Classification method for monitoring data of train-induced force response on high-speed railway bridges

Abstract

The embodiment of the application provides a high-speed railway bridge train actuating force response monitoring data classification method, which comprises the following steps: obtaining monitoring data of a first acceleration monitoring point and a second acceleration monitoring point; determining a marking point P s at which a train arrives at the first acceleration monitoring point or the second acceleration monitoring point and a marking point P e at which the train departs from the first acceleration monitoring point or the second acceleration monitoring point according to the monitoring data of the first acceleration monitoring point and the second acceleration monitoring point; determining a relative speed of the train, a running direction of the train and a marshalling number of the train according to the first acceleration monitoring data and the second acceleration monitoring data; and dividing the monitoring data into different categories according to the relative speed of the train, the running direction of the train and the marshalling number of the train determined in the above steps. The application realizes simplification of a train actuating force response monitoring data extraction process and improvement of train actuating force response monitoring data classification efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge structure monitoring, and in particular relates to a method for classifying monitoring data of train actuation force response on a high-speed railway bridge. Background Art

[0002] When a train passes a railway bridge, the train's passage will cause forced vibration of the bridge structure, which can significantly improve the signal-to-noise ratio of vibration monitoring data. The dynamic response monitoring data of the bridge structure under train excitation is usually used to evaluate the health status of the bridge structure, which can improve the accuracy and reliability of the evaluation results.

[0003] However, factors such as the train's number of trains, running speed and running direction will affect the load acting on the bridge.

[0004] Therefore, it is necessary to study the classification and extraction technology of vehicle-induced dynamic response monitoring data of high-speed railway bridge structures, classify and extract the dynamic response monitoring data of high-speed railway bridge structures under train excitation, and avoid the impact of changes in monitoring data type on damage identification results.

[0005] Related technologies use fuzzy clustering to classify vehicle-induced force response monitoring data from high-speed railway bridge structures. These methods combine time series analysis to extract data features and then determine the sample's category based on the membership of the cluster center based on these features. However, this method's time series model parameter selection process is complex, and the fuzzy clustering analysis process is computationally intensive, making it difficult to apply to real-time online analysis of health monitoring system data. Summary of the Invention

[0006] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, an embodiment of the present invention proposes a method for classifying monitoring data of actuating force response of a high-speed railway bridge train.

[0008] The high-speed railway bridge is provided with a first acceleration monitoring point and a second acceleration monitoring point;

[0009] The method for classifying high-speed railway bridge train actuation force response monitoring data according to an embodiment of the present invention comprises the following steps:

[0010] Step S100: Acquire monitoring data of the first acceleration monitoring point and the second acceleration monitoring point,

[0011] Determine the marking point P where the train arrives at the first acceleration monitoring point or the second acceleration monitoring point according to the monitoring data of the first acceleration monitoring point and the second acceleration monitoring point. s , and the marking point P where the train leaves the first acceleration monitoring point or the second acceleration monitoring pointe ;

[0012] Step S200: determining the relative speed of the train according to the first acceleration monitoring data and the second acceleration monitoring data;

[0013] Step S300: determining a running direction of the train according to the first acceleration monitoring data and the second acceleration monitoring data;

[0014] Step S400: determining the number of train formations according to the first acceleration monitoring data and the second acceleration monitoring data;

[0015] Step S500: Divide the monitoring data into different categories according to the relative speed, running direction and number of trains determined in step S200, step S300 and step S400.

[0016] In some embodiments, the monitoring data of the first acceleration monitoring point is the first acceleration monitoring data, and the marking point P in the first acceleration monitoring data is s Marked as point P s1 , the marker point P in the first acceleration monitoring data e Marked as point P e1 , get the marked point P s1 and the marked point P e1 The method comprises the following steps:

[0017] Step S111: pre-processing the first acceleration monitoring data to remove the data mean of the first acceleration monitoring data;

[0018] Step S112: Determine the maximum amplitude a of the first acceleration monitoring data of the first acceleration monitoring point when no train passes. max , will na max As the threshold, where n is the amplification factor.

[0019] Step S113: All amplitudes selected from the first acceleration monitoring data of the train passing through exceed na max The data points are recorded as set A;

[0020] Step S114: Obtain the marker point P from the data points in set A s1 , the marking point P s1 The amplitude of all the first acceleration monitoring data in the first 1s is less than na max , and the amplitudes of all first acceleration monitoring data within the next 1s are greater than na max data points;

[0021] Step S115: Obtain the marker point P from the data points in set A e1 , the marking point P e1 The amplitude of all the first acceleration monitoring data in the first 1s is greater than na max , and the amplitudes of all first acceleration monitoring data within the next 1s are less than na max data points;

[0022] Step S116: Mark the point P s1 and the marked point P e1 The corresponding time is recorded as time point t s1 and time point t e1 , the time point t s1 The time point t is the moment when the train arrives at the first acceleration monitoring point. e1 is the moment when the train leaves the first acceleration monitoring point.

[0023] In some embodiments, the monitoring data of the second acceleration monitoring point is the second acceleration monitoring data, and the marking point P in the second acceleration monitoring data is s Marked as point P s2 , the marker point P in the second acceleration monitoring data e Marked as point P e2 , get the marked point P s2 and the marked point P e2 The method comprises the following steps:

[0024] Step S121: pre-processing the second acceleration monitoring data to remove the data mean of the second acceleration monitoring data;

[0025] Step S122: Determine the maximum amplitude a2 of the second acceleration monitoring data of the second acceleration monitoring point when no train passes. max , na2 max As the threshold, where n is the amplification factor.

[0026] Step S123: All amplitudes selected from the second acceleration monitoring data of the train passing through exceed na2 max The data points are recorded as set A;

[0027] Step S124: Obtain the marker point P from the data points in set A s2 , the marking point P s2 The amplitude of all the second acceleration monitoring data in the first 1s is less than na2 max , and the amplitude of all the second acceleration monitoring data within 1s thereafter is greater than na2 max data points;

[0028] Step S124: Obtain the marker point P from the data points in set A e2 , the marking point P e2 The amplitude of all the second acceleration monitoring data in the first 1s is greater than na2 max , and the amplitude of all the second acceleration monitoring data within 1s is less than na2 max data points;

[0029] Step S126: Mark the point P s2 and the marked point P e2 The corresponding time is recorded as time point t s2 and time point t e2 , the time point t s2 The time point t is the moment when the train arrives at the second acceleration monitoring point. e2 is the moment when the train leaves the second acceleration monitoring point.

[0030] In some embodiments, the method for determining the relative speed of the train in step S200 includes the following steps:

[0031] Step S210: obtaining a distance D between the first acceleration monitoring point and the second acceleration monitoring point;

[0032] Step S220: Obtaining a time difference between when the train arrives at the first acceleration monitoring point and when the train leaves the first acceleration monitoring point and when the train leaves the second acceleration monitoring point.

[0033] Step S230: Calculate the relative speed of the train based on the distance D and the time difference

[0034] In some embodiments, the method for determining the number of train sets in step S400 includes the following steps:

[0035] Step S410: obtaining a distance D between the first acceleration monitoring point and the second acceleration monitoring point;

[0036] Step S420: Obtain the train length L;

[0037] Step S420: obtaining a time difference between when the train arrives at the first acceleration monitoring point and when it leaves the first acceleration monitoring point, or a time difference between when the train arrives at the second acceleration monitoring point and when it leaves the second acceleration monitoring point;

[0038] Step S440: Calculate the ratio H of the train length L to the distance D between the first acceleration monitoring point and the second acceleration monitoring point, and then obtain the number of train formations, where H=L / D.

[0039] In some embodiments, the distance D between the first acceleration monitoring point and the second acceleration monitoring point is calculated using the following formula:

[0040]

[0041] Where, Δt C The time difference between the train arriving at the first acceleration monitoring point and the second acceleration monitoring point or leaving the first acceleration monitoring point and the second acceleration monitoring point, Δt c =|t s1 -t s2 |=|t e1 -t e2 |.

[0042] In some embodiments, the train length L is calculated using the following formula:

[0043]

[0044] in, Indicates the relative speed of the train; Δt d Δt represents the time difference between the train arriving at the first acceleration monitoring point and leaving the first acceleration monitoring point, or the time difference between the train arriving at the second acceleration monitoring point and leaving the second acceleration monitoring point. d =|t s1 -t e1 |=|t s2 -t e2 |.

[0045] In some embodiments, the ratio H in step S440 is calculated using the following formula:

[0046]

[0047] Indicates the relative speed of the train; Δt C The time difference between the train arriving at the first acceleration monitoring point and the second acceleration monitoring point or leaving the first acceleration monitoring point and the second acceleration monitoring point, Δt c =|t s1 -t s2 |=||t e1 -|t e2 |;

[0048] Δt d Δt represents the time difference between the train arriving at the first acceleration monitoring point and leaving the first acceleration monitoring point, or the time difference between the train arriving at the second acceleration monitoring point and leaving the second acceleration monitoring point. d=|t s1 -t e1 |=|t s2 -t e2 |.

[0049] In some embodiments, in step S300, the direction of travel of the train is determined based on the order in which the train passes through the first acceleration monitoring point and the second acceleration monitoring point.

[0050] In some embodiments, step S500 includes the following steps:

[0051] Step S510: dividing the vehicle actuation force response monitoring data into different categories according to the number of identified train formations;

[0052] Step S520: classifying the vehicle actuating force response monitoring data into different categories according to the identified train running direction;

[0053] Step S530: Divide the vehicle actuating force response monitoring data into different categories according to the identified relative train speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a block diagram of a method for classifying monitoring data of actuating force response of a high-speed railway bridge train according to an embodiment of the present invention;

[0055] Figure 2 1. It is a layout diagram of monitoring points in a method for classifying monitoring data of actuating force response of a high-speed railway bridge train according to an embodiment of the present invention;

[0056] FIG3(a) and FIG3(b) are diagrams of monitoring data of monitoring points in the method for classifying monitoring data of actuating force response of a high-speed railway bridge train according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0058] The following describes a method for classifying high-speed railway bridge train actuation force response monitoring data according to an embodiment of the present invention with reference to the accompanying drawings.

[0059] In the high-speed railway bridge train actuation force response monitoring data classification method according to an embodiment of the present invention, a first acceleration monitoring point and a second acceleration monitoring point are arranged on the high-speed railway bridge.

[0060] like Figure 1 As shown, the high-speed railway bridge train actuation force response monitoring data classification method of the embodiment of the present invention includes the following steps:

[0061] Step S100: Acquire monitoring data of the first acceleration monitoring point and the second acceleration monitoring point,

[0062] Determine the marking point P where the train arrives at the first acceleration monitoring point or the second acceleration monitoring point according to the monitoring data of the first acceleration monitoring point and the second acceleration monitoring point s , and the marking point P where the train leaves the first acceleration monitoring point or the second acceleration monitoring point e ,

[0063] The monitoring data of the first acceleration monitoring point is the first acceleration monitoring data, and the monitoring data of the second acceleration monitoring point is the second acceleration monitoring data;

[0064] Step S200: determining the relative speed of the train according to the first acceleration monitoring data and the second acceleration monitoring data;

[0065] Step S300: determining the running direction of the train according to the first acceleration monitoring data and the second acceleration monitoring data;

[0066] Step S400: determining the number of train formations according to the first acceleration monitoring data and the second acceleration monitoring data;

[0067] Step S500: Divide the monitoring data into different categories according to the relative speed, running direction and number of trains determined in steps S200, S300 and S400.

[0068] The method for classifying monitoring data of train actuation force response on a high-speed railway bridge according to an embodiment of the present invention arranges two acceleration monitoring points, such as a first acceleration monitoring point and a second acceleration monitoring point, at a certain distance on the high-speed railway bridge.

[0069] Then, the marker point P is extracted from the monitoring data collected at the first acceleration monitoring point and the second acceleration monitoring point. s and the marker point P e , and the marker point P s and the marker point P e At the corresponding time point, the relative speed, running direction and number of trains are calculated, and the train-induced force response monitoring data are divided into different categories according to the relative speed, running direction and number of trains, thereby realizing effective online classification of the bridge structure dynamic response monitoring data caused by the train.

[0070] The following further describes the high-speed railway bridge train actuation force response monitoring data classification method according to an embodiment of the present invention with reference to the accompanying drawings.

[0071] In the high-speed railway bridge train actuation force response monitoring data classification method according to an embodiment of the present invention, a first acceleration monitoring point and a second acceleration monitoring point are arranged on the high-speed railway bridge.

[0072] Optionally, in order to effectively collect acceleration data at the first acceleration monitoring point and the second acceleration monitoring point, high-precision acceleration sensors should be respectively provided at the first acceleration monitoring point and the second acceleration monitoring point.

[0073] As shown in the figure, the method for classifying high-speed railway bridge train actuation force response monitoring data according to an embodiment of the present invention includes the following steps:

[0074] Step S100: Acquire monitoring data of the first acceleration monitoring point and the second acceleration monitoring point,

[0075] Determine the marking point P where the train arrives at the first acceleration monitoring point or the second acceleration monitoring point according to the monitoring data of the first acceleration monitoring point and the second acceleration monitoring point s , and the marking point P where the train leaves the first acceleration monitoring point or the second acceleration monitoring point e ,

[0076] The monitoring data of the first acceleration monitoring point is the first acceleration monitoring data, and the monitoring data of the second acceleration monitoring point is the second acceleration monitoring data;

[0077] Step S200: determining the relative speed of the train according to the first acceleration monitoring data and the second acceleration monitoring data;

[0078] Step S300: determining the running direction of the train according to the first acceleration monitoring data and the second acceleration monitoring data;

[0079] Step S400: determining the number of train formations according to the first acceleration monitoring data and the second acceleration monitoring data;

[0080] Step S500: Divide the monitoring data into different categories according to the relative speed, running direction and number of trains determined in steps S200, S300 and S400.

[0081] Unlike highway bridges, train tracks on high-speed railway bridges are fixed, train formations are typically fixed at 8 or 16 cars, and they typically operate within a specific speed range. This background provides favorable conditions for the data classification method for monitoring the actuating force response of trains on high-speed railway bridges in the embodiments of the present invention.

[0082] In other words, the relatively fixed train track, the relatively fixed number of train formations, and the relatively fixed train speed range greatly improve the accuracy of the high-speed railway bridge train actuation force response monitoring data classification method of the embodiment of the present invention.

[0083] The method for classifying monitoring data of a train actuating force response on a high-speed railway bridge according to an embodiment of the present invention arranges two acceleration monitoring points at a certain distance on the high-speed railway bridge, and extracts a marker point P from the monitoring data collected at the first acceleration monitoring point and the second acceleration monitoring point. s and the marker point P e , and the marker point P s and the marker point P e At the corresponding time point, the relative speed, running direction and number of trains are calculated, and the train-induced force response monitoring data are divided into different categories according to the relative speed, running direction and number of trains, thereby realizing effective online classification of the bridge structure dynamic response monitoring data caused by the train.

[0084] In some embodiments, the marker point P in the first acceleration monitoring data is s Marked as point P s1 , the marker point P in the first acceleration monitoring data e Marked as point P e1 , get the marker point P s1 and the marker point P e1 The method comprises the following steps:

[0085] Step S111: pre-processing the first acceleration monitoring data, and removing the data mean of the first acceleration monitoring data;

[0086] Step S112: Determine the maximum amplitude a of the first acceleration monitoring data of the first acceleration monitoring point when no train passes. max , will na max As the threshold, where n is the amplification factor.

[0087] Step S113: All amplitudes selected from the first acceleration monitoring data of the train passing through exceed na max The data points are recorded as set A;

[0088] Step S114: Obtain the marker point P from the data points in set A s1 , mark point P s1 The amplitude of all the first acceleration monitoring data in the first 1s is less than na max , and the amplitudes of all first acceleration monitoring data within the next 1s are greater than na max data points;

[0089] Step S115: Obtain the marker point P from the data points in set A e1 , mark point P e1 The amplitude of all the first acceleration monitoring data in the first 1s is greater than na max , and the amplitudes of all first acceleration monitoring data within the next 1s are less than na max data points;

[0090] Step S116: Mark point P s1 and the marker point P e1 The corresponding time is recorded as time point t s1 and time point t e1 , time point t s1 is the moment when the train arrives at the first acceleration monitoring point, time point t e1 The moment when the train leaves the first acceleration monitoring point.

[0091] Furthermore, the marker point P in the second acceleration monitoring data is s Marked as point P s2 , the mark point P in the second acceleration monitoring data e Marked as point P e2 , get the marker point P s2 and the marker point P e2 The method comprises the following steps:

[0092] Step S121: pre-processing the second acceleration monitoring data, and removing the data mean of the second acceleration monitoring data;

[0093] Step S122: Determine the maximum amplitude a2 of the second acceleration monitoring data of the second acceleration monitoring point when no train passes. max , na2 max As the threshold, where n is the amplification factor.

[0094] Step S123: All amplitudes selected from the second acceleration monitoring data of the train passing through exceed na2 max The data points are recorded as set A;

[0095] Step S124: Obtain the marker point P from the data points in set A s2 , mark point P s2 The amplitude of all the second acceleration monitoring data in the first 1s is less than na2 max , and the amplitude of all the second acceleration monitoring data within 1s thereafter is greater than na2 max data points;

[0096] Step S125: Obtain the marker point P from the data points in set A e2 , mark point Pe2 The amplitude of all the second acceleration monitoring data in the first 1s is greater than na2 max , and the amplitude of all the second acceleration monitoring data within 1s is less than na2 max data points;

[0097] Step S126: Mark point P s2 and the marker point P e2 The corresponding time is recorded as time point t s2 and time point t e2 , time point t s2 is the moment when the train arrives at the second acceleration monitoring point, time point t e2 The moment when the train leaves the second acceleration monitoring point.

[0098] In some embodiments, the method for determining the relative speed of the train in step S200 includes the following steps:

[0099] Step S210: obtaining a distance D between the first acceleration monitoring point and the second acceleration monitoring point;

[0100] Step S220: Obtaining a time difference between when the train arrives at the first acceleration monitoring point and when the train leaves the first acceleration monitoring point and when the train leaves the second acceleration monitoring point.

[0101] Step S230: Calculate the relative speed of the train based on the distance D and the time difference

[0102] Furthermore, the distance D between the first acceleration monitoring point and the second acceleration monitoring point is calculated by the following formula:

[0103]

[0104] Where, Δt c Indicates the time difference between the train arriving at the first acceleration monitoring point and the second acceleration monitoring point or leaving the first acceleration monitoring point and the second acceleration monitoring point, Δt c =|t s1 -t s2 |=|t e1 -t e2 |.

[0105] In some embodiments, the method for determining the number of train sets in step S400 includes the following steps:

[0106] Step S410: obtaining a distance D between a first acceleration monitoring point and a second acceleration monitoring point;

[0107] Step S420: Obtain the train length L;

[0108] Step S420: obtaining a time difference between when the train arrives at the first acceleration monitoring point and when it leaves the first acceleration monitoring point, or a time difference between when the train arrives at the second acceleration monitoring point and when it leaves the second acceleration monitoring point;

[0109] Step S440: Calculate the ratio H of the train length L to the distance D between the first acceleration monitoring point and the second acceleration monitoring point, and then calculate the number of train formations, where H=L / D.

[0110] Furthermore, the distance D between the first acceleration monitoring point and the second acceleration monitoring point is calculated by the following formula:

[0111]

[0112] Where, Δt C Indicates the time difference between the train arriving at the first acceleration monitoring point and the second acceleration monitoring point or leaving the first acceleration monitoring point and the second acceleration monitoring point, Δt c =|t s1 -t s2 |=|t e1 -t e2 |.

[0113] Furthermore, the train length L is calculated by the following formula:

[0114]

[0115] Where v represents the relative speed of the train; Δt d It represents the time difference between the train arriving at the first acceleration monitoring point and leaving the first acceleration monitoring point, or the time difference between the train arriving at the second acceleration monitoring point and leaving the second acceleration monitoring point, Δt d =|t s1 -t e1 |=|t s2 -t e2 |.

[0116] In some embodiments, the ratio H in step S440 is calculated using the following formula:

[0117]

[0118] Indicates the relative speed of the train; Δt C The time difference between the train arriving at the first acceleration monitoring point and the second acceleration monitoring point or leaving the first acceleration monitoring point and the second acceleration monitoring point, Δt c =|t s1 -t s2 |=|t e1 -te2 |;

[0119] Δt d Δt represents the time difference between the train arriving at the first acceleration monitoring point and leaving the first acceleration monitoring point, or the time difference between the train arriving at the second acceleration monitoring point and leaving the second acceleration monitoring point. d =|t s1 -t e1 |=|t s2 -t e2 |.

[0120] In some embodiments, in step S300 , the traveling direction of the train is determined based on the order in which the train passes through the first acceleration monitoring point and the second acceleration monitoring point.

[0121] In some embodiments, step S500 includes the following steps:

[0122] Step S510: Dividing the vehicle-actuated force response monitoring data into different categories according to the number of train formations;

[0123] Step S520: classifying the vehicle actuation force response monitoring data into different categories according to the running direction of the train;

[0124] Step S530: Divide the vehicle actuation force response monitoring data into different categories according to the relative speed of the train.

[0125] The high-speed railway bridge train actuation force response monitoring data classification method of the embodiment of the present invention sets a first acceleration monitoring point and a second acceleration monitoring point, and classifies the vehicle actuation force response monitoring data of the train passing through the high-speed railway bridge according to the number of train formations, the running direction of the train and the relative speed of the train obtained by monitoring. In this way, the vehicle actuation force response monitoring data divided according to the number of train formations, the running direction of the train and the relative speed of the train can be effectively obtained, so that the process of extracting the vehicle actuation force response monitoring data can be simplified and the classification efficiency of the vehicle actuation force response monitoring data can also be improved.

[0126] Therefore, the high-speed railway bridge train actuation force response monitoring data classification method of the embodiment of the present invention not only simplifies the vehicle actuation force response monitoring data extraction process, but also improves the vehicle actuation force response monitoring data classification efficiency.

[0127] The following combination Figure 2 3(a) and 3(b) further illustrate the high-speed railway bridge train actuation force response monitoring data classification method according to an embodiment of the present invention.

[0128] Figure 2Shown is the health monitoring system for the Yuxi River Bridge. The main bridge of the Yuxi River Bridge on the Shangqiu-Hefei-Hangzhou Railway features a (60+120+324+120+60)m twin-tower steel box truss cable-stayed bridge. It serves a double-track passenger-dedicated line, employing ballastless track and a design speed of 350 km / h. The bridge is 686 meters long, with a steel box truss main girder, reinforced concrete main towers, and double-plane cable-stayed cables.

[0129] The monitoring data of sensors 4-ZD03 and 14-ZD03 in the two time periods from 17:49:10 to 17:49:40 and from 18:33:20 to 18:33:50 on July 9, 2022 are shown in Figure 3.

[0130] First, a marking point is drawn on the time history curve of the monitoring data of the acceleration sensor. In this embodiment, the amplification factor n=10 is used.

[0131] In Figure 3(a), the left mark point of sensor 4-ZD03 corresponds to the time 17:49:28.845, and the right mark point corresponds to the time 17:49:31.803; the left mark point of sensor 14-ZD03 corresponds to the time 17:49:22.360, and the right mark point corresponds to the time 17:49:25.428.

[0132] Therefore, the time difference between the train arriving at the two sensors can be expressed as |t s1 -t s2 | Approximately, it is 6.485s.

[0133] The speed of this train is about 296.44 km / H (534 / 6.485×3.6=296.44).

[0134] Furthermore, since the train arrived at sensor 4 later than sensor 14, the train's direction of travel was from sensor 14 to sensor 4, meaning from Hangzhou to Shangqiu. The distance between sensors 4-ZD03 and 14-ZD03 is 534 meters. For an 8-car train, the ratio H of the train length to the distance between the two sensors is approximately 0.37 (200 / 534 ≈ 0.37). For a 16-car train, the ratio H is approximately 0.75 (400 / 534 ≈ 0.75). For sensor 4-ZD03, the train took 2.958 seconds to pass the sensor, resulting in a ratio H of 0.46, which is closer to 0.37. For sensor 14-ZD03, the train took 3.068 seconds to pass the sensor, resulting in a ratio H of 0.47, which is closer to 0.37. Therefore, this train is considered an 8-car train.

[0135] In Figure 3(b), the left mark point of sensor 4-ZD03 corresponds to the time 18:33:30.436, and the right mark point corresponds to the time 18:33:36.602; the left mark point of sensor 14-ZD03 corresponds to the time 18:33:37.190, and the right mark point corresponds to the time 18:33:42.621.

[0136] Therefore, the time difference between the train passing the two sensors is approximately 6.754 s, and the speed of this train is about 284.63 km / H (534 / 6.754×3.6=284.63).

[0137] Furthermore, since the train reached sensor 4 earlier than sensor 14, the train's direction of travel was from sensor 4 to sensor 14, that is, from Shangqiu to Hangzhou. For sensor 4-ZD03, the train took 6.166 seconds to pass the sensor, and the ratio H was 0.91, which is closer to 0.75. For sensor 14-ZD03, the train took 5.431 seconds to pass the sensor, and the ratio H was 0.80, which is closer to 0.75.

[0138] Therefore, it is judged that this train consists of 16 carriages.

[0139] Based on the above theoretical analysis, a classification and extraction program for vehicle-induced force response monitoring data was developed and applied to the online analysis of the Yuxi River Bridge health monitoring system. Observation of sample monitoring data from the Yuxi River Bridge health monitoring system indicates that the girder acceleration vibration process caused by train crossing the bridge does not exceed 30 seconds. Therefore, the program runs every 30 seconds, capturing the monitoring data within the previous 60 seconds for analysis each time to ensure that the girder acceleration vibration process caused by the train crossing the bridge is fully captured. The intercepted monitoring data is analyzed by first identifying the monitoring data markers. The markers are then used to identify the train's relative speed, direction of travel, and number of trains. The monitoring data is then classified into different categories based on the identification results.

[0140] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0141] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0142] In the present invention, unless otherwise specified or limited, the terms "installed," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0143] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0144] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0145] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for classifying monitoring data of actuating force response of a high-speed railway bridge train, characterized in that: The high-speed railway bridge is provided with a first acceleration monitoring point and a second acceleration monitoring point; The high-speed railway bridge train actuation force response monitoring data classification method comprises the following steps: Step S100: Acquire monitoring data of the first acceleration monitoring point and the second acceleration monitoring point, Determine the marking point P where the train arrives at the first acceleration monitoring point or the second acceleration monitoring point according to the monitoring data of the first acceleration monitoring point and the second acceleration monitoring point. s , and the marking point P where the train leaves the first acceleration monitoring point or the second acceleration monitoring point e ; Step S200: Determine the relative speed of the train based on the first acceleration monitoring data and the second acceleration monitoring data. The method for determining the relative speed of the train in step S200 includes the following steps: Step S210: obtaining a distance D between the first acceleration monitoring point and the second acceleration monitoring point; Step S220: obtaining a time difference between when the train arrives at the first acceleration monitoring point and when the train leaves the first acceleration monitoring point and when the train leaves the second acceleration monitoring point; Step S230: Calculate the relative speed of the train based on the distance D and the time difference Step S300: determining the running direction of the train based on the first acceleration monitoring data and the second acceleration monitoring data; in step S300, the running direction of the train is determined based on the order in which the train passes through the first acceleration monitoring point and the second acceleration monitoring point; Step S400: Determine the number of train sets based on the first acceleration monitoring data and the second acceleration monitoring data; the method for determining the number of train sets in step S400 includes the following steps: Step S410: obtaining a distance D between the first acceleration monitoring point and the second acceleration monitoring point; Step S420: Obtain the train length L; Step S420: obtaining a time difference between when the train arrives at the first acceleration monitoring point and when it leaves the first acceleration monitoring point, or a time difference between when the train arrives at the second acceleration monitoring point and when it leaves the second acceleration monitoring point; Step S440: Calculate the ratio H of the train length L to the distance D between the first acceleration monitoring point and the second acceleration monitoring point, and then calculate the number of train formations, where H=L / D; Step S500: Divide the monitoring data into different categories according to the relative speed, running direction and number of trains determined in step S200, step S300 and step S400.

2. The high-speed railway bridge train actuation force response monitoring data classification method according to claim 1 is characterized in that: The monitoring data of the first acceleration monitoring point is the first acceleration monitoring data. s Marked as point P s1 , the marker point P in the first acceleration monitoring data e Marked as point P e1 , get the marked point P s1 and the marked point P e1 The method comprises the following steps: Step S111: pre-processing the first acceleration monitoring data to remove the data mean of the first acceleration monitoring data; Step S112: Determine the maximum amplitude a of the first acceleration monitoring data of the first acceleration monitoring point when no train passes. max , will na max As the threshold, where n is the amplification factor; Step S113: All amplitudes selected from the first acceleration monitoring data of the train passing through exceed na max The data points are recorded as set A; Step S114: Obtain the marker point P from the data points in set A s1 , the marking point P s1 The amplitude of all the first acceleration monitoring data in the first 1s is less than na max , and the amplitudes of all first acceleration monitoring data within the next 1s are greater than na max data points; Step S115: Obtain the marker point P from the data points in set A e1 , the marking point P e1 The amplitude of all the first acceleration monitoring data in the first 1s is greater than na max , and the amplitudes of all first acceleration monitoring data within the next 1s are less than na max data points; Step S116: Mark the point P s1 and the marked point P e1 The corresponding time is recorded as time point t s1 and time point t e1 , the time point t s1 The time point t is the moment when the train arrives at the first acceleration monitoring point. e1 is the moment when the train leaves the first acceleration monitoring point.

3. The high-speed railway bridge train actuation force response monitoring data classification method according to claim 2 is characterized in that: The monitoring data of the second acceleration monitoring point is the second acceleration monitoring data. s Marked as point P s2 , the marker point P in the second acceleration monitoring data e Marked as point P e2 , get the marked point P s2 and the marked point P e2 The method comprises the following steps: Step S121: pre-processing the second acceleration monitoring data to remove the data mean of the second acceleration monitoring data; Step S122: Determine the maximum amplitude a2 of the second acceleration monitoring data of the second acceleration monitoring point when no train passes. max , na2 max As the threshold, where n is the amplification factor; Step S123: All amplitudes selected from the second acceleration monitoring data of the train passing through exceed na2 max The data points are recorded as set A; Step S124: Obtain the marker point P from the data points in set A s2 , the marking point P s2 The amplitude of all the second acceleration monitoring data in the first 1s is less than na2 max , and the amplitude of all the second acceleration monitoring data within 1s thereafter is greater than na2 max data points; Step S124: Obtain the marker point P from the data points in set A e2 , the marking point P e2 The amplitude of all the second acceleration monitoring data in the first 1s is greater than na2 max , and the amplitude of all the second acceleration monitoring data within 1s is less than na2 max data points; Step S126: Mark the point P s2 and the marked point P e2 The corresponding time is recorded as time point t s2 and time point t e2 , the time point t s2 The time point t is the moment when the train arrives at the second acceleration monitoring point. e2 is the moment when the train leaves the second acceleration monitoring point.

4. The high-speed railway bridge train actuation force response monitoring data classification method according to claim 3 is characterized in that: The distance D between the first acceleration monitoring point and the second acceleration monitoring point is calculated by the following formula: Where Δt c The time difference between the train arriving at the first acceleration monitoring point and the second acceleration monitoring point or leaving the first acceleration monitoring point and the second acceleration monitoring point, Δt c =|t s1 -t s2 |=|t e1 -t e2 |.

5. The high-speed railway bridge train actuation force response monitoring data classification method according to claim 3 is characterized in that: The train length L is calculated by the following formula: in, Indicates the relative speed of the train; Δt d Δt represents the time difference between the train arriving at the first acceleration monitoring point and leaving the first acceleration monitoring point, or the time difference between the train arriving at the second acceleration monitoring point and leaving the second acceleration monitoring point. d =|t s1 -t e1 |=|t s2 -t e2 |.

6. The high-speed railway bridge train actuation force response monitoring data classification method according to claim 5 is characterized in that: The ratio H in step S440 is calculated by the following formula: Indicates the relative speed of the train; Δt C The time difference between the train arriving at the first acceleration monitoring point and the second acceleration monitoring point or leaving the first acceleration monitoring point and the second acceleration monitoring point, Δt c =|t s1 -t s2 |=|t e1 -t e2 |; Δt d Δt represents the time difference between the train arriving at the first acceleration monitoring point and leaving the first acceleration monitoring point, or the time difference between the train arriving at the second acceleration monitoring point and leaving the second acceleration monitoring point. d =|t s1 -t e1 |=|t s2 -t e2 |.

7. The high-speed railway bridge train actuation force response monitoring data classification method according to claim 1 is characterized in that: The step S500 includes the following steps: Step S510: dividing the vehicle actuation force response monitoring data into different categories according to the number of identified train formations; Step S520: classifying the vehicle actuating force response monitoring data into different categories according to the identified train running direction; Step S530: Divide the vehicle actuating force response monitoring data into different categories according to the identified relative train speed.

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