A damage evaluation method and system for turnout components

Abstract

The present invention provides a method and system for evaluating the damage of turnout components, which relates to the field of rail transit safety technology, including obtaining detailed information of turnout components, including type, material properties and size parameters. The components are hierarchically analyzed by using the hierarchical analysis method, a hierarchical structure model is established, and a discriminant matrix is ​​constructed by pairwise comparison and normalization processing, and the eigenvector and the maximum eigenvalue are calculated to obtain a consistency index. Based on this, the reliability of the turnout components is calculated, and further, an evaluation parameter matrix is ​​constructed, including evaluation values ​​of fault severity, occurrence probability and detection difficulty, and fuzzification processing and confidence calculation are performed. Finally, the fuzzy decision matrix is ​​combined with the reliability, and the weighted average method and gray correlation theory are used to perform damage evaluation to obtain the damage evaluation value of the turnout components. The present invention can provide a scientific basis for the maintenance and replacement of railway turnouts and improve the safety and reliability of railway transportation.

Description

Technical Field

[0001] The present invention relates to the field of rail transit safety technology, and in particular to a method and system for evaluating damage of turnout components. Background Art

[0002] In the railway transportation system, turnouts are key track equipment, and their performance and safety are crucial to the smooth operation of trains. However, with the increase in train speed, axle weight and service life, the damage problem of turnout components is becoming increasingly serious. These damages not only affect driving efficiency, but may also cause serious safety accidents. Existing damage detection technologies, such as hand-pushed flaw detection trolleys, track circuit detection methods, optical fiber detection methods and ultrasonic guided wave detection methods, can detect turnout damage to a certain extent, but these methods have certain limitations, such as detection blind spots, low efficiency, and sensitivity to the environment. In actual operation, they require a lot of manpower and material resources, and it is difficult to meet the high efficiency and high precision requirements of high-speed railway turnout detection.

[0003] Therefore, there is an urgent need for a turnout component damage assessment method and system to solve the above-mentioned problems. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for evaluating the damage of turnout components to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a method for evaluating damage of a turnout component, comprising:

[0006] Acquiring information of a turnout component to be evaluated, wherein the information of the turnout component to be evaluated includes the component type, material properties and dimension parameter information of the turnout;

[0007] Performing a hierarchical analysis on the information of the turnout components to be evaluated based on the hierarchical analysis method to obtain the hierarchy of all the turnout components to be evaluated;

[0008] Sending the information of the turnout components to be evaluated to a preset reliability evaluation model for reliability calculation to obtain the reliability of all turnout components to be evaluated;

[0009] The levels of all the turnout components to be evaluated are numbered, and an evaluation parameter matrix is ​​constructed based on the level numbers of all the turnout components to be evaluated, so as to obtain an evaluation parameter matrix of each turnout component to be evaluated;

[0010] Constructing a fuzzy decision matrix based on the evaluation parameter matrix, and performing weight distribution on the fuzzy decision matrix according to a preset weight distribution ratio to obtain a fuzzy decision matrix after weight distribution;

[0011] The weighted fuzzy decision matrix and the reliability of all turnout components to be evaluated are sent to a preset damage evaluation model for evaluation to obtain damage evaluation values ​​of all turnout components to be evaluated.

[0012] In a second aspect, the present application also provides a turnout component damage assessment system, comprising:

[0013] An acquisition unit is used to acquire information of a turnout component to be evaluated, wherein the information of the turnout component to be evaluated includes the type of the turnout component, material properties and size parameter information;

[0014] An analysis unit, used for performing hierarchical analysis on the information of the turnout components to be evaluated based on a hierarchical analysis method to obtain the hierarchy of all the turnout components to be evaluated;

[0015] A calculation unit, used for sending the information of the turnout components to be evaluated to a preset reliability evaluation model for reliability calculation, so as to obtain the reliability of all the turnout components to be evaluated;

[0016] A numbering unit, used for numbering the levels of all the turnout components to be evaluated, and constructing an evaluation parameter matrix based on the level numbers of all the turnout components to be evaluated, so as to obtain an evaluation parameter matrix of each turnout component to be evaluated;

[0017] An allocation unit, used for constructing a fuzzy decision matrix based on the evaluation parameter matrix, and performing weight allocation on the fuzzy decision matrix according to a preset weight allocation ratio to obtain a fuzzy decision matrix after weight allocation;

[0018] The evaluation unit is used to send the weighted fuzzy decision matrix and the reliability of all turnout components to be evaluated to a preset damage evaluation model for evaluation, so as to obtain damage evaluation values ​​of all turnout components to be evaluated.

[0019] The beneficial effects of the present invention are:

[0020] The present invention realizes a comprehensive evaluation of the damage of turnout components by combining a variety of advanced technical means such as hierarchical analysis method, reliability evaluation model, fuzzy decision matrix and grey correlation theory. First, the hierarchical structure modeling of turnout component information is carried out through the hierarchical analysis method, and the components at each level are compared two by two, the discriminant matrix is ​​constructed, the eigenvector and the maximum eigenvalue are calculated, so as to obtain the hierarchical sorting of consistency index and component information. Then, the reliability evaluation model is used, combined with the available time of the turnout component and the Weibull distribution formula, to calculate the reliability value of the component and make corrections. In addition, the method also constructs an evaluation parameter matrix, and further refines the evaluation process through fuzzy processing and confidence calculation. Finally, the damage evaluation value of the turnout component is obtained by using the fuzzy decision matrix and reliability value, using the weighted average method and grey correlation theory. This method not only improves the accuracy and efficiency of the evaluation, but also provides a scientific decision-making basis for the maintenance and replacement of turnout components, thereby effectively improving the safety and reliability of railway transportation.

[0021] 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

[0022] 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.

[0023] Figure 1 A schematic diagram of a flow chart of a method for evaluating damage to a turnout component according to an embodiment of the present invention;

[0024] Figure 2 It is a schematic diagram of the structure of the damage assessment system for turnout components described in an embodiment of the present invention.

[0025] In the figure: 701, acquisition unit; 702, analysis unit; 703, calculation unit; 704, numbering unit; 705, allocation unit; 706, evaluation unit; 7021, first analysis subunit; 7022, second analysis subunit; 7023, third analysis subunit; 7024, fourth analysis subunit; 7031, first calculation subunit; 7032, second calculation subunit; 7033, third calculation subunit; 7041, first numbering subunit; 7042, second numbering subunit; 7043, third numbering subunit; 7061, first evaluation subunit; 7062, second evaluation subunit; 7063, third evaluation subunit; 70631, fourth evaluation subunit; 70632, fifth evaluation subunit; 70633, sixth evaluation subunit. DETAILED DESCRIPTION

[0026] 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.

[0027] 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:

[0028] This embodiment provides a method for evaluating damage of turnout components.

[0029] See also Figure 1 , the figure shows that the method includes step S1, step S2, step S3, step S4, step S5 and step S6.

[0030] Step S1, obtaining information of a turnout component to be evaluated, wherein the information of the turnout component to be evaluated includes the component type, material properties and size parameter information of the turnout;

[0031] It is understandable that in the railway transportation system, turnouts are key connecting components, and their performance directly affects the safety and efficiency of train operation. Obtaining information on the turnout components to be evaluated is the first step in the turnout damage evaluation method. The technical effect of this step is reflected in providing accurate and comprehensive data support for subsequent evaluation work. This information usually includes key data such as the component type, material properties and dimensional parameters of the turnout, which are the basis for evaluating the performance and status of the turnout. The component type involves each independent component in the turnout, such as the switch, fork, heart rail, etc. The type and status of these components directly affect the function of the turnout. Material properties include the material and strength of the rails, which determine the durability and reliability of the turnout components. Dimensional parameter information, such as the length, width and thickness of the rails, is crucial to ensure the precise matching and normal operation of the turnout. Among them, this information can be obtained through various methods such as on-site inspection, maintenance records and historical data.

[0032] Step S2, performing hierarchical analysis on the information of the turnout components to be evaluated based on the hierarchical analysis method to obtain the hierarchy of all the turnout components to be evaluated;

[0033] It can be understood that this step can decompose the complex turnout evaluation problem into sub-problems that are easier to manage and evaluate through the hierarchical analysis method. The hierarchical analysis method decomposes the decision-making target into multiple levels and factors by establishing a multi-level decision-making model, thereby making the decision-making process more systematic and organized. In this step, step S2 includes step S21, step S22, step S23 and step S24.

[0034] Step S21, analyzing all components in the turnout component information to be evaluated, and establishing a hierarchical model;

[0035] Step S22, comparing the component information of each level in the hierarchical structure model layer by layer, and normalizing and constructing a discriminant matrix based on the comparison results to obtain at least two discriminant matrices;

[0036] Step S23, calculating the eigenvector and the maximum eigenvalue of all discriminant matrices to obtain consistency indicators;

[0037] Step S24: When the consistency index is met, all component information of each layer is sorted based on the eigenvector and the maximum eigenvalue to obtain hierarchical information of the turnout component information to be evaluated.

[0038] It can be understood that this step identifies the key factors of turnout evaluation, such as component type, material properties, dimensional parameters, etc., through in-depth analysis of turnout component information, and determines the importance of these factors in the evaluation process. These factors constitute the criterion layer of the evaluation model, which are direct factors affecting turnout performance and safety.

[0039] Next, these factors are organized according to their relationships and influence levels to form a multi-level analysis structure model. In this model, the highest level is the overall performance and safety of the turnout, the middle level is the various components and characteristics that affect these performances, and the lowest level is the specific turnout components. This hierarchical representation method helps decision makers understand how various factors work together to affect the overall performance of the turnout and identify which factors are key decision drivers.

[0040] In addition, the establishment of the hierarchical model also helps in the construction and consistency test of the judgment matrix in the subsequent steps. Through this model, pairwise comparisons can be carried out more systematically to ensure the logic and consistency of the evaluation process. The application of this method not only improves the accuracy of the evaluation, but also enhances the interpretability and credibility of the evaluation results.

[0041] Step S3, sending the information of the turnout components to be evaluated to a preset reliability evaluation model for reliability calculation, and obtaining the reliability of all the turnout components to be evaluated;

[0042] It can be understood that the information of the turnout components to be evaluated is input into the preset reliability evaluation model to calculate the reliability of each component, which in turn affects the accuracy of subsequent damage evaluation. In this step, step S3 includes step S31, step S32 and step S33.

[0043] Step S31, determining the available time of each turnout component information based on the turnout component information to be evaluated;

[0044] It can be understood that the available time of each turnout component information in this step provides a necessary time reference for the reliability evaluation of the turnout component, so that the evaluation is more in line with the actual operation situation.

[0045] Step S32, calculating the reliability value of all turnout component information based on the available time of each turnout component information and a preset Weibull distribution formula;

[0046] It is understood that the Weibull distribution is a statistical method to describe the distribution of product life, which can simulate the failure probability of components at different time points. By combining the available time of the component with the Weibull distribution parameters, the risk of component failure within a specific time can be predicted. The technical effect of this step is to provide a quantitative method to evaluate the reliability of turnout components, providing a scientific basis for maintenance and replacement decisions. The application of Weibull distribution makes the evaluation results more accurate and can better reflect the actual performance and life of the components. The calculation formula of the reliability value is as follows:

[0047] ;

[0048] in, Represents the reliability value of turnout component information, represents the scale parameter, represents the shape parameter, Indicates the time from the installation of the turnout components to the failure of the bolts (such as breakage or wear). is the regression coefficient of the variable, Represents a variable.

[0049] Step S33: Correct the reliability values ​​of all turnout component information based on a preset correction coefficient to obtain the corrected reliability values ​​of all turnout components to be evaluated.

[0050] It is understandable that this step corrects the reliability value of the turnout component based on a preset correction coefficient. The correction coefficient may take into account the special use conditions, material properties, environmental factors, etc. of the component. The technical effect of this step is to improve the adaptability and accuracy of the reliability calculation. Through the correction, a reliability value that is more in line with the actual working conditions can be obtained, making the maintenance strategy more accurate and effective. The correction process is a necessary supplement to the calculation results of the Weibull distribution, which ensures the practicality and reliability of the evaluation results, wherein the correction method is to multiply the correction coefficient by the reliability value.

[0051] Step S4, numbering the levels of all the turnout components to be evaluated, and constructing an evaluation parameter matrix based on the level numbers of all the turnout components to be evaluated, to obtain an evaluation parameter matrix of each turnout component to be evaluated;

[0052] It is understandable that through this step, the evaluation parameter matrix of each turnout component to be evaluated can be obtained, providing a basis for subsequent fuzzy decision analysis and damage evaluation. These matrices not only contain rich component performance information, but also express this information in a quantitative way, making the evaluation process more objective and accurate. Ultimately, these evaluation parameter matrices will be used to calculate the comprehensive evaluation value of the turnout components, providing a scientific basis for the maintenance and replacement decisions of the turnout. In this step, step S4 includes step S41, step S42 and step S43.

[0053] Step S41: numbering the turnout components to be evaluated based on the hierarchy of all turnout components and the position of each turnout component in the hierarchy, and performing fault evaluation on each turnout component according to a preset fault mode evaluation standard to obtain an evaluation value of each turnout component, wherein the evaluation value of each turnout component includes a fault severity evaluation value, a fault occurrence probability evaluation value, and a fault detection difficulty evaluation value;

[0054] Step S42, fuzzifying the evaluation value of each turnout component, and establishing an evaluation parameter matrix with the fuzzified evaluation value;

[0055] Step S43: Calculate the confidence based on the evaluation parameter matrix, and combine the confidence matrices obtained by calculating the confidence to obtain the evaluation parameter matrix of each turnout component to be evaluated.

[0056] It can be understood that this step can not only number the structure of each level, but also perform multi-level numbering and decomposition. For example, in the first-level numbering, serial number 1 represents the switch, serial number 2 is the guide curve, and serial number 3 is the switch and guardrail; in the second-level numbering, serial number 1.1 under serial number 1 switch is the rail part, 1.2 is the fastener system, 1.3 is the connection system, and 1.4 is the foundation under the rail; under the second-level numbering, the failure modes of each part are sorted out and listed with three-level numbers. For example, 1.1.1 lateral point rail wear represents the first disease mode of the rail part (secondary number 1.1 under serial number 1) under the switch (first-level serial number 1). All possible failure modes of the components are listed in this numbering method.

[0057] It can be understood that the specific evaluation parameters of the fault mode in this step can include: fault severity (S), fault probability (O) and fault detection difficulty (D). The evaluation criteria are divided into 5 levels from low to high (i.e., low, medium-low, medium, medium-high and high), and are expressed by a 10-point score system (the corresponding 5-level scores are (1), (2,3), (4,5,6), (7,8), (9,10)). However, due to the large influence of subjective factors, taking the severity of bolt breakage (S) as an example, evaluators A, B, and C all believe that the risk is medium risk, but the scores are 4, 5, and 6 respectively. Assuming that A, B, and C also have the same evaluation on the other two items O and D, and the scores are 4, 5, and 6 respectively, for the same disease, although the risk level subjectively considered by the three is the same, the evaluation parameters given are 64, 125, and 216 respectively, and the values ​​are very different. Therefore, this method fuzzifies the evaluation parameter values ​​and the evaluation indicators, that is, the 10-point scoring indicators are (1, 2, 3) (3, 4, 5) (5, 6, 7) (7, 8, 9) (8, 9, 10), and establishes a confidence matrix, where the confidence matrix is ​​as follows:

[0058] ;

[0059] in, represents the confidence matrix, Indicates low level, Indicates low to medium level, Indicates the middle level, Indicates medium to high level, Indicates high level.

[0060] If the evaluator is unsure about a disease, such as thinking that the disease may be of low or medium grade, or medium grade, according to probability theory, It means that the probability of occurrence of medium-low grade and medium grade is 0.5; if the evaluator believes that the probability of occurrence of a certain disease is between medium-low grade and medium grade, then If the evaluator believes that the probability of a certain disease occurring may be at a medium-low level, a medium level, or a medium-high level, then it can be expressed as , indicating that the probabilities of occurring at medium-low, medium, and medium-high levels are 0.2, 0.6, and 0.2, respectively.

[0061] According to the above principles, the evaluation parameter matrix is ​​established as follows:

[0062]

[0063] in, represents the confidence matrix, Indicates low level, Indicates low to medium level, Indicates the middle level, Indicates medium to high level, Indicates high level, Indicates that the probability of occurrence is between low and medium-low levels. Indicates that the probability of occurrence is between low and medium. Indicates that the probability of occurrence is between low and medium-high levels. Indicates that the probability of occurrence is between low and high levels. Indicates that the probability of occurrence is between low-medium and medium levels. Indicates that the probability of occurrence is between medium-low and medium-high. Indicates that the probability of occurrence is between medium-low and high. Indicates that the probability of occurrence is between medium and medium-high. Indicates that the probability of occurrence is between medium and high. Indicates that the probability of occurrence is between medium-high and high.

[0064] Step S5, constructing a fuzzy decision matrix based on the evaluation parameter matrix, and performing weight distribution on the fuzzy decision matrix according to a preset weight distribution ratio to obtain a fuzzy decision matrix after weight distribution;

[0065] It can be understood that by constructing a fuzzy decision matrix and assigning weights in this step, we can transform the complex turnout component evaluation problem into an operational mathematical model. This method not only improves the systematic and scientific nature of the evaluation process, but also enhances the accuracy and reliability of the evaluation results. Ultimately, this helps the railway maintenance team to more effectively manage the maintenance of turnout components and ensure the safe and stable operation of the railway system. Among them, the specific example is, assuming that there are The failure or damage ( , , ..., ) is used for evaluation, a confidence matrix is ​​formed:

[0066] ;

[0067] ;

[0068] in, Indicates the evaluation personnel's understanding of the failure mode About Impact Factor The results of the evaluation are as follows: represents the nth failure mode, Indicates Impact factors, represents the confidence matrix OK, represents the confidence matrix Column; K means there are K evaluators, Indicates the relative importance of the evaluator among the Kth evaluators. Indicates the evaluation personnel's understanding of the failure mode About Impact Factor The results of the evaluation include is the fuzzy evaluation level, yes about Rated confidence level.

[0069] As mentioned above, under the condition of L influencing factors, the evaluation of N faults or damages by K evaluators can form a fuzzy decision matrix as follows:

[0070] ;

[0071] in, represents the evaluation matrix, It represents the evaluation value of the Nth fault or damage under the condition of L influencing factors.

[0072] Step S6: Send the weighted fuzzy decision matrix and the reliability of all turnout components to be evaluated to a preset damage evaluation model for evaluation, and obtain damage evaluation values ​​of all turnout components to be evaluated.

[0073] It can be understood that this step provides a scientific and quantitative damage evaluation of turnout components by comprehensively considering multiple evaluation indicators and component reliability information. This not only improves the accuracy of the evaluation, but also provides strong support for railway maintenance decisions, and helps to achieve preventive maintenance and optimal allocation of resources. In this step, step S6 includes step S61, step S62 and step S63.

[0074] Step S61, defuzzifying the weighted fuzzy decision matrix based on the weighted average method to obtain a clear matrix;

[0075] Step S62: performing correlation analysis on the clear matrix and the preset damage reference value based on the grey correlation theory to obtain a correlation coefficient matrix, wherein the correlation coefficient matrix includes grey correlation coefficients of the evaluation parameters of all the turnout components to be evaluated and the preset damage reference value;

[0076] The formula for association analysis is as follows:

[0077]

[0078] in, represents the correlation coefficient between each failure mode variable and the reference benchmark, Represents the reference matrix. Generally, the optimal or worst value of each influencing factor of the fault mode is selected as the benchmark, which is a fixed reference matrix. After defuzzification, the failure mode About Impact Factor The overall evaluation confidence structure of is the resolution coefficient, .

[0079] Step S63: weight the correlation coefficient matrix and the reliability of all turnout components to be evaluated based on the minimum discriminant principle to obtain damage evaluation values ​​of all turnout components to be evaluated.

[0080] It can be understood that, in this step, step S63 includes step S631, step S632 and step S633.

[0081] Step S631, establishing an objective function according to the minimum discriminant information principle, solving the objective function to construct a Lagrangian function;

[0082] It can be understood that the minimum discrimination information principle in this step describes the difference between the two probabilities respectively, and according to the minimum discrimination information principle, the combined weight should be as close to the two weights as possible without being biased towards any one weight, wherein this step uses the correlation coefficient matrix as the first weight and the reliability of all the switch components to be evaluated as the second weight.

[0083] It can be understood that the objective function is as follows:

[0084]

[0085] in, is the i-th first weight, is the ith second weight, is the combined weight vector of the i-th first weight and the second weight, and n is the total number of first weights and second weights;

[0086] It can be understood that the Lagrangian function is constructed as follows:

[0087] ;

[0088] ;

[0089] Where L is the Lagrangian function, Lagrange constant, is the i-th first weight, is the ith second weight, is the combined weight vector of the ith first weight and the second weight, and n is the total number of first weights and second weights.

[0090] Step S632: Substitute the correlation coefficient matrix and the reliability of all turnout components to be evaluated into the Lagrangian function for solution to obtain the combined weight of the evaluation value of each turnout component;

[0091] It can be understood that the Lagrangian function solution formula in this step is as follows:

[0092] ;

[0093] in, is the i-th first weight, is the ith second weight, is the combined weight vector of the ith first weight and the second weight, and n is the total number of first weights and second weights.

[0094] Step S633: multiply the combined weight of the evaluation value of each turnout component by the evaluation value of each turnout component to obtain the damage evaluation values ​​of all turnout components to be evaluated.

[0095] It can be understood that this step converts the comprehensive information of multiple evaluation indicators into a quantitative damage evaluation value through weighted product calculation, which provides a scientific and reasonable basis for railway maintenance decision-making. This method not only improves the accuracy of the evaluation, but also helps to optimize the maintenance strategy and ensure the safety and efficiency of railway transportation. Embodiment 2:

[0096] like Figure 2 As shown, this embodiment provides a turnout component damage assessment system, see Figure 2 The system includes an acquisition unit 701 , an analysis unit 702 , a calculation unit 703 , a numbering unit 704 , an allocation unit 705 and an evaluation unit 706 .

[0097] An acquisition unit 701 acquires information of a turnout component to be evaluated, wherein the information of the turnout component to be evaluated includes the type of the turnout component, material properties and size parameter information;

[0098] An analysis unit 702 is used to perform a hierarchical analysis on the information of the turnout components to be evaluated based on a hierarchical analysis method to obtain the hierarchy of all the turnout components to be evaluated;

[0099] The analysis unit 702 includes a first analysis subunit 7021 , a second analysis subunit 7022 , a third analysis subunit 7023 and a fourth analysis subunit 7024 .

[0100] The first analysis subunit 7021 is used to analyze all components in the turnout component information to be evaluated and establish a hierarchical structure model;

[0101] The second analysis subunit 7022 is used to compare the component information of each level in the hierarchical structure model layer by layer, and to normalize and construct a discriminant matrix based on the comparison results to obtain at least two discriminant matrices;

[0102] The third analysis subunit 7023 is used to calculate the eigenvector and the maximum eigenvalue of all discriminant matrices to obtain the consistency index;

[0103] The fourth analysis subunit 7024 is used to sort all the component information of each layer based on the eigenvector and the maximum eigenvalue when the consistency index is met, so as to obtain the hierarchical information of the turnout component information to be evaluated.

[0104] The calculation unit 703 is used to send the information of the turnout components to be evaluated to a preset reliability evaluation model for reliability calculation to obtain the reliability of all the turnout components to be evaluated;

[0105] The calculation unit 703 includes a first calculation subunit 7031 , a second calculation subunit 7032 and a third calculation subunit 7033 .

[0106] The first calculation subunit 7031 is used to determine the available time of each turnout component information based on the turnout component information to be evaluated;

[0107] The second calculation subunit 7032 is used to calculate the reliability value of all turnout component information based on the available time of each turnout component information and a preset Weibull distribution formula;

[0108] The third calculation subunit 7033 is used to correct the reliability values ​​of all turnout component information based on a preset correction coefficient to obtain the corrected reliability values ​​of all turnout components to be evaluated.

[0109] A numbering unit 704 is used to number the levels of all the turnout components to be evaluated, and to construct an evaluation parameter matrix based on the level numbers of all the turnout components to be evaluated, so as to obtain an evaluation parameter matrix of each turnout component to be evaluated;

[0110] The numbering unit 704 includes a first numbering subunit 7041 , a second numbering subunit 7042 and a third numbering subunit 7043 .

[0111] The first numbering subunit 7041 is used to number the turnout components to be evaluated based on the hierarchy of all turnout components and the position of each turnout component in the hierarchy, and to perform fault evaluation on each turnout component according to a preset fault mode evaluation standard to obtain an evaluation value of each turnout component, wherein the evaluation value of each turnout component includes a fault severity evaluation value, a fault occurrence probability evaluation value and a fault detection difficulty evaluation value;

[0112] The second numbering subunit 7042 is used to perform fuzzy processing on the evaluation value of each turnout component and establish an evaluation parameter matrix with the fuzzy-processed evaluation value;

[0113] The third numbering subunit 7043 is used to perform confidence calculation based on the evaluation parameter matrix, and to combine the confidence matrices obtained by calculating the confidence, so as to obtain the evaluation parameter matrix of each turnout component to be evaluated.

[0114] The allocation unit 705 is used to construct a fuzzy decision matrix based on the evaluation parameter matrix, and to perform weight allocation on the fuzzy decision matrix according to a preset weight allocation ratio to obtain a fuzzy decision matrix after weight allocation;

[0115] The evaluation unit 706 is used to send the weighted fuzzy decision matrix and the reliability of all turnout components to be evaluated to a preset damage evaluation model for evaluation, so as to obtain damage evaluation values ​​of all turnout components to be evaluated.

[0116] The evaluation unit 706 includes a first evaluation subunit 7061 , a second evaluation subunit 7062 and a third evaluation subunit 7063 .

[0117] The first evaluation subunit 7061 is used to perform defuzzification processing on the weighted fuzzy decision matrix based on the weighted average method to obtain a clear matrix;

[0118] The second evaluation subunit 7062 is used to perform correlation analysis on the clear matrix and the preset damage reference value based on the grey correlation theory to obtain a correlation coefficient matrix, wherein the correlation coefficient matrix includes the grey correlation coefficients of the evaluation parameters of all the turnout components to be evaluated and the preset damage reference value;

[0119] The third evaluation subunit 7063 is used to distribute weights on the correlation coefficient matrix and the reliability of all turnout components to be evaluated based on the minimum discriminant principle to obtain damage evaluation values ​​of all turnout components to be evaluated.

[0120] The third evaluation subunit 7063 includes a fourth evaluation subunit 70631 , a fifth evaluation subunit 70632 and a sixth evaluation subunit 70633 .

[0121] The fourth evaluation subunit 70631 is used to establish an objective function according to the minimum discriminant information principle, solve the objective function and thus construct a Lagrangian function;

[0122] The fifth evaluation subunit 70632 is used to substitute the correlation coefficient matrix and the reliability of all turnout components to be evaluated into the Lagrangian function for solving, so as to obtain the combined weight of the evaluation value of each turnout component;

[0123] The sixth evaluation subunit 70633 is used to calculate the product of the combined weight of the evaluation value of each turnout component and the evaluation value of each turnout component to obtain the damage evaluation value of all the turnout components to be evaluated.

[0124] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0125] 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.

[0126] 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 method for evaluating damage of turnout components, characterized in that: include: Acquiring information of a turnout component to be evaluated, wherein the information of the turnout component to be evaluated includes the component type, material properties and dimension parameter information of the turnout; Performing a hierarchical analysis on the information of the turnout components to be evaluated based on the hierarchical analysis method to obtain the hierarchy of all the turnout components to be evaluated; Sending the information of the turnout components to be evaluated to a preset reliability evaluation model for reliability calculation to obtain the reliability of all turnout components to be evaluated; The levels of all the turnout components to be evaluated are numbered, and an evaluation parameter matrix is ​​constructed based on the level numbers of all the turnout components to be evaluated, so as to obtain an evaluation parameter matrix of each turnout component to be evaluated; Constructing a fuzzy decision matrix based on the evaluation parameter matrix, and performing weight distribution on the fuzzy decision matrix according to a preset weight distribution ratio to obtain a fuzzy decision matrix after weight distribution; Sending the weighted fuzzy decision matrix and the reliability of all turnout components to be evaluated to a preset damage evaluation model for evaluation to obtain damage evaluation values ​​of all turnout components to be evaluated; The evaluation is performed based on sending the weighted fuzzy decision matrix and the reliability of all turnout components to be evaluated to a preset damage evaluation model, including: Defuzzifying the weighted fuzzy decision matrix based on the weighted average method to obtain a definite matrix; Based on the grey correlation theory, a correlation analysis is performed on the clear matrix and the preset damage reference value to obtain a correlation coefficient matrix, wherein the correlation coefficient matrix includes the grey correlation coefficients of the evaluation parameters of all the turnout components to be evaluated and the preset damage reference value; Based on the minimum discriminant principle, the correlation coefficient matrix and the reliability of all turnout components to be evaluated are weighted to obtain the damage evaluation values ​​of all turnout components to be evaluated.

2. The method for evaluating damage of turnout components according to claim 1, characterized in that , the hierarchical analysis method is used to perform a hierarchical analysis on the information of the turnout components to be evaluated, and the hierarchy of all the turnout components to be evaluated is obtained, including: Analyze all components in the turnout component information to be evaluated and establish a hierarchical structure model; Comparing the component information of each level in the hierarchical structure model layer by layer, and normalizing and constructing a discriminant matrix based on the comparison results to obtain at least two discriminant matrices; Calculate the eigenvector and maximum eigenvalue of all discriminant matrices to obtain consistency indicators; In the case where the consistency index is met, all component information of each layer is sorted based on the eigenvector and the maximum eigenvalue to obtain the hierarchical information of the turnout component information to be evaluated.

3. The method for evaluating damage of turnout components according to claim 1, characterized in that , sending the information of the turnout components to be evaluated to a preset reliability evaluation model for reliability calculation, and obtaining the reliability of all turnout components to be evaluated, including: Determining the availability time of each turnout component information based on the turnout component information to be evaluated; Calculate the reliability value of all turnout component information based on the available time of each turnout component information and a preset Weibull distribution formula; The reliability values ​​of all turnout component information are corrected based on a preset correction coefficient to obtain the corrected reliability values ​​of all turnout components to be evaluated.

4. The method for evaluating damage of turnout components according to claim 2, characterized in that , the levels of all the turnout components to be evaluated are numbered, and an evaluation parameter matrix is ​​constructed based on the level numbers of all the turnout components to be evaluated, and an evaluation parameter matrix of each turnout component to be evaluated is obtained, including: Based on the hierarchy of all turnout components to be evaluated and the position of each turnout component in the hierarchy, the turnout components are numbered, and the fault evaluation of each turnout component is performed according to the preset fault mode evaluation standard to obtain the evaluation value of each turnout component, wherein the evaluation value of each turnout component includes the fault severity evaluation value, the fault occurrence probability evaluation value and the fault detection difficulty evaluation value; The evaluation value of each turnout component is fuzzy processed, and the evaluation parameter matrix is ​​established with the fuzzy processed evaluation value; The confidence calculation is performed based on the evaluation parameter matrix, and the confidence matrix obtained based on the confidence calculation is combined to obtain the evaluation parameter matrix of each turnout component to be evaluated.

5. A turnout component damage assessment system, characterized in that: include: An acquisition unit is used to acquire information of a turnout component to be evaluated, wherein the information of the turnout component to be evaluated includes the type of the turnout component, material properties and size parameter information; An analysis unit, used for performing hierarchical analysis on the information of the turnout components to be evaluated based on a hierarchical analysis method to obtain the hierarchy of all the turnout components to be evaluated; A calculation unit, used for sending the information of the turnout components to be evaluated to a preset reliability evaluation model for reliability calculation, so as to obtain the reliability of all the turnout components to be evaluated; A numbering unit, used for numbering the levels of all the turnout components to be evaluated, and constructing an evaluation parameter matrix based on the level numbers of all the turnout components to be evaluated, so as to obtain an evaluation parameter matrix of each turnout component to be evaluated; An allocation unit, used for constructing a fuzzy decision matrix based on the evaluation parameter matrix, and performing weight allocation on the fuzzy decision matrix according to a preset weight allocation ratio to obtain a fuzzy decision matrix after weight allocation; An evaluation unit, used for sending the weighted fuzzy decision matrix and the reliability of all turnout components to be evaluated to a preset damage evaluation model for evaluation, so as to obtain damage evaluation values ​​of all turnout components to be evaluated; Wherein, the evaluation unit comprises: A first evaluation subunit is used to perform a defuzzification process on the weighted fuzzy decision matrix to obtain a definite matrix based on a weighted average method; The second evaluation subunit is used to perform correlation analysis on the clear matrix and the preset damage reference value based on the grey correlation theory to obtain a correlation coefficient matrix, wherein the correlation coefficient matrix includes the grey correlation coefficients of the evaluation parameters of all the turnout components to be evaluated and the preset damage reference value; The third evaluation subunit is used to distribute weights to the correlation coefficient matrix and the reliability of all turnout components to be evaluated based on the minimum discrimination principle to obtain damage evaluation values ​​of all turnout components to be evaluated.

6. The turnout component damage evaluation system according to claim 5, characterized in that: The analysis unit comprises: A first analysis subunit is used to analyze all components in the turnout component information to be evaluated and establish a hierarchical structure model; A second analysis subunit is used to compare the component information of each level in the hierarchical structure model layer by layer, and to normalize and construct a discriminant matrix based on the comparison results to obtain at least two discriminant matrices; The third analysis subunit is used to calculate the eigenvector and the maximum eigenvalue of all discriminant matrices to obtain the consistency index; The fourth analysis subunit is used to sort all component information of each layer based on the eigenvector and the maximum eigenvalue when the consistency index is met, so as to obtain the hierarchical information of the turnout component information to be evaluated.

7. The turnout component damage evaluation system according to claim 5, characterized in that: The computing unit comprises: A first calculation subunit, used for determining the available time of each turnout component information based on the turnout component information to be evaluated; A second calculation subunit, used for calculating the reliability value of all turnout component information based on the available time of each turnout component information and a preset Weibull distribution formula; The third calculation subunit is used to correct the reliability values ​​of all turnout component information based on a preset correction coefficient to obtain the corrected reliability values ​​of all turnout components to be evaluated.

8. The turnout component damage evaluation system according to claim 6, characterized in that: The numbering unit comprises: A first numbering subunit is used to number the turnout components to be evaluated based on the hierarchy of all turnout components and the position of each turnout component in the hierarchy, and to perform a fault evaluation on each turnout component according to a preset fault mode evaluation standard to obtain an evaluation value of each turnout component, wherein the evaluation value of each turnout component includes a fault severity evaluation value, a fault occurrence probability evaluation value, and a fault detection difficulty evaluation value; The second numbering subunit is used to perform fuzzy processing on the evaluation value of each turnout component and establish an evaluation parameter matrix with the fuzzy-processed evaluation value; The third numbering subunit is used to perform confidence calculation based on the evaluation parameter matrix, and to combine the confidence matrix obtained by calculating the confidence to obtain the evaluation parameter matrix of each turnout component to be evaluated.

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