Method and device for identifying track roughness and determining maintenance standards in railway hubs

Abstract

The present invention discloses a method and device for identifying and determining the roughness of rail tracks in railway hubs, and relates to the field of railway track monitoring, including: collecting the unevenness spectrum data of rails of each line of railway hubs and the vehicle type and speed of each line of trains; collecting vibration and noise data of functional areas of railway hubs; constructing a mapping relationship between vibration and noise data and vehicle type and driving line under unit roughness excitation based on the unevenness spectrum data and the vehicle type and speed of each line of trains; converting real-time vibration and noise data into real-time track unevenness data based on the mapping relationship; determining the limit value data and maintenance standard of rail roughness of each wavelength according to vibration and noise related specifications; maintaining each line of tracks according to the limit value data, maintenance standard and track unevenness data, and performing track detection to update unevenness spectrum data. The present invention can realize dynamic identification of rail roughness of railway hubs, and improve vibration and noise problems when trains pass through railway hubs.

Description

Technical Field

[0001] The present invention belongs to the field of railway track monitoring, and in particular relates to a method and a device for identifying the roughness of a railway hub track and determining a maintenance standard. Background Art

[0002] With the rapid development of my country's high-speed rail construction, high-speed rail stations are gradually developing towards large-scale multifunctional railway hubs, and the vibration and noise in the functional areas of the station buildings are gradually attracting people's attention. Especially when the train passes through the railway hub at high speed, the vibration and noise in each area are relatively large, which seriously affects the comfort of waiting passengers and staff.

[0003] During the operation of railway hubs, trains accelerate and decelerate frequently, so this part of the track is more prone to damage and defects such as corrugation, side wear, and cracks, which may increase the roughness of the rails, thereby increasing structural vibration and noise. It is necessary to monitor and warn the roughness of the rails in real time, and determine the maintenance standards for the roughness of the rails in railway hubs. Since the vehicle-induced vibration caused by the unevenness of the rails is the main vibration source load of the railway hub, it is feasible to monitor the unevenness and rail roughness using the vibration and noise of the station functional area.

[0004] At present, the vibration and noise evaluation standards for railway hubs are not clear, and there is a lack of research and technology related to the monitoring and maintenance standards of rail roughness in railway hubs. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a method and device for identifying the roughness of railway hub tracks and determining maintenance standards to solve the problems existing in the above prior art.

[0006] To achieve the above object, the present invention provides a method for identifying track roughness and determining maintenance standards in a railway hub, comprising the following steps:

[0007] Collect the unevenness spectrum data of rail tracks on each track of the railway hub and the type and speed of trains on each track;

[0008] Collect vibration and noise data of railway hub functional areas through measuring devices;

[0009] Based on the roughness spectrum data and the vehicle type and speed of each line train, a mapping relationship between the vibration and noise data and the vehicle type and the driving line under unit roughness excitation is constructed;

[0010] Based on the mapping relationship, the real-time vibration and noise data are converted into real-time track irregularity data of corresponding wavelengths, and the irregularity changes of each track are identified and monitored;

[0011] According to the vibration and noise related specifications, determine the limit data and maintenance standards of rail roughness at each wavelength;

[0012] The tracks of each lane are maintained according to the limit data, maintenance standards and real-time track irregularity data, and track inspection is performed to update the irregularity spectrum data.

[0013] Preferably, the method for collecting the unevenness spectrum data and the type and speed of trains on each line includes:

[0014] Using detection tools to perform static or dynamic detection of track irregularity to obtain rail irregularity spectrum data of each track in the railway hub, the detection tools include track ruler, rail smoothness meter and track inspection vehicle;

[0015] Cameras or sensors are installed above the structural columns next to each train line in the railway hub to collect real-time data on the type and speed of trains running on each train line in the railway hub.

[0016] Preferably, the method for collecting vibration and noise data of a railway hub functional area includes:

[0017] For railway hub functional areas with densely populated areas or non-enclosed rooms, vibration accelerometers are used to measure vibration responses. The densely populated areas include waiting halls and platforms.

[0018] For independent room functional areas, sound pressure sensors are used to measure the indoor noise response of each measuring point. The independent room functional areas include business waiting areas, office areas and meeting rooms.

[0019] The vibration or noise data of the measuring points during the period when the train passes are intercepted, and the data of the measuring points close to the driving line are screened out from the intercepted data to calculate the vibration level or noise level of the functional area.

[0020] Preferably, the method for constructing the mapping relationship includes:

[0021] The spatial domain track irregularity data is processed by Fourier transform to obtain the irregularity frequency domain spectrum;

[0022] The vehicle speed data of the train is obtained according to the vehicle identification device, and the influence value of the uneven frequency domain spectrum on the vibration and noise response at the corresponding vehicle speed is calculated;

[0023] The energy average calculation is performed on the frequency domain response of the measuring points in each functional area of ​​the station building to remove the influence of the uneven frequency domain spectrum and obtain the measuring point response of each vehicle model under unit roughness excitation;

[0024] Based on the impact values ​​and measurement point responses, a machine learning method is used to establish a mapping relationship between the vibration and noise responses of each functional area and the vehicle line and vehicle model under unit roughness excitation.

[0025] Preferably, the frequency domain response of the measuring points in each functional area of ​​the station building satisfies the formula:

[0026]

[0027] Where a0 is the reference acceleration or sound pressure, r0 is the unit roughness, a is the vibration or sound pressure measurement point response under unit roughness excitation in 1 / 3 octave band, and a m is the measured value of structural vibration acceleration or sound pressure, r m It is the measured track irregularity spectrum.

[0028] Preferably, the method of converting into real-time track irregularity data of corresponding wavelength comprises:

[0029] Fourier transform is used to calculate the real-time frequency domain response of each functional area and perform energy averaging to obtain average data;

[0030] Determine the impact of vehicle type and driving route on the response of measuring points in each functional area based on the mapping relationship;

[0031] Based on the average data and the influence of vehicle type and driving line on the response of measuring points in each functional area, the real-time track roughness data is calculated by the train speed.

[0032] Preferably, the limit value data of rail roughness at each wavelength

[0033] The calculation formula for the limit data is:

[0034]

[0035] Where r limit is the rail roughness maintenance limit, a limit Structural vibration acceleration or sound pressure limit, a0 is the reference acceleration or sound pressure, r0 is the unit roughness, a m is the measured value of structural vibration acceleration or sound pressure, r m It is the measured track irregularity spectrum.

[0036] The present invention also provides a device for identifying the roughness of a railway hub track and determining a maintenance standard, comprising:

[0037] Data input module, used to input measured track irregularity data and applicable regulatory limits;

[0038] The collection and screening module is used to collect information on the type, speed and route of trains passing through the railway hub, as well as the vibration acceleration or sound pressure response of the measuring points in each functional area;

[0039] The data processing module is used to establish the mapping relationship between the vibration acceleration and sound pressure response of each functional area under unit roughness excitation and the vehicle type and driving line, identify and monitor the changes in track unevenness of each line, and determine the rail roughness limit data and maintenance standards for each wavelength;

[0040] A database module is used to store the mapping relationship between each vehicle line and vehicle model and the response of each measuring point;

[0041] The data judgment module is used to determine whether there is any abnormal change in track unevenness and whether the rail roughness exceeds the limit and requires maintenance.

[0042] Preferably, the collection and screening module includes a first collection unit, a second collection unit and a screening unit;

[0043] The first collection unit is used to collect real-time data on the types and speeds of trains running on each driving line of the railway hub;

[0044] The second acquisition unit is used to collect the vibration acceleration response of each measuring point in the railway hub functional area with dense personnel or non-enclosed rooms, and to collect the indoor sound pressure response of each measuring point in the functional area of ​​an independent room;

[0045] The screening unit is used to intercept the vibration or noise data of the measuring points during the period when a train passes by, and screen out the measuring points that are closer to the driving line to calculate the vibration level or noise level of the functional area.

[0046] Preferably, the data processing module includes a preliminary processing unit, an influence value calculation unit, a measurement point response acquisition unit, a mapping relationship construction unit, a roughness acquisition unit and a maintenance limit index calculation unit;

[0047] The preliminary processing unit is used to process the track irregularity data in the spatial domain by Fourier transform to obtain an irregularity frequency domain spectrum;

[0048] The influence value calculation unit is used to calculate the influence value of the uneven frequency domain spectrum on the vibration and noise response at the corresponding vehicle speed according to the vehicle speed data obtained by the vehicle identification device;

[0049] The measuring point response acquisition unit is used to perform energy average calculation on the frequency domain response of the measuring points in each functional area of ​​the station building, remove the influence caused by the uneven frequency domain spectrum, and obtain the measuring point response of each vehicle model under unit roughness excitation;

[0050] The mapping relationship building unit is used to use a machine learning method to establish a mapping relationship between the vibration and noise response of each functional area and the vehicle line and vehicle model under unit roughness excitation and store it in a database;

[0051] The roughness acquisition unit is used to calculate the frequency domain response of each functional area by Fourier transform and perform energy averaging, determine the influence of the vehicle type and the driving line on the response of the measuring point of each functional area according to the mapping relationship, and calculate the real-time roughness data of the track according to the speed of the train passing;

[0052] The maintenance limit index calculation unit is used to calculate the rail roughness maintenance limit index of each track according to the input specification limit and the response of each functional area.

[0053] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for identifying the roughness of a railway hub track and determining maintenance standards is implemented.

[0054] The present invention also provides a computer-readable storage medium, which stores a computer program for executing the method for identifying the roughness of railway hub tracks and determining maintenance standards.

[0055] Compared with the prior art, the present invention has the following advantages and technical effects:

[0056] 1. The present invention innovatively realizes the real-time monitoring of the roughness of rails in large railway hubs, and provides a reference for the rail maintenance standards of railway hubs.

[0057] 2. Compared with the existing track unevenness or disease detection technical solutions, the present invention can obtain the unevenness of the rails more timely and accurately, which is helpful for monitoring and timely maintenance of the railway status and improving work efficiency.

[0058] 3. The present invention combines the vibration and noise problems of railway hubs with rail status monitoring, realizes the monitoring of vibration comfort and noise of high-speed railway station buildings, and helps to improve the vibration and noise environment of railway hubs. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0060] Figure 1 This is a flow chart of a method for identifying the roughness of a railway hub track and determining maintenance standards according to an embodiment of the present invention;

[0061] Figure 2 This is a working principle diagram of a device for identifying track roughness and determining maintenance standards for a railway hub according to an embodiment of the present invention;

[0062] Figure 3 A schematic diagram of a computer device according to an embodiment of the present invention;

[0063] Figure 4 Schematic diagram of a device for identifying track roughness and determining maintenance standards for a railway hub according to an embodiment of the present invention;

[0064] Explanation of markings in the figure: 1. Track-bearing layer; 2. Platform; 3. Structural column; 4. Departure layer; 5. Vibration measuring point of departure layer; 6. Vibration measuring point of platform; 7. Vehicle type and speed recognition device. DETAILED DESCRIPTION

[0065] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0066] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0067] Embodiment 1

[0068] like Figure 1 As shown, this embodiment provides a method for identifying the roughness of a railway hub track and determining a maintenance standard, comprising the following steps:

[0069] Collect the unevenness spectrum data of rail tracks on each track of the railway hub and the type and speed of trains on each track;

[0070] Collect vibration and noise data of railway hub functional areas through measuring devices;

[0071] Based on the roughness spectrum data and the vehicle type and speed of each line train, a mapping relationship between the vibration and noise data and the vehicle type and the driving line under unit roughness excitation is constructed;

[0072] Based on the mapping relationship, the real-time vibration and noise data are converted into real-time track irregularity data of corresponding wavelengths, and the irregularity changes of each track are identified and monitored;

[0073] According to the vibration and noise related specifications, determine the limit data and maintenance standards of rail roughness at each wavelength;

[0074] The tracks of each lane are maintained according to the limit data, maintenance standards and track irregularity data, and track inspection is performed to update the irregularity spectrum data.

[0075] The specific steps are as follows:

[0076] S101: Obtain unevenness spectrum data collected by static or dynamic detection of each track;

[0077] In S101, the unevenness spectrum data of each track line collected by static or dynamic detection is obtained; in a specific embodiment, the unevenness spectrum data of each track line in the railway hub is obtained by using static or dynamic detection methods of track unevenness such as track rulers, rail smoothness meters or track inspection vehicles.

[0078] S102: obtaining data collected by a vehicle model and speed recognition system for each lane, wherein the recognition system is installed on a structural column 3 next to the lane;

[0079] In S102, data collected by the vehicle type and speed identification system of each lane is obtained, and the identification system is installed above the structural column 3 next to the lane; in a specific embodiment, the vehicle type and speed identification device 7 uses a camera to record and identify the vehicle type and running speed of the train passing through the railway hub.

[0080] S103: Obtaining vibration and noise data collected by measuring devices at various locations in the station building, wherein the measuring devices are installed in a railway hub functional area;

[0081] In S103, vibration and noise data collected by measuring devices at various positions in the station building are obtained, and the measuring devices are installed in the railway hub functional area; in a specific embodiment, for railway hub functional areas with dense crowds or non-enclosed rooms such as waiting halls and platform 2, vibration accelerometers are used to measure the vibration response of each measuring point, and the measuring points include the departure floor vibration measuring point 5 and the platform vibration measuring point 6.

[0082] Specifically, the arrangement of vibration measuring points shall meet the criterion of uniform distribution; further, for functional areas with larger building areas, it should be ensured that there are measuring points above each track; for functional areas located on the departure layer 4, arrival layer, etc., which are connected to the track-bearing layer 1 or platform 2 through structural columns 3, the measuring points should be evenly distributed with the top or bottom of the structural column as the center.

[0083] For independent room functional areas such as business waiting areas, office areas, and conference rooms, sound pressure sensors are used to measure the indoor noise response of each measuring point.

[0084] Specifically, the arrangement of the sound pressure measurement points should meet the uniform distribution criterion; further, the sound pressure measurement points should be arranged in the middle of the room as much as possible, and the height should be 1.5m from the ground.

[0085] The vibration or noise data of the measuring points during the period when trains pass are intercepted, and the measuring points close to the driving line are screened out. After excluding the measuring points far away, the vibration level or noise level of the functional area is calculated.

[0086] S104: establishing a mapping relationship between vibration and noise of each functional area and vehicle type and driving route under unit roughness excitation according to the collected roughness spectrum and running vehicle speed;

[0087] In S104, a mapping relationship between vibration and noise of each functional area and vehicle type and driving route under unit roughness excitation is established; in a specific embodiment, if the input irregularity data is spatial domain data, Fourier transform is required to process the spatial domain track irregularity to obtain an irregularity frequency domain spectrum; according to the vehicle speed data obtained by the vehicle identification device, the impact value of the irregularity spectrum on the vibration and noise response at the corresponding vehicle speed is calculated; the frequency domain response of the measuring points of each functional area of ​​the station building is averaged by energy calculation to remove the impact caused by the irregularity spectrum, and obtain the measuring point response of each vehicle type under unit roughness excitation;

[0088] Specifically, the response of the measuring points in each functional area of ​​the railway hub satisfies the formula:

[0089]

[0090] Where a0 is the reference acceleration or sound pressure, r0 is the unit roughness, a is the vibration or sound pressure measurement point response under unit roughness excitation in 1 / 3 octave band, and a m is the measured value of structural vibration acceleration or sound pressure, r m It is the measured track irregularity spectrum.

[0091] Furthermore, for the responses of the measuring points in each functional area, if the number of measuring points is large, the energy average value or the sum of the energy mean and three times the standard deviation can be used as the vibration or noise response of the functional area in a statistical concept; if the number of measuring points is small, the energy average value or the maximum value can be used as the vibration or noise response of the functional area.

[0092] In a specific embodiment, machine learning and other methods are used to establish a mapping relationship between the vibration and noise response of each functional area and the vehicle line and vehicle model under unit roughness excitation.

[0093] In this embodiment, the machine learning method adopts a supervised machine learning method, including:

[0094] The model and speed data of trains passing through are taken as variables, the vibration or noise response of each functional area at each frequency is taken as the dependent variable, and the data from the initial operation of the railway hub is used as the training set to train the support vector machine (SVM) model. The functional relationship between the response of vibration and noise of each functional area at each frequency under unit roughness excitation and the vehicle line and model is obtained.

[0095] S105: According to the mapping relationship, the real-time structural vibration and noise data are converted into track irregularity data of corresponding wavelengths, and the changes of track irregularity of each lane are identified and monitored;

[0096] In S105, the real-time structural vibration and noise data are converted into track irregularity data of corresponding wavelengths, and the changes in track irregularity of each line are identified and monitored. In a specific embodiment, Fourier transform is used to calculate the frequency domain response of the measuring points in each functional area and perform energy averaging. The influence of the vehicle type and the driving line on the response of the measuring points in each functional area is determined based on the mapping relationship. The real-time track roughness data is calculated based on the passing speed of the train.

[0097] Specifically, for the responses of the measuring points in each functional area, if the number of measuring points is large, the energy average value or the sum of the energy mean and three times the standard deviation can be used as the vibration or noise response of the functional area in a statistical concept; if the number of measuring points is small, the energy average value or the maximum value can be used as the vibration or noise response of the functional area.

[0098] S106: Determine the rail roughness limit data and maintenance standards for each wavelength according to the vibration and noise related specifications;

[0099] In S106, according to the vibration and noise related specifications, the rail roughness limit data and maintenance standards for each wavelength are determined; in a specific embodiment, according to the types of functional areas of the railway hub, the applicable specifications and vibration and noise limit standards are determined.

[0100] In this embodiment, since there are many trains passing through, they mainly operate during the day and passengers and employees stay for a long time, the daytime repetitive vibration evaluation standard is adopted. The use scenarios involving rest, administrative office and meeting, such as the executive VIP room and business waiting room, adopt the standards of residential or conference areas; the waiting hall, the mezzanine of the travel service, and the mixed office and commercial areas of the staff office area adopt the standards of commercial or office areas; platform 2 and the equipment room meet the standards of the most relaxed limits such as workshops.

[0101] In this embodiment, the specification limit is adopted, and the response of each functional area is comprehensively considered to calculate the rail roughness maintenance limit index of each track.

[0102] Specifically, the rail roughness maintenance limit index satisfies the calculation formula:

[0103]

[0104] Where a0 is the reference acceleration or sound pressure, r0 is the unit roughness, and a m is the measured value of structural vibration acceleration or sound pressure, r m is the measured track irregularity spectrum, r limit is the rail roughness maintenance limit, a limit Structural vibration acceleration or sound pressure limit.

[0105] S107: Maintain each track according to the maintenance standard, perform track inspection and update the unevenness spectrum.

[0106] In S107, each track is maintained according to the maintenance standard, and the track is inspected and the unevenness spectrum is updated. In a specific embodiment, when it is found that the vibration and noise of each functional area exceed the limit or the real-time rail unevenness shows abnormal values, the rail roughness maintenance limit value is used as the standard to grind, maintain or replace the rails of each track. The maintained rails are inspected to determine the track unevenness spectrum after maintenance, and the data is updated.

[0107] like Figure 2 As shown, the embodiment of the present invention also provides a device for identifying the roughness of a railway hub track and determining a maintenance standard, comprising:

[0108] Data input module, used to input measured track irregularity data and applicable regulatory limits;

[0109] The collection and screening module is used to collect information on the type, speed and route of trains passing through the railway hub, as well as the vibration acceleration or sound pressure response of the measuring points in each functional area;

[0110] Data processing module, used to establish the mapping relationship between vibration and noise of each functional area and vehicle type and driving line under unit roughness excitation, identify and monitor the changes of track unevenness of each line, and determine the limit value data and maintenance standards of rail roughness of each wavelength;

[0111] A database module is used to store the mapping relationship between each vehicle line and vehicle model and the response of each measuring point;

[0112] The data judgment module is used to determine whether there is any abnormal change in track unevenness and whether the rail roughness exceeds the limit and requires maintenance.

[0113] In this embodiment, the data input module inputs data of the rail irregularity spectrum of each line of the railway hub obtained by static or dynamic detection methods of track irregularity such as track gauge, rail smoothness meter or track inspection vehicle into the device;

[0114] Furthermore, the vibration and noise limit standards determined according to the types of functional areas in the railway hub are input into the device.

[0115] In this embodiment, the collection and screening module collects real-time data on the types and speeds of trains running on each driving line of the railway hub;

[0116] Furthermore, the vibration or noise data of the measuring points during the period when the train passes by are intercepted, and the measuring points close to the driving line are screened out to calculate the vibration level or noise level of the functional area.

[0117] In this embodiment, the data processing module uses Fourier transform to process track irregularities in the spatial domain to obtain an irregularity frequency domain spectrum; based on the vehicle speed data obtained by the vehicle identification device, the impact value of the irregularity spectrum on the vibration and noise response at the corresponding vehicle speed is calculated;

[0118] Furthermore, the energy average calculation is performed on the frequency domain response of the measuring points in each functional area of ​​the station building to remove the influence of the uneven spectrum and obtain the measuring point response of each vehicle model under unit roughness excitation. The mapping relationship between the vibration and noise response of each functional area and the vehicle line and vehicle model under unit roughness excitation is established by using machine learning and other methods and stored in the database.

[0119] Furthermore, Fourier transform is used to calculate the frequency domain response of each functional area and perform energy averaging. The influence of vehicle type and driving line on the response of measuring points in each functional area is determined according to the mapping relationship, and the real-time track roughness data is calculated according to the passing speed of the train. Based on the input specification limit, the response of each functional area is integrated to calculate the maintenance limit index of rail roughness of each line.

[0120] In this embodiment, the database module needs to store the vibration and noise response curves of each functional area under different vehicle lines and vehicle models;

[0121] Furthermore, the functional relationship between the vibration and noise response of each functional area and the vehicle line and vehicle model under unit roughness excitation is stored and updated with the input of new data.

[0122] In this embodiment, the data judgment module judges whether the track irregularity data of the entire band has changed according to the track irregularity limit value, and if so, issues an early warning of the abnormal change of the irregularity;

[0123] Furthermore, it is determined whether the full-band track unevenness data exceeds a given value of the evaluation standard. If so, a reminder for rail roughness maintenance is issued.

[0124] An embodiment of the present invention also provides an embodiment of a computer device for implementing all or part of the contents of the above-mentioned method for identifying the roughness of the railway hub track and determining the maintenance standard. The computer device specifically includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor performs the functions of identifying the roughness of the railway hub track and determining the maintenance standard.

[0125] Figure 3 Schematic diagram of computer equipment for railway hub track roughness identification and maintenance standard determination in this embodiment of the present application. The computer equipment may include a central processing unit, a manual input unit, an equipment import unit, a display unit, a data storage unit, a signal output unit, and a power unit. It is worth noting that Figure 3 It is exemplary; other types of structures may also be used to supplement or replace this structure to implement computing functions or other functions.

[0126] In this embodiment, the functions of track roughness identification and maintenance standard determination can be integrated into the central processing unit to realize the functions of each module in the above-mentioned railway hub track roughness identification and maintenance standard determination device.

[0127] The manual input device provides manual data input to the central processing unit. In this embodiment, the manual input device is a keyboard or touch screen input, which is used to input the specification limit values ​​related to vibration and noise.

[0128] The equipment introduction device provides collected data input to the central processing unit. In this embodiment, the equipment introduction device is a sensor or a camera or a track inspection vehicle, which is used to input the type of trains and track data passing through the station building, functional area vibration and noise data or track irregularity data.

[0129] The display device provides data visualization for the central processing unit. In this embodiment, the display device is a display screen for displaying real-time train passing conditions, functional area vibration and noise data, and track irregularity data.

[0130] The data storage device is used to store the application programs and functional programs of the central processor, and store the input train type and line data, functional area vibration and noise data or track irregularity data. The data storage device can be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc.

[0131] The signal output device is used for early warning of irregularity changes and rail roughness maintenance reminders. In this embodiment, the signal output device is a speaker, which gives a reminder when irregularity changes or exceeds the limit.

[0132] The power device provides power supply for the above devices.

[0133] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for implementing the above-mentioned functions of track roughness identification and maintenance standard determination.

[0134] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A method for identifying track roughness and determining maintenance standards for railway hubs, characterized in that: The following steps are involved: Collect the unevenness spectrum data of rail tracks on each track of the railway hub and the type and speed of trains on each track; Collect vibration and noise data of railway hub functional areas through measuring devices; Based on the roughness spectrum data and the vehicle type and speed of each line train, a mapping relationship between the vibration and noise data and the vehicle type and the driving line under unit roughness excitation is constructed; Based on the mapping relationship, the real-time vibration and noise data are converted into real-time track irregularity data of corresponding wavelengths, and the irregularity changes of each track are identified and monitored; According to the vibration and noise related specifications, determine the limit data and maintenance standards of rail roughness at each wavelength; The tracks of each lane are maintained according to the limit data, maintenance standards and real-time track irregularity data, and track inspection is performed to update the irregularity spectrum data.

2. The method for identifying the roughness of railway hub tracks and determining maintenance standards according to claim 1, characterized in that: The method for collecting the unevenness spectrum data and the vehicle type and speed of each line train includes: Using detection tools to perform static or dynamic detection of track irregularity to obtain rail irregularity spectrum data of each track in the railway hub, the detection tools include track ruler, rail smoothness meter and track inspection vehicle; Install cameras or sensors above the structural columns beside each lane of the railway hub to collect real-time data on the types and speeds of trains running on each lane of the railway hub; Methods for collecting vibration and noise data in railway hub functional areas include: For railway hub functional areas with densely populated areas or non-enclosed rooms, vibration accelerometers are used to measure vibration responses. The densely populated areas include waiting halls and platforms. For independent room functional areas, sound pressure sensors are used to measure the indoor noise response of each measuring point. The independent room functional areas include business waiting areas, office areas and meeting rooms. The vibration or noise data of the measuring points during the period when the train passes are intercepted, and the data of the measuring points close to the driving line are screened out from the intercepted data to calculate the vibration level or noise level of the functional area.

3. The method for identifying the roughness of railway hub tracks and determining maintenance standards according to claim 1, characterized in that: Methods for constructing mapping relationships include: The spatial domain track irregularity data is processed by Fourier transform to obtain the irregularity frequency domain spectrum; The vehicle speed data of the train is obtained according to the vehicle identification device, and the influence value of the uneven frequency domain spectrum on the vibration and noise response at the corresponding vehicle speed is calculated; The energy average calculation is performed on the frequency domain response of the measuring points in each functional area of ​​the station building to remove the influence of the uneven frequency domain spectrum and obtain the measuring point response of each vehicle model under unit roughness excitation; Based on the impact value and the measurement point response, a machine learning method is used to establish a mapping relationship between the vibration and noise response of each functional area and the vehicle line and vehicle model under unit roughness excitation; The frequency domain response of the measuring points in each functional area of ​​the station building satisfies the formula: Where a0 is the reference acceleration or sound pressure, r0 is the unit roughness, a is the vibration or sound pressure measurement point response under unit roughness excitation in 1 / 3 octave band, and a m is the measured value of structural vibration acceleration or sound pressure, r m It is the measured track irregularity spectrum.

4. The method for identifying the roughness of railway hub tracks and determining maintenance standards according to claim 1, characterized in that: Methods for converting to real-time track irregularity data of corresponding wavelength include: Fourier transform is used to calculate the real-time frequency domain response of each functional area and perform energy averaging to obtain average data; Determine the impact of vehicle type and driving route on the response of measuring points in each functional area based on the mapping relationship; Based on the average data and the influence of vehicle type and driving line on the response of measuring points in each functional area, the real-time track roughness data is calculated by the train speed.

5. The method for identifying the roughness of railway hub tracks and determining maintenance standards according to claim 1, characterized in that: Rail roughness limit data for each wavelength The calculation formula for the limit data is: Where r limit is the rail roughness maintenance limit, a limit Structural vibration acceleration or sound pressure limit, a0 is the reference acceleration or sound pressure, r0 is the unit roughness, a m is the measured value of structural vibration acceleration or sound pressure, r m It is the measured track irregularity spectrum.

6. A device for identifying track roughness and determining maintenance standards for railway hubs, characterized in that: include: Data input module, used to input measured track irregularity data and applicable regulatory limits; The collection and screening module is used to collect information on the type, speed and route of trains passing through the railway hub, as well as the vibration acceleration or sound pressure response of the measuring points in each functional area; The data processing module is used to establish the mapping relationship between the vibration acceleration and sound pressure response of each functional area under unit roughness excitation and the vehicle type and driving line, identify and monitor the changes in track unevenness of each line, and determine the rail roughness limit data and maintenance standards for each wavelength; A database module is used to store the mapping relationship between each vehicle line and vehicle model and the response of each measuring point; The data judgment module is used to determine whether there is any abnormal change in track unevenness and whether the rail roughness exceeds the limit and requires maintenance.

7. The device for identifying track roughness and determining maintenance standards for railway hubs according to claim 6, characterized in that: The acquisition and screening module includes a first acquisition unit, a second acquisition unit and a screening unit; The first collection unit is used to collect real-time data on the types and speeds of trains running on each driving line of the railway hub; The second acquisition unit is used to collect the vibration acceleration response of each measuring point in the railway hub functional area with dense personnel or non-enclosed rooms, and to collect the indoor sound pressure response of each measuring point in the functional area of ​​an independent room; The screening unit is used to intercept the vibration or noise data of the measuring points during the period when a train passes by, and screen out the measuring points that are closer to the driving line to calculate the vibration level or noise level of the functional area.

8. The device for identifying track roughness and determining maintenance standards for railway hubs according to claim 6, characterized in that: The data processing module includes a preliminary processing unit, an influence value calculation unit, a measurement point response acquisition unit, a mapping relationship construction unit, a roughness acquisition unit and a maintenance limit index calculation unit; The preliminary processing unit is used to process the track irregularity data in the spatial domain by Fourier transform to obtain an irregularity frequency domain spectrum; The influence value calculation unit is used to calculate the influence value of the uneven frequency domain spectrum on the vibration and noise response at the corresponding vehicle speed according to the vehicle speed data obtained by the vehicle identification device; The measuring point response acquisition unit is used to perform energy average calculation on the frequency domain response of the measuring points in each functional area of ​​the station building, remove the influence caused by the uneven frequency domain spectrum, and obtain the measuring point response of each vehicle model under unit roughness excitation; The mapping relationship building unit is used to use a machine learning method to establish a mapping relationship between the vibration and noise response of each functional area and the vehicle line and vehicle model under unit roughness excitation and store it in a database; The roughness acquisition unit is used to calculate the frequency domain response of each functional area by Fourier transform and perform energy averaging, determine the influence of the vehicle type and the driving line on the response of the measuring point of each functional area according to the mapping relationship, and calculate the real-time roughness data of the track according to the speed of the train passing; The maintenance limit index calculation unit is used to calculate the rail roughness maintenance limit index of each track according to the input specification limit and the response of each functional area.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 5.

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