CN118094797A - High-speed railway track fine tuning optimization method considering multi-chord constraint - Google Patents
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Abstract
The invention discloses a high-speed railway track fine tuning optimization method considering multi-chord constraint, which comprises the following steps: establishing constraint conditions of a high-speed railway track fine adjustment model; establishing a model, constructing an objective function of the adjustment node, and deducing an evaluation matrix from the original data; obtaining optimized deviation data by generating an adjustment measurement vector and superposing the adjustment measurement vector with the original deviation; after generating an adjustment measurement vector and optimizing the linearity by using an iterative algorithm, completing the 1 st iteration; for subsequent iterations, using the optimized line shape as input bias data; then, converting the deviation data into an adjustment space matrix and an evaluation matrix, and recalculating the optimized line shape after the 2 nd iteration; the invention can be applied to daily maintenance of track irregularity operation, realizes quick output of a fine adjustment scheme, and avoids complexity of manual scheme generation.
Description
Technical Field
The invention relates to the technical field of railways and rail transit, in particular to a high-speed railway track fine tuning optimization method considering multi-chord constraint.
Background
Track irregularity is an important source of disturbance for trains and is a major cause of vibration and wheel-rail interaction. Track irregularity severely affects the safety, stability, comfort and service life of the vehicle, and is also a major limiting factor for train speed. For adjusting the irregularity of the ballastless track, the upper limit of the adjustment amount of the fastener system and various smoothness indexes are considered, the constraint is complex, and manual adjustment is difficult.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide the high-speed railway track fine tuning optimization method taking the multi-chord constraint into consideration, which can be applied to daily track irregularity operation maintenance, realizes the rapid output of a fine tuning scheme and avoids the complexity of manual scheme generation.
In order to achieve the above purpose, the invention adopts the following technical scheme: a high-speed railway track fine tuning optimization method considering multi-chord constraint comprises the following steps:
Step 1, establishing constraint conditions of a high-speed railway track fine adjustment model;
step 2, establishing a model, constructing an objective function of the adjustment node, and deducing an evaluation matrix from the original data;
Step 3, obtaining optimized deviation data by generating an adjustment measurement vector and superposing the adjustment measurement vector and the original deviation;
Step 4, using an iterative algorithm, and completing the 1 st iteration after generating an adjustment measurement vector and optimizing the linearity; for subsequent iterations, using the optimized line shape as input bias data; the deviation data is then converted into an adjusted spatial matrix and an evaluation matrix, and the optimized line shape is recalculated after the 2 nd iteration.
As a further improvement of the present invention, in step1, the constraint conditions include a track adjustment space and a track smoothness constraint.
As a further improvement of the present invention, in step 2, the objective function includes an objective function of an adjustment amount of each adjustment node, a mid-chord measurement objective function, and an objective function of a vector difference method.
As a further improvement of the present invention, the objective function of the adjustment amount of each adjustment node is specifically as follows:
Where i is the tuning node number, a lim is the design elevation deviation limit, To adjust policy vector,/>For adjusting the objective function corresponding to node i.
As a further improvement of the invention, the construction method of the mid-chord measurement objective function is specifically as follows:
calculating an adjusting node as an average value of a middle point, an end point and a starting point of a detected chord in a chord measurement method:
Wherein, The average value of chord measurement when the detection point is at i, c j1 is the detected chord length, and p is absolute deviation data; the mid-point chord measurement average value changes of the middle point, the end point and the starting point of the front and rear detection chords are adjusted as follows:
Wherein, To adjust the corresponding detected chord change after the ith node;
The i-th adjustment node is a constraint that the sum of the squares of the mid-point chord measurement average changes of the mid-point, end point and start point of the detected chord before and after adjustment is divided by three times, as follows:
As a further improvement of the invention, the construction method of the objective function of the vector difference method is specifically as follows:
calculating the adjusting node as an average value of an end point of a moving chord, a starting point of the moving chord, an end point of a detecting chord and a starting point of the detecting chord in the vector distance difference method:
For the average value of vector distance difference when the detection point is at i, the end point of the moving chord, the starting point of the moving chord, the end point of the detecting chord and the average value of the starting point of the detecting chord are adjusted to be as follows:
Objective function of ith adjustment node vector difference method To adjust the sum of the squares of the end point of the moving chord back and forth, the start point of the moving chord, the end point of the detecting chord, and the mean change of the start point of the detecting chord divided by the four times vector distance difference constraint value is as follows:
As a further improvement of the present invention, in step 4, for the calculation of the adjustment evaluation score, the inputted adjustment amount is the accumulated adjustment amount, and the adjustment space matrix is synchronized and updated after each iteration to ensure that the optimized line is within the adjustment space.
As a further development of the invention, the weights of all objective functions need to meet the following conditions:
Wherein, w 1 is the adjustment weight, w 2 is the 10m chord weight, w 3 is the 60m chord weight, w 4 is the 5/30m vector distance difference weight, and w 5 is the 150/300m vector distance difference weight; by increasing the overrun chord constraint, a feasible weight combination meeting all the indexes is output.
The beneficial effects of the invention are as follows:
1. The invention provides a track irregularity intelligent fine tuning algorithm considering multi-chord constraint based on vector calculation, which aims to solve the problems that the existing artificial fine tuning algorithm lacks intelligence, has large calculation amount and cannot conduct fine tuning simultaneously aiming at various frequency band indexes of irregularity. The method only needs to collect original deviation data of track irregularity, realizes automatic output of an adjustment scheme by setting a irregularity evaluation index and a target weight of adjustment quantity, and ensures relatively smaller accumulated adjustment quantity. Meanwhile, the model enables the fine adjustment scheme under the combination of the output weights to meet the requirement of track smoothness by means of a corresponding auxiliary algorithm. The method has practicability, can be applied to daily maintenance of track irregularity operation, realizes quick output of a fine adjustment scheme, and avoids the complexity of manual scheme generation;
2. According to the invention, track fine adjustment calculation is regarded as a multi-objective optimization problem, a chord measurement method and a vector distance difference method corresponding to long and short waves can be regarded as targets for adjustment respectively, and the adjustment quantity amplitude of each fastener is considered; compared with the traditional filtering algorithm, the method can consider the adjustment amplitude of the track fastener system; compared with a linear programming method, the method reduces the calculated amount and is easy to apply.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a track adjustment section structure according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a structure for adjusting a space matrix according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a structure in which an adjustment node is a middle point, an end point and a start point of a detected chord in a chord measurement method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a structure in which an adjustment node is an end point of a moving chord, a start point of the moving chord, an end point of a detecting chord, and a start point of the detecting chord in a vector difference method according to an embodiment of the present invention;
FIG. 6 is a diagram showing the comparison of the manual adjustment scheme with the adjustment method according to the embodiment of the present invention;
FIG. 7 is a schematic diagram of a process for determining appropriate weight combinations in an embodiment of the invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Examples
As shown in fig. 1, a method for optimizing fine tuning of a high-speed railway track by considering multi-chord constraint comprises the following steps:
Step 1, model constraint:
step 1.1, track adjustment space:
for track fine tuning, each tie adjustment range is not infinite, and depends on the current state of each tie fastener system. Due to the limitations of the type, number and thickness of the track pads, the adjustment space of the single sleeper is discrete and bounded. For the WJ-8 fastener, the pad consists of a kneeling pad and an adjusting pad, and the influence of the number and thickness of the selected pads on the adjusting space is shown in figure 2. The highest elevation of the rail comprises a 10mm rail pad plus two 8mm adjustment pads, totaling 26mm, and the lowest elevation comprises a 5mm rail pad totaling 5mm. The highest and lowest elevation difference is 21mm, which is the adjustment interval, or may be referred to as interpolation of the adjustment upper and lower bounds.
For ballastless tracks, the adjustment amount is a multiple of 0.5mm, so that the adjustment is limited discrete for a single sleeper, the interpolation interval is 0.5mm, and the vector size is 43. Each tie adjustment vector is stitched to form an adjustment space matrix a, the matrix size being 43×n, N being the number of adjustment nodes (ties), assuming fine upward adjustment is positive, fig. 3 illustrates how the adjustment space matrix is determined.
Step 1.2, track smoothness constraint:
The following table shows the track ride constraints. Although this method focuses on the adjustment of the level irregularity, the level irregularity fine adjustment is satisfied as well.
Step 2, establishing a model:
step 2.1, objective function:
Step 2.1.1, adjustment amount:
The generated adjustment strategy should reduce the adjustment margin as much as possible. Therefore, the adjustment amount of each adjustment node should be a key index. The adjustment amount objective function is as follows:
i is the adjustment node number; a lim is the deviation from design elevation limit, 10mm; to adjust policy vector,/> For adjusting the objective function corresponding to node i.
Step 2.1.2, mid-chord measurement objective function:
analyzing and adjusting the influence of a single node on the measured value of the central point, wherein each node can be a starting point/end point of the detected chord, or an intermediate point, and the adjustment of each node also affects the corresponding measured value of the chord. Fig. 4 shows all the possibilities, and the following table shows the range of influence of the adjustment node.
(A) The adjusting node is the middle point of the chord measurement method; (b) adjusting the node to detect the chord endpoint; (c) adjusting the node to be the origin of the detected chord:
To reduce the amount of computation, the average of the mid-point chord measurements for the above three cases is calculated as follows:
Wherein, The average value of chord measurement when the detection point is at i, c j1 is the detected chord length, and p is absolute deviation data; the change of the chord measurement average value in the three conditions before and after adjustment is as follows:
Wherein, To adjust the corresponding detected chord change after the ith node;
The i-th node chord measurement objective function is the sum of the squares of the averages of the above three cases divided by the three times limit, as follows:
step 2.1.3, vector distance difference method:
The effect of the vector difference method was also analyzed. For its calculation, each node may be one of the start and end points of the detected strings, or one of the start and end points of the moving strings in the detected strings. The four cases are shown in fig. 5, and the corresponding ranges of influence are shown in the following table:
(a) Adjusting the node as the end point of the moving chord; (b) adjusting the node as a starting point for moving the chord; (c) adjusting the node as an endpoint of the detected chord; (d) adjusting the node as a starting point for detecting the chord:
The corresponding average value calculation formula:
And (3) as the average value of vector distance differences when the detection point is at i, the average value is changed after adjustment:
target value function The sum of squares of the average of the four cases divided by the four times the vector difference constraint.
Step 2.2, optimization algorithm:
According to the above procedure, the evaluation matrix E can be derived from the raw data. Subsequently, by generating an adjustment measurement vector and superimposing it with the original deviation, optimized deviation data can be obtained. It is worth mentioning that the result may not represent the optimal solution for the current weight combination. As previously mentioned, the relationship between the adjustment of each node and the metrics is not independent. Specifically, the mid-chord is affected by three nodes and the pair-vector difference is affected by four nodes. Thus, adjustments made to a node may affect the actual scores of all four metrics associated with the node, resulting in complex interactions between these factors.
To reduce this effect, the present embodiment designs an iterative algorithm. After generating the adjustment measurement vector and optimizing the alignment, iteration 1 is completed. For subsequent iterations, the optimized line shape is used as input bias data. The deviation data is then converted into an adjusted spatial matrix and an evaluation matrix, and the optimized line shape is recalculated after the 2 nd iteration. It is important to note that for the calculation of the adjustment evaluation score, the input adjustment amount should be the accumulated adjustment amount, not the adjustment amount of the nth iteration. Furthermore, the adjustment space matrix needs to be synchronized and updated after each iteration to ensure that the optimized line shape is within the adjustment space.
In addition, the MSC-FT is intended to output an optimized tuning strategy for a particular mileage segment. However, the process of obtaining the evaluation matrix requires absolute deviation data of neighboring nodes within a certain range. Some nodes cannot obtain the corresponding evaluation vectors because the calculation requires absolute deviation data beyond the adjustment range. Thus, two sequences, whose element values are equal to the deviation of the start and end points, are connected to both sides of the original data. Thus, an evaluation array of the aforementioned nodes can be calculated. The mileage extension is only for calculating the evaluation function. The irregularity curves of the subsequent portions do not include an extension.
Step 2.3, a feasible weight combination acquisition algorithm:
one possible weight combination means that the adjustment amount is as minimum as possible if all the criteria are met. The weights of all objective functions need to meet the following conditions:
Wherein, w 1 is the adjustment weight, w 2 is the 10m chord weight, w 3 is the 60m chord weight, w 4 is the 5/30m vector distance difference weight, and w 5 is the 150/300m vector distance difference weight; in order to minimize the total adjustment amount, the larger and better the weight of the adjustment amount needs to be, while the weight of the other index is reduced. By increasing the overrun chord constraint, feasible weight combinations that meet all the criteria can be output.
The scientificity and feasibility of the method of the embodiment are verified by using an ballastless track roadbed arch as an engineering example. In this example, due to the expansion of the subgrade soil, the track irregularity presents an upward curve, reducing the overall smoothness of the track and greatly increasing the difficulty of fine adjustment scheme output. The manual adjustment scheme was compared with this invention as shown in fig. 6.
Compared with a manual method, the method of the embodiment has the following advantages:
1. the cumulative adjustment amount is obviously reduced, and the reduction of 58% is about from 1657.1mm to 703 mm.
2. The adjustment interval is also greatly reduced. Manual adjustment requires adjustment of 370 nodes, but for the method herein only 203 nodes are adjusted with 45% reduction.
3. The adjustment scheme is more humanized. The staff can directly reserve the adjustment space of fastener through adjusting index weight combination, makes things convenient for the adjustment next time.
This embodiment is further described below:
The embodiment of the invention provides a track fine adjustment scheme output method suitable for high-speed railway maintenance.
A ballastless railroad engineering case is used herein to demonstrate the effectiveness of the algorithm. In this case, the roadbed is deformed upward due to the expansive soil, so that a place is obviously arched upward, and the difficulty of the track fine adjustment operation is greatly increased.
Inputting corresponding adjustment parameters into the model, wherein the adjustment parameters comprise: absolute deviation data of the track, corresponding adjustment upper and lower bounds of each fastener system, peak value constraint of each index and initial weight combination. After calculation, an adjustment policy under the initial weight may be generated.
Since the weights of the different variables have different effects on the adjustment plan, in order to generate an appropriate plan with all the metrics below the limits, appropriate weight combinations for the five targets should be determined. Under the constraints already mentioned, the appropriate combination should minimize the overall adjustment. Fig. 7 illustrates a process for determining the appropriate weight combinations. The increment step size is set to 0.02.
The results of fig. 7 show that the intercept of the different filled regions on the y-axis represents the corresponding weight in one period. Initially, the 150/300ACO index exceeded the limit of 10mm, so the adjustment amount and 150/300ACO weight continued to increase, and the weights of the other three indexes were squeezed. As shown in (b) of fig. 7, the peak value of each index varies with the variation of the weight combination. Since weights of 10 mMCO, 60 mMCO and 5/30ACO are reduced, their peaks are slightly increased, but they do not eventually exceed the limit. The best weight combination occurs at iteration 98, at [0.335,0.029,0.029,0.029,0.579].
By static detection of railway irregularities, absolute deviation data is output assuming a WJ-8 fastener system is used. Since the status of the fastener system of each sleeper is unknown, it is assumed that the track under all nodes has one 10mm pad plus 0.5mm adjustment pad, for a total of 10.5mm. From the previous section, the adjustment range of all nodes is [ -10.5, 10.5], the adjustment boundary can be derived. The measured values and upper/lower boundaries are shown in fig. 6.
As can be seen from fig. 6, the maximum deviation reached 20mm, four evaluation indexes about the level irregularity were selected, and the peaks of the indexes were respectively: 10m chord measurement 1.92mm,60m chord measurement 5.91mm,5/30 vector distance difference 5.13mm,150/300 chord measurement 19.32mm. The two vector distance difference indexes exceed the limit.
Y=0 is a design line shape. However, since the deviation of the height irregularity is large, it is difficult to adjust to the design alignment. Through testing, the method has better applicability.
For the method of this embodiment, a 150/300 vector difference satisfies the ride comfort requirement about 10 iterations. The cumulative adjustment can be further optimized by sacrificing the weight of the other indicators. In 100 iterations, the iteration number corresponding to the minimum accumulated adjustment amount scheme is 98.
The foregoing examples merely illustrate specific embodiments of the invention, which are described in greater detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (8)
1. The high-speed railway track fine tuning optimization method considering multi-chord constraint is characterized by comprising the following steps of:
Step 1, establishing constraint conditions of a high-speed railway track fine adjustment model;
step 2, establishing a model, constructing an objective function of the adjustment node, and deducing an evaluation matrix from the original data;
Step 3, obtaining optimized deviation data by generating an adjustment measurement vector and superposing the adjustment measurement vector and the original deviation;
Step 4, using an iterative algorithm, and completing the 1 st iteration after generating an adjustment measurement vector and optimizing the linearity; for subsequent iterations, using the optimized line shape as input bias data; the deviation data is then converted into an adjusted spatial matrix and an evaluation matrix, and the optimized line shape is recalculated after the 2 nd iteration.
2. The method for fine tuning a high-speed railway track taking into account multi-chord constraints according to claim 1, wherein in step 1, the constraints include track adjustment space and track smoothness constraints.
3. The high-speed railway track fine-tuning optimization method considering multi-chord constraints according to claim 1, wherein in step 2, the objective function comprises an objective function of adjustment quantity of each adjustment node, a mid-chord measurement objective function and an objective function of a vector difference method.
4. The high-speed railway track fine tuning optimization method considering multi-chord constraint according to claim 3, wherein the objective function of the adjustment amount of each adjustment node is specifically as follows:
Where i is the tuning node number, a lim is the design elevation deviation limit, To adjust policy vector,/>For adjusting the objective function corresponding to node i.
5. The high-speed railway track fine tuning optimization method considering multi-chord constraint according to claim 4, wherein the construction method of the mid-chord measurement objective function is specifically as follows:
calculating an adjusting node as an average value of a middle point, an end point and a starting point of a detected chord in a chord measurement method:
Wherein, The average value of chord measurement when the detection point is at i, c j1 is the detected chord length, and p is absolute deviation data; the mid-point chord measurement average value changes of the middle point, the end point and the starting point of the front and rear detection chords are adjusted as follows:
Wherein, To adjust the corresponding detected chord change after the ith node;
Point chord measurement objective function in ith adjustment node The limit of dividing the sum of the squares of the median chord measurement average changes for the middle point, end point and start point of the adjusting front and rear detected chords by three times is as follows:
6. The high-speed railway track fine tuning optimization method considering multi-chord constraint according to claim 5, wherein the construction method of the objective function of the vector difference method is specifically as follows:
calculating the adjusting node as an average value of an end point of a moving chord, a starting point of the moving chord, an end point of a detecting chord and a starting point of the detecting chord in the vector distance difference method:
for the average value of vector distance difference when the detection point is at i, the end point of the front-back moving chord, the starting point of the moving chord, the end point of the detecting chord and the average value change/>, of the starting point of the detecting chord are adjusted The following are provided:
Objective function of ith adjustment node vector difference method To adjust the sum of the squares of the end point of the moving chord back and forth, the start point of the moving chord, the end point of the detecting chord, and the mean change of the start point of the detecting chord divided by the four times vector distance difference constraint value is as follows:
7. the method for fine tuning of a high-speed railway track taking into account the multi-chord constraint according to claim 6, wherein in step 4, for the calculation of the adjustment evaluation score, the inputted adjustment amount is the accumulated adjustment amount, and the adjustment space matrix is synchronized and updated after each iteration to ensure that the optimized line is within the adjustment space.
8. The high-speed railway track fine-tuning optimization method considering multi-chord constraints according to claim 7, wherein the weights of all objective functions need to satisfy the following conditions:
Wherein, w 1 is the adjustment weight, w 2 is the 10m chord weight, w 3 is the 60m chord weight, w 4 is the 5/30m vector distance difference weight, and w 5 is the 150/300m vector distance difference weight; by increasing the overrun chord constraint, a feasible weight combination meeting all the indexes is output.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102561118A (en) * | 2011-12-15 | 2012-07-11 | 江西日月明铁道设备开发有限公司 | Quick fine tuning method for ballastless track of high-speed rail based on track deviation |
| CN115758061A (en) * | 2023-01-10 | 2023-03-07 | 西南交通大学 | Track irregularity fine adjustment method based on adjacent sleeper coupling analytic calculation |
| CN116127631A (en) * | 2022-12-21 | 2023-05-16 | 西南交通大学 | Multi-target fine tuning method for high-speed railway track under data physical fusion |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102561118A (en) * | 2011-12-15 | 2012-07-11 | 江西日月明铁道设备开发有限公司 | Quick fine tuning method for ballastless track of high-speed rail based on track deviation |
| CN116127631A (en) * | 2022-12-21 | 2023-05-16 | 西南交通大学 | Multi-target fine tuning method for high-speed railway track under data physical fusion |
| CN115758061A (en) * | 2023-01-10 | 2023-03-07 | 西南交通大学 | Track irregularity fine adjustment method based on adjacent sleeper coupling analytic calculation |
Non-Patent Citations (2)
| Title |
|---|
| 杜威等: "基于IPSO-LSTM的高速铁路无砟轨道不平顺预测", 铁道科学与工程学报, vol. 20, no. 3, 17 May 2022 (2022-05-17), pages 753 - 761 * |
| 马青松等: "基于逆向强化学习的铁路线路方案优选研究", 铁道建筑, vol. 63, no. 7, 20 July 2023 (2023-07-20), pages 1 - 7 * |
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