A method and device for evaluating the state of road maintenance machinery based on fuzzy analytic hierarchy process

Abstract

The application provides a road maintenance machine state evaluation method and device based on fuzzy analytic hierarchy process. The method provided by the application comprises the following steps: establishing a simulation model of the road maintenance machine, simulating a working process, and obtaining stress distribution of the simulation model; determining a first position point of the road maintenance machine based on the stress distribution; determining an evaluation structure according to the first position point and the structure of the road maintenance machine; determining an evaluation position under each evaluation structure according to the stress distribution; calculating first weights of each evaluation structure by using a fuzzy analytic hierarchy process; calculating an influence coefficient of each evaluation position according to the stress distribution in the evaluation structure, and calculating second weights of each evaluation position under the evaluation structure based on the first weights and the influence coefficient of the evaluation position corresponding to the evaluation structure; collecting real-time running state parameters of the road maintenance machine; and evaluating the real-time running state of the road maintenance machine based on the real-time running state parameters, the first weights and the second weights, so that the precision of state evaluation is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fault diagnosis processing, in particular to a road maintenance machinery state evaluation method and device based on fuzzy analytic hierarchy process. BACKGROUND

[0002] In the field of road maintenance machinery state evaluation, the traditional method usually adopts fuzzy analytic hierarchy process (FAHP) to construct an evaluation model, and realizes comprehensive evaluation of the running state of the machinery through multi-level weight calculation. However, when determining the weight of the multi-level evaluation index, the existing method needs to perform repeated information entropy calculation on each index according to the same process. The weight calculation method leads to high algorithm complexity and large calculation amount, especially when the evaluation object structure is complex or there are many evaluation indexes, the calculation amount is large, and real-time performance is difficult to guarantee. In addition, the current fuzzy analytic hierarchy process applied to state evaluation often selects evaluation indexes based on expert experience or common sense, and lacks dynamic correlation with the actual running state of the machinery. This subjective index selection method is easy to ignore key influencing factors, resulting in large deviation between the evaluation result and the real working condition, and it is difficult to accurately reflect the real-time health state of the road maintenance machinery.

[0003] Therefore, there is an urgent need for a road maintenance machinery state evaluation method with efficient calculation and scientific indexes to solve the problems of weight calculation redundancy and insufficient evaluation accuracy in the prior art. SUMMARY

[0004] The first aspect of the present application provides a road maintenance machinery state evaluation method based on fuzzy analytic hierarchy process, which comprises:

[0005] establishing a simulation model of the road maintenance machinery;

[0006] performing working process simulation based on the simulation model to obtain stress distribution of the simulation model;

[0007] determining a first position point of the road maintenance machinery based on the stress distribution;

[0008] determining an evaluation structure according to the first position point and the structure of the road maintenance machinery;

[0009] determining an evaluation position under each evaluation structure according to the stress distribution;

[0010] calculating a first weight of each evaluation structure by using fuzzy analytic hierarchy process;

[0011] calculating an influence coefficient of each evaluation position according to the stress distribution in the evaluation structure, and calculating a second weight of each evaluation position under the evaluation structure based on the first weight of the corresponding evaluation structure of the evaluation position and the influence coefficient;

[0012] acquire real-time running state parameters of the track maintenance machine;

[0013] evaluate the real-time running state of the track maintenance machine based on the real-time running state parameters, the first weight and the second weight.

[0014] The second aspect of the application provides a track maintenance machine state evaluation device based on fuzzy analytic hierarchy process, the device comprises:

[0015] a modeling module for establishing a simulation model of the track maintenance machine;

[0016] a simulation module for performing working process simulation based on the simulation model to obtain stress distribution of the simulation model;

[0017] a screening module for determining a first position point of the track maintenance machine based on the stress distribution;

[0018] The screening module is further configured to determine a participating structure according to the first position point and the structure of the track maintenance machine, and determine a participating position under each participating structure according to the stress distribution;

[0019] a weight calculation module for calculating a first weight of each participating structure by using fuzzy analytic hierarchy process, calculating an influence coefficient of each participating position according to the stress distribution in the participating structure, and calculating a second weight of each participating position under the participating structure based on the first weight of the corresponding participating structure of the participating position and the influence coefficient;

[0020] a monitoring module for acquiring real-time running state parameters of the track maintenance machine;

[0021] an evaluation module for evaluating the real-time running state of the track maintenance machine based on the real-time running state parameters, the first weight and the second weight.

[0022] The method and device for evaluating the state of road maintenance machinery based on fuzzy analytic hierarchy process provided by the application realize adaptive selection of evaluation indexes and accurate and rapid calculation of state evaluation values through simulation modeling, stress analysis, weight calculation and real-time evaluation, are suitable for different types of road maintenance machinery and reduce the implementation threshold. In the first aspect, when calculating the weight of the evaluation index, the first weight of the upper evaluation structure is calculated first, and then the second weight of the lower evaluation position is calculated in combination with stress distribution, thereby avoiding the problem of global repeated calculation of all indexes in the traditional fuzzy analytic hierarchy process (FAHP) and reducing redundant operation; the weight of the evaluation position is not only dependent on the structure weight but also dynamically adjusted in combination with stress distribution data, so that the weight distribution is more scientific and the calculation burden caused by manual intervention is reduced. In the second aspect, the stress analysis based on the simulation model determines the key position point (the first position point) and the evaluation structure, thereby avoiding the problem of subjective selection of evaluation indexes by experts in the traditional method, making the evaluation more objective, having high automation degree, reducing the dependence of the fuzzy analytic hierarchy process on expert experience and being suitable for large-scale engineering application. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 A flowchart of the method for evaluating the state of road maintenance machinery based on fuzzy analytic hierarchy process provided by the first embodiment of the application is shown in FIG. 1.

[0024] Figure 2 A structural schematic diagram of the device for evaluating the state of road maintenance machinery based on fuzzy analytic hierarchy process provided by the second embodiment of the application is shown in FIG. 2. DETAILED DESCRIPTION

[0025] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The following description is with reference to the drawings, in which like numerals refer to like elements throughout. The implementations described in the following exemplary embodiments are illustrative of all implementations in which the present application could be used.

[0026] The terms used in the present application are merely for the purpose of describing particular embodiments and are not intended to limit the present application. As used in the present application, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "and / or," as used herein, refer to and encompass any or all possible combinations of one or more of the associated listed items.

[0027] It should be understood that, although the terms first, second, third, etc. can be employed in this application to describe various information, the information should not be limited to these terms. These terms are only used to differentiate one piece of information from another. For example, without departing from the scope of the application, the first information can also be called the second information, and similarly, the second information can also be called the first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon determination" or "in response to determining".

[0028] The specific embodiments are given below to introduce the technical solutions of the application in detail.

[0029] Figure 1 The flow chart of the road maintenance machinery state evaluation method based on fuzzy analytic hierarchy provided by the embodiment of the application is shown in Figure 1 The method provided by the embodiment can include:

[0030] S1: Establishing a simulation model of the road maintenance machinery.

[0031] The road maintenance machinery refers to the engineering machinery used for the maintenance of infrastructure such as railways and highways, such as tamping machines, screen cleaning machines, rail grinding cars, etc. Its structure is complex, and it is easy to be damaged by fatigue due to long-term dynamic load. A virtual three-dimensional model established by a computer software can simulate the geometric structure, material properties, mechanical behavior, etc. of the machinery, and perform numerical simulation (such as stress and vibration analysis) on its working process. The modeling method can use three-dimensional geometric modeling, use CAD software (such as SolidWorks, CATIA) to establish an accurate three-dimensional model of the machinery, including key components (such as hydraulic cylinders, transmission mechanisms, support frames, etc.). After the three-dimensional geometric model is established, the CAD model is imported into the finite element software (such as ANSYS, ABAQUS), the mesh is divided, the material properties (such as elastic modulus, Poisson's ratio) and boundary conditions (such as fixed constraints, load application) are defined, so as to perform working process simulation later. For example, a three-dimensional model of a tamping machine is established in SolidWorks, including tamping arms, hydraulic systems, walking mechanisms, etc. Then the model is imported into ANSYS, the key components (such as tamping arms) are refined, the material is defined as high-strength alloy steel, and the impact force of the tamping pick on the sleeper (such as 10kN dynamic load) is simulated as a load during tamping operation. The stress distribution of the tamping machine under typical working conditions is calculated, and the vulnerable parts are identified. Specifically, the stress concentration at the root of the tamping arm is found through the stress nephogram, which can be listed as a key evaluation position.

[0032] S2: Based on the simulation model, the working process simulation is performed to obtain the stress distribution of the simulation model.

[0033] Specifically, by computer simulation of the dynamic behavior of road maintenance machinery in actual operation (such as vibration, load change, etc.), the mechanical response is predicted, and the spatial distribution of the stress (internal force per unit area) generated inside the mechanical structure under stress is analyzed, usually in the form of a cloud chart, which is used to identify high stress areas. Generally speaking, the specific work content of road maintenance machinery (such as track tamping, ballast cleaning, etc.) corresponds to different motion patterns and load conditions, and different mechanical structural components are required, and the working environment is also different. Among them, the working environment refers to the external conditions of the machinery during operation, including terrain, climate, track condition, etc., which affect the external force (such as impact, friction). In order to accurately analyze the stress distribution of the simulation model, a virtual working scene model is first established, including the ground, track, obstacles, etc., and the interaction force between the machinery and the environment is simulated according to the actual working scene. Under the interaction of different environmental forces and the internal mechanical connection structure constraints of the road maintenance machinery, the conduction path of energy loss (such as stress, wear) of the machinery in operation is divided into internal (transmission between structural components) and external (interaction between environment and machinery). In the process of interaction force transmission, the stress distribution at each position of the road maintenance machinery is simulated, and the stress distribution is a three-dimensional atlas, usually represented by color gradient to indicate stress size, for example, red for high stress area, showing the stress value of each point inside and on the surface of the road maintenance machinery after work is completed.

[0034] The working process simulation based on the simulation model obtains the stress distribution of the simulation model, including: determining the working task and working environment of the road maintenance machinery; determining the type of machinery operation (such as tamping, grinding) and environmental parameters (such as track irregularity, temperature). A three-dimensional environment model is established according to the working environment, which at least includes the external force of the simulation model; a virtual environment including terrain, track, and external load (such as wind load, impact force) is constructed using simulation software (such as ANSYS, ADAMS). The simulation model is placed in the three-dimensional environment model, and the simulation model of the road maintenance machinery is imported into the environment model to drive it to move according to the preset task, and the dynamic load data is recorded. The working task is executed to determine the internal loss transmission path of the simulation model; through multi-body dynamics analysis, the force transmission chain between mechanical components is determined, such as hydraulic cylinder → connecting rod → tamping arm. The force of the environment on the machinery is calculated, such as track reaction force, vibration excitation, and the external loss transmission path of the simulation model is calculated according to the external force; the stress distribution map inside the simulation model is calculated by integrating the internal loss transmission path and the external loss transmission path; the mechanical stress cloud chart is output by using finite element analysis (FEA) based on the mechanical data of the internal and external paths, for example, the position where the stress is greater than 80% of the material yield strength is marked in red to remind the user.

[0035] As a specific example, the tamping machine work task is set to "railway sleeper tamping", and the environment parameters include sleeper spacing 600 mm and ballast stiffness 50 MPa. A three-dimensional environment model is established in ANSYS, the sleepers, ballast and dynamic load (simulating the impact force of 10 kN of the tamper) are added, the tamping machine model is placed in the environment, and the simulation of the tamping arm down-lifting cycle motion is performed for 5 working periods. The path of the hydraulic system pressure fluctuation transmitted to the root of the tamping arm is identified, the peak stress appears at the hinge point, and the uneven support of the ballast causes the tamping machine chassis to bear periodic torsional load. The stress distribution map is generated, showing stress concentration (120 MPa) at the hinge point. The method provided by the application reduces the stress prediction error by about 30% through dynamic interaction of the environment and double-path analysis, improves the simulation accuracy, realizes accurate prediction of the stress distribution of the road maintenance machine through high-fidelity environment modeling and multi-physical field coupling simulation, solves the evaluation deviation problem caused by simplifying the environment or single-path analysis in the traditional method, and provides reliable data support for mechanical design, state evaluation and maintenance.

[0036] S3: determining a first position point of the road maintenance machine based on the stress distribution.

[0037] Specifically, the first position point is a key position that needs to be monitored or optimized in the road maintenance machine structure, which is a high-stress concentration area or a sensitive point of scattered distribution, and the first candidate position point is a potential key position point preliminarily screened out, which can be a point with a stress distribution value greater than a preset threshold.

[0038] The method for determining the first position point of the road maintenance machine based on the stress distribution includes: obtaining a stress distribution map of the simulation model; screening a plurality of first candidate position points based on the stress distribution values of each point in the stress distribution map; calculating the distribution dispersion degree according to the three-dimensional coordinates of the plurality of first candidate position points; and correcting the plurality of first candidate position points to obtain the first position point, with the maximum distribution dispersion degree as the target. The method for screening a plurality of first candidate position points based on the stress distribution values of each point in the stress distribution map includes: dividing the stress distribution map into regions according to the structure of the road maintenance machine; counting the point density of points with stress values exceeding a preset threshold in each region; calculating the point selection ratio of each region based on the ratio of the point density of each region; and selecting a plurality of first candidate position points in descending order of the stress values, and the ratio of the number of first candidate position points in each region is the point selection ratio.

[0039] Specifically, after obtaining the stress distribution map, the first candidate position points exceeding the preset threshold are screened according to the threshold, that is, the positions most prone to wear and tear during operation are taken as the basis for state evaluation, so that the state of the road maintenance machine can be evaluated in advance and accurately. After obtaining a plurality of first candidate positions, the stress distribution map is first divided into regions according to the connection structure. The stress distribution map is the stress value at each position of the road maintenance machine, which contains the position attribute. The structure of the road maintenance machine includes a plurality of functionally independent functional modules, such as a walking module and a cleaning module. The stress distribution map corresponding to the functional modules is divided into regions, for example, regions A, B and C are obtained, and the stress distribution of each functional module corresponding to the region is obtained. Next, the number of first candidate position points in each region is counted, for example, 5, 10 and 15 points. The ratio of the number to the total number of first candidate position points in the stress distribution map is taken as the point density of the region. Taking the total number of points as 100, the point densities of regions A, B and C are 0.05, 0.1 and 0.15 respectively. After calculating the point density, the ratio of the point density is taken as the point selection ratio, that is, 5:10:15. The nearest point to any candidate position point is taken as the adjacent point, and a first screening threshold is divided above the preset threshold. The first candidate position with a stress value greater than the first screening threshold is taken as the point participating in the dispersion calculation. The three-dimensional coordinate dispersion of the candidate point is calculated by the sum of the Euclidean distances between the adjacent points. Replace any point participating in the dispersion calculation to determine whether the dispersion is larger. Replace repeatedly until the combination of the points participating in the dispersion calculation with the largest dispersion is obtained as the first position point after screening. In the replacement process, the number of first position points selected in each region is always constrained by the point selection ratio. For example, the two points with the lowest dispersion in region A are replaced by the second highest stress points in regions B and C, and the final dispersion is increased to 1.5. Ten first position points are determined. The present application realizes the scientific selection of key position points of the road maintenance machine through stress threshold screening and spatial dispersion optimization, ensures that the high stress area is not missed, reduces the error by 40% compared with manual selection, and solves the problem of experience dependence and one-sidedness of the traditional method.

[0040] The first position point is obtained to screen out key position points inside and outside the structure according to the stress distribution, and then the state data of the key position points are collected and judged to accurately obtain the evaluation result of the road maintenance machine. After obtaining the first position point, the area whose stress distribution reflects the need for attention can be screened out inside and outside the structure of the road maintenance machine. On the basis of this area, the evaluation structure and position are screened.

[0041] S4: determining an evaluation structure according to the first position point and the structure of the road maintenance machine, and determining an evaluation position under each evaluation structure according to the stress distribution.

[0042] Specifically, the function components are determined according to the functions and connection structures of the track maintenance machine; the corresponding function components are matched according to the first position points in the position of the track maintenance machine, and the matched function components are taken as the evaluation structures; the sub-distribution conditions of each evaluation structure are matched from the stress distribution conditions; and the multiple evaluation positions in each evaluation structure are determined according to the stress value ranking in the sub-distribution conditions. According to different execution tasks, the functions performed by each component of the track maintenance machine are different, and the splitting methods are also different. The split function units are taken as the division of the structure to obtain multiple sub-structure units. According to the three-dimensional space coordinates of the multiple first position points, it is judged which sub-structure unit the first position points belong to, that is, it is determined which sub-structure unit the important first position points belong to, and N evaluation position points with a preset number of stress value rankings are selected from each sub-structure unit as the indicators at each level for calculation by the fuzzy analytic hierarchy process.

[0043] S5: calculating the first weight of each evaluation structure by the fuzzy analytic hierarchy process.

[0044] The calculation of the first weight of each evaluation structure by the fuzzy analytic hierarchy process includes: obtaining the average subjective weight of each evaluation structure; determining the importance ratio according to the number of points in each evaluation structure whose stress value exceeds a preset threshold; calculating the objective weight of each evaluation structure based on the importance ratio and the fuzzy analytic hierarchy process; and calculating the first weight based on the weighted sum of the average subjective weight and the objective weight.

[0045] The first weight is calculated by Fuzzy-AHP combined with the Delphi method. This method can calculate the weight of each factor or decision scheme and conduct comprehensive evaluation. The Delphi method is a method of expert investigation, which obtains consistent consensus from expert groups through multiple rounds of questionnaire investigation and opinion collection. In this study, Fuzzy-AHP combined with the Delphi method can evaluate, discuss and analyze the weight results of the weight and state of each component point of the experts.

[0046] On large industrial and electrical maintenance equipment, various sensors such as temperature, pressure, etc. that can collect vehicle state information are installed. When evaluating the state of large industrial and electrical maintenance equipment, the function components of large industrial and electrical maintenance equipment are divided into diesel engines, power transmission systems, running systems, braking systems, and car coupler buffer devices. After determining the evaluation index system, different weight coefficients are assigned to each evaluation index, and Fuzzy-AHP is used to determine the weight of each system and evaluation index.

[0047] The fuzzy set of influence system and evaluation criteria is constructed, and the specific steps are as follows: ① Construct the set of participating institutions, U={diesel engine U1, power transmission system U2, running system U3, brake system U4, coupler buffer device U5}. ② Construct the participating position of the next level of the participating institution, V={brake beam V1, brake shoe V2, brake cylinder V3,..., other Vn}. ③ Construct the fuzzy comprehensive evaluation set, X={normal region X1, deterioration region X2, warning region X3, danger region X4}.

[0048] First, the first weight of the participating institution needs to be calculated, and then the second weight of the participating position is calculated, and then the real-time state data collected is evaluated. For the first weight, first calculate the average subjective weight of each participating institution, collect the weight of each participating institution from experts by Delphi method, the subjective weight is to determine the weight parameter of each participating institution according to the judgment of experts, construct n anti-symmetric matrices by n experts' weight judgment of different participating institutions, and the difference between the weight judgment of different participating institutions by each expert is calculated by the standard deviation of the overall sample. Calculate the arithmetic mean of the anti-symmetric matrix constructed by n experts' weight judgment of different participating institutions to obtain matrix A. According to the steps of AHP method, the square root method is used to normalize the maximum eigenvalue and eigenvector of A, and the weight vector of the participating institution is obtained. Finally, the judgment matrix is constructed, when the judgment matrix meets the consistency condition, the maximum eigenvalue and the corresponding eigenvector are obtained by using the eigenvalue method, and the subjective weight is obtained by normalization.

[0049] The objective weight is calculated by using information entropy, according to the definition of information entropy, the entropy value of each participating institution is calculated, and the objective weight is calculated according to the information entropy of each participating institution. Finally, the weight of the participating institution to the system is determined by using Fuzzy-AHP method, the product of the weight and the objective weight, the average subjective weight is calculated, the sum is divided by 2 to obtain the first weight, and the information entropy and the average subjective weight in the calculation process are recorded for reference when calculating the second weight of the participating position.

[0050] S6: Calculate the influence coefficient of each participating position according to the stress distribution of the participating structure, calculate the second weight of each participating position of the participating structure based on the first weight of the corresponding participating position of the participating structure and the influence coefficient.

[0051] The influence coefficient of each evaluation position is calculated according to the stress distribution in the evaluation structure, the second weight of each evaluation position in the evaluation structure is calculated based on the first weight of the evaluation position corresponding to the evaluation structure and the influence coefficient, and the second weight of each evaluation position in the evaluation structure is calculated based on the stress ratio value calculated according to the stress distribution value of each evaluation position in each structure, the influence coefficient of each evaluation position is calculated based on the proportion of the sub-value of each evaluation position in the stress ratio value in the total value of the stress ratio value, the subjective weight of the evaluation position is calculated according to the average subjective weight of the first weight and the influence coefficient, the information entropy of the evaluation position is calculated based on the product of the information entropy of the objective weight in the first weight and the influence coefficient, the objective weight of the evaluation position is calculated according to the information entropy of the evaluation position, and the weighted sum of the subjective weight of the evaluation position and the objective weight of the evaluation position is calculated as the second weight.

[0052] S7: Collect real-time running state parameters of the track maintenance machine.

[0053] During the working process of the track maintenance machine, a plurality of sensors are arranged to collect real-time running state parameters, the real-time running state parameters include at least one of the following: mechanical vibration frequency spectrum data, hydraulic system pressure value, power device rotation speed parameter, working device displacement, environmental temperature and humidity data, etc., which can be realized by a multi-source sensor array installed on the track maintenance machine, such as a vibration sensor (e.g. an accelerometer), a pressure transmitter, a rotation speed encoder, a GPS positioning module, a temperature sensor. For example, during the working process of the tamper vehicle, the parameter collection is implemented by the following methods: a three-axis accelerometer is arranged at the vibration frame, the sampling frequency is set to 1 kHz, a pressure transmitter is installed on the hydraulic circuit to monitor the oil pressure change in real time, the rotation speed data of the engine ECU is obtained through the CAN bus, and the RTK-GPS module is used to record the equipment track.

[0054] Preferably, after obtaining the real-time running state parameters, the method further includes amplifying and filtering the original signal, converting the processed analog signal into a digital signal, performing outlier rejection and data normalization, and uploading the preprocessed data to a cloud server through a 4G / 5G network.

[0055] S8: Based on the real-time running state parameters, the first weight and the second weight, the real-time running state of the track maintenance machine is evaluated.

[0056] The method comprises the following steps: determining the monitoring position corresponding to each real-time operation state parameter; matching the target weight corresponding to each monitoring position from the first weight and the second weight; calculating the weighted sum of each real-time operation state parameter value and the corresponding target weight to obtain a comprehensive evaluation value; and determining the real-time operation state according to the comprehensive evaluation value and a fuzzy comprehensive evaluation set. Specifically, after the real-time operation state data is collected, the monitoring position corresponding to each real-time operation state data is determined, and each monitoring position is traversed. For any monitoring position, the monitoring position is first matched with each position point in the second weight. If the distance is less than a preset threshold, the second weight corresponding to the matched position point is taken as the target weight. If the distance is greater than or equal to the preset threshold, the monitoring position is matched with each evaluation structure position range in the first weight. If the monitoring position is within the evaluation structure position range, the first weight corresponding to the evaluation structure is taken as the target weight. The weighted sum of each real-time operation state data and the target weight is calculated to obtain the comprehensive evaluation value. According to the interval range of each evaluation result in the fuzzy comprehensive evaluation set, the evaluation result corresponding to the comprehensive evaluation value is obtained.

[0057] After the real-time operation state of the track maintenance machine is evaluated based on the real-time operation state parameters, the first weight and the second weight, the method further comprises the following steps: determining a real-time operation state level; matching a corresponding fault handling mode according to the real-time operation state level; and feeding back the fault handling mode and the parameter value with the largest weight in the real-time operation state parameters as a fault diagnosis result.

[0058] Corresponding to the foregoing embodiment of the method for evaluating the state of the track maintenance machine based on fuzzy analytic hierarchy process, the present application further provides an embodiment of a device for evaluating the state of the track maintenance machine based on fuzzy analytic hierarchy process.

[0059] Figure 2 The structure schematic diagram of the method and device for evaluating the state of the track maintenance machine based on fuzzy analytic hierarchy process provided in Embodiment Two of the present application is shown in FIG. 1. Figure 2 The device provided in the present embodiment comprises:

[0060] A modeling module is configured to establish a simulation model of the track maintenance machine.

[0061] A simulation module is configured to perform working process simulation based on the simulation model to obtain the stress distribution of the simulation model.

[0062] A screening module is configured to determine the first position point of the track maintenance machine based on the stress distribution.

[0063] The screening module is further configured to determine a participating structure according to the first position point and the structure of the track maintenance machine, and determine a participating position under each participating structure according to the stress distribution;

[0064] The weight calculation module is configured to calculate a first weight of each participating structure by using a fuzzy analytic hierarchy process, calculate an influence coefficient of each participating position according to the stress distribution of the participating structure, and calculate a second weight of each participating position under the participating structure based on the first weight of the participating position corresponding to the participating structure and the influence coefficient;

[0065] The monitoring module is configured to collect a real-time running state parameter of the track maintenance machine;

[0066] The evaluation module is configured to evaluate the real-time running state of the track maintenance machine based on the real-time running state parameter, the first weight and the second weight.

[0067] The device of the embodiment can be used to execute Figure 1 The steps of the method embodiment are similar to the specific implementation principle and implementation process, and will not be described here.

[0068] The implementation process of the functions and roles of each unit in the above device is specifically described in the implementation process of the corresponding steps in the above method, and will not be described here.

[0069] For the device embodiment, since it basically corresponds to the method embodiment, the related parts can be referred to the part of the method embodiment. The device embodiment described above is only schematic, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. According to the actual needs, some or all of the modules can be selected to achieve the purpose of the scheme of the present application. Those skilled in the art can understand and implement without creative labor.

[0070] The above is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for assessing the condition of track maintenance machinery based on fuzzy hierarchical analysis, characterized in that, The method includes: Establish simulation models for road maintenance machinery; The working process is simulated based on the simulation model to obtain the stress distribution of the simulation model; The first position point of the road maintenance machinery is determined based on the stress distribution. The evaluation structure is determined based on the first location point and the structure of the road maintenance machinery; The evaluation position under each of the evaluation structures is determined based on the stress distribution. The first weight of each participating structure is calculated using the fuzzy hierarchical analysis method. Calculate the influence coefficient of each evaluation position based on the stress distribution of the evaluation structure, and calculate the second weight of each evaluation position under the evaluation structure based on the first weight of the evaluation position corresponding to the evaluation structure and the influence coefficient. Collect real-time operating status parameters of the road maintenance machinery; The real-time operating status of the road maintenance machinery is evaluated based on the real-time operating status parameters, the first weight, and the second weight.

2. The method according to claim 1, characterized in that, The process simulation based on the simulation model to obtain the stress distribution of the simulation model includes: Determine the working tasks and working environment of the road maintenance machinery; A three-dimensional environment model is established based on the working environment, and the three-dimensional environment model includes at least the external forces of the simulation model; The simulation model is placed within the three-dimensional environment model, and the task is executed to determine the loss propagation path within the simulation model. Calculate the external loss transmission path of the simulation model based on the external forces applied. By combining the internal loss transmission path and the external loss transmission path, the stress distribution diagram inside the simulation model is calculated.

3. The method according to claim 1, characterized in that, Determining the first position point of the road maintenance machinery based on the stress distribution includes: Obtain the stress distribution diagram of the simulation model; Multiple first candidate location points are selected based on the stress distribution values ​​of each point in the stress distribution map; The distribution dispersion is calculated based on the three-dimensional coordinates of multiple first candidate location points; With the goal of maximizing the distribution dispersion, multiple first candidate location points are corrected to obtain the first location point.

4. The method according to claim 3, characterized in that, The process of filtering multiple first candidate location points based on the stress distribution values ​​at each point in the stress distribution map includes: The stress distribution map is divided into regions based on the structure of the road maintenance machinery. Statistically determine the density of points in each region where the stress value exceeds a preset threshold; The proportion of points selected in each region is calculated based on the ratio of point density in each region. Multiple first candidate location points are selected according to the stress value from largest to smallest, and the ratio of the number of first candidate location points in each region is the point selection ratio.

5. The method according to claim 1, characterized in that, The process of determining the evaluation structure based on the first location point and the structure of the road maintenance machinery, and determining the evaluation position under each of the evaluation structures based on the stress distribution, includes: The functional components are determined based on the function and connection structure of the road maintenance machinery; Based on the first location point, match the corresponding functional component at the location of the road maintenance machinery, and use the matched functional component as the evaluation structure; Match the sub-distribution of each participating structure with the stress distribution; Multiple evaluation positions within each evaluation structure are determined based on the stress value sorting in the sub-distribution.

6. The method according to claim 1, characterized in that, The calculation of the first weight of each participating structure using fuzzy hierarchical analysis includes: Obtain the average subjective weight of each participating structure; The importance ratio is determined based on the number of points in each participating structure whose internal stress value exceeds a preset threshold. The objective weights of each participating structure are calculated based on the importance ratio and the fuzzy hierarchical analysis method. The first weight is calculated based on the weighted sum of the average subjective weight and the objective weight.

7. The method according to claim 1, characterized in that, The step of calculating the influence coefficient of each evaluation position based on the stress distribution within the evaluated structure, and calculating the second weight of each evaluation position under the evaluated structure based on the first weight of the evaluated structure corresponding to the evaluation position and the influence coefficient, includes: The stress ratio is calculated based on the stress distribution values ​​at each evaluation location within each structure. The influence coefficient of each evaluation position within the evaluated structure is calculated based on the proportion of each evaluation position's sub-value in the stress ratio to the total stress ratio. The subjective weight of the evaluation position is calculated based on the average subjective weight and the influence coefficient used to calculate the first weight. The information entropy of the evaluation location is calculated by multiplying the information entropy of the objective weight in the first weight and the influence coefficient. Calculate the objective weight of the participating location based on the entropy of the participating location information; The weighted sum of the subjective weight and the objective weight of the evaluation position is used as the second weight.

8. The method according to claim 1, characterized in that, The evaluation of the real-time operating status of the road maintenance machinery based on the real-time operating status parameters, the first weight, and the second weight includes: Determine the monitoring location corresponding to each of the aforementioned real-time operating status parameters; Match the target weight corresponding to each monitoring location from the first weight and the second weight; Calculate the weighted sum of each real-time operating status parameter value and its corresponding target weight to obtain the comprehensive evaluation value; The real-time operating status is determined based on the comprehensive evaluation value and the fuzzy comprehensive evaluation set.

9. The method according to claim 1, characterized in that, After evaluating the real-time operating status of the road maintenance machinery based on the real-time operating status parameters, the first weight, and the second weight, the method further includes: Determine the real-time operational status level; Match the corresponding fault handling method according to the real-time operating status level; The fault handling method and the parameter value with the largest weight among the real-time operating status parameters are used as the fault diagnosis result for feedback.

10. A road maintenance machinery condition assessment device based on fuzzy hierarchical analysis, characterized in that, The device includes: The modeling module is used to create simulation models of road maintenance machinery; The simulation module is used to simulate the working process based on the simulation model and obtain the stress distribution of the simulation model. The screening module is used to determine the first position point of the road maintenance machinery based on the stress distribution. The screening module is also used to determine the evaluation structure based on the first location point and the structure of the road maintenance machinery, and to determine the evaluation position under each of the evaluation structures based on the stress distribution. The weight calculation module is used to calculate the first weight of each participating structure using fuzzy hierarchical analysis, calculate the influence coefficient of each participating position based on the internal stress distribution of the participating structure, and calculate the second weight of each participating position under the participating structure based on the first weight of the participating structure corresponding to the participating position and the influence coefficient. The monitoring module is used to collect real-time operating status parameters of the road maintenance machinery; The evaluation module is used to evaluate the real-time operating status of the road maintenance machinery based on the real-time operating status parameters, the first weight, and the second weight.

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