Method for fine adjustment of small-radius curve track considering deviation contribution and ultra-high compensation

Abstract

The application discloses a small-radius curve track fine adjustment method considering bias contribution degree and super-high compensation, and the method comprises the following steps: acquiring small-radius curve track geometric parameters and curve design parameters, and performing geometric parameter bias calculation for each mile point; based on the obtained geometric parameter bias, the contribution degree of each geometric parameter bias is calculated and sorted, and the bias processing priority is determined according to the sorting result; according to the determined bias processing priority, the track fine adjustment strategy is formulated, the track fine adjustment is performed, and the fine adjustment result is checked and verified in multiple levels. The application provides a fine adjustment scheme generation method fusing bias contribution degree sorting and super-high compensation quantification, and is suitable for new high-speed rail fine adjustment and finishing operation after track over-difference (dynamic TQI>2.4) in the operation and maintenance period.

Description

Technical Field

[0001] This invention relates to the field of fine-tuning technology for ballasted tracks in high-speed railways, specifically to a fine-tuning method for small-radius curve tracks that considers deviation contribution and superelevation compensation. Background Technology

[0002] The rationality of the fine-tuning scheme for small-radius curves directly determines the TQI compliance effect. However, the traditional scheme generation method has two major drawbacks: First, it does not consider the contribution of deviations, treating deviations such as track alignment and elevation as well as deviations such as gauge and superelevation in the same way, resulting in key deviations not being corrected first, making it difficult for the static TQI to be ≤2.0. Second, the superelevation compensation is set based on experience (such as a fixed addition of 2mm), without considering the adaptability of the curve radius. After lateral deformation of the track bed on small-radius curves (such as 600m), the superelevation deviation exceeds the standard, and the dynamic TQI is >2.4. In addition, the traditional scheme lacks a multi-level review mechanism, and scheme errors lead to a rework rate as high as 15%, which seriously affects the project progress. Summary of the Invention

[0003] This application provides a fine-tuning method for small-radius curve tracks that considers the contribution of deviation and ultra-high compensation, in order to solve the problems of traditional fine-tuning schemes, such as failure to distinguish deviation priorities, inaccurate ultra-high compensation, and lack of verification.

[0004] According to the first aspect, one embodiment provides a method for fine-tuning small-radius curve tracks that considers deviation contribution and superelevation compensation, the method comprising: Obtain the geometric parameters of the small-radius curve track and the curve design parameters, and calculate the geometric parameter deviation at each mile point; Based on the obtained geometric parameter deviations, the contribution of each geometric parameter deviation is calculated and sorted, and the deviation processing priority is determined according to the sorting results. Based on the determined deviation handling priorities, a track fine-tuning strategy is formulated and track fine-tuning is carried out, and the fine-tuning results are verified and checked at multiple levels.

[0005] Furthermore, the geometric parameters of the small-radius curve track and the curve design parameters are obtained, and the geometric parameter deviations are calculated at each mileage point, specifically including: The inertial navigation track inspection instrument is used to collect the geometric parameters of small radius curve tracks, including the measured track gauge S, measured horizontal H, measured vertical G, and measured track orientation X. Obtain curve design parameters, including curve radius R, design superelevation Hset, design gauge Sset, design elevation Gset, and design orientation Xset; Outlier handling: Outliers are removed using the 3σ criterion and replaced with the mean of adjacent points; elevation and alignment data are filtered using a 5-point moving average to retain the true continuous deviation.

[0006] Furthermore, the geometric parameters of the small-radius curve track and the curve design parameters are obtained, and the geometric parameter deviations are calculated at each mileage point, specifically including: Calculate the geometric parameter deviations at every 0.5m mileage point using the formulas: ΔS=S-Sset, ΔH=H-Hset, ΔG=G-Gset, ΔX=X-Xset, where ΔS is the gauge deviation, ΔH is the superelevation deviation, ΔG is the elevation deviation, and ΔX is the track alignment deviation.

[0007] Furthermore, based on the obtained geometric parameter deviations, the contribution of each geometric parameter deviation is calculated and ranked. The priority of deviation processing is determined according to the ranking results, specifically including: Calculate the contribution of each parameter deviation using the following formula: Ck=(ΣΔPk 2 / ΣΔPi 2 ) × 100% Where Ck is the deviation contribution of the k-th parameter, k is a certain parameter, i is all parameters, ΔPk is the deviation value of the k-th parameter, and ΣΔPk 2 Let ΣΔPi be the sum of squared deviations of the k-th term. 2 This is the sum of squares of the total deviations of the four parameters; Sort by contribution from highest to lowest and set the priority for handling deviations.

[0008] Furthermore, a track fine-tuning strategy is formulated and track fine-tuning is performed based on the determined deviation handling priority, and the fine-tuning results are verified through multi-level checks, specifically including: We have developed a track fine-tuning strategy that prioritizes high-priority deviations over low-priority deviations and prioritizes benchmark stocks over non-benchmark stocks.

[0009] Furthermore, a track fine-tuning strategy is formulated and track fine-tuning is performed based on the determined deviation handling priority, and the fine-tuning results are verified through multi-level checks, specifically including: Based on the radius R of the small-radius curve, the superelevation compensation ΔHbu is determined according to the following rules: When 800m < R ≤ 1000m, set the initial value ΔH. bu = 2mm; When 600m < R ≤ 800m, set the initial value ΔH. bu = 2.5mm; When R ≤ 600m, the initial value ΔH is set. bu = 3mm; Compensation verification: Select 10 sleepers at the midpoint of the curve and measure the horizontal deviation with an electronic level. If the horizontal deviation after compensation is ≤ ±0.5mm, confirm the value of ΔHbu; otherwise, fine-tune ΔHbu by ±0.2mm until the accuracy is met.

[0010] Furthermore, a track fine-tuning strategy is formulated and track fine-tuning is performed based on the determined deviation handling priority, and the fine-tuning results are verified through multi-level checks, specifically including: Track alignment adjustment: For transition curve sections, a quadratic parabola is used for fitting, and the track alignment amount ΔXtiao = fitted track alignment - measured track alignment, with a single track alignment amount ≤ 3mm; for circular curve sections, a circular curve is used for fitting, and ΔXtiao = designed track alignment - measured track alignment, ensuring that the track alignment deviation contribution Cx after adjustment is lower than the preset value. High and low level adjustment: The baseline track level adjustment amount Gbase = Design height and low level Gset - Measured height and low level G + 0.5mm, where 0.5mm is the track bed settlement compensation amount, ensuring that the track alignment deviation contribution Cg after adjustment is lower than the preset value; Horizontal adjustment: Non-benchmark starting volume Gnon = Gbase - design superelevation Hset + ΔHbu, ensuring that the superelevation deviation contribution Ch after adjustment is lower than the preset value; Track gauge adjustment: Track gauge adjustment amount ΔStiao = set track gauge Sset - measured track gauge S, ensuring that the track gauge deviation Cs is lower than the preset value after adjustment.

[0011] Furthermore, the fine-tuning results undergo multi-level verification and validation, specifically including: Level 1 Review: Technicians conduct self-checks to ensure that the adjustment logic conforms to the "priority sorting" and that the excessive compensation amount matches the curve radius, ensuring that the solution is free of logical errors; Secondary verification: The person in charge of measurement will verify the measurement, randomly select 20% of the mileage points, and manually check the track alignment amount. If the deviation is ≤ ±0.2mm, it is considered qualified; if it is not qualified, the superelevation compensation amount or track alignment amount will be recalculated. Level 3 review: The supervising engineer reviews the plan, focusing on the high-priority deviation correction plan and the rationality of the excessive compensation amount. The plan can only be implemented after the engineer signs and confirms it. If the dynamic TQI is greater than 2.4, the deviation contribution will be re-analyzed and the plan will be optimized.

[0012] According to a second aspect, one embodiment provides a fine-tuning system for small-radius curve tracks that considers deviation contribution and ultra-high compensation, the system comprising: The deviation calculation module is used to obtain the geometric parameters of small-radius curve tracks and curve design parameters, and to calculate the geometric parameter deviations at each mileage point. The deviation contribution calculation module is used to calculate the contribution of each geometric parameter deviation based on the obtained geometric parameter deviations and sort them, and determine the deviation processing priority based on the sorting results. The fine-tuning and verification module is used to formulate a track fine-tuning strategy based on the determined deviation handling priority, perform track fine-tuning, and perform multi-level verification of the fine-tuning results.

[0013] This application provides a method for fine-tuning small-radius curve tracks that considers deviation contribution and ultra-high compensation, which has the following advantages: 1. Improved accuracy of the solution: Deviations are corrected first based on contribution, and ultra-high compensation is adapted to the curve radius, with static TQI ≤ 2.0 and dynamic TQI ≤ 2.4; 2. Reduced rework rate: Multi-level review avoids errors in the design, reducing the rework rate from 15% to below 3%; 3. Wheel-rail force optimization: The superelevation compensation is reasonable, and the peak wheel-rail lateral force is ≤75kN (15% lower than the traditional construction method), reducing the track defect incidence rate by 70%; 4. Strong applicability: It is compatible with curves of different radii from 600 to 1000m and can be directly applied to new construction and maintenance projects. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a method for fine-tuning a small-radius curve track that considers deviation contribution and ultra-high compensation, as provided in one embodiment of the present invention. Detailed Implementation

[0015] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0016] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0017] The first embodiment of this invention provides a method for fine-tuning small-radius curve tracks that considers deviation contribution and superelevation compensation. It integrates a method for generating fine-tuning schemes by ranking deviation contribution and quantifying superelevation compensation. This method is applicable to fine-tuning of newly built high-speed railways and fine-tuning operations after track deviations (dynamic TQI > 2.4) during the operation and maintenance period. The following section discusses... Figure 1Please provide a detailed explanation.

[0018] like Figure 1 As shown, in step S100, the geometric parameters of the small radius curve track and the curve design parameters are obtained, and the geometric parameter deviation is calculated for each mileage point.

[0019] The above steps specifically include: S110, Data Acquisition: Acquire the geometric parameters of the small-radius curve track (measured track gauge S, measured horizontal H, measured vertical G, measured track orientation X) collected by the inertial navigation track inspection instrument, with an acquisition frequency of 100Hz and a resolution of 0.5cm; acquire the curve design parameters (curve radius R, design superelevation Hset, design track gauge Sset = 1435mm, design vertical Gset, design track orientation Xset); S120, Outlier Handling: The “3σ criterion” is used to remove outliers such as gauge jumps >3mm and elevation changes >2mm, and replace them with the average of adjacent points; the elevation and alignment data are filtered by a 5-point moving average to retain the true continuous deviation; S130, Deviation Calculation: Calculate the geometric parameter deviation for each 0.5m mileage point according to the formula "ΔS=S-Sset, ΔH=H-Hset, ΔG=G-Gset, ΔX=X-Xset".

[0020] like Figure 1 As shown, in step S200, based on the obtained geometric parameter deviations, the contribution of each geometric parameter deviation is calculated and sorted, and the deviation processing priority is determined according to the sorting results.

[0021] The above steps specifically include: S210, Contribution Calculation: According to the formula Ck=(ΣΔPk) 2 / ΣΔPi 2 ) × 100%, where Ck is the deviation contribution of the k-th parameter, k is a certain parameter, i is all parameters, ΔPk is the deviation value of the k-th parameter, ΣΔPk 2 Let ΣΔPi be the sum of squared deviations of the k-th term. 2 The contribution of deviations in orientation (Cx), elevation (Cg), levelness (Ch), and gauge (Cs) is calculated as the sum of squares of the total deviations of the four parameters. S220, Priority sorting: Sort by contribution from high to low, set priority: Cx (>40%) > Cg (>30%) > Ch (>10%) > Cs (<10%), prioritize the processing of high priority deviations (e.g., when Cx=46%, correct the trajectory first). S230, Defect Labeling: Generate a "Deviation Contribution Heatmap", label the mileage segments of high-priority deviations (such as the circular curve K2+300-K2+400, Cx=48%), and clarify the key areas for correction in the plan.

[0022] like Figure 1 As shown, in step S300, a track fine-tuning strategy is formulated and track fine-tuning is performed according to the determined deviation processing priority, and the fine-tuning results are verified through multi-level checks.

[0023] The above steps specifically include: S310, ultra-high compensation amount accurately determined Based on the radius R of the small-radius curve, the superelevation compensation ΔHbu is determined according to the following rules: When 800m < R ≤ 1000m, the initial value ΔHbu is set to 2mm (the lateral deformation of the track bed is relatively small). When 600m < R ≤ 800m, the initial value ΔHbu is set to 2.5mm (moderate lateral deformation of the track bed). When R≤600m, the initial value is set to ΔHbu = 3mm (for tracks with large lateral deformation, such as curves with a radius of 600m). Compensation verification: Select 10 sleepers at the midpoint of the curve and measure the horizontal deviation with an electronic level (accuracy ±0.05mm / m). If the horizontal deviation after compensation is ≤±0.5mm, confirm the value of ΔHbu; otherwise, fine-tune ΔHbu by ±0.2mm until the accuracy is met.

[0024] S320, Fine-tuning scheme generation In this embodiment, the adjustment logic is set according to the following logic generation scheme: "high priority deviation first, then low priority deviation" and "benchmark stock first, then non-benchmark stock".

[0025] Specific parameter calculations: Track alignment adjustment (high priority): For transition curve sections, a quadratic parabola fitting is used, with the track alignment adjustment amount ΔXtiao = fitted track alignment - measured track alignment, and the single adjustment amount ≤ 3mm; for circular curve sections, a circular curve fitting is used, with ΔXtiao = design track alignment - measured track alignment, ensuring that the track alignment deviation contribution Cx after adjustment is ≤ 45%; High / low elevation repair (medium-high priority): The lifting amount Gbase of the benchmark strip (outer rail) = design elevation / low elevation Gset - measured elevation / low elevation G + 0.5mm, where 0.5mm is the amount of track bed settlement compensation, ensuring that the contribution of track alignment deviation Cg after repair is ≤35%; Horizontal adjustment (medium priority): Non-benchmark section (inner rail) lifting amount Gnon = Gbase - design superelevation Hset + ΔHbu, ensuring that the superelevation deviation contribution Ch after adjustment is ≤15%; Track gauge adjustment (low priority): Track gauge adjustment amount ΔStiao = set track gauge Sset - measured track gauge S, ensuring that the track gauge deviation Cs ≤ 10% after adjustment; Output of the scheme: Generate the "Small Radius Curve Fine Adjustment Scheme", which includes the "Early Track Adjustment Parameter Table for Each 0.5m Mileage" and the "Suggested Table of Large Machine Tamping Parameters" (Circular curve tamping frequency 28Hz, clamping force 200kN, and transition curve clamping force 180kN).

[0026] S330, multi-level review of the solution Level 1 Review (Technician Self-Check): Check whether the adjustment logic conforms to the "priority sorting", whether the over-compensation amount matches the curve radius, and ensure that the solution has no logical errors; Secondary verification (verification by the person in charge of measurement): Randomly select 20% of the mileage points and manually verify the initial track alignment amount. A deviation of ≤±0.2mm is considered qualified; if it is not qualified, recalculate the superelevation compensation amount or track alignment amount. Level 3 review (supervising engineer review): Focus on reviewing the high-priority deviation correction plan and the rationality of the excessive compensation amount. It can only be implemented after the engineer signs and confirms it. If the dynamic TQI is greater than 2.4, return to step S200 to re-analyze the deviation contribution and optimize the plan.

[0027] Application example: Taking the fine-tuning of two small-radius curves, 600m (R=600m) and 800m (R=800m), on the Shenyang-Baishan High-Speed ​​Railway as an example: 1. Data Preprocessing Curve with a radius of 600m: ΔS = -0.3-0.2mm, ΔH = -0.6-0.4mm, ΔG = -1.3-0.9mm, ΔX = -1.4-1.2mm; 800m radius curve: ΔS=-0.2-0.3mm, ΔH=-0.5-0.5mm, ΔG=-1.1-1.0mm, ΔX=-1.2-1.0mm; Outlier handling: Remove one outlier of ΔX=1.5mm from the 600m curve; after filtering at 5 points, the data becomes stable.

[0028] 2. Ranking of Deviation Contribution 600m curve: Cx=47%, Cg=33%, Ch=16%, Cs=4%, priority: track direction > elevation > horizontal > gauge; 800m curve: Cx=43%, Cg=32%, Ch=15%, Cs=10%, priority: track direction > elevation > horizontal > gauge.

[0029] 3. Determination of Over-Height Compensation 600m curve: R≤600m, ΔH bu= 3mm, after verification, the horizontal deviation = ±0.4mm ≤ ±0.5mm; 800m curve: 600m < R ≤ 800m, ΔH bu =2.5mm, after verification, the horizontal deviation is ±0.3mm≤±0.5mm.

[0030] 4. Scheme generation and review 600m curve scheme: track alignment adjustment 0.4-0.6mm, reference track starting amount 1.3-2.1mm, non-reference track starting amount 1.3-2.1-125+3=-120.7-119.9mm; 800m curve scheme: track alignment adjustment 0.3-0.5mm, reference track starting amount 1.1-2.0mm, non-reference track starting amount 1.1-2.0-110+2.5=-106.4-105.5mm (800m radius design superelevation 110mm). Level 3 verification: Manual calculation at 20% mileage points, deviation ≤ ±0.2mm, with supervisor's signature confirmation.

[0031] 5. Implementation Results 600m curve: After fine-tuning, static TQI = 1.8, dynamic TQI = 2.3; 800m curve: After fine-tuning, static TQI = 1.9, dynamic TQI = 2.2; All met the standards, with no rework required. The peak lateral force of the wheel and rail was 71kN and 70kN respectively, which met the standards.

[0032] Corresponding to the aforementioned method for fine-tuning small-radius curve tracks considering deviation contribution and ultra-high compensation, this invention also discloses a system for fine-tuning small-radius curve tracks considering deviation contribution and ultra-high compensation, which specifically includes: The deviation calculation module is used to obtain the geometric parameters of small-radius curve tracks and curve design parameters, and to calculate the geometric parameter deviations at each mileage point. The deviation contribution calculation module is used to calculate the contribution of each geometric parameter deviation based on the obtained geometric parameter deviations and sort them, and determine the deviation processing priority based on the sorting results. The fine-tuning and verification module is used to formulate a track fine-tuning strategy based on the determined deviation handling priority, perform track fine-tuning, and perform multi-level verification of the fine-tuning results.

[0033] It should be noted that for a detailed description of the small radius curve track fine-tuning system considering deviation contribution and ultra-high compensation provided in the embodiments of the present invention, please refer to the relevant description of the small radius curve track fine-tuning method considering deviation contribution and ultra-high compensation provided in the embodiments of this application, which will not be repeated here.

[0034] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for fine-tuning small-radius curve tracks considering deviation contribution and superelevation compensation, characterized in that, The method includes: Obtain the geometric parameters of the small-radius curve track and the curve design parameters, and calculate the geometric parameter deviation at each mile point; Based on the obtained geometric parameter deviations, the contribution of each geometric parameter deviation is calculated and sorted, and the deviation processing priority is determined according to the sorting results. Based on the determined deviation handling priorities, a track fine-tuning strategy is formulated and track fine-tuning is carried out, and the fine-tuning results are verified and checked at multiple levels.

2. The method for fine-tuning small-radius curve tracks considering deviation contribution and superelevation compensation as described in claim 1, characterized in that, Obtain the geometric parameters of the small-radius curve track and the curve design parameters, and calculate the geometric parameter deviation at each mileage point, specifically including: The inertial navigation track inspection instrument is used to collect the geometric parameters of small radius curve tracks, including the measured track gauge S, measured horizontal H, measured vertical G, and measured track orientation X. Obtain curve design parameters, including curve radius R, design superelevation Hset, design gauge Sset, design elevation Gset, and design orientation Xset; Outlier handling: Outliers are removed using the 3σ criterion and replaced with the mean of adjacent points; elevation and alignment data are filtered using a 5-point moving average to retain the true continuous deviation.

3. The method for fine-tuning small-radius curve tracks considering deviation contribution and superelevation compensation as described in claim 2, characterized in that, Obtain the geometric parameters of the small-radius curve track and the curve design parameters, and calculate the geometric parameter deviation at each mileage point, specifically including: Calculate the geometric parameter deviations at every 0.5m mileage point using the formulas: ΔS=S-Sset, ΔH=H-Hset, ΔG=G-Gset, ΔX=X-Xset, where ΔS is the gauge deviation, ΔH is the superelevation deviation, ΔG is the elevation deviation, and ΔX is the track alignment deviation.

4. The method for fine-tuning small-radius curve tracks considering deviation contribution and superelevation compensation as described in claim 3, characterized in that... Based on the obtained geometric parameter deviations, the contribution of each geometric parameter deviation is calculated and ranked. The priority of deviation processing is determined according to the ranking results, specifically including: Calculate the contribution of each parameter deviation using the following formula: Ck=(SΔPk 2 / SDPi 2 )×100% Where Ck is the deviation contribution of the k-th parameter, k is a certain parameter, i is all parameters, ΔPk is the deviation value of the k-th parameter, and ΣΔPk 2 Let ΣΔPi be the sum of squared deviations of the k-th term. 2 This is the sum of squares of the total deviations of the four parameters; Sort by contribution from highest to lowest and set the priority for handling deviations.

5. The method for fine-tuning small-radius curve tracks considering deviation contribution and superelevation compensation as described in claim 1, characterized in that, Based on the determined deviation handling priorities, a track fine-tuning strategy is formulated and track fine-tuning is performed. The fine-tuning results are then subject to multi-level verification and validation, specifically including: Formulate a track fine-tuning strategy that prioritizes high-priority deviations over low-priority deviations and prioritizes benchmark stocks over non-benchmark stocks.

6. The method for fine-tuning small-radius curve tracks considering deviation contribution and superelevation compensation as described in claim 5, characterized in that, Based on the determined deviation handling priorities, a track fine-tuning strategy is formulated and track fine-tuning is performed. The fine-tuning results are then subject to multi-level verification and validation, specifically including: Based on the radius R of the small-radius curve, the superelevation compensation ΔHbu is determined according to the following rules: When 800m < R ≤ 1000m, set the initial value ΔH. bu = 2mm; When 600m < R ≤ 800m, set the initial value ΔH. bu = 2.5mm; When R ≤ 600m, the initial value ΔH is set. bu = 3mm; Compensation verification: Select 10 sleepers at the midpoint of the curve and measure the horizontal deviation with an electronic level. If the horizontal deviation after compensation is ≤ ±0.5mm, confirm the value of ΔHbu; otherwise, fine-tune ΔHbu by ±0.2mm until the accuracy is met.

7. The method for fine-tuning small-radius curve tracks considering deviation contribution and superelevation compensation as described in claim 6, characterized in that, Based on the determined deviation handling priorities, a track fine-tuning strategy is formulated and track fine-tuning is performed. The fine-tuning results are then subject to multi-level verification and validation, specifically including: Track alignment adjustment: For transition curve sections, a quadratic parabola is used for fitting, and the track alignment amount ΔXtiao = fitted track alignment - measured track alignment, with a single track alignment amount ≤ 3mm; for circular curve sections, a circular curve is used for fitting, and ΔXtiao = designed track alignment - measured track alignment, ensuring that the track alignment deviation contribution Cx after adjustment is lower than the preset value. High and low level adjustment: The baseline track level adjustment amount Gbase = Design height and low level Gset - Measured height and low level G + 0.5mm, where 0.5mm is the track bed settlement compensation amount, ensuring that the track alignment deviation contribution Cg after adjustment is lower than the preset value; Horizontal adjustment: Non-benchmark starting volume Gnon = Gbase - design superelevation Hset + ΔHbu, ensuring that the superelevation deviation contribution Ch after adjustment is lower than the preset value; Track gauge adjustment: Track gauge adjustment amount ΔStiao = set track gauge Sset - measured track gauge S, ensuring that the track gauge deviation Cs is lower than the preset value after adjustment.

8. The method for fine-tuning small-radius curve tracks considering deviation contribution and superelevation compensation as described in claim 1, characterized in that, The fine-tuning results are subject to multi-level verification and validation, specifically including: Level 1 Review: Technicians conduct self-checks to ensure that the adjustment logic conforms to the "priority sorting" and that the excessive compensation amount matches the curve radius, ensuring that the solution is free of logical errors; Secondary verification: The person in charge of measurement will verify the measurement, randomly select 20% of the mileage points, and manually check the track alignment amount. If the deviation is ≤ ±0.2mm, it is considered qualified; if it is not qualified, the superelevation compensation amount or track alignment amount will be recalculated. Level 3 review: The supervising engineer reviews the plan, focusing on the high-priority deviation correction plan and the rationality of the excessive compensation amount. The plan can only be implemented after the engineer signs and confirms it. If the dynamic TQI is greater than 2.4, the deviation contribution will be re-analyzed and the plan will be optimized.

9. A fine-tuning system for small-radius curve tracks that considers deviation contribution and ultra-high compensation, characterized in that, The system includes: The deviation calculation module is used to obtain the geometric parameters of small-radius curve tracks and curve design parameters, and to calculate the geometric parameter deviations at each mileage point. The deviation contribution calculation module is used to calculate the contribution of each geometric parameter deviation based on the obtained geometric parameter deviations and sort them, and determine the deviation processing priority based on the sorting results. The fine-tuning and verification module is used to formulate a track fine-tuning strategy based on the determined deviation handling priority, perform track fine-tuning, and perform multi-level verification of the fine-tuning results.

Images