A method for monitoring the health state of a subway train

Abstract

The present application relates to the field of state monitoring, especially to a kind of monitoring method for the health state of subway train, by determining track regularity and track stress distribution characteristic value;Calculate the load bearing characteristic coefficient of the opposite track of subway train, determine the change coefficient in combination with the historical track load bearing characteristic coefficient, determine whether the track exists abnormal tendency, determine the processing method;If the track does not exist abnormal tendency, then keep the continuous monitoring state to the opposite track of subway train;If the track exists abnormal tendency, then determine the moving load of subway train, calculate the derailment risk coefficient in combination with the track regularity, determine the derailment early warning state of subway train;In response to the result of subway train derailment early warning state, locate the risk point, determine the risk point and the running distance of subway train, adjust the running speed of subway train;Or determine that subway train needs to be stopped.The present application determines the processing method by monitoring the track operable state of subway train, improves the running efficiency and safety factor.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of state monitoring, in particular to a subway train health state monitoring method. BACKGROUND

[0002] Subway trains usually operate in various complex environments such as high temperature, high cold, wind and sand, and have the characteristics of long distance, large capacity and high speed continuous operation. These complex operating environments will cause different degrees of wear, fatigue and damage to various components of the train and the track, increasing the risk of train failure. Therefore, it is necessary to monitor the health status of the train and the track in real time to timely discover and handle potential problems and ensure the safe operation of the train. In recent years, the rapid development of emerging technologies such as Internet of Things, big data, artificial intelligence and digital twin has provided strong technical support for train health state monitoring. These technologies make it possible to collect, transmit and analyze train operation data in real time, and through the establishment of corresponding models and algorithms, the health status of the train can be accurately evaluated and predicted, thereby realizing intelligent operation and maintenance of the train.

[0003] Chinese patent publication No. CN117782647A discloses a subway running part bearing health state monitoring system, which includes a storage module for saving bearing historical detection data and bearing historical health score; a training module for introducing an initial model and retraining the initial model to obtain a bearing health prediction model; a terminal module for real-time collection of bearing vibration data, bearing temperature data, bearing load data, bearing speed data and bearing friction coefficient; a prediction module for inputting the bearing vibration data, bearing temperature data, bearing load data, bearing speed data and bearing friction coefficient into the bearing health prediction model to predict the bearing predicted health score at the next time; an alarm module for generating an alarm maintenance instruction according to the matching state score range interval of the bearing predicted health score; and a maintenance module for maintenance of the corresponding subway running part bearing according to the alarm maintenance instruction. The present application improves the durability and safety of the subway running part bearing.

[0004] The Chinese patent publication No. CN115892111A discloses a train running part health state monitoring system, comprising: a host monitoring subsystem and a slave monitoring subsystem, the host monitoring subsystem is installed on a train trailer, and the slave monitoring subsystem is installed on a train motor car; the host monitoring subsystem comprises: a monitoring host, a host front-end processor and a host composite sensor; the slave monitoring subsystem comprises: a monitoring slave, a slave front-end processor and a slave composite sensor. The invention can realize real-time monitoring of the working state of the running part, provide a large amount of real data for supporting operation and maintenance, realize early warning, fault diagnosis and maintenance decision suggestion of the state of the running part and the track state through the multi-parameter diagnosis mechanism technology combining temperature, vibration and impact monitoring. According to the real-time monitoring and diagnosis analysis result, the planned maintenance is converted to condition-based maintenance, the efficiency of operation and maintenance is improved, and the real-time monitoring provides effective protection for safe and effective operation of the subway.

[0005] However, the prior art still has the following problems,

[0006] In actual situations, when monitoring the health state of a subway train, global monitoring is usually performed, the monitoring accuracy is low, especially when encountering slight undulations on the ground, if the judgment is wrong, the long-term existence of the undulations will cause the track to be abnormal, thereby affecting the running efficiency of the train, and even causing the train to derail. SUMMARY

[0007] Therefore, the present application provides a monitoring method for the health state of a subway train to solve the problem that in actual situations, when monitoring the health state of a subway train, global monitoring is usually performed, the monitoring accuracy is low, especially when encountering slight undulations on the ground, if the detection is wrong, the long-term existence of the undulations will cause the track to be abnormal, thereby affecting the running efficiency of the train, and even causing the train to derail.

[0008] To achieve the above purpose, the present application provides a monitoring method for the health state of a subway train, comprising:

[0009] Periodically acquiring position information of the opposite tracks of the subway train, determining track smoothness, acquiring real-time stress data of the opposite tracks of the subway train, and determining track stress distribution characteristic values;

[0010] Based on the track smoothness and the track stress distribution characteristic values, a track bearing representation coefficient of the opposite tracks of the subway train is calculated, a change coefficient is determined in combination with historical track bearing representation coefficients to determine whether the track has an abnormal tendency, and a processing method is determined;

[0011] If the track does not have an abnormal tendency, the continuous monitoring state of the opposite tracks of the subway train is maintained;

[0012] If the track has an abnormal tendency, determine the subway train moving load, calculate the derailment risk coefficient combined with the track smoothness, determine the subway train derailment early warning state, and judge whether the subway train needs to be stopped;

[0013] In response to the result of the subway train derailment early warning state, locate the risk point, determine the risk point and the subway train running distance, and adjust the subway train running speed if necessary;

[0014] Or, determine whether the subway train needs to be stopped;

[0015] The position information includes vertical position and horizontal position.

[0016] Further, the process of determining the track smoothness includes,

[0017] Divide the track on the opposite side of the subway train into several track sections;

[0018] Determine the double-side track height difference and the horizontal track distance difference of each track section according to the position information;

[0019] Determine the average value of the sum of the double-side track height difference and the horizontal track distance difference as the track smoothness.

[0020] Further, the process of determining the track stress distribution characteristic value includes,

[0021] Determine the stress distribution of each track section;

[0022] Calculate the stress dispersion value of the track section based on the stress distribution;

[0023] Determine the stress dispersion value as the track stress distribution characteristic value.

[0024] Further, the process of calculating the bearing representation coefficient of the track on the opposite side of the subway train includes,

[0025] Determine the ratio of the track smoothness to the reference track smoothness as the smoothness influence factor;

[0026] Determine the ratio of the track stress distribution characteristic value to the reference track stress distribution characteristic value as the characteristic value influence factor;

[0027] Determine the weighted sum of the smoothness influence factor and the characteristic value influence factor as the bearing representation coefficient.

[0028] Further, the process of determining the change coefficient combined with the historical track bearing representation coefficient includes,

[0029] Determine the ratio of the bearing representation coefficient to the historical track bearing representation coefficient as the change coefficient;

[0030] The historical track bearing representation coefficient is an average value of a plurality of historical track bearing representation coefficients.

[0031] Further, the determination method of whether the track has an abnormal tendency, wherein,

[0032] If the change coefficient is greater than a preset change coefficient threshold, the track has an abnormal tendency, the subway train moving load is determined, the track smoothness is combined to calculate the derailment risk coefficient, the subway train derailment early warning state is determined, and it is judged whether the subway train needs to be stopped;

[0033] If the change coefficient is less than or equal to the preset change coefficient threshold, the track does not have an abnormal tendency, and the continuous monitoring state of the subway train on the opposite track is maintained.

[0034] Further, the process of determining the subway train moving load comprises,

[0035] Based on historical data, the heavy load of the subway train on each track section is predicted;

[0036] Based on historical data, the operation load time domain curve of each track section during the operation of the subway train is drawn;

[0037] The average value of the variance of the corresponding values of each point on the operation load time domain curve is determined as the operation load;

[0038] The sum of the heavy load and the operation load is determined as the subway train moving load.

[0039] Further, the process of combining the track smoothness to calculate the derailment risk coefficient comprises,

[0040] The ratio of the subway train moving load to the reference subway train moving load is determined as a moving load influence factor;

[0041] The weighted sum of the smoothness influence factor and the moving load influence factor is determined as the derailment risk coefficient.

[0042] Further, the determination of the subway train derailment early warning state and the judgment of whether the subway train needs to be stopped, wherein,

[0043] If the derailment risk coefficient is greater than a preset derailment risk coefficient threshold, it is judged that the subway train needs to be stopped;

[0044] If the derailment risk coefficient is less than or equal to the preset derailment risk coefficient threshold, the risk point is located, the risk point and the subway train operation distance are determined, and the subway train operation speed needs to be adjusted.

[0045] Further, the adjustment of the subway train operation speed, wherein,

[0046] determine a time length of the metro train reaching the risk point based on the running distance and the metro train running speed;

[0047] if the time length is greater than or equal to the maintenance time length, the metro train running speed needs to be maintained;

[0048] if the time length is less than the maintenance time length, the metro train running speed needs to be reduced.

[0049] Compared with the prior art, the present application periodically acquires the position information of the opposite track of the metro train, determines the track smoothness, acquires the stress data of the opposite track of the metro train in real time, determines the track stress distribution characteristic value, calculates the bearing representation coefficient of the opposite track of the metro train, combines the historical track bearing representation coefficient to determine the change coefficient, judges whether the track has an abnormal tendency, determines the processing method, if the track does not have an abnormal tendency, the continuous monitoring state of the opposite track of the metro train is maintained, if the track has an abnormal tendency, the train moving load is determined, the derailment risk coefficient is calculated combined with the track smoothness, the train derailment early warning state is determined, the train running speed is adjusted, in response to the result of the train derailment early warning state, the risk point is located, the risk point and the metro train running distance are determined, the train running speed is reduced, or it is judged that the metro train needs to be stopped. The present application determines the processing method by monitoring the running state of the train track, improves the running efficiency and safety coefficient.

[0050] Especially, by calculating the bearing representation coefficient of the opposite track of the metro train, determining the change coefficient, judging whether the track has an abnormal tendency, and then determining the corresponding processing method for different categories, in actual situations, the health state of the metro train is often determined by global and wide-range monitoring, the monitoring accuracy of local details (such as slight deformation and uneven settlement) of the track is relatively low, and such insufficient accuracy often makes it difficult to accurately and truly reflect the actual running state of each part of the track, if the judgment deviates, it may affect the running efficiency of the metro train, and even cause the train to derail, based on this, the present application divides the track into several track sections, focuses on local details, improves the detection sensitivity and accuracy of local slight abnormalities of the track, determines the track smoothness to represent the uneven settlement of the track, determines the track stress distribution characteristic value to represent the slight deformation of the track, calculates the bearing representation coefficient of the several track sections, analyzes the track sections, avoids that the error is too large due to the too long track, judges whether the track has an abnormal tendency, provides data basis for the track abnormal running category, and improves the running efficiency and safety coefficient of the metro train.

[0051] Especially, in view of the abnormal tendency of the track, the track derailment risk coefficient is calculated in combination with the train moving load and the track smoothness, the train derailment early warning is carried out, and then the train running speed is adjusted. In actual situation, the traditional processing method is often to immediately take emergency stop train for repair after finding the problem. Although this method can ensure safety, when facing non-emergency, local and slight abnormalities or problems that can be quickly repaired on the track, if the repair work can be completed before the train reaches the risk point, the operation of stopping the train will not only affect the train operation efficiency, but also reduce the operation efficiency of the whole subway system. Based on this, the train derailment early warning state is determined by calculating the derailment risk coefficient, so as to judge whether the train running speed needs to be adjusted. For slight problems or problems that can be quickly repaired, the train running efficiency and safety coefficient are improved by reasonable speed limit instead of complete stop.

[0052] Especially, by determining the risk point in the track section, the running speed of the subway train is adjusted to ensure that the repair of the abnormal track operation category is completed before the subway train runs to the risk point, thereby improving the running efficiency and safety coefficient of the subway train. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The step schematic diagram of the method for monitoring the health state of the subway train according to the embodiment of the application;

[0054] Figure 2 The logic block diagram of the method for determining whether the track has an abnormal tendency and determining the processing method according to the embodiment of the application;

[0055] Figure 3 The logic block diagram of the method for determining the train derailment early warning state and judging whether the subway train needs to be stopped according to the embodiment of the application;

[0056] Figure 4 The logic block diagram of the method for adjusting the running speed of the subway train according to the embodiment of the application. DETAILED DESCRIPTION

[0057] In order to make the purpose and advantages of the application more clear and obvious, the application will be further described below in combination with embodiments. It should be understood that the specific embodiments described herein are only used to explain the application, and do not limit the application.

[0058] The preferred embodiments of the application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the application, and do not limit the protection scope of the application.

[0059] Please refer to Figure 1 , Figure 1The application discloses a monitoring method for a health state of a subway train.

[0060] In step S1, position information of opposite tracks of the subway train is periodically acquired, track smoothness is determined, track stress data of the opposite tracks of the subway train is acquired in real time, and a characteristic value of track stress distribution is determined.

[0061] In step S2, a track bearing representation coefficient of the opposite tracks of the subway train is calculated based on the track smoothness and the characteristic value of the track stress distribution, a change coefficient is determined by combining a historical track bearing representation coefficient, whether the track has an abnormal tendency is judged, and a processing method is determined.

[0062] If the track does not have the abnormal tendency, a continuous monitoring state of the opposite tracks of the subway train is kept.

[0063] If the track has the abnormal tendency, a moving load of the subway train is determined, a derailment risk coefficient is calculated by combining the track smoothness, a derailment early warning state of the subway train is determined, and whether the subway train needs to be stopped is judged.

[0064] In step S3, in response to a result of the derailment early warning state of the subway train, a risk point is located, a risk point and a running distance of the subway train are determined, and the running speed of the subway train needs to be adjusted.

[0065] Or, it is judged that the subway train needs to be stopped.

[0066] The position information includes a vertical position and a horizontal position.

[0067] Specifically, the acquisition mode of the position information is not limited, and only the position information needs to be obtained, for example, a non-contact laser displacement sensor is arranged on both sides of the track, the left and right tracks are continuously and dynamically scanned and data are collected in a train running state, so that position coordinates of the track in a vertical direction and a horizontal plane direction are captured, of course, other modes can also be adopted by those skilled in the art, and any sensing technology (such as a machine vision system, an inertial reference method combined with a mileage positioning, optical triangulation and the like) capable of accurately acquiring three-dimensional space position data of the track is within the technical scheme range of the application, and will not be repeated here.

[0068] Specifically, the acquisition mode of the stress data is not limited, and only the stress data needs to be obtained, for example, a non-contact / contact sensing system can be arranged on the track, preferably, a track temperature stress monitor is used to measure the track stress in a continuous and dynamic manner in the train running state, of course, other modes can also be adopted by those skilled in the art, and any technical means capable of acquiring track stress distribution data in real time, in situ and non-destructively is within the technical scheme range of the application, and will not be repeated here.

[0069] It can be understood that when the speed of the subway train is adjusted, there is a minimum speed of the subway train,

[0070] If the adjusted speed is less than the minimum speed of the subway train, it is also determined that the subway train needs to be stopped, and the minimum speed represents that when the subway train runs to the risk point at the minimum speed, the time used is the same as that when the train reaches the risk point at the original speed after the train is stopped, at this time, reducing the running speed of the subway train will not improve the train operation efficiency, and even, it may cause track problems to be aggravated due to continuous train operation.

[0071] Specifically, the process of determining the track smoothness includes,

[0072] Dividing the track on the opposite side of the subway train into a plurality of track sections;

[0073] According to the position information, the height difference of the double-track and the horizontal track distance difference of each track section are determined;

[0074] The average of the sum of the height difference of the double-track and the horizontal track distance difference is determined as the track smoothness.

[0075] Specifically, the division method of the track section is not limited, for example, in the implementation, the track section can be divided into 5% of the total line, of course, those skilled in the art can divide the track section according to the actual situation, and it is reasonable, which will not be repeated here.

[0076] Specifically, the process of determining the track stress distribution characteristic value includes,

[0077] Determine the stress distribution of each track section;

[0078] Based on the stress distribution, the stress dispersion value of the track section is calculated;

[0079] The stress dispersion value is determined as the track stress distribution characteristic value.

[0080] Specifically, the stress distribution is determined by determining a plurality of stress points, it can be understood that the stress data measured by the track temperature stress monitor is recorded as the stress point which exceeds the stress peak point, wherein the value corresponding to the stress peak point is the average value of the stress data detected in the historical normal operation process of the subway train.

[0081] Specifically, in the implementation, the dispersion value is determined by variance, and the specific steps are as follows,

[0082] The stress distribution is counted in the same coordinate system;

[0083] Determine the average value of a plurality of stress points on the X-axis and the Y-axis;

[0084] determining the variance of the stress points on the X axis and the variance on the Y axis;

[0085] determining the average of the variance on the X axis and the variance on the Y axis as a discrete value;

[0086] Of course, other numerical values can also be used to represent the discrete value by those skilled in the art, and reasonable values are acceptable, which will not be repeated here.

[0087] Specifically, the process of calculating the bearing representation coefficient of the subway train on the side track includes,

[0088] determining the ratio of the track regularity to the reference track regularity as a regularity influence factor;

[0089] determining the ratio of the track stress distribution characteristic value to the reference track stress distribution characteristic value as a characteristic value influence factor;

[0090] determining the weighted sum of the regularity influence factor and the characteristic value influence factor as the bearing representation coefficient.

[0091] Specifically, the reference track regularity is data obtained in advance, which is determined according to the topography during track design, and can be obtained from the design scheme. It will not be repeated here. It can be understood that the closer the reference track regularity is to 1, the more smooth the track is.

[0092] Specifically, the reference track stress distribution characteristic value is obtained according to historical data, and the average of a plurality of historical track stress distribution characteristic values of the subway train during normal driving is determined as the reference track stress distribution characteristic value.

[0093] Specifically, the sum of the weight coefficients of the regularity influence factor and the characteristic value influence factor is 1, the weight coefficient of the regularity influence factor is 0.53, and the weight coefficient of the characteristic value influence factor is 0.47.

[0094] Specifically, the process of determining the change coefficient in combination with the historical track bearing representation coefficient includes,

[0095] determining the ratio of the bearing representation coefficient to the historical track bearing representation coefficient as a change coefficient;

[0096] The historical track bearing representation coefficient is the average of a plurality of historical track bearing representation coefficients.

[0097] Specifically, by calculating the side track bearing coefficient of the subway train, the change coefficient is determined, whether the track has an abnormal tendency is determined, and the corresponding processing method is determined for different categories. In actual situations, the health status of the subway train is often determined by global and wide range monitoring, and the monitoring accuracy of local details of the track (such as slight deformation and uneven settlement) is relatively low. Such insufficient accuracy often makes it difficult to accurately and truly reflect the actual operational status of each part of the track. If the judgment deviates, it may affect the operation efficiency of the subway train, and even cause the train to derail. Based on this, the track is divided into several track sections, the local details are focused, the detection sensitivity and accuracy of the local slight abnormalities of the track are improved, the uneven settlement of the track is represented by the track smoothness, the slight deformation of the track is represented by the track stress distribution characteristic value, the bearing representation coefficient of the several track sections is calculated, the track section is analyzed in sections, the error caused by the excessive length of the track is avoided, whether the track has an abnormal tendency is determined, data basis is provided for the track abnormal operation category, and the operation efficiency and safety coefficient of the subway train are improved.

[0098] Please refer to Figure 2 , Figure 2 The logic block diagram for determining whether the track has an abnormal tendency and determining the processing method of the embodiment of the application. Specifically, whether the track has an abnormal tendency is determined, and the processing method is determined, wherein,

[0099] If the change coefficient is greater than the preset change coefficient threshold, the track has an abnormal tendency, the moving load of the subway train is determined, the derailment risk coefficient is calculated in combination with the track smoothness, the derailment early warning state of the subway train is determined, and the train running speed is adjusted.

[0100] If the change coefficient is less than or equal to the preset change coefficient threshold, the track does not have an abnormal tendency, and the continuous monitoring state of the side track of the subway train is maintained.

[0101] Specifically, the preset change coefficient threshold is determined according to historical data, and the average value of the change coefficients of the subway train during normal driving is determined as the preset change coefficient threshold.

[0102] Specifically, the process of determining the train moving load includes,

[0103] The heavy load of the train on each track section is predicted based on historical data;

[0104] The running load time domain curve of each track section during the train running process is drawn based on historical data;

[0105] The average value of the variance of the corresponding values of each point on the running load time domain curve is determined as the running load.

[0106] The sum of the heavy load and the running load is determined as a metro train moving load.

[0107] Specifically, the manner of obtaining the historical data is not limited, for example, the data uploaded by a person skilled in the art, or the data in an existing open source database, and the historical data is authorized, which will not be repeated here.

[0108] It can be understood that the running load represents the pressure value of the track during the train running.

[0109] Specifically, the process of calculating the derailment risk coefficient in combination with the track regularity includes,

[0110] The ratio of the metro train moving load to the reference metro train moving load is determined as a moving load influence factor.

[0111] The weighted sum of the regularity influence factor and the moving load influence factor is determined as a derailment risk coefficient.

[0112] Specifically, the reference metro train moving load is obtained through historical running data of the metro train, and a plurality of metro train moving loads during normal driving of the metro train are obtained in advance, and the average value of the plurality of metro train moving loads is determined as the reference metro train moving load.

[0113] Specifically, the sum of the weight coefficients of the regularity influence factor and the moving load influence factor is 1, the weight coefficient of the regularity influence factor is 0.55, and the weight coefficient of the moving load influence factor is 0.45.

[0114] Specifically, in view of the abnormal tendency of the track, the derailment risk coefficient is calculated in combination with the train moving load and the track regularity, the train derailment warning is performed, and then the train running speed is adjusted. In actual situations, the traditional processing method often tends to immediately take emergency stop train for repair after finding the problem. Although this method can ensure safety, when facing non-emergency, local, and minor abnormalities or problems that can be quickly repaired on the track, if the repair work can be completed before the train reaches the risk point, continuing to take the stop operation will not only affect the train operation efficiency, but also reduce the operation efficiency of the entire metro system. Therefore, the derailment risk coefficient is calculated to determine the train derailment warning state, so as to judge whether the train running speed needs to be adjusted. For minor problems or problems that can be quickly repaired, the train running efficiency and safety coefficient are improved by reasonable speed limit instead of complete stop.

[0115] Please refer to Figure 3 , Figure 3The logic block diagram for determining the derailment early warning state of the subway train and judging whether the subway train needs to be stopped. Specifically, the derailment early warning state of the subway train is determined, and whether the subway train needs to be stopped is judged, wherein,

[0116] If the derailment risk coefficient is greater than the preset derailment risk coefficient threshold, it is judged that the subway train needs to be stopped.

[0117] If the derailment risk coefficient is less than or equal to the preset derailment risk coefficient threshold, the risk point is located, the risk point and the subway train running distance are determined, and the subway train running speed needs to be adjusted.

[0118] Specifically, the preset derailment risk coefficient threshold represents the boundary value of the subway train needing to be stopped. When the derailment risk coefficient exceeds the preset derailment risk coefficient threshold, stopping is needed. The preset derailment risk coefficient threshold is set to be the product of the average value of a plurality of historical derailment risk coefficients and an influence coefficient. To ensure safety, the influence coefficient is set to 0.8. The historical derailment risk coefficient is the derailment risk coefficient under the historical stopping state of the subway train.

[0119] Specifically, in the implementation, the process of locating the risk point includes,

[0120] Determining the track section corresponding to the derailment risk coefficient less than or equal to the preset derailment risk coefficient threshold;

[0121] Arranging the points with the track section stress distribution intensity exceeding the predetermined intensity in descending order;

[0122] Determining the first point in the order as the risk point;

[0123] Of course, those skilled in the art can also locate the risk point in other ways, which will not be described here.

[0124] Specifically, the track section stress distribution intensity is the number of stress peak points that can be identified in a unit area, and the predetermined intensity is the average value of the track section stress distribution intensity.

[0125] Specifically, by determining the risk point in the track section, the running speed of the subway train is adjusted to ensure that the abnormal track operation category is repaired before the subway train runs to the risk point, and the running efficiency and safety coefficient of the subway train are improved.

[0126] Please refer to Figure 4 , Figure 4 The logic block diagram for adjusting the running speed of the subway train according to the embodiment of the application. Specifically, the running speed of the subway train is adjusted, wherein,

[0127] The time length for the subway train to reach the risk point is determined based on the running distance and the running speed of the subway train.

[0128] If the time length is greater than or equal to the maintenance time length, the subway train running speed needs to be maintained;

[0129] If the time length is less than the maintenance time length, the subway train running speed needs to be reduced.

[0130] Specifically, the ratio of the running distance to the running speed is determined as the time length.

[0131] Specifically, the maintenance time length is determined according to the accident content. In implementation, a plurality of historical accidents are acquired in advance, and the corresponding historical maintenance time lengths are recorded. The maintenance time length is determined as the average of the historical maintenance time lengths of the same accident.

[0132] It can be understood that the maintenance time length is negatively correlated with the subway train running speed.

[0133] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

[0134] The above description is only the preferred embodiments of the present application and is not used to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for monitoring the health status of subway trains, characterized in that, include: Periodically acquire track position information on the opposite side of the subway train to determine track smoothness, and acquire track stress data on the opposite side of the subway train in real time to determine track stress distribution characteristic values; The load-bearing capacity characterization coefficient of the opposite track of the subway train is calculated based on the track smoothness and the track stress distribution characteristic values, including: The ratio of the track smoothness to the reference track smoothness is determined as the smoothness influence factor. The ratio of the characteristic value of track stress distribution to the characteristic value of the reference track stress distribution is determined as the characteristic value influence factor. The weighted sum of the smoothness influence factor and the eigenvalue influence factor is determined to be the carrying capacity characterization coefficient; By combining historical data with the track bearing capacity characteristic coefficients, a variation coefficient is determined to ascertain whether the track exhibits any abnormal tendencies and to determine the appropriate handling method. If there are no abnormal trends on the track, continue monitoring of the track opposite the subway train. If there is an abnormal tendency in the track, the moving load of the subway train is determined, and the derailment risk factor is calculated in conjunction with the track smoothness, including... The ratio of the moving load of a subway train to the benchmark moving load of a subway train is determined as the moving load influence factor. The weighted sum of the ride comfort factor and the moving load factor is determined to be the derailment risk coefficient. Determine the derailment warning status of the subway train and decide whether it is necessary to stop the train. If the derailment risk coefficient is greater than the preset derailment risk coefficient threshold, then it is determined that the subway train needs to be stopped. If the derailment risk coefficient is less than or equal to a preset derailment risk coefficient threshold, then the risk point is located, the distance between the risk point and the subway train is determined, and the subway train speed needs to be adjusted, including... The time it takes for the subway train to reach the risk point is determined based on the travel distance and the subway train's operating speed. If the stated duration is greater than or equal to the maintenance duration, then the subway train operating speed must be maintained. If the specified duration is less than the maintenance duration, the subway train speed needs to be reduced. The location information includes both vertical and horizontal positions.

2. The method for monitoring the health status of subway trains according to claim 1, characterized in that, The process of determining track smoothness includes: The subway train's opposite track is divided into several track segments; The difference in track height between the two sides and the difference in horizontal track distance between each track segment are determined based on the location information. The average of the sum of the height difference between the two sides of the track and the distance difference between the horizontal track is determined as the track smoothness.

3. The method for monitoring the health status of subway trains according to claim 2, characterized in that, The process of determining the characteristic values ​​of track stress distribution includes, Determine the stress distribution of each track segment; Calculate the stress discrete value of the track segment based on the stress distribution; The stress discrete value is determined to be a characteristic value of the track stress distribution.

4. The method for monitoring the health status of subway trains according to claim 1, characterized in that, The process of determining the variation coefficient by combining the historical track bearing capacity characterization coefficient includes: The ratio of the load-bearing characterization coefficient to the historical track load-bearing characterization coefficient is determined as the variation coefficient; The historical track bearing capacity characterization coefficient is the average value of several historical track bearing capacity characterization coefficients.

5. The method for monitoring the health status of subway trains according to claim 1, characterized in that, The determination of whether the trajectory exhibits an abnormal tendency and the determination of the handling method are as follows: If the change coefficient is greater than the preset change coefficient threshold, the track has an abnormal tendency. The moving load of the subway train is determined, and the derailment risk coefficient is calculated in combination with the track smoothness to determine the derailment warning status of the subway train and to determine whether the subway train needs to be stopped. If the change coefficient is less than or equal to the preset change coefficient threshold, then there is no abnormal tendency in the track, and the continuous monitoring of the track opposite the subway train is maintained.

6. The method for monitoring the health status of subway trains according to claim 1, characterized in that, The process of determining the moving load of the subway train includes... Predict the heavy load of subway trains on each track section based on historical data; Based on historical data, time-domain curves of the operating load of each track section during subway train operation were plotted. The mean variance of the values ​​corresponding to each point on the time-domain curve of the operating load is determined as the operating load. The sum of the heavy load and the running load is determined as the moving load of the subway train.

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