Method, medium and device for evaluating the smoothness of a maglev track based on multi-class bias

Abstract

The application discloses a kind of based on multi-class deviation's magnetic levitation track smoothness evaluation method, medium and equipment, belong to magnetic levitation track analysis field.This method uses hierarchical amplitude evaluation criterion and hierarchical mean evaluation criterion, assesses the smoothness state of target track, wherein determining hierarchical amplitude evaluation criterion and hierarchical mean evaluation criterion includes: based on the layered deformation characteristics of track beam and stator core system, establish multi-class irregularity superposition model;Based on the superposition model, give the random amplitude of multi-class track geometric deviation in line with the set statistical distribution, and generate theoretical track random irregularity sample;Calculate the power spectral density of theoretical track random irregularity sample, and determine the multilevel confidence interval of power spectral density and corresponding track spectral area value;Based on the multilevel confidence interval of power spectral density, define track smoothness hierarchical amplitude evaluation criterion;Based on track spectral area value, define hierarchical mean evaluation criterion.The evaluation result of the application is more scientific, objective and comprehensive.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of maglev track analysis, and more particularly to a maglev track smoothness evaluation method based on multi-class deviation, a medium and an equipment. BACKGROUND

[0002] High-speed maglev transportation system has very high requirements for track smoothness (or smoothness). Its non-contact suspension principle makes the vehicle very sensitive to track geometric deviation.

[0003] Unlike wheel-rail railways that have formed a standardized evaluation system, at present, the field of high-speed maglev generally lacks a recognized and authoritative track irregularity evaluation benchmark. Existing evaluation methods usually rely on measured data of specific lines or simplified theoretical structures, resulting in different sources of evaluation benchmarks, lack of universality and statistical representativeness. This not only makes it difficult to objectively benchmark different research results, but also cannot provide a unified and clear graded decision basis for the maintenance and repair of operating lines.

[0004] Therefore, it is urgent to establish an evaluation method that can reflect the general physical characteristics and statistical laws of high-speed maglev tracks to solve the problems of lack of current evaluation standards and strong subjectivity of state evaluation. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a maglev track smoothness evaluation method based on multi-class deviation, a medium and an equipment.

[0006] According to a first aspect of the present application, a maglev track smoothness evaluation method based on multi-class deviation is provided. The method comprises the following steps:

[0007] For the target track to be evaluated, the power spectral density and spectral area value of the irregularity data are obtained;

[0008] Based on the power spectral density and the spectral area value, the smoothness state of the target track is evaluated using predetermined grading amplitude evaluation criteria and grading mean value evaluation criteria;

[0009] The grading amplitude evaluation criteria and the grading mean value evaluation criteria are determined according to the following steps:

[0010] Collect the track structure parameters and geometric deviation management values of the maglev line, and based on the layered deformation characteristics of the track beam system and the stator core system, establish a multi-class irregularity superposition model, which covers longitudinal irregularities and vertical irregularities;

[0011] Based on the multi-class unevenness superposition model, random amplitudes conforming to a set statistical distribution are given to multi-class track geometry deviations, and continuous theoretical track random unevenness samples are generated through Monte Carlo sampling and spatial interpolation;

[0012] Power spectral densities of the theoretical track random unevenness samples are calculated, and a multi-level confidence interval of the power spectral densities and track spectral area values corresponding to the multi-level confidence interval are constructed based on statistical results;

[0013] Based on the multi-level confidence interval of the power spectral densities, a grading amplitude evaluation criterion of track smoothness state is defined, and the grading amplitude evaluation criterion is used to divide amplitude states of local characteristic wavelengths of the track into multiple levels;

[0014] Based on the track spectral area values, a grading mean value evaluation criterion of the track smoothness state is defined, and the grading mean value evaluation criterion is used to divide mean value states of the track as a whole into multiple levels.

[0015] According to a second aspect of the present application, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium, wherein the computer program is executed by a processor to implement the steps of the above-mentioned magnetic levitation track smoothness evaluation method based on multi-class deviations.

[0016] According to a third aspect of the present application, a computer device is provided, comprising a memory and a processor, and a computer program capable of running on the processor is stored on the memory, wherein the processor implements the steps of the above-mentioned magnetic levitation track smoothness evaluation method based on multi-class deviations when executing the computer program.

[0017] Compared with the prior art, the present application has the following advantages:

[0018] 1) The present application combines the amplitude and mean value of the power spectral density through the construction of a refined model based on physical mechanisms and large-scale random simulation, and for the first time proposes a two-dimensional evaluation system, which can not only identify local mutation problems of specific wavelengths, but also evaluate the overall energy level of the track section, and form a probabilistic evaluation benchmark with statistical significance, overcoming the limitations of traditional methods relying on specific samples or subjective experience, making the evaluation results more scientific, objective and comprehensive.

[0019] 2. The present application systematically considers multiple key deviation sources and their coupling relationship at two levels of track beams and stator cores, and the generated benchmark model can better represent the general state of the line under the influence of various random factors, and has high statistical representativeness.

[0020] 3. The application provides a complete and unified evaluation process and grading criteria, which can be used as a standard tool for smoothness state evaluation, transverse benchmarking and longitudinal tracking of different lines or different periods of the same line, and provides clear and quantitative scientific basis for maintenance decision-making, and has strong engineering practical value.

[0021] Other features of the present application, and their advantages, will become apparent in the non-limiting description of the examples of the application that follows. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and, together with the description, serve to explain the principles of the application.

[0023] Figure 1 is a flowchart of a multi-class bias-based maglev track smoothness evaluation method according to an embodiment of the application;

[0024] Figure 2 is a process diagram of a multi-class bias-based maglev track smoothness evaluation method according to an embodiment of the application;

[0025] Figure 3 is a schematic diagram of high-speed maglev line structure deformation and irregularity composition according to an embodiment of the application;

[0026] Figure 4 is a waveform diagram of a single theoretical track random irregularity sample generated according to an embodiment of the application;

[0027] Figure 5 is a schematic diagram of a constructed track irregularity power spectral density probability confidence interval according to an embodiment of the application;

[0028] Figure 6 is a schematic diagram of a constructed track irregularity grading evaluation interval according to an embodiment of the application. DETAILED DESCRIPTION

[0029] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of the components and steps set forth in the embodiments, numerical expressions, and numerical values are not limiting to the scope of the present application unless otherwise specifically stated.

[0030] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way limiting to the scope of the application or its applications or uses.

[0031] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, such techniques, methods, and devices can be viewed as part of the specification.

[0032] In all of the examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as a limitation. Thus, other examples of the exemplary embodiments can have different values.

[0033] It should be noted that like reference numerals and letters refer to like items throughout the several views, and as a result, further discussion of such items is not necessary in the subsequent views once such items have been defined in one view.

[0034] In conjunction with Figure 1 and Figure 2 As shown, the provided multi-class bias-based maglev track smoothness evaluation method includes the following steps:

[0035] Step S1, collect the track structure parameters and geometric bias management values of the maglev line, and based on the layered deformation characteristics of the track beam and stator core system, establish a multi-class irregularity superposition model, which covers longitudinal irregularities and vertical irregularities.

[0036] Step S1 mainly collects data and models multi-class irregularities, aiming to establish a mathematical model that can accurately describe the geometric shape of the track.

[0037] For example, data collection includes basic structural parameters of the track line (such as track beam standard length , stator core standard length , etc.) and control limit values of various geometric biases specified in technical specifications. By collecting the track structure parameters and geometric bias management values of the high-speed maglev line, based on the layered deformation characteristics of the track beam system and the stator core system, a multi-class irregularity superposition model covering longitudinal and vertical irregularities is established.

[0038] Specifically, as shown in Figure 3 , a layered modeling strategy is used to decompose the track system into beam span subsystems and stator core subsystems, and the multi-class irregularity superposition model is established according to the following steps.

[0039] Step S11, define nodes: define the head and tail nodes of each track beam and the head and tail nodes of each stator core along the line mileage direction.

[0040] Through node definition, a discrete geometric basis can be provided for subsequent coordinate calculation and deviation superposition.

[0041] Step S12, longitudinal irregularity superposition is performed to determine the accurate longitudinal coordinates of all nodes of the line, thereby establishing the longitudinal mileage coordinate recursive relationship of the beam span system and the stator core system, respectively.

[0042] For example, the recursive relationship of the beam span system is defined by the following formula:

[0043] (1)

[0044] In the formula, , For the first i The mileage coordinates of the first and last nodes of each beam span; The standard span length of the beam is 24.768m. This is the basic value for longitudinal clearance; This represents the change in the longitudinal gap between the i-th and i+1-th beam spans; i For the beam span number, i =1,…, N .

[0045] The recursive relationship of the stator core system is defined by the following formula:

[0046] (2)

[0047] In the formula, , For the first i The first span of the beam j Mileage coordinates of the first and last nodes of each stator core The standard length of the stator core is 1.032 m. For the first j Variation in the longitudinal gap of each stator core, j It is the stator core serial number. j =1,…,24.

[0048] For ease of overall calculation, the above relationships can be converted into matrix form:

[0049] (3)

[0050] (4)

[0051] In the formula, The beam span mileage matrix; This is the beam span mileage coefficient matrix. The beam span gap matrix, Stator core mileage matrix; This is the stator core mileage coefficient matrix. This is the stator core mileage gap matrix.

[0052] Finally, through coordinate transformation and superposition, the overall longitudinal irregularity of the line is obtained, and its mathematical model is as follows:

[0053] (5)

[0054] In the formula, Indicates the firsti The first span of the beam j stator core k Node relative mileage coordinates Let k be the absolute mileage coordinates of the j-th stator core in the i-th beam span. Extract the beam span mileage vector. The first node mileage is included, and the sample size and stator core mileage vector are expanded. They are consistent. Combining the two according to the above formula, we get:

[0055] (6)

[0056] In the formula, The absolute mileage vector of the stator core; This represents the expanded beam span mileage vector. Simultaneously, to ensure the total length of a single beam span remains unchanged, the following constraints must be satisfied:

[0057] (7)

[0058] Step S13: Perform vertical irregularity superposition to determine the precise vertical deviation of all nodes of the line, and establish the vertical deviation recursive relationship of the two subsystems respectively.

[0059] For example, the vertical deviation of a beam span system is caused by beam deformation and beam end misalignment, and their relationship can be described by the following formula:

[0060] (8)

[0061] In the formula, , Let be the vertical deviation between the first and last nodes of the i-th beam span; For the first i Deformation of each beam span; For the first i The beam ends of each span are vertically misaligned.

[0062] The vertical deviation of the stator core system is caused by core deformation, misalignment, and unevenness, and their relationship can be described by the following formula:

[0063] (9)

[0064] In the formula, , The vertical deviation of the first and last nodes of the j-th stator core in the i-th beam span; For the deformation of the j-th stator core; For the first j Vertical misalignment of the ends of the stator core.

[0065] The unevenness reflects the fluctuation of the position within the two iron core ranges. The end of the former iron core and the beginning of the subsequent iron core are respectively taken into the unevenness calculation formula due to the consideration of the longitudinal gap between the stators, and the specific method is as follows:

[0066] (10)

[0067] In the formula, is the unevenness with the end of the i-th stator iron core as the center. j

[0068] For the convenience of overall calculation, the above relationship can also be transformed into a matrix form:

[0069] (11)

[0070] (12)

[0071] In the formula, is the beam span vertical deviation matrix; is the beam span vertical deviation coefficient matrix, is the beam end vertical deviation error matrix, is the stator iron core vertical deviation matrix; and is the stator iron core vertical deviation coefficient matrix, and is the stator iron core vertical deviation error and unevenness matrix.

[0072] Finally, the vertical unevenness of the whole line is obtained through linear superposition and synthesis, and the mathematical model is:

[0073] (13)

[0074] In the formula, is the superimposed vertical unevenness of the node k of the j-th stator iron core of the i-th beam span, , are the vertical deviation and the relative mileage coordinate of the node k, , are the vertical deviations of the head and tail nodes of the beam span i, is the standard length of the beam span.

[0075] The samples in the beam span vertical deviation vector are extracted, a new vector is established according to the above formula, and is combined with the stator iron core vertical deviation vector , and the expression is as follows:

[0076] (14)

[0077] In the formula, ​is a vertical deviation vector of the stator core; is a vertical deviation vector of the expanded beam span; is a coefficient vector.

[0078] Step S2, based on the established multi-class irregularity superposition model, gives the multi-class track geometry deviation a random amplitude conforming to the set statistical distribution, and generates a large number of continuous theoretical track random irregularity samples through Monte Carlo sampling and spatial interpolation.

[0079] Step S2 is used for random irregularity assignment and sample generation, which aims to give each type of track geometry deviation a random amplitude conforming to a specific statistical distribution according to the multi-class irregularity superposition model established in step S1, and generate a large number of continuous theoretical track random irregularity samples through Monte Carlo sampling and spatial interpolation.

[0080] Specifically, based on the established multi-class irregularity superposition model, a large number of theoretical track irregularity samples conforming to the statistical characteristics of engineering practice are generated. In implementation, random amplitudes are given to the seven types of key geometry deviation random quantities involved in the model. It is assumed that these deviation quantities are independent of each other, and their amplitude distributions all follow t-distribution. Taking the control requirements in the technical specification as a benchmark, the upper and lower limits of each type of deviation are randomly generated within the preset fluctuation amplitude, and the specific parameters of the corresponding t-distribution are inversely calculated through scale transformation based on this.

[0081] Subsequently, using Monte Carlo simulation technology, random sampling is performed according to the determined t-distribution parameters, and the sampling values are substituted into the superposition model of step S1 to calculate the total irregularity amplitude at each node. Finally, spatial interpolation is performed on the discrete node values to generate a large number of (e.g., 1000) continuous theoretical track irregularity samples. Figure 4 One of the generated irregularity sample waveforms is shown, where the horizontal coordinate represents location in meters (m), and the vertical coordinate represents track irregularity in millimeters (mm).

[0082] Step S3, calculate the power spectral density of the theoretical track random irregularity sample, and based on the statistical results, construct a multi-level confidence interval of the power spectral density and calculate the track spectral area value corresponding to the multi-level confidence interval.

[0083] This step S3 calculates the power spectral density of all theoretical track random irregularity samples generated in step S2, constructs a multi-level confidence interval of the power spectral density based on the statistical results, and calculates the track spectral area value corresponding to the multi-level confidence interval.

[0084] Step S3 aims to extract statistically significant evaluation criteria from a large number of irregularity samples. For example, for each irregularity sample generated in step S2, the power spectral density (PSD) is calculated using digital signal processing methods such as periodogram method, and for each discrete spatial frequency point, a statistical sample set consisting of a large number of PSD values is obtained.

[0085] Subsequently, the PSD statistical sample set at each frequency point is sorted, and its statistical results, including the minimum value, maximum value, and 5%, 25%, 75%, and 95% quantiles, are calculated. Connecting the PSD values with the same statistical significance at all frequency points forms the multi-level confidence interval boundary of the power spectral density, as shown in Figure 5 , where the vertical coordinate is the PSD value and the horizontal coordinate is the wavelength. This confidence interval framework is the probabilistic evaluation criterion constructed by the present application.

[0086] Finally, the area enclosed by each confidence boundary spectrum line (i.e., the spectrum line corresponding to the minimum value, maximum value, 25% quantile, and 75% quantile) and the coordinate axes is calculated to obtain the corresponding spectral area value.

[0087] Step S4 defines a graded amplitude evaluation criterion for the track smoothness state based on the constructed multi-level confidence interval of the power spectral density, which is used to divide the amplitude state of the local characteristic wavelength of the track into multiple levels.

[0088] Step S4 defines a graded amplitude evaluation criterion for the track smoothness state based on the power spectral density confidence interval constructed in step S3, which divides the amplitude state of the local characteristic wavelength of the track into multiple levels.

[0089] For example, based on the PSD confidence interval constructed in step S3, the following clear five-level track smoothness amplitude evaluation criterion is established, and these graded evaluation intervals divided by the confidence interval are shown in Figure 6 .

[0090] Level 1 (excellent): When the PSD curve of the track to be evaluated falls mostly below the minimum value boundary in its main frequency range, the state is rated as excellent.

[0091] Level 2 (good): When the PSD curve falls mainly between the minimum value boundary and the 25% quantile boundary, the state is rated as good.

[0092] Level 3 (average): When the PSD curve falls mainly between the 25% quantile boundary and the 75% quantile boundary, the state is rated as average.

[0093] Level 4 (poor): When the PSD curve falls mainly between the 75% quantile boundary and the maximum value boundary, the state is rated as poor.

[0094] 5th grade (degradation): the PSD curve crosses the maximum value boundary at some frequency points or frequency bands, and the state is evaluated as degradation.

[0095] Step S5, based on the calculated track spectrum area value, define the grading mean evaluation criterion of track smoothness state, which is used to divide the mean state of the whole track into multiple grades.

[0096] This step S5 defines the grading mean evaluation criterion of track smoothness state according to the track spectrum area value constructed in step S3, and divides the mean state of the whole track into multiple grades.

[0097] For example, according to the track spectrum area value calculated in step S3, the following clear five-grade track smoothness mean evaluation criterion is established:

[0098] 1st grade (excellent): the spectrum area value of the track to be evaluated is lower than the minimum spectrum area value.

[0099] 2nd grade (good): the spectrum area value is between the minimum spectrum area value and the 25% quantile spectrum area value.

[0100] 3rd grade (general): the spectrum area value is between the 25% and 75% quantile spectrum area value.

[0101] 4th grade (poor): the spectrum area value is between the 75% quantile spectrum area value and the maximum spectrum area value.

[0102] 5th grade (degradation): the spectrum area value is higher than the maximum spectrum area value.

[0103] Step S6, for the target track to be evaluated, the grading amplitude evaluation criterion and the grading mean evaluation criterion are used to obtain the smoothness state evaluation of the target track.

[0104] Step S6 aims to evaluate the measured data and compare the effects of different applications. The application process includes: calculating the power spectral density and spectrum area value of the measured track irregularity data to be evaluated, and evaluating the track smoothness state of the target track according to the grading amplitude evaluation criterion defined in step S4 and the grading amplitude evaluation criterion defined in step S5.

[0105] In one application example, first, the measured irregularity data of the track to be evaluated is obtained by the track detection device, and the power spectral density and spectrum area value are calculated. Then, the measured PSD curve is superimposed and compared with the grading amplitude evaluation criterion established in step S4 to evaluate the local state of the track at different characteristic wavelengths. At the same time, the measured spectrum area value is compared with the grading mean evaluation criterion established in step S5 to evaluate the overall state of the track section. Through this two-dimensional evaluation, a comprehensive understanding of the track smoothness state can be obtained.

[0106] In another application example, the measured track spectrum of different sources is compared and analyzed. Specifically, track irregularity data collected at the same line at different times, or track irregularity data collected on lines of different technical standards, can be calculated for their power spectral density and spectral area value respectively, and placed in the same set of hierarchical evaluation system established by the present application for comparison. By comparing their rating results in two dimensions of amplitude and mean value, the time sequence evolution law of track state can be quantitatively evaluated, or the smoothness characteristic difference of lines of different technical standards can be identified, thereby providing a powerful analysis tool for long-term performance tracking and technical benchmarking of the line.

[0107] To sum up, the present application provides a high-speed maglev track irregularity hierarchical evaluation method based on multi-class bias refinement superposition model. The method generally includes: establishing a hierarchical superposition model covering multi-class geometric bias of track beam and stator core system; generating a large number of theoretical track irregularity samples by Monte Carlo sampling; calculating the power spectral density of the samples and constructing multi-level probability confidence interval based on the statistical quantile, and calculating the track spectral area value corresponding to each confidence interval; defining the hierarchical amplitude evaluation criteria according to the confidence interval, and defining the hierarchical mean value evaluation criteria according to the spectral area value; finally, comparing the power spectral density and spectral area value of the measured track spectrum with the dual criteria to evaluate its smoothness grade. The present application solves the problem of lack of unified and objective evaluation benchmark in the field of high-speed maglev, and provides a scientific, systematic and well-engineered track smoothness quantitative hierarchical evaluation method by combining the refined physical model with the probabilistic statistical benchmark, which can provide more reliable decision basis for the maintenance of the line.

[0108] The present application can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present application.

[0109] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0110] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0111] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0112] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0113] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0114] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0115] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0116] Embodiments of the present application have been described above, and the description is intended to be illustrative, and not restrictive, of the disclosed embodiments. Many modifications and variations of the disclosed embodiments are possible in light of the above teachings. It is therefore to be understood that within the scope of the disclosed embodiments, modifications and variations of the disclosed embodiments can be practiced. It is also to be understood that the specific order or hierarchy of steps in the processes disclosed is an illustration of exemplary processes. Based upon the description and illustrations provided herein, those skilled in the art will understand that changes can be made to the order of steps in the processes and that many of the individual steps can be modified or eliminated. Additionally, the description and illustrations provided herein are not meant to limit the scope of the disclosed embodiments. The scope of the disclosed embodiments is limited only by the claims.

Claims

1. A magnetic levitation track smoothness evaluation method based on multi-class bias, characterized in that, The method comprises the following steps: For the target track to be evaluated, the power spectral density and the spectral area value of the irregularity data are obtained; Based on the power spectral density and the spectral area value, the smoothness state of the target track is evaluated by using a predetermined grading amplitude evaluation criterion and a grading mean value evaluation criterion; Wherein, the grading amplitude evaluation criterion and the grading mean value evaluation criterion are determined according to the following steps: Collect the track structure parameters and the geometric deviation management value of the maglev line, and based on the layered deformation characteristics of the track beam system and the stator core system, a multi-category irregularity superposition model is established, which covers longitudinal irregularities and vertical irregularities; Based on the multi-category irregularity superposition model, random amplitudes conforming to the set statistical distribution are given to the multi-category track geometric deviations, and continuous theoretical track random irregularity samples are generated through Monte Carlo sampling and spatial interpolation; The power spectral density of the theoretical track random irregularity samples is calculated, and based on the statistical results, a multi-level confidence interval of the power spectral density and the track spectral area value corresponding to the multi-level confidence interval are constructed; Based on the multi-level confidence interval of the power spectral density, a grading amplitude evaluation criterion of the track smoothness state is defined, which is used to divide the amplitude state of the local characteristic wavelength of the track into multiple levels; Based on the track spectral area value, a grading mean value evaluation criterion of the track smoothness state is defined, which is used to divide the mean value state of the whole track into multiple levels.

2. The method of claim 1, wherein, The multi-category irregularity superposition model is established according to the following steps: Define the head and tail nodes of the track beam and the head and tail nodes of each stator core along the line mileage direction; Establish the longitudinal mileage coordinate recursive relationship of the track beam system and the stator core system respectively, and obtain the longitudinal superimposed irregularity of the whole line through coordinate transformation, which is mathematically represented as: Establish the vertical deviation recursive relationship of the track beam system and the stator core system respectively, and linearly superimpose the macro vertical deformation of the beam span and the local vertical deformation of the core to obtain the vertical superimposed irregularity of the whole line, which is mathematically represented as: wherein, is the post-stacked vertical irregularity of node k of the jth stator core of the ith beam span, is the vertical deviation of node k, is the relative mileage coordinate of node k, is the vertical deviation of the head node of beam span i, is the vertical deviation of the tail node of beam span i, is the beam span standard length, is the absolute mileage coordinate of node k of the jth stator core of the ith beam span, is the head node mileage coordinate of beam span i.

3. The method of claim 1, wherein, Based on the multi-category irregularity superposition model, random amplitudes conforming to the set statistical distribution are given to the multi-category track geometric deviations, and continuous theoretical track random irregularity samples are generated through Monte Carlo sampling and spatial interpolation, which includes: Assuming that each type of deviation is independent and follows a t-distribution, the upper and lower limits of the deviation amplitude for this simulation are randomly generated within the preset fluctuation amplitude based on the control requirements of each type of irregularity specified in the technical specification, and then the t-distribution parameters corresponding to each simulation are determined through scale transformation, and Monte Carlo sampling and spatial interpolation are performed according to the t-distribution parameters.

4. The method of claim 1, wherein, For the multi-level confidence interval of the power spectral density, the confidence levels include: 5% quantile, 25% quantile, 75% quantile, 95% quantile, and maximum value, minimum value, thereby forming the corresponding confidence boundary.

5. The method of claim 4, wherein, For the track spectrum area value corresponding to the multi-level confidence interval, the area surrounded by the minimum value, the maximum value, the 25% quantile, the 75% quantile corresponding power spectrum line and the horizontal axis are calculated respectively as the spectrum area value corresponding to the minimum value, the spectrum area value corresponding to the maximum value, the spectrum area value corresponding to the 25% quantile, and the spectrum area value corresponding to the 75% quantile.

6. The method of claim 1, wherein, Also comprising: For the track of different periods or different lines, or collecting irregular data on the track of different technical standards, and then using the hierarchical amplitude evaluation criterion and the hierarchical mean evaluation criterion for comparison to quantify the state evolution of the target track or identify the technical feature difference.

7. The method of claim 1, wherein, For the irregularity sample, the power spectral density is calculated by using the periodogram method, and for each discrete spatial frequency point, a statistical sample set composed of multiple power spectral density values is obtained.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

9. A computer device comprising a memory and a processor, having stored on the memory a computer program capable of running on the processor, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7. The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

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