A method and system for adjusting the geometry deviation of a high-speed maglev track based on dynamic and static string measurement

Abstract

The application relates to the technical field of track engineering, in particular to a method and system for adjusting geometric deviation of a high-speed maglev track with constant guide based on dynamic and static chord measurement, which comprises the following steps: acquiring a data set and extracting a dynamic and static geometric deviation sequence of a long-stator surface and a guide surface of the high-speed maglev track; constructing three groups of virtual detection chords corresponding to short waves, medium waves and long waves, converting the dynamic and static geometric deviation sequence into chord measurement distance sequences of the short waves, the medium waves and the long waves; establishing a correlation between the chord measurement distance sequences of the short waves, the medium waves and the long waves and track structure deformation, and comparing the differences between the dynamic and static chord measurement distance values; then, establishing a high-speed maglev track geometric deviation optimization adjustment model; and finally, globally optimizing and solving the high-speed maglev track geometric deviation optimization adjustment model. Under the premise of ensuring the construction feasibility, the application realizes targeted treatment of multi-band geometric deviation, and improves the suspension guide stability and riding comfort of the high-speed maglev train.

Description

Technical Field

[0001] This application relates to the field of track engineering technology, and in particular to a method and system for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement. Background Technology

[0002] In conventional high-speed maglev trains operating at speeds of 430-600 km / h, the levitation and guidance stability and ride comfort are highly dependent on the geometric smoothness of the track. Geometric deviations in high-speed maglev tracks exhibit complex wavelength characteristics. The impact of geometric degradation across different wavelength bands—short-wave (e.g., functional component misalignment, angles), medium-wave (e.g., track beam deformation), and long-wave (e.g., bridge pier settlement)—on the train's levitation system varies significantly. Existing adjustment methods largely rely on local corrections or empirical judgments, lacking a systematic approach to accurately correlate multi-wavelength geometric deviations with specific track structural deformations. Furthermore, when developing adjustment plans, it is difficult to comprehensively consider the constraints of the actual construction operation's limit adjustment amount and track geometric change rate, resulting in residual deviations in the track after adjustment, failing to meet the stringent requirements for stable operation of high-speed maglev trains. Therefore, there is an urgent need for a method for optimizing and adjusting high-speed maglev track structural components that can accurately identify short, medium, and long-wavelength geometric deviations and establish a correlation with structural deformation, in order to improve the operational quality of high-speed maglev trains. Summary of the Invention

[0003] This application provides a method and system for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement. It is used to optimize and adjust the geometric deviation of the structure and functional components of the normal-conducting high-speed maglev track in order to improve the levitation stability and ride comfort of the maglev train under high-speed operation.

[0004] To address the aforementioned technical problems, in a first aspect, embodiments of this application provide a method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement, comprising the following steps: Step S1: Obtain a dynamic and static track irregularity detection dataset, and preprocess the dataset to extract the dynamic and static geometric deviation sequences of the long stator surface and guide surface of the high-speed maglev track from the preprocessed dataset; Step S2: For the dynamic and static geometric deviation sequences, construct three sets of virtual detection strings corresponding to short waves, medium waves, and long waves based on the principle of chord measurement, and convert the dynamic and static geometric deviation sequences of the track into short wave, medium wave, and long wave chord measurement vector sequences using a moving window chord measurement algorithm; Step S3: Establish the short wave, medium wave, and long wave chord measurement vector sequences and the track... The correlation between track structure deformation and the difference between dynamic and static chord measurement values ​​are used to identify the location of abnormal track beam stiffness. Step S4: Using the displacement adjustment amount of track functional components and track beam supports at each mileage point as decision variables, and taking the minimum adjustment scale and the minimum chord measurement value of the track residual geometric deviation after the adjustment operation as the objective function, the limit adjustment amount of the actual construction operation and the track geometric change rate at adjacent mileage points are introduced as constraints to establish a high-speed maglev track geometric deviation optimization adjustment model. Step S5: The high-speed maglev track geometric deviation optimization adjustment model is globally optimized to obtain the track geometric deviation adjustment amount at each mileage point, and targeted and precise adjustments are made to the high-speed maglev functional components and track beam supports according to the correlation.

[0005] In some exemplary embodiments, in step S1, the dataset consists of dynamic detection data and static track irregularity detection data of the same mileage section of the high-speed maglev track; the preprocessing includes: achieving accurate alignment of the dynamic and static irregularity data on the mileage coordinates through spatial positioning and interpolation resampling, and performing preprocessing using digital filtering technology.

[0006] In some exemplary embodiments, the precise alignment process includes: performing mileage positioning on the raw dynamic detection data based on the Global Positioning System and ranging beacons, and resampling it into equally spaced discrete data points using a spline interpolation algorithm to correspond to the mileage points of the static irregular data; and performing preprocessing using digital filtering techniques, including: using a zero-phase bandpass filter to eliminate trend terms and high-frequency non-stationary measurement noise, and extracting the orbital geometric deviation sequence of the effective band. y ( x i Let the original discrete spatial signal be... s ( x i The frequency domain transfer function of a bandpass filter is expressed as: H (ω), then the track geometric deviation after filtering can be expressed as: (1) In the formula,F and F -1 These are the Fourier transform and the inverse Fourier transform, respectively. x i For equal interval mileage, ω Spatial frequency.

[0007] In some exemplary embodiments, in step S2, when constructing three sets of virtual detection strings corresponding to shortwave, medium wave, and long wave, the shortwave detection string length is set to 3m, the medium wave detection string length to 30m, and the long wave detection string length to 300m; the track dynamic and static geometric deviation sequences are converted into shortwave, medium wave, and long wave chord measurement vector sequences using a moving window chord measurement algorithm, including: using each detection node x i Centered on, establish lengths of... L c The moving calculation window; where, L c ∈{3m, 30m, 300m}; Within each window, the endpoints of the detected chord will be in the orbital geometric deviation sequence. y ( x i The line connecting the points on the window is used as a virtual detection chord. The deviation of the actual line shape at the center point of the window from the midpoint of the virtual detection chord is calculated, and the single geometric deviation sequence is converted into three independent chord measurement vector sequences. v c ( x i The calculation formula is as follows: (2) In the formula, v c ( x i The sequence of 3m chordal distance measurement, 30m chordal distance measurement, and 300m chordal distance measurement for high-speed maglev tracks. c It equals 3, 30, or 300.

[0008] In some exemplary embodiments, in step S3, the track structure includes: functional component misalignment, bends, track beam deformation, and pier settlement.

[0009] In some exemplary embodiments, in step S3, establishing a mapping guide for the correlation between shortwave, medium-wave, and long-wave chord measurement vector sequences and track structure deformation includes: when the peak value of the 3m shortwave chord measurement vector significantly increases and exceeds the limit, it is determined that the track shortwave geometric deviation has deteriorated, and the corresponding track structure component adjustment measures are: adjusting the structural misalignment at the joint of the functional component, correcting the installation angle of the functional component, or the misalignment at the end of the track beam; when the peak value of the 30m medium-wave chord measurement vector significantly increases and exceeds the limit, it is determined that the track medium-wave geometric deviation has deteriorated, and the corresponding track structure component adjustment measures are: correcting the overall installation alignment of the track beam, or compensating for the upward and downward camber deformation of the beam; when the peak value of the 300m long-wave chord measurement vector significantly increases and exceeds the limit, it is determined that the track long-wave geometric deviation has deteriorated, and the corresponding track structure component adjustment measures are: adjusting the pier support pads to compensate for the long-wave deformation of the track caused by uneven foundation settlement.

[0010] In some exemplary embodiments, in step S3, comparing the differences between dynamic and static chord measurement values ​​to identify the location of abnormal track beam stiffness includes: extracting the dynamic and static chord measurement values ​​corresponding to the same chord length under the same detection section and the same mileage node; when the absolute difference between the dynamic and static chord measurement values ​​at the same mileage location exceeds a threshold, it is determined that there is an abnormality in track beam stiffness at that location.

[0011] In some exemplary embodiments, in step S4, the objective function is to adjust the scale of the operation and the residual track geometric deviation after the operation. The residual track geometric deviation is expressed as a chordal distance of 3m, 30m, and 300m, respectively. The objective function is expressed by the following formula: (3) In the formula, t c ( x i () represents the track adjustment amount corresponding to 3m chordal distance, 30m chordal distance, and 300m chordal distance.

[0012] In some exemplary embodiments, in step S4, constraints on the amplitude of chordal moment measurement at 3m, 30m, and 300m, the limit adjustment amount of actual construction operations, and the geometric change rate of the track at adjacent mileage points are introduced as constraints; the formula for the amplitude constraint of chordal moment measurement is as follows: (4) In equation (4), v c max The chord length is c The maximum value of the distance measured by the meter string; The limit adjustment constraints for actual construction operations are expressed by the following formula: (5) In equation (5), t c max The chord length is c The maximum adjustment range for the meter-chord distance measurement; The constraint on the geometric rate of change of the track between adjacent mileage points is expressed by the following formula: (6) In equation (6), Δ v c max The chord length is c The maximum allowable rate of change of the distance in a meter-chord survey.

[0013] Secondly, this application provides a system for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static string measurement. This system is used to implement the method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static string measurement described in the above embodiments. The system includes: a data acquisition and preprocessing module, a detection string processing module, a deformation mapping and rigidity assessment module, a global optimization modeling module, and a precise adjustment module connected in sequence. The data acquisition and preprocessing module acquires a dynamic and static track irregularity detection dataset and preprocesses the dataset, extracting the dynamic and static geometric deviation sequences of the long stator surface and guide surface of the high-speed maglev track from the preprocessed dataset. The detection string processing module constructs three sets of virtual detection strings corresponding to short waves, medium waves, and long waves based on the string measurement principle for the dynamic and static geometric deviation sequences, and converts the dynamic and static geometric deviation sequences into... The system is divided into three modules: shortwave, medium wave, and long wave chord measurement vector sequences; deformation mapping and rigidity assessment module; global optimization modeling module; and precise adjustment module. The global optimization modeling module is used to establish the correlation between the shortwave, medium wave, and long wave chord measurement vector sequences and the deformation of the track structure. At the same time, it compares the differences between dynamic and static chord measurement vector values ​​to identify the location of abnormal track beam stiffness. The global optimization modeling module uses the displacement adjustment amount of track functional components and track beam supports at each mileage point as decision variables, and the objective function is to minimize the chord measurement vector of the track residual geometric deviation after the adjustment operation. It introduces the limit adjustment amount of the actual construction operation and the track geometric change rate of adjacent mileage points as constraints to establish a high-speed maglev track geometric deviation optimization adjustment model. The precise adjustment module is used to perform global optimization solution on the high-speed maglev track geometric deviation optimization adjustment model, obtain the track geometric deviation adjustment amount at each mileage point, and perform targeted precise adjustment of high-speed maglev functional components and track beam supports according to the correlation.

[0014] The technical solution provided in this application has at least the following advantages: This application provides a method and system for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement. The method includes the following steps: Step S1: Obtain a dynamic and static track irregularity detection dataset and preprocess the dataset to extract the dynamic and static geometric deviation sequences of the long stator surface and guide surface of the high-speed maglev track from the preprocessed dataset; Step S2: For the dynamic and static geometric deviation sequences, construct three sets of virtual detection strings corresponding to short waves, medium waves, and long waves based on the principle of chord measurement, and convert the dynamic and static geometric deviation sequences into short wave, medium wave, and long wave chord measurement vector sequences using a moving window chord measurement algorithm; Step S3: Establish the short wave, medium wave, and long wave chord measurement vector sequences and the track structure deformation... The correlation is established, and the differences between dynamic and static chord measurement values ​​are compared to identify the abnormal locations of track beam stiffness. Step S4: Using the displacement adjustment amount of track functional components and track beam supports at each mileage point as decision variables, and taking the minimum adjustment scale and the minimum chord measurement value of the track residual geometric deviation after the adjustment operation as the objective function, the limit adjustment amount of the actual construction operation and the track geometric change rate at adjacent mileage points are introduced as constraints to establish a high-speed maglev track geometric deviation optimization adjustment model. Step S5: The high-speed maglev track geometric deviation optimization adjustment model is globally optimized to obtain the track geometric deviation adjustment amount at each mileage point, and targeted and precise adjustments are made to the high-speed maglev functional components and track beam supports according to the correlation.

[0015] This application provides a method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement. On the one hand, by constructing a multi-band virtual detection string covering short, medium, and long waves, the track geometric deviation is transformed into an independent chord measurement vector sequence. Furthermore, the correlation between deviations in different bands and structural deformations such as misalignment, bends, track beam deformation, and pier settlement is established, enabling precise adjustment of track components at each level. Simultaneously, by comparing the differences between dynamic and static chord measurement vector values, the location of abnormal track beam stiffness can be effectively identified.

[0016] On the other hand, this application constructs a global optimization model with the adjustment amount of track components at each mileage point as the decision variable and the goal of minimizing the chordal distance of the residual geometric deviation after adjustment. It also introduces the limit adjustment amount of actual on-site construction operations and the geometric change rate of adjacent mileage points as constraints. Under the premise of ensuring construction feasibility, it minimizes the residual deviation of the track and improves the levitation guidance stability and ride comfort of the normal-conducting high-speed maglev train in high-speed operation. Attached Figure Description

[0017] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments, and unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0018] Figure 1 This is a flowchart illustrating a method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static string measurement, provided as an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of a typical structure of a conventional high-speed maglev track provided in an embodiment of this application.

[0020] Figure 3 This is a flowchart illustrating the specific process architecture of a method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static string measurement, provided in an embodiment of this application. Detailed Implementation

[0021] As can be seen from the background technology, the existing technology lacks a systematic method to establish a precise correlation between multi-band geometric deviations and specific track structural deformations. Furthermore, when formulating adjustment plans, it is difficult to comprehensively consider the constraints such as the limit adjustment amount and track geometric change rate of actual construction operations. This results in residual deviations still existing in the track after adjustment, making it difficult to meet the stringent requirements for the smooth operation of high-speed maglev trains.

[0022] To address the aforementioned issues, this application provides a method and system for adjusting the geometric deviation of a conventional high-speed maglev track based on dynamic and static chord measurement. The method first acquires dynamic and static detection data of the high-speed maglev track, achieving precise mileage alignment through spatial positioning and interpolation resampling, and extracting the track geometric deviation sequence. Second, it constructs a virtual detection chord to transform the track geometric deviation into short, medium, and long-wave chord measurement vector sequences, establishing the correlation between deviations in different wavebands and the deformation of track component structures, and identifying track beam stiffness anomalies. Using the adjustment amount of track components at each mileage point as decision variables, a global optimization control model is constructed based on the objective of minimizing the residual geometric deviation after adjustment, and constraints are set for the limit adjustment amount and geometric change rate during on-site construction operations. Finally, the model is optimized to guide the precise adjustment of track structural components. This application, while ensuring construction feasibility, achieves targeted treatment of multi-waveband geometric deviations, improving the levitation and guidance stability and ride comfort of high-speed maglev trains.

[0023] The embodiments of this application will now be described in detail with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the embodiments of this application to facilitate a better understanding of the application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0024] See Figure 1 This application provides a method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement, including the following steps: Step S1: Obtain the dynamic and static track irregularity detection dataset, and preprocess the dataset to extract the dynamic and static geometric deviation sequences of the long stator surface and guide surface of the high-speed maglev track from the preprocessed dataset.

[0025] Step S2: For the dynamic and static geometric deviation sequences of the track, three sets of virtual detection strings corresponding to short wave, medium wave and long wave are constructed based on the principle of string measurement. The dynamic and static geometric deviation sequences of the track are converted into short wave, medium wave and long wave string measurement vector sequences by the moving window string measurement algorithm.

[0026] Step S3: Establish the correlation between the shortwave, medium wave, and long wave chord measurement vector sequences and the deformation of the track structure. At the same time, compare the differences between the dynamic and static chord measurement vector values ​​to identify the location of abnormal track beam stiffness.

[0027] Step S4: Using the displacement adjustment of track functional components and track beam supports at each mileage point as decision variables, and taking the minimum adjustment scale and the minimum chordal distance of the track residual geometric deviation after the adjustment operation as the objective function, the limit adjustment amount of the actual construction operation and the track geometric change rate at adjacent mileage points are introduced as constraints to establish a high-speed maglev track geometric deviation optimization adjustment model.

[0028] Step S5: Perform global optimization on the high-speed maglev track geometric deviation optimization and adjustment model to obtain the track geometric deviation adjustment amount at each mileage point, and make targeted and precise adjustments to the high-speed maglev functional components and track beam supports based on the correlation.

[0029] In some embodiments, the dataset obtained in this application consists of dynamic detection data and static track irregularity detection data of high-speed maglev track in the same mileage section; the preprocessing includes: achieving accurate alignment of dynamic and static irregularity data on mileage coordinates through spatial positioning and interpolation resampling, and performing preprocessing using digital filtering technology.

[0030] Figure 2 A schematic diagram of a typical structure of a conventional high-speed maglev track provided in an embodiment of this application is shown. Figure 2 As can be seen, the conventional high-speed maglev track system is mainly composed of functional components (including long stator surfaces and guide surfaces), track beams, supports, and lower piers. The spatial deformation state of these structural components directly determines the geometric deviation characteristics of the multi-band track. Specifically, the geometric state of the functional component surfaces and the structural misalignment and installation angles at adjacent joints mainly correspond to the geometric deviation of the short-wave track with a chord length of 3m; the overall installation alignment of the track beam and its upward and downward camber deformation during service mainly correspond to the geometric deviation of the medium-wave track with a chord length of 30m; and the pier displacement caused by factors such as uneven foundation settlement mainly corresponds to the geometric deviation of the long-wave track with a chord length of 300m. This typical structural composition constitutes the physical basis for the implementation of this method.

[0031] This application provides a geometric deviation adjustment system for a normal-conducting high-speed maglev track based on dynamic and static string measurement. This system is used to implement the geometric deviation adjustment method for a normal-conducting high-speed maglev track based on dynamic and static string measurement described in the above embodiment. The system includes: a data acquisition and preprocessing module, a detection string processing module, a deformation mapping and stiffness evaluation module, a global optimization modeling module, and a precise adjustment module connected in sequence; its architecture diagram is shown below. Figure 3 As shown.

[0032] Data acquisition and preprocessing module ( Figure 3 Module 1) is used for data acquisition and preprocessing. First, dynamic detection data and static track irregularity detection data of the high-speed maglev track in the same mileage section are acquired. Through spatial positioning and interpolation resampling, the dynamic and static irregularity data are accurately aligned on the mileage coordinates. Digital filtering technology is used for preprocessing to extract the dynamic and static geometric deviation sequences of the long stator surface and guide surface of the high-speed maglev track.

[0033] Furthermore, the specific implementation method for preprocessing and extracting the dynamic and static geometric deviation sequences of the track in the data acquisition and preprocessing module is as follows: (1) Data spatial alignment. For a 600km / h high-speed maglev line, the original dynamic detection data is used for mileage positioning based on the Global Positioning System and ranging beacons. Considering that the high speed of dynamic inspection leads to non-uniform spatial distribution of sampling points, spline interpolation algorithm is used to resample the data into equally spaced Δ points. x The discrete data points are used to precisely correspond to the mileage points of the static, uneven data.

[0034] (2) Data filtering preprocessing. A zero-phase bandpass filter is used to eliminate trend terms and high-frequency non-stationary measurement noise, and the orbital geometric deviation sequence of the effective band is extracted. y ( x i Let the original discrete spatial signal be... s ( x i The frequency domain transfer function of a bandpass filter is expressed as: H (ω), then the track geometric deviation after filtering can be expressed as: (1) In equation (1), F and F -1 These are the Fourier transform and the inverse Fourier transform, respectively. x i For equal interval mileage, ω Spatial frequency.

[0035] String processing module ( Figure 3 Module 2) is used to construct a virtual detection string containing short-wave, medium-wave, and long-wave based on the principle of string measurement for dynamic and static track geometric deviation sequences. The dynamic and static track geometric deviation sequences are converted into short-wave, medium-wave, and long-wave string measurement vector sequences through the moving window string measurement algorithm.

[0036] Furthermore, the specific implementation of the virtual detection string and moving window string detection algorithm in the detection string processing module is as follows: (1) Construct three sets of virtual detection strings corresponding to short waves, medium waves and long waves. Based on the typical structural dimensions of high-speed maglev tracks, the length of the short wave detection string is set to 3m (corresponding to the span of a single long stator or functional component), the length of the medium wave detection string is set to 30m (corresponding to the length of a single span track beam), and the length of the long wave detection string is set to 300m (corresponding to the long wave deformation section of the roadbed or bridge pier).

[0037] (2) Implement the moving window chord detection algorithm. Using each detection node... x i Centered on, establish lengths of... L c ( L c A moving calculation window (∈{3m, 30m, 300m}) is used. Within each window, the endpoints of the detected chord are considered in the orbital geometric deviation sequence. y ( x i The line connecting the points on the window is used as a virtual detection chord, and the deviation of the actual line shape at the center point of the window from the midpoint of this virtual detection chord is calculated. This decouples the single geometric deviation sequence into three independent chordal distance sequences. v c ( x i The calculation formula is as follows: (2) In the formula, v c ( x i The sequence of 3m chordal distance measurement, 30m chordal distance measurement, and 300m chordal distance measurement for high-speed maglev tracks. c It equals 3, 30, or 300.

[0038] Deformation mapping and stiffness assessment module ( Figure 3 Module 3) is used to establish the correlation between short, medium and long wave chord measurement vectors and track structure deformations such as functional component misalignment, bends, track beam deformation and pier settlement. At the same time, it compares the differences between dynamic and static chord measurement vector values ​​to identify the location of abnormal track beam stiffness.

[0039] Furthermore, the specific methods for multi-band decoupling mapping and stiffness assessment in the deformation mapping and stiffness assessment module are as follows: (1) Establish a mapping guide for the correlation between short, medium and long wave chord measurement vector sequences and track structure deformation: When the peak value of the 3m short wave chord measurement vector increases significantly and exceeds the limit, it is determined that the short wave geometric deviation of the track is deteriorated. The corresponding structural component adjustment measures are: adjust the structural misalignment at the joint of the functional component, correct the installation angle of the functional component or the misalignment at the end of the track beam; when the peak value of the 30m medium wave chord measurement vector increases significantly and exceeds the limit, it is determined that the medium wave of the track is deteriorated. The corresponding adjustment measures are: correct the overall installation alignment of the track beam, or compensate for the upward and downward deformation of the beam; when the peak value of the 300m long wave chord measurement vector increases significantly and exceeds the limit, it is determined that the long wave of the track is deteriorated. The corresponding adjustment measures are: adjust the bridge pier support pads to compensate for the long wave deformation of the track caused by uneven settlement of the foundation.

[0040] (2) Anomaly detection of track beam stiffness. Extract the dynamic chord measurement vector corresponding to the same chord length under the same detection section and the same mileage node. v c,dyn ( x i ) and static chordal distance measurement v c,sta ( x i When the absolute difference between the dynamic and static chord distances at the same mileage location (see formula (3)) exceeds the preset safety assessment threshold, it is determined that the dynamic coupling deflection of the vehicle and track at that location is too large, and there is an abnormality in the track beam stiffness.

[0041] (3) In the formula, Δ v c ( x i ) represents mileage x i The absolute difference between the dynamic and static chord distances.

[0042] Global optimization modeling module ( Figure 3 Module 4) is used to take the displacement adjustment of track functional components and track beam supports at each mileage point as decision variables, and the objective function is to minimize the chordal distance of the adjustment operation and the residual geometric deviation of the track after the adjustment operation. The limit adjustment amount of the actual construction operation and the geometric change rate of the track at adjacent mileage points are introduced as constraints. The geometric deviation sequence of the track after adjustment is smoothed by combining digital filters to establish a high-speed maglev track geometric deviation optimization adjustment model.

[0043] Furthermore, the global optimization adjustment model in the global optimization modeling module specifically includes: (1) Constructing the objective function. The objective function is to adjust the scale of the operation and the residual track geometric deviation after the operation. The residual track geometric deviation is represented by chordal distance measurement of 3m, 30m, and 300m respectively. The objective function is expressed by the following formula: (4) In the formula, t c ( x i () represents the track adjustment amount corresponding to 3m chordal distance, 30m chordal distance, and 300m chordal distance.

[0044] (2) Setting multiple constraints. To ensure the physical feasibility of on-site construction operations, the following boundary constraints are introduced: ① The amplitude constraint of chord-based vector distance measurement is expressed by the following formula: (5) In the formula, v c max The chord length is c The maximum value of the distance measured by the meter string.

[0045] ② The limit adjustment constraints for actual construction operations are expressed by the following formula: (6) In the formula, t c max The chord length is c The maximum adjustment range for the meter-chord distance measurement.

[0046] ③ Constraints on the geometric rate of change of the track between adjacent mileage points, expressed by the following formula: (7) In the formula, Δ v c max The chord length is c The maximum allowable rate of change of the distance in a meter-chord survey.

[0047] Precision adjustment module ( Figure 3 Module 5) is used to perform global optimization of the high-speed maglev track geometric deviation optimization and adjustment model, obtain the track geometric deviation adjustment amount at each mileage point, and make targeted and precise adjustments to the high-speed maglev functional components and track beam supports based on the aforementioned correlation with track structure deformation.

[0048] Furthermore, the specific implementation process for precisely adjusting the module is as follows: (1) The above control model is solved by using global optimization algorithms such as sequential quadratic programming, and the output is a sequence of shortwave chordal distance adjustment, medium wave chordal distance adjustment, and long wave chordal distance adjustment for each mileage point that minimizes the residual deviation of the whole band.

[0049] (2) Implement targeted regulation. Combine with step S3 ( Figure 3 The structural deformation correlation established in Module 3) is precisely targeted through automated mechanical and hydraulic adjustment terminals: short-wave chordal distance adjustment is used to adjust the forward and backward movement of the long stator support arm and positioning shims; medium-wave chordal distance adjustment is used to adjust the track beam connection and support fasteners; and long-wave chordal distance adjustment is used to adjust the pier supports. For sections identified as having abnormal track beam stiffness, local structural reinforcement measures such as adding external prestressing are implemented. This achieves closed-loop rectification of track geometric deviations across all characteristic wavebands, effectively improving the high-speed operation safety and ride comfort of high-speed maglev trains.

[0050] Based on the above technical solutions, this application provides a method and system for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static string measurement. The method includes the following steps: Step S1: Obtain a dynamic and static track irregularity detection dataset and preprocess the dataset to extract the dynamic and static geometric deviation sequences of the long stator surface and guide surface of the high-speed maglev track from the preprocessed dataset; Step S2: For the dynamic and static geometric deviation sequences, construct three sets of virtual detection strings corresponding to short waves, medium waves, and long waves based on the string measurement principle, and convert the dynamic and static geometric deviation sequences of the track into short wave, medium wave, and long wave string measurement vector sequences using a moving window string measurement algorithm; Step S3: Establish the short wave, medium wave, and long wave string measurement vector sequences and the track structure... The correlation between structural deformation and the difference between dynamic and static chord measurement values ​​are used to identify the abnormal location of track beam stiffness. Step S4: Using the displacement adjustment amount of track functional components and track beam supports at each mileage point as decision variables, and taking the minimum adjustment scale and the minimum chord measurement value of the track residual geometric deviation after the adjustment operation as the objective function, the limit adjustment amount of the actual construction operation and the track geometric change rate of adjacent mileage points are introduced as constraints to establish a high-speed maglev track geometric deviation optimization adjustment model. Step S5: The high-speed maglev track geometric deviation optimization adjustment model is globally optimized to obtain the track geometric deviation adjustment amount at each mileage point, and targeted and precise adjustments are made to the high-speed maglev functional components and track beam supports according to the correlation.

[0051] This application provides a method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement. On the one hand, by constructing a multi-band virtual detection string covering short, medium, and long waves, the track geometric deviation is transformed into an independent chord measurement vector sequence. Furthermore, the correlation between deviations in different bands and structural deformations such as misalignment, bends, track beam deformation, and pier settlement is established, enabling precise adjustment of track components at each level. Simultaneously, by comparing the differences between dynamic and static chord measurement vector values, the location of abnormal track beam stiffness can be effectively identified.

[0052] On the other hand, this application constructs a global optimization model with the adjustment amount of track components at each mileage point as the decision variable and the goal of minimizing the chordal distance of the residual geometric deviation after adjustment. It also introduces the limit adjustment amount of actual on-site construction operations and the geometric change rate of adjacent mileage points as constraints. Under the premise of ensuring construction feasibility, it minimizes the residual deviation of the track and improves the levitation guidance stability and ride comfort of the normal-conducting high-speed maglev train in high-speed operation.

[0053] Those skilled in the art will understand that the above-described embodiments are specific examples of implementing this application, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of this application. Any person skilled in the art can make their own modifications and alterations without departing from the spirit and scope of this application; therefore, the scope of protection of this application should be determined by the scope defined in the claims.

Claims

1. A method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement, characterized in that, Includes the following steps: Step S1: Obtain the dynamic and static track irregularity detection dataset, and preprocess the dataset to extract the dynamic and static geometric deviation sequences of the long stator surface and guide surface of the high-speed maglev track from the preprocessed dataset. Step S2: For the dynamic and static geometric deviation sequences of the track, three sets of virtual detection strings corresponding to short wave, medium wave and long wave are constructed based on the principle of string measurement. The dynamic and static geometric deviation sequences of the track are converted into short wave, medium wave and long wave string measurement vector sequences by the moving window string measurement algorithm. Step S3: Establish the correlation between shortwave, medium wave, and long wave chord measurement vector sequences and track structure deformation. At the same time, compare the differences between dynamic and static chord measurement vector values ​​to identify the location of track beam stiffness anomalies. Step S4: Using the displacement adjustment amount of track functional components and track beam supports at each mileage point as decision variables, and taking the minimum adjustment operation scale and the minimum chordal distance of the track residual geometric deviation after the adjustment operation as the objective function, the limit adjustment amount of the actual construction operation and the track geometric change rate of adjacent mileage points are introduced as constraints to establish a high-speed maglev track geometric deviation optimization adjustment model. Step S5: Perform global optimization solution on the high-speed maglev track geometric deviation optimization and adjustment model to obtain the track geometric deviation adjustment amount at each mileage point, and make targeted and precise adjustments to the high-speed maglev functional components and track beam supports according to the correlation. In step S2, when constructing the three sets of virtual detection strings corresponding to short wave, medium wave and long wave, the length of the short wave detection string is set to 3m, the length of the medium wave detection string is set to 30m, and the length of the long wave detection string is set to 300m. The orbital dynamic and static geometric deviation sequences are converted into shortwave, medium-wave, and long-wave chord measurement vector distance sequences using a moving window chord measurement algorithm, including: Based on each detection node x i Centered on, establish lengths of... L c The moving calculation window; where, L c ∈{3m, 30m, 300m}; Within each window, the endpoints of the detected chord are placed in the orbital geometric deviation sequence. y ( x i The line connecting the points on the window is used as a virtual detection chord. The deviation of the actual line shape at the center point of the window from the midpoint of the virtual detection chord is calculated, and the single geometric deviation sequence is converted into three independent chord measurement vector sequences. v c ( x i The calculation formula is as follows: (2) In the formula, v c ( x i The sequence of 3m chordal distance measurement, 30m chordal distance measurement, and 300m chordal distance measurement for high-speed maglev tracks. c It equals 3, 30, or 300.

2. The method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement according to claim 1, characterized in that, In step S1, the dataset consists of dynamic detection data and static track irregularity detection data of high-speed maglev track in the same mileage section; The preprocessing includes: achieving precise alignment of dynamic and static irregularities on mileage coordinates through spatial positioning and interpolation resampling, and performing preprocessing using digital filtering technology.

3. The method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement according to claim 2, characterized in that, The process of precise alignment includes: Based on the Global Positioning System and ranging beacons, the original dynamic detection data is used to determine the mileage, and the spline interpolation algorithm is used to resample it into discrete data points with equal intervals so that it corresponds to the mileage points of the static irregular data. Preprocessing is performed using digital filtering techniques, including: A zero-phase bandpass filter is used to eliminate trend terms and high-frequency non-stationary measurement noise, and the orbital geometric deviation sequence in the effective band is extracted. y ( x i Let the original discrete spatial signal be... s ( x i The frequency domain transfer function of a bandpass filter is expressed as: H (ω), then the track geometric deviation after filtering can be expressed as: (1) In the formula, F and F -1 These are the Fourier transform and the inverse Fourier transform, respectively. x i For equal interval mileage, ω Spatial frequency.

4. The method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement according to claim 1, characterized in that, In step S3, the track structure includes: functional component misalignment, bends, track beam deformation, and pier settlement.

5. The method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement according to claim 1, characterized in that, In step S3, a mapping guide is established to link the shortwave, medium-wave, and long-wave chord measurement vector sequences with the deformation of the orbital structure, including: When the peak value of the 3m shortwave chord measurement vector distance increases significantly and exceeds the limit, it is determined that the shortwave geometric deviation of the track has deteriorated. The corresponding adjustment measures for track structural components are: adjusting the structural misalignment at the joint of the functional components, correcting the installation angle of the functional components or the misalignment at the end of the track beam. When the peak value of the 30m medium wave chord measurement vector distance increases significantly and exceeds the limit, it is determined that the geometric deviation of the medium wave in the track has deteriorated. The corresponding adjustment measures for the track structure components are: correcting the overall installation alignment of the track beam, or compensating for the upward and downward camber deformation of the beam. When the peak value of the 300m long-wave chord measurement vector distance increases significantly and exceeds the limit, it is determined that the geometric deviation of the long-wave track has deteriorated. The corresponding adjustment measures for the track structure components are: adjusting the pier support pads to compensate for the long-wave deformation of the track caused by uneven foundation settlement.

6. The method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement according to claim 1, characterized in that, In step S3, the differences between dynamic and static chord measurement values ​​are compared to identify the locations of abnormal track beam stiffness, including: Extract the dynamic and static chord measurement vector distances corresponding to the same chord length in the same detection section and at the same mileage node. When the absolute difference between the dynamic and static chord measurement vector distances at the same mileage location exceeds the threshold, it is determined that there is an abnormality in the track beam stiffness at that location.

7. The method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement according to claim 1, characterized in that, In step S4, the objective function is to adjust the scale of the operation and the residual track geometric deviation after the operation. The residual track geometric deviation is expressed by chordal distance measurement of 3m, 30m, and 300m respectively. The objective function is expressed by the following formula: (3) In the formula, t c ( x i () represents the track adjustment amount corresponding to 3m chordal distance, 30m chordal distance, and 300m chordal distance.

8. The method for adjusting the geometric deviation of a normal-conducting high-speed maglev track based on dynamic and static chord measurement according to claim 7, characterized in that, In step S4, the chordal distance amplitude constraints of 3m, 30m, and 300m, the limit adjustment amount of actual construction operations, and the geometric change rate of the track at adjacent mileage points are introduced as constraint conditions. The formula for constraining the amplitude of chord-based vector distance measurement is as follows: (4) In equation (4), v c max The chord length is c The maximum value of the distance measured by the meter string; The limit adjustment constraints for actual construction operations are expressed by the following formula: (5) In equation (5), t c max The chord length is c The maximum adjustment range for the meter-chord distance measurement; The constraint on the geometric rate of change of the track between adjacent mileage points is expressed by the following formula: (6) In equation (6), Δ v c max The chord length is c The maximum allowable rate of change of the distance in a meter-chord survey.

9. A geometric deviation adjustment system for a normal-conducting high-speed maglev track based on dynamic and static chord measurement, the system being used to implement the geometric deviation adjustment method for a normal-conducting high-speed maglev track based on dynamic and static chord measurement as described in any one of claims 1 to 8, characterized in that, The system includes: a data acquisition and preprocessing module, a detection string processing module, a deformation mapping and stiffness assessment module, a global optimization modeling module, and a precision adjustment module, connected in sequence; among them, The data acquisition and preprocessing module is used to acquire dynamic and static track irregularity detection datasets, and preprocess the datasets to extract the dynamic and static geometric deviation sequences of the long stator surface and guide surface of the high-speed maglev track from the preprocessed datasets. The detection string processing module is used to construct three sets of virtual detection strings corresponding to short wave, medium wave and long wave respectively based on the string measurement method principle for the dynamic and static geometric deviation sequences of the track. The dynamic and static geometric deviation sequences of the track are converted into short wave, medium wave and long wave string measurement vector sequences through the moving window string measurement algorithm. The deformation mapping and stiffness assessment module is used to establish the correlation between short-wave, medium-wave, and long-wave chord measurement vector sequences and track structure deformation. At the same time, it compares the differences between dynamic and static chord measurement vector values ​​to identify the location of track beam stiffness anomalies. The global optimization modeling module is used to take the displacement adjustment of track functional components and track beam supports at each mileage point as decision variables, and the objective function is to minimize the scale of adjustment operation and the chordal distance of the track residual geometric deviation after adjustment operation. The module introduces the limit adjustment amount of actual construction operation and the track geometric change rate of adjacent mileage points as constraints to establish a high-speed maglev track geometric deviation optimization adjustment model. The precision adjustment module is used to perform global optimization of the high-speed maglev track geometric deviation optimization adjustment model, obtain the track geometric deviation adjustment amount at each mileage point, and make targeted precision adjustments to the high-speed maglev functional components and track beam supports according to the correlation.

Images