Time-frequency domain joint evaluation method, device, equipment and medium for track smoothness

Abstract

The present invention provides a time-frequency domain joint evaluation method, device, equipment and medium for track smoothness, which relates to the technical field of railway track detection and evaluation, including obtaining detection data of multiple groups of track unevenness parameters; calculating the statistical characteristics and distribution characteristics of each group of unevenness parameters and the local peak value and section mean of all unevenness parameters to form the time domain characteristics of each group of unevenness parameters; using the power spectrum estimation method to perform frequency domain analysis on each group of unevenness parameters to obtain the track unevenness spectrum corresponding to each group of unevenness parameters; fitting each group of track unevenness spectrum to obtain a standard expression of the track unevenness spectrum, and obtaining the frequency domain characteristics of each group of unevenness parameters; using the time domain characteristics and frequency domain characteristics of each group of unevenness detection data to construct a corresponding time-frequency domain joint evaluation model. The present invention is used to solve the technical problem that the existing evaluation method lacks in-depth analysis of the frequency domain characteristics of track unevenness and cannot fully reflect its frequency domain performance.

Description

Technical Field

[0001] The present invention relates to the technical field of railway track detection and evaluation, and in particular to a time-frequency domain joint evaluation method, device, equipment and medium for evaluating the smoothness of a track. Background Art

[0002] Rail transit lines will be affected by many factors during long-term operation, including train loads, environmental loads, material and structural fatigue aging, etc. The combined effect of these factors may lead to problems such as poor track smoothness, wear of line equipment, and breeding of line diseases, affecting the operational quality and service performance of the line. At present, the evaluation of the service status of rail transit lines mainly relies on traditional time domain evaluation methods. However, with the rapid development of science and technology and the continuous improvement of people's quality of life, the requirements for the service status of rail transit lines are also increasing. Relying solely on time domain methods represented by TQI lacks in-depth analysis of the frequency domain characteristics of track irregularities and cannot fully reflect the frequency domain performance of the track. Summary of the invention

[0003] The purpose of the present invention is to provide a time-frequency domain joint evaluation method, device, equipment and medium for track smoothness to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0004] In a first aspect, the present application provides a time-frequency domain joint evaluation method for track smoothness, comprising:

[0005] Obtaining detection data of multiple groups of track irregularity parameters;

[0006] According to the detection data of each group of roughness parameters, the statistical characteristics and distribution characteristics of each group of roughness parameters and the local peak values ​​and section means of all roughness parameters are calculated;

[0007] The time domain characteristics of each group of roughness parameters are composed of the local peak value, the section mean value, and the statistical characteristics and distribution characteristics of each group of roughness parameters;

[0008] The power spectrum estimation method is used to analyze each group of irregularity parameters in the frequency domain, and the track irregularity spectrum corresponding to each group of irregularity parameters is obtained;

[0009] The nonlinear least square method is used to fit each group of track irregularity spectra, and the standard expression of the track irregularity spectrum corresponding to each group of irregularity parameters is generated. The frequency domain characteristics of each group of irregularity parameters are obtained according to the standard expression of the track irregularity spectrum.

[0010] The time domain characteristics and frequency domain characteristics of each group of irregularity detection data are used to construct the corresponding frequency domain joint evaluation model to evaluate the smoothness of the track under each group of irregularity parameters.

[0011] In a second aspect, the present application also provides a time-frequency domain joint evaluation device for track smoothness, comprising:

[0012] Acquisition module: acquires detection data of multiple groups of track irregularity parameters;

[0013] Calculation module: Calculate the statistical characteristics and distribution characteristics of each group of irregularity parameters and the local peak value and section mean value of all irregularity parameters according to the detection data of each group of irregularity parameters;

[0014] The first component module: the time domain characteristics of each group of roughness parameters are formed by the local peak value, the section mean value, and the statistical characteristics and distribution characteristics of each group of roughness parameters;

[0015] Frequency domain analysis module: Use power spectrum estimation method to perform frequency domain analysis on each group of irregularity parameters to obtain the track irregularity spectrum corresponding to each group of irregularity parameters;

[0016] Fitting module: Use nonlinear least squares method to fit each group of track irregularity spectrum, generate the standard expression of track irregularity spectrum corresponding to each group of irregularity parameters, and obtain the frequency domain characteristics of each group of irregularity parameters according to the standard expression of track irregularity spectrum;

[0017] Model building module: The time domain characteristics and frequency domain characteristics of each set of irregularity detection data are used to build a corresponding frequency domain joint evaluation model to evaluate the smoothness of the track under each set of irregularity parameters.

[0018] In a third aspect, the present application also provides a time-frequency domain joint evaluation device for track smoothness, including:

[0019] Memory for storing computer programs;

[0020] A processor is used to implement the steps of the time-frequency domain joint evaluation method of the track smoothness state when executing the computer program.

[0021] In a fourth aspect, the present application also provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned time-frequency domain joint evaluation method based on track smoothness are implemented.

[0022] The beneficial effects of the present invention are:

[0023] The present invention analyzes the detection data of the irregularity parameters, extracts the time domain characteristics and frequency domain characteristics of the irregularity parameters, and uses the time domain characteristics and frequency domain characteristics of the irregularity parameters to construct a comprehensive evaluation model. The model can comprehensively reflect the dynamic performance of the track structure in service, improve the perception of changes in track smoothness performance, reduce misjudgments and missed judgments, help to prevent and deal with problems in a timely manner, and provide scientific and quantitative decision support for track maintenance.

[0024] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 It is a schematic flow chart of a method for joint evaluation of track smoothness in time-frequency domains according to an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of random sequence division described in an embodiment of the present invention;

[0028] Figure 3 Schematic diagram of the structure of the time-frequency domain joint evaluation device for track smoothness according to an embodiment of the present invention;

[0029] Figure 4 Schematic diagram of the structure of the time-frequency domain joint assessment device for track smoothness described in an embodiment of the present invention.

[0030] Markings in the figure:

[0031] 800. Time-frequency domain joint assessment device for track smoothness; 801. processor; 802. memory; 803. multimedia component; 804. I / O interface; 805. communication component. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0033] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. Embodiment 1:

[0034] This embodiment provides a time-frequency domain joint evaluation method for track smoothness.

[0035] See also Figure 1 , the figure shows that the method includes:

[0036] S1. Obtaining detection data of multiple sets of track irregularity parameters;

[0037] In this embodiment, the unevenness parameters are 7 groups of parameters including left height unevenness, right height unevenness, left track direction unevenness, right track direction unevenness, track gauge unevenness, horizontal unevenness and triangular pit unevenness.

[0038] Based on the above embodiments, the method further includes:

[0039] S2. Calculate the statistical characteristics and distribution characteristics of each group of irregularity parameters and the local peak value and segment mean value of all irregularity parameters according to the detection data of each group of irregularity parameters;

[0040] Specifically, step S2 includes:

[0041] S21. Calculate the mean and standard deviation of the detection data of each set of unevenness parameters in turn to obtain the statistical characteristics of each set of unevenness parameters;

[0042] Specifically, the mean value is calculated as follows:

[0043] ; (1)

[0044] In the formula, It represents the mean, reflects the central trend of the data, and is used to describe the overall level of track irregularity data. represents the total number of data points for a set of unsmoothness parameters, Indicates Test data points.

[0045] The standard deviation is calculated as:

[0046] ; (2)

[0047] In the formula, Represents the standard deviation, which is used to measure the fluctuation of track irregularities.

[0048] S22. Calculate the kurtosis, skewness and probability density of the detection data of each set of unevenness parameters in turn to obtain the distribution characteristics of each set of unevenness parameters;

[0049] Specifically, the peak value is calculated as follows:

[0050] ; (3)

[0051] In the formula, Indicates the peak value, which is used to analyze the distribution of track irregularity and determine whether there are abnormal peaks in the track irregularity data.

[0052] The skewness is calculated as:

[0053] ; (4)

[0054] In the formula, Represents skewness, which is used to analyze the symmetry of track irregularity data.

[0055] The probability density is calculated as follows:

[0056] ; (5)

[0057] In the formula, and They represent the lower and upper limits of the integral, i.e. the boundaries of the range of values ​​of the random variable. In track smoothness assessment, they are used to describe the distribution of track irregularity data, help determine whether there is a specific range of track irregularities, and guide maintenance work.

[0058] S23. Obtaining detection data of each set of irregularity parameters in a preset section, and calculating the standard deviation of each set of irregularity parameters in the preset section;

[0059] In this embodiment, the length of the preset section is 200m. The standard deviation of the group irregularity parameter within the preset range is:

[0060] ; (6)

[0061] In the formula, Indicates The standard deviation of the group irregularity parameter within the preset range, Indicates The first Detection data points, Indicates The average value of the group roughness parameter within the preset range.

[0062] S24. Calculate the section mean value by the standard deviation of each group of irregularity parameters in the preset section :

[0063] ; (7)

[0064] Specifically, the It is a comprehensive indicator that evaluates the overall roughness of the track in a preset section by calculating the sum of the standard deviations of the roughness parameters.

[0065] S25. Obtaining the evaluation level and evaluation criteria of the detection data, and dividing the detection data of each group of roughness parameters into different evaluation levels according to the evaluation criteria. In this embodiment, the detection data is divided into 4 evaluation levels;

[0066] S26. According to the division result, the local peak value is calculated by using the local peak deduction method:

[0067] Specifically, step S26 includes:

[0068] S261. Obtain the score corresponding to each evaluation level and the weight coefficient of each set of unevenness parameters;

[0069] S262. Calculate the number of test data for each group of roughness parameters at each evaluation level;

[0070] S263. Calculate the deduction value of each group of unevenness parameters based on the evaluation level score, the weight coefficient of each group of unevenness parameters and the number of test data;

[0071] S264. Sum the deduction values ​​of each group of unevenness parameters to obtain a local peak value:

[0072] ; (8)

[0073] In the formula, Indicates Evaluation level, Indicates The deduction value of the evaluation level, Indicates The evaluation level The number of test data points for the group irregularity parameter, Indicates The weight coefficient of the group irregularity parameter, It represents the local peak value, that is, the comprehensive penalty value of the track under various uneven conditions.

[0074] Based on the above embodiments, the method further includes:

[0075] S3. The time domain characteristics of each group of irregularity parameters are formed by the local peak value, the segment mean value, and the statistical characteristics and distribution characteristics of each group of irregularity parameters;

[0076] Based on the above embodiments, the method further includes:

[0077] S4. Perform frequency domain analysis on each group of irregularity parameters using power spectrum estimation method to obtain track irregularity spectra corresponding to each group of irregularity parameters;

[0078] Specifically, step S4 includes:

[0079] S41. Convert a set of irregular parameters into a set of random sequences , divide the random sequence into Segment data, each segment data length is , the overlap between two adjacent segments of data is ,like Figure 2 As shown;

[0080] ; (9)

[0081] In the formula, Indicates a single length The window function of Represents the index in each piece of data, Indicates Segment data.

[0082] S42. Perform Fourier transform on each data segment to obtain frequency domain signal:

[0083] ; (10)

[0084] In the formula, it represents No. The frequency domain signal of the segment data, represents the imaginary unit, Represents the angular frequency.

[0085] S43. Windowing the frequency domain signal using a window function to generate a periodogram power spectral density :

[0086] ; (11)

[0087] S44. Calculate the average value of the power spectrum density function of the periodogram of several segments of data to obtain the track irregularity spectrum of the current irregularity parameter .

[0088] ; (12)

[0089] Based on the above embodiments, the method further includes:

[0090] S5. Fit each group of track irregularity spectra using a nonlinear least squares method to generate a standard expression of the track irregularity spectrum corresponding to each group of irregularity parameters, and obtain the frequency domain characteristics of each group of irregularity parameters according to the standard expression of the track irregularity spectrum;

[0091] Specifically, step S5 includes:

[0092] S51. Construct a characterization function based on the track's unevenness , and use the track irregularity spectrum to initialize the parameter set of the characterization function;

[0093] In this embodiment, the track irregularity spectrum is expressed in the form of a data sequence:

[0094] ,in, Indicates frequency;

[0095] set up yes Nonlinear functions, is a parameter to be determined, As initial value ;

[0096] S52. Calculate the initial deviation value of each parameter in the parameter set based on the track irregularity spectrum :

[0097] ; (13)

[0098] S53. Performing Taylor series expansion on the characterization function and obtaining a constant term and a linear term of each initial deviation value;

[0099] Specifically, At the point Perform Taylor series expansion operation at to obtain:

[0100] ; (14)

[0101] in, , which means that in the parameter equal When, function The value of , taking partial derivatives on both sides of the equation can be obtained:

[0102] ; (15)

[0103] in, express The initial value of

[0104] S54. Construct the objective function using the constant term and the linear term of the initial deviation value , and calculate the partial derivatives and Jacobian matrix of the objective function;

[0105] in, ; (16)

[0106] represents the total number of data points, represents the actual observed value of the i-th data point;

[0107] Substituting formula (14) into formula (16), we obtain:

[0108] ; (17)

[0109] Taking partial derivative of the above formula, we get the partial derivative as:

[0110] ; (18)

[0111] when When , we can get:

[0112] ; (19)

[0113] make , then we get the Jacobian matrix :

[0114] ; (20)

[0115] Therefore, the above formula can be written as:

[0116] ;(twenty one)

[0117] In the formula, Represents the deviation value, which is the amount by which the model parameters need to be updated in each iteration; The observation value vector contains all the actual observed values. For each data point 𝑖, its observation value is .

[0118] S55. Iteratively update each parameter based on the partial derivative and Jacobian matrix, and obtain a final parameter set when the change of the parameter is less than a preset accuracy threshold;

[0119] Specifically, solving equation (21) yields:

[0120] ;(twenty two)

[0121] therefore:

[0122] ;(twenty three)

[0123] when When the value of is large, it is advisable The above steps are repeated until the parameter change is less than the preset accuracy threshold through iterative calculation.

[0124] S56. Constructing a standard mathematical expression of the track irregularity spectrum according to the final parameter set;

[0125] In this embodiment, since the standard mathematical expressions of each set of roughness parameters are different, they are not listed one by one here.

[0126] The frequency domain characteristics of the irregularity parameters are obtained from standard mathematical expressions, including: band range, periodic wavelength, sensitive wavelength, spectrum area, etc.

[0127] Based on the above embodiments, the method further includes:

[0128] S6. Using the time domain features and frequency domain features of each set of irregularity detection data, a corresponding frequency domain joint evaluation model is constructed to evaluate the smoothness of the track under each set of irregularity parameters;

[0129] Specifically, step S6 includes:

[0130] S61. The time domain evaluation index is calculated based on the time domain characteristics of a set of irregularity parameters:

[0131] Specifically, ;(twenty four)

[0132] In the formula, They are time domain features, including mean, standard deviation, segment mean and local peak.

[0133] S62. Calculate the frequency domain evaluation index based on the frequency domain characteristics of the irregularity parameter ;

[0134] Specifically, ; (25)

[0135] In the formula, Frequency domain features include band range, periodic wavelength, sensitive wavelength and spectrum area;

[0136] S63. Obtaining weight allocation coefficients of time domain evaluation indicators and frequency domain evaluation indicators respectively;

[0137] S64. Construct a frequency domain joint evaluation model based on the time domain evaluation index and the frequency domain evaluation index and their corresponding weight allocation coefficients:

[0138] ; (26)

[0139] In the formula, It represents the overall evaluation index of the track service status. is the quantitative index of other accidental and random factors, such as the system construction factors, environmental factors, accidental factors of the vehicle-track system, etc. 𝑎 and 𝑏 are the weight distribution coefficients of the time domain evaluation index and the frequency domain evaluation index, respectively, and .

[0140] In this embodiment, each set of unevenness parameters is calculated in turn, and the smaller the value, the better the smoothness of the track. Embodiment 2:

[0141] like Figure 3 As shown, this embodiment provides a time-frequency domain joint evaluation device for track smoothness, the device comprising:

[0142] Acquisition module: acquires detection data of multiple groups of track irregularity parameters;

[0143] Calculation module: Calculate the statistical characteristics and distribution characteristics of each group of irregularity parameters and the local peak value and section mean value of all irregularity parameters according to the detection data of each group of irregularity parameters;

[0144] The first component module: the time domain characteristics of each group of roughness parameters are formed by the local peak value, the section mean value, and the statistical characteristics and distribution characteristics of each group of roughness parameters;

[0145] Frequency domain analysis module: Use power spectrum estimation method to perform frequency domain analysis on each group of irregularity parameters to obtain the track irregularity spectrum corresponding to each group of irregularity parameters;

[0146] Fitting module: Use nonlinear least squares method to fit each group of track irregularity spectrum, generate the standard expression of track irregularity spectrum corresponding to each group of irregularity parameters, and obtain the frequency domain characteristics of each group of irregularity parameters according to the standard expression of track irregularity spectrum;

[0147] Model building module: The time domain characteristics and frequency domain characteristics of each set of irregularity detection data are used to build a corresponding time-frequency domain joint evaluation model to evaluate the smoothness of the track under each set of irregularity parameters.

[0148] Based on the above embodiment, the calculation module includes:

[0149] The first calculation unit calculates the mean and standard deviation of the detection data of each group of roughness parameters in turn to obtain the statistical characteristics of each group of roughness parameters;

[0150] A first acquisition unit is configured to acquire detection data of each set of roughness parameters in a preset section, and calculate a standard deviation of each set of roughness parameters in the preset section;

[0151] The second calculation unit is used to calculate the section mean value by the standard deviation of each group of roughness parameters in the preset section;

[0152] A second acquisition unit is configured to acquire an evaluation level and an evaluation standard of the detection data, and to divide the detection data of each group of roughness parameters into different evaluation levels according to the evaluation standard;

[0153] The third calculation unit: according to the division result, the local peak value is calculated by using the local peak deduction method.

[0154] Based on the above embodiment, the third calculation unit includes:

[0155] The third acquisition unit is used to acquire the score corresponding to each evaluation level and the weight coefficient of each group of roughness parameters;

[0156] The fourth calculation unit is used to calculate the number of detection data of each group of roughness parameters at each evaluation level;

[0157] A fifth calculation unit: calculating a deduction value of each group of roughness parameters based on the score of the evaluation level, the weight coefficient of each group of roughness parameters and the number of detection data;

[0158] The sixth calculation unit is to sum up the deduction values ​​of each group of roughness parameters to obtain a local peak value.

[0159] Based on the above embodiment, the frequency domain analysis module includes:

[0160] The first conversion unit: converts a set of irregularity parameters into a set of random sequences, and divides the random sequences into a number of data segments;

[0161] The second conversion unit: performs Fourier transformation on each segment of data to obtain a frequency domain signal;

[0162] Windowing processing unit: performing windowing processing on the frequency domain signal using a window function to generate a periodogram power spectrum density;

[0163] The seventh calculation unit: calculates the average value of the power spectrum density function of the periodogram of several segments of data to obtain the track irregularity spectrum of the current irregularity parameter.

[0164] Based on the above embodiment, the fitting module includes:

[0165] The first construction unit: construct a characterization function according to the track's irregularity characteristics, and initialize the parameter set of the characterization function using the track's irregularity spectrum;

[0166] The eighth calculation unit: obtains the initial deviation value of each parameter in the parameter set according to the track irregularity spectrum;

[0167] A ninth calculation unit: performing Taylor series expansion on the characterization function, and obtaining a constant term and a linear term of each initial deviation value;

[0168] The second construction unit: constructs the objective function using the constant term and the linear term of the initial deviation value, and calculates the partial derivative and Jacobian matrix of the objective function;

[0169] Iteration unit: iteratively updates each parameter based on the partial derivative and Jacobian matrix, and obtains a final parameter set when the change of the parameter is less than a preset accuracy threshold;

[0170] The third construction unit: constructs a standard mathematical expression of the track irregularity spectrum according to the final parameter set.

[0171] Based on the above embodiment, the model building module includes:

[0172] The tenth calculation unit: calculates a time domain evaluation index according to a set of time domain characteristics of the roughness parameters;

[0173] An eleventh calculation unit: calculates a frequency domain evaluation index according to the frequency domain characteristics of the roughness parameter;

[0174] A fourth acquisition unit: respectively acquires weight allocation coefficients of the time domain evaluation index and the frequency domain evaluation index;

[0175] The fourth construction unit: constructing a frequency domain joint evaluation model based on the time domain evaluation index and the frequency domain evaluation index and their corresponding weight allocation coefficients.

[0176] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here. Embodiment 3:

[0177] Corresponding to the above method embodiment, this embodiment also provides a time-frequency domain joint evaluation device for the track smoothness state. The time-frequency domain joint evaluation device for the track smoothness state described below and the time-frequency domain joint evaluation method for the track smoothness state described above can be referenced to each other.

[0178] Figure 4 FIG. 8 is a block diagram of a time-frequency domain joint evaluation device 800 for track smoothness according to an exemplary embodiment. Figure 4 As shown, the track smoothness time-frequency domain joint assessment device 800 may include: a processor 801, a memory 802. The track smoothness time-frequency domain joint assessment device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0179] The processor 801 is used to control the overall operation of the track smoothness time-frequency domain joint evaluation device 800 to complete all or part of the steps in the track smoothness time-frequency domain joint evaluation method. The memory 802 is used to store various types of data to support the operation of the track smoothness time-frequency domain joint evaluation device 800, which may include, for example, instructions for any application or method operating on the track smoothness time-frequency domain joint evaluation device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the time-frequency domain joint evaluation device 800 of the track smoothness and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0180] In an exemplary embodiment, the time-frequency domain joint evaluation device 800 for the track smoothness state can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processing Device, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components, and are used to execute the above-mentioned time-frequency domain joint evaluation method for the track smoothness state.

[0181] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned time-frequency domain joint evaluation method for track smoothness are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions may be executed by the processor 801 of the time-frequency domain joint evaluation device 800 for track smoothness to complete the above-mentioned time-frequency domain joint evaluation method for track smoothness. Embodiment 4:

[0182] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the time-frequency domain joint evaluation method of the track smoothness described above can refer to each other.

[0183] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the time-frequency domain joint evaluation method for track smoothness of the above method embodiment.

[0184] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0185] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0186] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A time-frequency domain joint evaluation method for track smoothness, characterized in that: include: Obtaining detection data of multiple groups of track irregularity parameters; According to the detection data of each group of roughness parameters, the statistical characteristics and distribution characteristics of each group of roughness parameters and the local peak values ​​and section means of all roughness parameters are calculated; The time domain characteristics of each group of roughness parameters are composed of the local peak value, the section mean value, and the statistical characteristics and distribution characteristics of each group of roughness parameters; The power spectrum estimation method is used to analyze each group of irregularity parameters in the frequency domain, and the track irregularity spectrum corresponding to each group of irregularity parameters is obtained; The nonlinear least square method is used to fit each group of track irregularity spectra, and the standard expression of the track irregularity spectrum corresponding to each group of irregularity parameters is generated. The frequency domain characteristics of each group of irregularity parameters are obtained according to the standard expression of the track irregularity spectrum. The time-domain and frequency-domain features of each set of irregularity detection data are used to construct a corresponding time-frequency domain joint evaluation model to evaluate the smoothness of the track under each set of irregularity parameters, including: The time domain evaluation index is calculated based on the time domain characteristics of a set of irregularity parameters; Calculating a frequency domain evaluation index according to the frequency domain characteristics of the roughness parameter; Obtaining weight allocation coefficients of time domain evaluation indicators and frequency domain evaluation indicators respectively; A frequency domain joint evaluation model is constructed based on time domain evaluation indicators, frequency domain evaluation indicators and their corresponding weight allocation coefficients.

2. The time-frequency domain joint evaluation method for track smoothness according to claim 1 is characterized in that ,According to the detection data of each group of irregularity parameters, the statistical characteristics and distribution characteristics of each group of irregularity parameters and the local peak value and section mean value of all irregularity parameters are calculated, including: The mean and standard deviation of the detection data of each group of irregularity parameters are calculated in turn to obtain the statistical characteristics of each group of irregularity parameters; Acquire detection data of each set of roughness parameters in a preset section, and calculate the standard deviation of each set of roughness parameters in the preset section; The section mean is calculated by the standard deviation of each group of irregularity parameters in the preset section; Acquiring evaluation levels and evaluation criteria for the detection data, and dividing the detection data of each group of roughness parameters into different evaluation levels according to the evaluation criteria; According to the division results, the local peak value is calculated using the local peak deduction method.

3. The time-frequency domain joint evaluation method for track smoothness according to claim 1 is characterized in that ,The power spectrum estimation method is used to perform frequency domain analysis on each group of irregularity parameters, and the track irregularity spectrum corresponding to each group of irregularity parameters is obtained, including: Convert a set of irregular parameters into a set of random sequences, and divide the random sequences into several segments of data; Perform Fourier transform on each piece of data to obtain the frequency domain signal; Performing windowing processing on the frequency domain signal using a window function to generate a periodogram power spectral density; The average value of the power spectrum density function of the periodogram of several segments of data is calculated to obtain the track irregularity spectrum of the current irregularity parameter.

4. The time-frequency domain joint evaluation method for track smoothness according to claim 1 is characterized in that ,The nonlinear least square method is used to fit each set of track irregularity spectra, and the standard expression of track irregularity spectrum corresponding to each set of irregularity parameters is generated, including: A characterization function is constructed according to the track irregularity characteristics, and the parameter set of the characterization function is initialized using the track irregularity spectrum; The initial deviation value of each parameter in the parameter set is obtained according to the track irregularity spectrum; Perform Taylor series expansion on the characterization parameters, and obtain a constant term and a linear term of each initial deviation value; The objective function is constructed using the constant term and the linear term of the initial deviation value, and the partial derivative and Jacobian matrix of the objective function are calculated; Iteratively updating each parameter based on the partial derivatives and the Jacobian matrix, and obtaining a final parameter set when the change in the parameter is less than a preset accuracy threshold; A standard mathematical expression for the track irregularity spectrum is constructed based on the final parameter set.

5. A time-frequency domain joint evaluation device for track smoothness, used in the time-frequency domain joint evaluation method for track smoothness according to any one of claims 1 to 4, characterized in that: include: Acquisition module: acquires detection data of multiple groups of track irregularity parameters; Calculation module: Calculate the statistical characteristics and distribution characteristics of each group of irregularity parameters and the local peak value and section mean value of all irregularity parameters according to the detection data of each group of irregularity parameters; The first component module: the time domain characteristics of each group of roughness parameters are formed by the local peak value, the section mean value, and the statistical characteristics and distribution characteristics of each group of roughness parameters; Frequency domain analysis module: Use power spectrum estimation method to perform frequency domain analysis on each group of irregularity parameters to obtain the track irregularity spectrum corresponding to each group of irregularity parameters; Fitting module: Use nonlinear least squares method to fit each group of track irregularity spectrum, generate the standard expression of track irregularity spectrum corresponding to each group of irregularity parameters, and obtain the frequency domain characteristics of each group of irregularity parameters according to the standard expression of track irregularity spectrum; Model building module: The time domain characteristics and frequency domain characteristics of each set of irregularity detection data are used to build a corresponding time-frequency domain joint evaluation model to evaluate the smoothness of the track under each set of irregularity parameters.

6. The time-frequency domain joint evaluation device for track smoothness according to claim 5, characterized in that: The calculation module comprises: The first calculation unit calculates the mean and standard deviation of the detection data of each group of roughness parameters in turn to obtain the statistical characteristics of each group of roughness parameters; A first acquisition unit is configured to acquire detection data of each set of roughness parameters in a preset section, and calculate a standard deviation of each set of roughness parameters in the preset section; The second calculation unit is used to calculate the section mean value by the standard deviation of each group of roughness parameters in the preset section; A second acquisition unit is configured to acquire an evaluation level and an evaluation standard of the detection data, and to divide the detection data of each group of roughness parameters into different evaluation levels according to the evaluation standard; The third calculation unit: according to the division result, the local peak value is calculated by using the local peak deduction method.

7. The time-frequency domain joint evaluation device for track smoothness according to claim 5, characterized in that: The frequency domain analysis module comprises: The first conversion unit: converts a set of irregularity parameters into a set of random sequences, and divides the random sequences into a number of data segments; The second conversion unit: performs Fourier transformation on each segment of data to obtain a frequency domain signal; Windowing processing unit: performing windowing processing on the frequency domain signal using a window function to generate a periodogram power spectrum density; The seventh calculation unit: calculates the average value of the power spectrum density function of the periodogram of several segments of data to obtain the track irregularity spectrum of the current irregularity parameter.

8. The time-frequency domain joint evaluation device for track smoothness according to claim 5, characterized in that: The fitting module comprises: The first construction unit: construct a characterization function according to the track irregularity characteristics, and initialize the parameter set of the characterization function using the track irregularity spectrum; The eighth calculation unit: obtains the initial deviation value of each parameter in the parameter set according to the track irregularity spectrum; A ninth calculation unit: performing Taylor series expansion on the characterization parameter, and obtaining a constant term and a linear term of each initial deviation value; The second construction unit: constructs the objective function using the constant term and the linear term of the initial deviation value, and calculates the partial derivative and Jacobian matrix of the objective function; Iteration unit: iteratively updates each parameter based on the partial derivative and Jacobian matrix, and obtains a final parameter set when the change of the parameter is less than a preset accuracy threshold; The third construction unit: constructs a standard mathematical expression of the track irregularity spectrum according to the final parameter set.

9. A time-frequency domain joint evaluation device for track smoothness, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the time-frequency domain joint assessment method for track smoothness as claimed in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the time-frequency domain joint evaluation method for track smoothness as claimed in any one of claims 1 to 4.

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