshold is 5 meters, the preset first sampling distance is 0.25 meters, and accordingly, the preset number of values is 5/0.25 — 20. Corresponding to YtThe correction unit in the corresponding target correction data may be specifically expressed in the following form: qt＝[qt(i)|i＝1,2,…,N+1,…,N+20]. Wherein q ist(i) Correcting data for object with YtTrack gage irregularity data (dynamic track gage irregularity data) of data points numbered i in the corresponding correction unit numbered t.
Further, according to a preset correlation rule, the correction units in the target correction data are sequentially determined and corrected one by one from front to back by utilizing the plurality of calibration units in sequence, so that the mileage precision of the dynamic detection data is improved.
In some embodiments, the determining and correcting each correction unit in the target correction data sequentially by using a plurality of calibration units according to a preset correlation rule to obtain a plurality of corrected units may include the following steps: determining and correcting a current correction unit in the target correction data in the following manner:
s1: determining a current correction unit in the target correction data according to the last corrected unit;
s2: 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;
s3: screening out a correlation coefficient with the largest value from the plurality of correlation coefficients to serve as a current correlation coefficient;
s4: and correcting the current correction unit according to the current correlation coefficient and the mileage in the current calibration unit to obtain the current corrected unit.
In some embodiments, the calculating a plurality of correlation coefficients by using a plurality of data points in the current calibration unit and a plurality of data points included in the current correction unit may include the following steps:
and calculating a number k correlation coefficient in the plurality of correlation coefficients according to the following formula:
where ρ isj(k) The number of the current correction unit is k, j is the number of the current correction unit, k is the number of the correlation coefficient in the current correction unit, yj(i) Track gauge irregularity data for data points numbered i in the current calibration unit, qj(i + k-1) is the number in the current correction unitTrack gauge irregularity data for data points of i + k-1.
Wherein k is 1,2, …,2l/d1. L is a preset mileage error threshold value, d1Is a preset first sampling distance.
In some embodiments, when using calibration unit Yt-1Correction unit Q with corrected number t-1t-1Obtaining a corrected unit Q with the number t-1t-1' thereafter, the current correction unit to be corrected is the correction unit Q numbered tt。
Specifically, the corrected unit Q may be immediately preceded in the target correction datat-1' at the position of the current correction unit Q is determinedt. And using a calibration unit YtThe included data points and the data points included in the current correction unit are calculated to obtain 2l/d1A correlation coefficient.
And comparing the plurality of correlation coefficients, and finding out one correlation coefficient with the maximum value, and recording the correlation coefficient 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 QtIn order to obtain the current corrected unit Q with mileage accuracy meeting the requirementt′。
Then, the corrected unit Q can be based ontNext, the next correction unit is determined and corrected in the target correction data in the same manner as described above until all correction units in the target correction data are corrected.
By the correction mode, the current correction unit can be determined and corrected by using the last corrected unit every time of correction, so that the search range can be reduced, the correction efficiency is improved, the target correction data can be efficiently and accurately corrected, and the corrected dynamic detection data with the mileage precision meeting the requirement and reaching the mileage precision level of the static detection data can be obtained.
In some embodiments, after the correction is completed in the above manner, it is considered that the sampling interval (denoted as the preset second sampling distance) used by the dynamic detection data during the detection is often different from the preset first sampling distance used by the static detection data. For example, the preset first sampling distance may be 0.625 meters, and the preset second sampling distance may be 0.25 meters.
Therefore, after the plurality of correction units are corrected to obtain a plurality of corresponding corrected units, the plurality of corrected units can be further subjected to resampling processing, so that the data in the corrected units correspond to the preset second sampling distance, and the corrected dynamic detection data meeting the requirements can be obtained.
In some embodiments, the modifying the current correction unit according to the current correlation coefficient and the mileage in the current calibration unit to obtain the current modified unit may include: determining a current offset distance corresponding to the current correlation coefficient; subtracting the current offset distance from the range of each data point in the current calibration unit to obtain a current corrected unit.
In some embodiments, the resampling the plurality of corrected units to obtain the corrected dynamic detection data may include, in specific implementation: and according to the sleeper interval (a preset second sampling distance, for example, 0.625 m), corresponding the dynamic long-wave height irregularity data and the rail irregularity data in the corrected units to the corresponding sleepers (serving as measuring points) to obtain the corrected dynamic detection data.
In some embodiments, the determining, by using the modified dynamic detection data, an adjustment amount of each measurement point on the target track based on the preset multi-chord control optimization algorithm may include the following steps:
s1: constructing an objective function about the adjustment quantity sum of a plurality of measuring points on the target track;
s2: constructing a first class of constraint conditions for controlling the linear smoothness of the track by using a plurality of preset chord lengths; the combination of the effective detection wave bands corresponding to the preset chord lengths covers a preset wave band range;
s3: constructing a second class of constraint conditions according to the limiting effect of the target object on the track;
s4: and based on the first class constraint condition and the second class constraint condition, determining the adjustment quantity of each measuring point on the target track by solving the target function by utilizing the dynamic long-wavelength height irregularity data and the rail irregularity data in the modified dynamic detection data.
The preset wavelength range may be greater than or equal to 1.5 m and less than or equal to 120 m, and 120 m is a cut-off wavelength of the dynamic long wave. The target object specifically may include a bridge, a tunnel, a catenary, and other structures, and a rail fastener. The adjustment may specifically include an adjustment to an elevation and/or a plane of the track gauge locations.
In some embodiments, the plurality of preset chord lengths may specifically include: the chord length is 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 four preset chord lengths can better cover the required preset band range.
Specifically, the four effective detection bands with preset chord lengths can be obtained according to the amplitude-frequency gain characteristic between the input and the output of the midpoint chord measuring method, and are respectively: 3-10 meters, 7-20 meters, 20-60 meters and 40-120 meters; after combination, the multi-chord constraint can control the wave band 1.5-120 m contained in the dynamic long-wave irregularity data, namely, the preset wave band range can be covered.
In some embodiments, in implementation, according to the dynamic long-wave height irregularity data or/and the rail irregularity data in the modified dynamic detection data, equal-space resampling may be performed at a sleeper interval of 0.625 m, and the corresponding irregularity data may be located on a corresponding sleeper. The dynamic long wave height irregularity data and/or the track irregularity data of the ith sleeper (corresponding to the measuring point position with the number of i) can be represented as p (i), the adjustment amount at each measuring point position can be represented as t (i), i is 1,2, …, and M are the number of sleepers. The adjusted dynamic long wave height irregularity data and/or the rail irregularity data p' (i) can be calculated according to the following equations: p' (i) ═ p (i) + t (i).
In some embodiments, the constructing an objective function regarding the adjustment amount sum of the plurality of measurement points on the target track may include, in specific implementation: the objective function is constructed according to the following equation:
where f (i) is the objective function, and t (i) is the adjustment amount of the measurement point with number i. N may specifically be the number of measurement points included in a fourth preset chord length of 60 meters.
In specific implementation, the above objective function can be used to find an optimal solution so as to minimize the sum of absolute values of the adjustment amounts of the track, thereby reducing the disturbance to the track alignment and weakening the adverse effect of the track 'memory' on the track effect.
In some embodiments, the constructing the first class of constraint conditions for controlling the linearity and smoothness of the track by using a plurality of preset chord lengths may include: constructing a first class of constraints according to the following equation:
wherein, deltaLIs a sine vector allowable limit value, p ' (i), p ', corresponding to a preset chord length with a chord length of L meters 'L,1(i)、p′L,2(i) The dynamic long wave height irregularity data and the rail irregularity data are respectively obtained after the starting point, the middle point and the end point of the preset chord length with the chord length of L meters are adjusted. The value of L may specifically include: 5. 10, 30 and 60 corresponding to the first preset chord length, the second preset chord length, the third preset chord length and the fourth preset chord length, respectively.
In specific implementation, the first class of constraint conditions can be utilized to control the mid-point chord vector distance of the four preset chord lengths, so that the smoothness of the adjusted line shape is ensured, and the smoothness of the track is optimized.
In some embodiments, the second type of constraint condition is constructed according to the constraint effect of the target object on the track, and when implemented, the second type of constraint condition may include the following: the second class of constraints is constructed according to the following equation:
α(i)≤t(i)≤β(i)
where β (i) is an upper limit value of the limiting action of the target object on the measurement point of track number i, and α (i) is a lower limit value of the limiting action of the target object on the measurement point of track number i.
During specific implementation, the second type constraint conditions can be utilized, and the effects of structures such as bridges, tunnels and contact networks and rail fasteners on the rails are introduced so as to further constrain the adjustment amount.
In some embodiments, the first type constraint and the second type constraint may be combined to obtain the following total constraint:
further, based on the total constraint conditions and the objective function, calculating the dynamic long-wave height irregularity data and the rail irregularity data of the measured point i after adjustment through the adjustment amount t (i); and repeating the optimization process until the adjustment quantities of all the N measuring points are calculated to obtain the adjustment quantities of all the measuring points on the target track by changing i to i + 1.
In some embodiments, after determining the adjustment amount of each measurement point on the target track, when the method is implemented, the following may be further included:
s1: calculating the dynamic long wave height irregularity data and the rail irregularity data (which can be recorded as p' (i)) of each measuring point after adjustment according to the adjustment amount of each measuring point on the target track;
s2: performing smooth filtering processing on the adjusted dynamic long-wave height irregularity data and the adjusted rail direction irregularity data of each measuring point by using a preset filter to obtain the smooth filtered dynamic long-wave height irregularity data and the rail direction irregularity data (which can be recorded as p' (i)) of each measuring point;
s3: and determining the adjustment quantity of each measuring point after smooth filtering according to the dynamic long wave height irregularity data and the rail irregularity data of each measuring point after smooth filtering. For example, t' (i) ═ p "(i) -p (i).
The preset filter may specifically include: Savitzky-Golay filters, and the like.
The adjustment quantity after smooth filtering of each measuring point obtained by the method is utilized to carry out track shaping, so that the finally shaped track is smoother, abrupt change characteristics such as burrs, steps and the like can not occur, and a relatively better track shaping effect can be obtained.
In some embodiments, the above-mentioned performing smooth adjustment on the target track according to the adjustment amount of each measurement point of the target track may include the following steps: and according to the adjustment quantity of each measuring point, performing targeted adjustment and maintenance on the track at the position corresponding to the measuring point on the target track so as to enable the smoothness of the target track to meet the requirement. Therefore, the whole maintenance of the target track can be well completed.
As can be seen from the above, based on the method for adjusting track smoothness provided in the embodiments of the present specification, after obtaining dynamic detection data of a target track with a mileage error in a manner with higher efficiency and lower cost, the mileage in the dynamic detection data may be corrected based on the principle of optimal correlation by using reference detection data of the target track that is easily obtained according to a preset correlation rule, so as to obtain corrected dynamic detection data with a mileage positioning accuracy meeting the requirement; determining the adjustment amount of each measuring point which can enable the adjusted track to have higher smoothness by using the corrected dynamic detection data based on a preset multi-chord control optimization algorithm; and then the smoothness of the target track can be adjusted according to the adjustment amount of each measuring point. Therefore, the dynamic detection data which is high in detection density and can reflect the real state of the line can be obtained and effectively utilized, smoothness adjustment can be efficiently and accurately carried out on the target track, the track arrangement cost can be effectively reduced, track arrangement efficiency is improved, meanwhile, the track smoothness of the target track full wave band can be well controlled, and a good track arrangement effect is obtained.
An embodiment of the present specification further provides an electronic device, including a processor and a memory for storing processor-executable instructions, where the processor, when implemented, may perform the following steps according to the instructions: acquiring reference detection data and dynamic detection data of a target track; the reference detection data comprise historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track; according to a preset correlation rule, the mileage in the dynamic detection data is corrected by using the reference detection data, and corrected dynamic detection data is obtained; based on a preset multi-chord control optimization algorithm, determining the adjustment quantity of each measuring point on the target track by using the modified dynamic detection data; and carrying out smoothness adjustment on the target track according to the adjustment quantity of each measuring point of the target track.
In order to more accurately complete the above instructions, referring to fig. 2, another specific electronic device is provided in the embodiments of the present specification, wherein the electronic device includes a network communication port 201, a processor 202, and a memory 203, and the above structures are connected by an internal cable, so that the structures may perform specific data interaction.
The network communication port 201 may be specifically configured to acquire reference detection data and dynamic detection data of a target track; the reference detection data comprises historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track.
The processor 202 may be specifically configured to correct the mileage in the dynamic detection data by using the reference detection data according to a preset correlation rule, so as to obtain corrected dynamic detection data; based on a preset multi-chord control optimization algorithm, determining the adjustment quantity of each measuring point on the target track by using the modified dynamic detection data; and carrying out smooth adjustment on the target track according to the adjustment quantity of each measuring point of the target track.
The memory 203 may be specifically configured to store a corresponding instruction program.
In this embodiment, the network communication port 201 may be a virtual port that is bound to different communication protocols, so that different data can be sent or received. For example, the network communication port may be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for mail data communication. In addition, the network communication port can also be a communication interface or a communication chip of an entity. For example, it may be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it may also be a bluetooth chip.
In the present embodiment, the processor 202 may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The description is not intended to be limiting.
In this embodiment, the memory 203 may include multiple layers, and in a digital system, the memory may be any memory as long as it can store binary data; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.
The present specification further provides a computer-readable storage medium based on the above adjusting method for track smoothness, where the computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the computer program instructions implement: acquiring reference detection data and dynamic detection data of a target track; the reference detection data comprise historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track; according to a preset correlation rule, the mileage in the dynamic detection data is corrected by using the reference detection data, and corrected dynamic detection data is obtained; based on a preset multi-chord control optimization algorithm, determining the adjustment quantity of each measuring point on the target track by using the modified dynamic detection data; and carrying out smoothness adjustment on the target track according to the adjustment quantity of each measuring point of the target track.
In this embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.
In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer-readable storage medium can be explained in comparison with other embodiments, and are not described herein again.
Referring to fig. 3, in a software level, an embodiment of the present disclosure further provides an adjusting device for track smoothness, which may specifically include the following structural modules:
the obtaining module 301 may be specifically configured to obtain reference detection data and dynamic detection data of a target track; the reference detection data comprise historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track;
the modification module 302 is specifically configured to modify the mileage in the dynamic detection data by using the reference detection data according to a preset correlation rule, so as to obtain modified dynamic detection data;
the determining module 303 is specifically configured to determine an adjustment amount of each measuring point on the target track by using the modified dynamic detection data based on a preset multi-chord control optimization algorithm;
the adjusting module 304 may be specifically configured to perform smooth adjustment on the target track according to the adjustment amount of each measuring point of the target track.
It should be noted that, the units, devices, modules, etc. illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. It is to be understood that, in implementing the present specification, functions of each module may be implemented in one or more pieces of software and/or hardware, or a module that implements the same function may be implemented by a combination of a plurality of sub-modules or sub-units, or the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
It can be seen from above that, based on the adjusting device of track ride comfort that this specification embodiment provided, can be through acquireing and effectively utilizing the dynamic detection data that detection density is higher, can reflect the circuit true state more, carry out the ride comfort adjustment to the target track high-efficiently, accurately, can reduce the putting together a track cost effectively, improve putting together a track efficiency, simultaneously, can also control the track ride comfort of the target track full wave section better, obtain better putting together a track effect.
In a specific scenario example, the track smoothness adjustment method provided by the embodiments of the present specification may be applied to adjust and maintain a certain track.
In the present scenario example, the mileage error of the dynamic detection data may be corrected (the corrected dynamic detection data is obtained) based on the correlation optimization principle by using the static track gauge irregularity data (e.g., the reference detection data) as a reference. And then, formulating track adjustment quantity based on a multi-chord control optimization algorithm to adjust the track. Therefore, dynamic detection data which is higher in detection density and can reflect the real state of the line can be effectively used for guiding maintenance of the high-speed railway, the contradiction between the precision measurement efficiency and the frequent maintenance of the line in the existing track precision adjusting and tamping technology is solved, and the whole track scheme is adapted to track smoothness management.
In specific implementation, the method can be performed according to the following steps.
The method comprises the following steps: and correcting the mileage error of the dynamic detection data based on the optimal principle of the correlation (correspondingly, the mileage in the dynamic detection data is corrected by using the reference detection data according to the preset correlation rule to obtain the corrected dynamic detection data).
Specifically, for example, the maximum mileage error (for example, a preset mileage error threshold) of the obtained motion detection data (dynamic detection data) is 60 meters, the length of one calibration unit is 50 meters, and the method provided by the application is adopted to firstly perform mileage error correction on the 1 st calibration unit. The rule of the change of the correlation coefficient with the unit moving distance is obtained, as shown in fig. 4, the correlation of the unit moving distance at-7.5 m is optimal (maximum value), and the correlation coefficient is 0.94. Furthermore, the dynamic detection data mileage of the calibration unit can be increased by 7.5 meters, so that correction is realized. The other cells are corrected one by one in the same way.
After the mileage deviation of the dynamic test data of all the calibration units is corrected, resampling can be performed at each sampling point of the static test data, and the obtained mileage correction result is shown in fig. 5 (corresponding to K16+ 000-K16 + 800). Further, referring to fig. 6 (corresponding to K16+050 to K16+063.2), a detailed view of a section K16+060.8-K16+074 is shown, the mileage deviation of the live test data reaches 3 meters before correction, and the mileage error is controlled within 0.6 meter of one sleeper interval after correction.
Step two: and (4) formulating track adjustment quantity based on a multi-chord control optimization algorithm (correspondingly, based on a preset multi-chord control optimization algorithm, determining the adjustment quantity of each measuring point on the target track by using the modified dynamic detection data).
Specifically, for example, with dynamic long wave height irregularity after mileage correction (cutoff wavelength 120 m) as an optimization target, the constraint conditions of track smoothness are set to 60 m chord 1 mm, 30 m chord 0.5 mm, 10 m chord 0.3 mm and 5 m chord 0.1 mm, and the optimization results and the adjustment amount obtained by using the algorithm of the present invention can be referred to fig. 7 (60 m chord comparison before and after adjustment), fig. 8 (30 m chord comparison before and after adjustment), fig. 9 (10 m chord comparison before and after adjustment), fig. 10 (5 m chord comparison before and after adjustment), fig. 11 (120 m length wave height irregularity comparison before and after adjustment) and fig. 12 (height adjustment amount).
Wherein the maximum absolute value reaches 9.2 mm before the string of 60 m is adjusted, and is controlled within 1 mm after being optimized; the absolute value of the maximum value before the string of 30 meters is adjusted reaches 8.3 mm, and the absolute value is controlled within 0.5 mm after the string is optimized; the absolute value of the maximum value before the string of 10 meters is adjusted reaches 4.0 mm, and the absolute value is controlled within 0.3 mm after optimization; the absolute value of the maximum value before the string of 5 meters is adjusted reaches 3.2 millimeters, and the maximum value after the string of 5 meters is optimized is controlled within 0.1 millimeter. The above 4 smoothness indexes are all strictly controlled. The maximum value of the dynamic long wave height irregularity reaches 6.4 mm before adjustment, the minimum value reaches-7.0, and the maximum value and the minimum value are respectively controlled at 2.9 mm and-2.3 mm after optimization. The maximum value of the adjustment amount reaches +5.3 mm, and the minimum value reaches-5.0 mm.
Through the scene example, it is verified that the method for adjusting the track smoothness provided by the embodiment of the present specification can efficiently and accurately adjust the smoothness of the target track by acquiring and effectively utilizing the dynamic detection data which has higher detection density and can better reflect the real state of the line.
Although the present specification provides method steps as described in the examples or flowcharts, additional or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an apparatus or client product in practice executes, it may execute sequentially or in parallel (e.g., in a parallel processor or multithreaded processing environment, or even in a distributed data processing environment) according to the embodiments or methods shown in the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. The terms first, second, etc. are used to denote names, but not any particular order.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer-readable storage media including memory storage devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present specification can be implemented by software plus necessary general hardware platform. With this understanding in mind, the technical solutions in the present specification may be essentially embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a mobile terminal, an electronic device, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.
The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The description is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, electronic device computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
While the specification has been described with examples, those skilled in the art will appreciate that there are numerous variations and permutations of the specification that do not depart from the spirit of the specification, and it is intended that the appended claims include such variations and modifications that do not depart from the spirit of the specification.
Claims (10)
1. A method for adjusting track smoothness is characterized by comprising the following steps:
acquiring reference detection data and dynamic detection data of a target track; the reference detection data comprise historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track;
according to a preset correlation rule, the mileage in the dynamic detection data is corrected by using the reference detection data, and corrected dynamic detection data is obtained;
based on a preset multi-chord control optimization algorithm, determining the adjustment quantity of each measuring point of the target track by using the modified dynamic detection data;
and carrying out smoothness adjustment on the target track according to the adjustment quantity of each measuring point of the target track.
2. The method of claim 1, wherein the referencing detection data includes at least: static gauge irregularity data for the target track.
3. The method of claim 2, wherein the step of modifying the mileage in the dynamic detection data by using the reference detection data according to a preset correlation rule to obtain modified dynamic detection data comprises:
dividing static track gauge irregularity data of the target track into a plurality of calibration units; wherein each calibration unit of the plurality of calibration units comprises 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 value and dynamic track gauge irregularity data of a target track in the dynamic detection data;
according to a preset correlation rule, sequentially determining and correcting each correction unit in the target correction data by using a plurality of calibration units according to a sequence to obtain a plurality of corrected units;
and performing resampling processing on the plurality of corrected units to obtain corrected dynamic detection data.
4. The method of claim 3, wherein sequentially determining and correcting each correction unit in the target correction data in sequence by using a plurality of calibration units according to a preset correlation rule to obtain a plurality of corrected units comprises:
determining and correcting a current correction unit in the target correction data in the following manner:
determining a 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;
screening out a correlation coefficient with the largest value from the plurality of correlation coefficients to serve as a current correlation coefficient;
and correcting the current correction unit according to the current correlation coefficient and the mileage in the current calibration unit to obtain the current corrected unit.
5. The method of claim 4, wherein calculating a plurality of correlation coefficients using the plurality of data points in the current calibration cell and the plurality of data points included in the current correction cell comprises:
and calculating a number k correlation coefficient in the plurality of correlation coefficients according to the following formula:
where ρ isj(k) The number of the current correction unit is k, j is the number of the current correction unit, k is the number of the correlation coefficient of the current correction unit, yj(i) Track gauge irregularity data for data points numbered i in the current calibration unit, qj(i + k-1) is the track gauge irregularity data for the data point numbered i + k-1 in the current correction unit.
6. The method of claim 1, wherein determining the adjustment amount of each measuring point of the target track by using the modified dynamic detection data based on a preset multi-chord control optimization algorithm comprises:
constructing an objective function about the adjustment quantity sum of a plurality of measuring points on the target track;
constructing a first class of constraint conditions for controlling the linear smoothness of the track by using a plurality of preset chord lengths; the combination of the effective detection wave bands corresponding to the preset chord lengths covers a preset wave band range;
constructing a second class of constraint conditions according to the limiting effect of the target object on the track;
and based on the first class constraint condition and the second class constraint condition, determining the adjustment quantity of each measuring point of the target track by solving the target function by utilizing the dynamic long-wavelength height irregularity data and the rail irregularity data in the modified dynamic detection data.
7. The method of claim 6, wherein the plurality of preset chord lengths comprises: the chord length is 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.
8. The method of claim 6, wherein after determining the amount of adjustment for each of the stations on the target track, the method further comprises:
calculating the adjusted dynamic long wave height irregularity data and the adjusted rail irregularity data of each measuring point according to the adjustment quantity of each measuring point on the target track;
performing smooth filtering processing on the adjusted dynamic long-wave height irregularity data and the adjusted rail direction irregularity data of each measuring point by using a preset filter to obtain the smooth filtered dynamic long-wave height irregularity data and the rail direction irregularity data of each measuring point;
and determining the adjustment quantity of each measuring point after smooth filtering according to the dynamic long wave height irregularity data and the rail irregularity data of each measuring point after smooth filtering.
9. An apparatus for adjusting track smoothness, comprising:
the acquisition module is used for acquiring reference detection data and dynamic detection data of the target track; the reference detection data comprise historical static detection data of the target track or static detection data obtained by adopting a relative measurement mode on the target track;
the correction module is used for 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;
the determining module is used for determining the adjustment quantity of each measuring point on the target track by using the modified dynamic detection data based on a preset multi-chord control optimization algorithm;
and the adjusting module is used for performing smooth adjustment on the target track according to the adjustment quantity of each measuring point of the target track.
10. A computer-readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115758528A (en) * | 2022-11-18 | 2023-03-07 | 中国铁路设计集团有限公司 | Comprehensive optimization calculation method for railway track lifting and lining improvement scheme |
| CN118494245A (en) * | 2024-07-18 | 2024-08-16 | 国网山东省电力公司东营供电公司 | Scheduling method, system, terminal and medium for full-area mobile charging robot |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105083320A (en) * | 2015-05-15 | 2015-11-25 | 西南交通大学 | Detection method and device of track regularity state |
| CN113071529A (en) * | 2021-04-09 | 2021-07-06 | 中国铁道科学研究院集团有限公司 | Track elasticity detection method and device |
-
2021
- 2021-11-03 CN CN202111293912.9A patent/CN113987819A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105083320A (en) * | 2015-05-15 | 2015-11-25 | 西南交通大学 | Detection method and device of track regularity state |
| CN113071529A (en) * | 2021-04-09 | 2021-07-06 | 中国铁道科学研究院集团有限公司 | Track elasticity detection method and device |
Non-Patent Citations (2)
| Title |
|---|
| 杨飞;赵文博;高芒芒;孙加林;: "运营期高速铁路轨道长波不平顺静态测量方法及控制标准", 中国铁道科学, no. 03, 15 May 2020 (2020-05-15), pages 43 - 51 * |
| 殷华;朱洪涛;王志勇;吴维军;金哲民;: "基于多弦模型的轨道短波不平顺测量研究", 振动与冲击, no. 14, 28 July 2017 (2017-07-28), pages 178 - 182 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115758528A (en) * | 2022-11-18 | 2023-03-07 | 中国铁路设计集团有限公司 | Comprehensive optimization calculation method for railway track lifting and lining improvement scheme |
| CN115758528B (en) * | 2022-11-18 | 2023-09-29 | 中国铁路设计集团有限公司 | Comprehensive optimization calculation method for railway track lifting and lining treatment scheme |
| CN118494245A (en) * | 2024-07-18 | 2024-08-16 | 国网山东省电力公司东营供电公司 | Scheduling method, system, terminal and medium for full-area mobile charging robot |
| CN118494245B (en) * | 2024-07-18 | 2024-09-24 | 国网山东省电力公司东营供电公司 | Scheduling method, system, terminal and medium for full-area mobile charging robot |
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