Train crosswind early warning method, train and storage medium

Abstract

The invention provides a train crosswind early warning method, a train and a storage medium, and is applied to the technical field of rail transit and the technical field of computers. The method comprises the following steps: acquiring target real-time radial wind speeds in a preset measurement range in front of a train head in two axis wave beam directions of a laser radar measured by a double-axis cross-coherent Doppler wind measurement laser radar; vector calculation is carried out on the target real-time radial wind speeds in the two axial wave beam directions, and wind speed components, perpendicular to the track direction, of the train are obtained; vector decomposition is conducted on the obtained train speed of the train running on the track, and a speed component, perpendicular to the track direction, of the train is obtained; determining the real crosswind speed of the train in the direction perpendicular to the track according to the wind speed component and the speed component of the train in the direction perpendicular to the track; and according to the real crosswind speed of the train in the direction perpendicular to the track, crosswind early warning is conducted on the train.

Description

Technical Field

[0001] The present disclosure relates to the fields of rail transportation technology and computer technology, and more specifically, to a train crosswind warning method, a train, and a storage medium. Background Art

[0002] Under the action of crosswind, the aerodynamic performance of high-speed trains deteriorates, which not only rapidly increases the air resistance and lateral force of high-speed trains, but also affects the lateral stability of high-speed trains, and in severe cases may cause railway traffic accidents.

[0003] The traditional method of measuring crosswinds requires setting up a large number of in-situ anemometers along the railway. However, this observation distance is short and can only observe crosswinds and eddies near the railway. It is impossible to predict danger before it arrives, nor can it provide sufficient response time for high-speed trains. In addition, the project is large in scale, costly, and difficult to maintain. Summary of the Invention

[0004] In view of this, the present disclosure provides a train crosswind warning method, a train and a storage medium.

[0005] One aspect of the present disclosure provides a train crosswind warning method, comprising: obtaining a target real-time radial wind speed in two-axis beam directions of a laser radar measured by a dual-axis cross-coherent Doppler wind measurement laser radar within a preset measurement range in front of a train head; performing vector calculation on the target real-time radial wind speed in the two-axis beam directions respectively to obtain a wind speed component of the train perpendicular to the track direction; performing vector decomposition on the obtained train speed running on the track to obtain a speed component of the train perpendicular to the track direction; determining the actual crosswind speed of the train in the direction perpendicular to the track based on the wind speed component and speed component of the train in the direction perpendicular to the track direction; and providing a crosswind warning to the train based on the actual crosswind speed of the train in the direction perpendicular to the track direction.

[0006] According to an embodiment of the present disclosure, the target real-time radial wind speed in the two axial beam directions of the lidar within a preset measurement range in front of the train head measured by the dual-axis cross-coherent Doppler wind measurement lidar is performed by the following operations: using the dual-axis cross-coherent Doppler wind measurement lidar to measure the original real-time radial wind speed and the signal-to-noise ratio corresponding to the original real-time radial wind speed in the two axial beam directions of the lidar within a preset measurement range in front of the train head; determining the signal-to-noise ratio corresponding to the original real-time radial wind speed that meets the signal-to-noise ratio threshold in each axial beam direction as the target signal-to-noise ratio in each axial beam direction; and determining the original real-time radial wind speed corresponding to the target signal-to-noise ratio in each axial beam direction as the target real-time radial wind speed in each axial beam direction.

[0007] According to an embodiment of the present disclosure, the preset measurement range includes the intersection angle of the two axes and the distance from the front of the train head;

[0008] The method comprises the following steps: based on the cross angle of the two axes, transmitting the emitted light generated by the dual-axis cross-coherent Doppler wind measurement laser radar into the atmosphere through the dual-axis synchronous emission to generate the atmospheric backscattering signal; the dual-axis cross-coherent Doppler wind measurement laser radar synchronously receives the atmospheric backscattering signal, and performs optical mixing with the local oscillator light generated by the dual-axis cross-coherent Doppler wind measurement laser radar to obtain the difference frequency signal generated by the atmospheric backscattering signal and the local oscillator light; performing frequency domain conversion and noise processing on the difference frequency signal to obtain the target Doppler shift and the noise signal; calculating the original real-time radial wind speed in the preset measurement range in front of the train head in the two axis beam directions of the laser radar according to the Doppler shift; and calculating the signal-to-noise ratio corresponding to the original real-time radial wind speed according to the difference frequency signal and the noise signal.

[0009] According to the embodiments of the present disclosure, the preset measurement range includes the cross angle of the two axes, and the cross angle includes a first angle of the first cross axis with the train advancing direction and a second angle of the second cross axis with the train advancing direction, and the first angle and the second angle are equal.

[0010] The target real-time radial wind speed in the two axis beam directions is respectively vector calculated to obtain the wind speed component of the train perpendicular to the track direction, which comprises: performing vector decomposition on the target real-time radial wind speed in the first axis beam direction according to the first angle to obtain the first decomposition component of the target real-time radial wind speed in the first axis beam direction; performing vector decomposition on the target real-time radial wind speed in the second axis beam direction according to the second angle to obtain the second decomposition component of the target real-time radial wind speed in the second axis beam direction; and performing vector synthesis on the first decomposition component and the second decomposition component to obtain the wind speed component of the train perpendicular to the track direction.

[0011] According to the embodiments of the present disclosure, the real crosswind wind speed of the train perpendicular to the track direction is determined according to the wind speed component of the train perpendicular to the track direction and the speed component, which comprises: performing difference operation on the wind speed component of the train perpendicular to the track direction and the speed component to obtain a difference component; and determining the real crosswind wind speed of the train perpendicular to the track direction according to the difference component.

[0012] According to the embodiments of the present disclosure, the train is given a crosswind warning according to the real crosswind wind speed of the train perpendicular to the track direction, which comprises: obtaining the real-time state information of the train; determining the dynamic crosswind warning threshold in the preset measurement range in front of the train head according to the real-time state information of the train; and determining whether the train needs to be given a crosswind warning according to the real crosswind wind speed of the train perpendicular to the track direction and the dynamic crosswind warning threshold.

[0013] According to an embodiment of the present disclosure, the real-time state information of the train currently includes train current speed information, position information and train model identification.

[0014] According to the real-time state information of the train currently, the dynamic crosswind early warning threshold in the preset measurement range in front of the train head is determined, including: according to the train model identification, the critical crosswind speed and speed curve corresponding to the train model identification is retrieved from the database; according to the train current speed information, the critical crosswind speed theoretical value corresponding to the train current speed information is calculated from the critical crosswind speed and speed curve; the dynamic safety factor corresponding to the critical crosswind speed theoretical value corresponding to the train current speed information is determined, wherein the dynamic safety factor represents the dynamic scaling factor of the additional buffer space reserved by the train on the basis of the critical crosswind speed theoretical value; according to the train current position information and the preset measurement range in front of the train head, the geographical type safety factor in the preset measurement range in front of the train head is determined; according to the critical crosswind speed theoretical value, the dynamic safety factor and the geographical type safety factor, the dynamic crosswind early warning threshold in the preset measurement range in front of the train head is determined.

[0015] According to an embodiment of the present disclosure, the method further comprises: in response to the real crosswind speed of the train perpendicular to the track direction being greater than or equal to the dynamic crosswind early warning threshold of different levels, performing the early warning measures corresponding to the different levels on the train.

[0016] Another aspect of the present disclosure provides a train, comprising:

[0017] A vehicle body, in which a vehicle sensor is arranged for detecting vehicle operation data of the vehicle; a dual-axis cross-coherent Doppler wind measurement laser radar is carried on the vehicle body for measuring the target real-time radial wind speed in the preset measurement range in front of the train head in the two-axis beam direction of the laser radar; an edge computing device is configured to have a communication connection with the vehicle sensor and the dual-axis cross-coherent Doppler wind measurement laser radar respectively, and the edge computing device comprises: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0018] Another aspect of the present disclosure provides a computer readable storage medium, which stores computer executable instructions, the instructions being used to implement the above method when executed.

[0019] According to the embodiment of the present disclosure, by adopting the dual-axis cross-coherent Doppler wind measurement laser radar, the real-time radial wind speed of the target within the preset measurement range in front of the train head in the two-axis beam directions of the laser radar is measured, and the real-time radial wind speed of the target in the two-axis beam directions is respectively subjected to vector calculation to obtain the wind speed component of the train perpendicular to the track direction, the train speed obtained when the train runs on the track is subjected to vector decomposition to obtain the speed component of the train perpendicular to the track direction, and the real wind speed of the train perpendicular to the track direction is determined according to the wind speed component and the speed component of the train perpendicular to the track direction. Based on the real wind speed, the technical means for warning the train of the crosswind, at least partially solves the technical problem that in the traditional crosswind measurement, the danger caused by the crosswind cannot be predicted in time due to the short observation distance, and the high-speed train cannot be provided with sufficient response time, and further realizes that through the two beams of the dual-axis cross-coherent Doppler wind measurement laser radar, the system stability in the vehicle-mounted harsh environment is improved, and the technical effect of improving the crosswind warning result is achieved through the calculated real wind speed. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of the embodiments of the present disclosure taken in conjunction with the accompanying drawings, in which:

[0021] Figure 1 An exemplary system architecture to which the train crosswind warning method according to the embodiment of the present disclosure can be applied is schematically shown;

[0022] Figure 2 A flowchart of the train crosswind warning method according to the embodiment of the present disclosure is schematically shown;

[0023] Figure 3 A flowchart of the method for obtaining the real-time radial wind speed according to the embodiment of the present disclosure is schematically shown;

[0024] Figure 4 A hardware diagram of the dual-axis cross-coherent Doppler wind measurement laser radar according to the embodiment of the present disclosure is schematically shown;

[0025] Figure 5 A schematic diagram of the train crosswind warning method according to the embodiment of the present disclosure is schematically shown;

[0026] Figure 6 A block diagram of the train crosswind warning device according to the embodiment of the present disclosure is schematically shown; and

[0027] Figure 7 A block diagram of the edge computing device suitable for implementing the train crosswind warning method according to the embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood, however, that the description is merely exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description of the embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it would be apparent to those skilled in the art that the embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known structures and methods are not described in detail in order to avoid obscuring the concepts of the present disclosure.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the term "includes" and tautological expressions thereof, such as "including," "includes," "include," "contains," "containing," and so on, mean the presence of stated features, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0030] All terms used herein, including technical and scientific terms, have the meanings commonly understood by one of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having meanings consistent with the context of the specification, and should not be interpreted in an idealized or overly formal manner.

[0031] In the case of using expressions similar to "at least one of A, B, and C, etc.", it should generally be interpreted to include any of the possibilities of one of A, one of B, one of C, a combination of A and B, a combination of A and C, a combination of B and C, and / or a combination of A, B, and C, etc.

[0032] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) comply with the relevant legal regulations, are used for legal purposes, and do not violate public order and good customs. In particular, necessary measures have been taken to prevent illegal access to user personal information data, and to maintain user personal information security and network security

[0033] In the embodiments of the present disclosure, the authorization or consent of the user is obtained before the user's personal information is acquired or collected.

[0034] In the related art, a traditional all-fiber coherent Doppler wind measurement laser radar can be used to extract a Doppler frequency shift from the collected wind field raw data to calculate a wind speed. However, when the radar is carried on a high-speed train, the radar moves with the train, and the wind speed obtained directly from the Doppler frequency shift contains the relative wind speed generated by the train running, which cannot meet the wind speed observation and calculation requirements of being carried on a high-speed train, thereby causing the cross-wind early warning to be unable to be accurately realized.

[0035] Therefore, the embodiment of the present disclosure provides a train crosswind early warning method, comprising: acquiring target real-time radial wind speed in a preset measurement range in front of a train head in two-axis beam directions of a laser radar measured by a dual-axis cross-coherent Doppler wind measurement laser radar; performing vector calculation on the target real-time radial wind speed in the two-axis beam directions respectively to obtain a wind speed component of the train perpendicular to the track direction; performing vector decomposition on the train speed of the train running on the track to obtain a speed component of the train perpendicular to the track direction; determining a real crosswind wind speed of the train perpendicular to the track direction according to the wind speed component and the speed component of the train perpendicular to the track direction; and performing crosswind early warning on the train according to the real crosswind wind speed of the train perpendicular to the track direction.

[0036] Figure 1 An exemplary system architecture to which the train crosswind early warning method according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that, Figure 1 The shown is only an example of the system architecture to which the embodiment of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiment of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0037] As Figure 1 shown, the system architecture 100 according to the embodiment can include a train vehicle 101, a dual-axis cross-coherent Doppler wind measurement laser radar (not shown in the figure) carried on the train vehicle 101, a network 102 and a server 103. The network 102 is used as a medium to provide a communication link between the train vehicle 101 and the server 103. The network 102 can include various connection types, such as wired and / or wireless communication links, etc.

[0038] A plurality of vehicle sensors can be installed on the train vehicle 101 for detecting vehicle operation data of the train. The train vehicle 101 can also include an edge computing device, which can interact with the server 103 through the network 102, and is configured to have a communication connection with the vehicle sensors and the dual-axis cross-coherent Doppler wind measurement laser radar respectively. The dual-axis cross-coherent Doppler wind measurement laser radar carried on the train vehicle 101 is used to measure target real-time radial wind speed in a preset measurement range in front of a train head in two-axis beam directions of a laser radar.

[0039] The server 103 can be a server providing various services, such as a background management server (only as an example) providing support for websites browsed by users using the first terminal device 101, the second terminal device 102 and the third terminal device 103. The background management server can analyze and process the received user requests and other data, and feed back the processing results (such as web pages, information or data generated or obtained according to user requests) to the terminal device.

[0040] It should be noted that the train crosswind early warning method provided by the embodiments of the present disclosure can generally be executed by the server 103. Accordingly, the train crosswind early warning device provided by the embodiments of the present disclosure can generally be arranged in the server 103. The train crosswind early warning method provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from the server 103 and capable of communicating with the train vehicle 101 and / or the server 103. Accordingly, the train crosswind early warning device provided by the embodiments of the present disclosure can also be arranged in a server or a server cluster different from the server 103 and capable of communicating with the train vehicle 101 and / or the server 103.

[0041] Figure 2 A flowchart of a train crosswind early warning method according to an embodiment of the present disclosure is schematically shown.

[0042] As shown in FIG. 2, the method includes operations S210-S250. Figure 2

[0043] At operation S210, target real-time radial wind speeds in a preset measurement range ahead of a train head along two axial beam directions of a dual-axis cross-coherent Doppler wind lidar are acquired.

[0044] According to an embodiment of the present disclosure, the dual-axis cross-coherent Doppler wind lidar is mounted on a high-speed train, which is a powerful atmospheric wind field remote sensing device. By emitting two laser beams in different planes but intersecting at a certain height into the atmosphere, the radar can simultaneously measure the wind speed component in the line-of-sight direction of the laser beam, i.e., the target real-time radial wind speed.

[0045] According to an embodiment of the present disclosure, the dual-axis cross-coherent Doppler wind lidar can be used to measure the target real-time radial wind speed along the two axial beam directions. The target real-time radial wind speed is measured within a preset measurement range ahead of the train head. The preset measurement range may, for example, be a range of 3 kilometers from the train head.

[0046] According to an embodiment of the present disclosure, the axial beam direction can be the direction along which the laser beam is emitted by the two telescopes with a cross angle on the dual-axis cross-coherent Doppler wind lidar.

[0047] At operation S220, vector calculations are respectively performed on the target real-time radial wind speeds along the two axial beam directions to obtain a wind speed component of the train perpendicular to the track direction.

[0048] ​According to an embodiment of the present disclosure, for example, the real-time radial wind speed in the first axial beam direction is decomposed into a crosswind perpendicular to the track direction and a headwind along the track direction, respectively.

[0049] According to an embodiment of the present disclosure, based on the crosswind perpendicular to the track direction decomposed from the real-time radial wind speed in the first axial beam direction and the crosswind perpendicular to the track direction decomposed from the real-time radial wind speed in the second axial beam direction, the wind speed component of the train perpendicular to the track direction can be determined. Similarly, the wind speed component of the train along the track direction can be determined.

[0050] In operation S230, the train speed obtained by the train running on the track is decomposed into a speed component perpendicular to the track direction.

[0051] In operation S240, based on the wind speed component and the speed component of the train perpendicular to the track direction, the real crosswind speed of the train perpendicular to the track direction is determined.

[0052] In operation S250, based on the real crosswind speed of the train perpendicular to the track direction, the train is warned of crosswind.

[0053] According to an embodiment of the present disclosure, the train speed obtained by the train running on the track is decomposed into a speed component perpendicular to the track direction and a speed component along the track direction.

[0054] According to an embodiment of the present disclosure, the difference between the wind speed component of the train perpendicular to the track direction and the speed component perpendicular to the track direction can be used to determine the real crosswind speed of the train perpendicular to the track direction; similarly, the difference between the wind speed component of the train along the track direction and the speed component along the track direction can be used to determine the real headwind speed of the train along the track direction.

[0055] According to an embodiment of the present disclosure, the real crosswind speed of the train perpendicular to the track direction can be compared with a crosswind warning threshold to determine whether the train needs to be warned of crosswind.

[0056] According to the embodiment of the present disclosure, by adopting the dual-axis cross-coherent Doppler wind measurement laser radar, the real-time radial wind speed of the target within the preset measurement range in front of the train head in the two-axis beam directions of the laser radar is measured, and the real-time radial wind speed of the target in the two-axis beam directions is respectively subjected to vector calculation to obtain the wind speed component of the train perpendicular to the track direction. The train speed obtained when the train runs on the track is subjected to vector decomposition to obtain the speed component of the train perpendicular to the track direction. According to the wind speed component and the speed component of the train perpendicular to the track direction, the real crosswind wind speed of the train perpendicular to the track direction is determined. Based on the real crosswind wind speed, the technical means for crosswind warning of the train is adopted, which at least partially solves the technical problem that in the traditional crosswind measurement, the danger caused by the crosswind cannot be predicted in time due to the short observation distance, and the high-speed train cannot be provided with sufficient response time, and further realizes that through the two beams of the dual-axis cross-coherent Doppler wind measurement laser radar, the system stability in the vehicle-mounted harsh environment is improved, and the technical effect of improving the crosswind warning result through the calculated real crosswind wind speed is achieved.

[0057] Figure 3 A flowchart of a method for obtaining a target real-time radial wind speed according to an embodiment of the present disclosure is schematically shown.

[0058] As shown in Figure 3 , the method 300 includes operations S310-S330.

[0059] In operation S310, the original real-time radial wind speed within the preset measurement range in front of the train head in the two-axis beam directions of the laser radar and the signal-to-noise ratio corresponding to the original real-time radial wind speed are measured by using the dual-axis cross-coherent Doppler wind measurement laser radar.

[0060] In operation S320, the signal-to-noise ratio corresponding to the original real-time radial wind speed that meets the signal-to-noise ratio threshold in each axis beam direction is determined as the target signal-to-noise ratio in each axis beam direction.

[0061] In operation S330, the original real-time radial wind speed corresponding to the target signal-to-noise ratio in each axis beam direction is determined as the target real-time radial wind speed in each axis beam direction.

[0062] According to the embodiment of the present disclosure, the original real-time radial wind speed can be the radial wind speed initially measured by the dual-axis cross-coherent Doppler wind measurement laser radar (hereinafter referred to as laser radar), and the original real-time radial wind speed data corresponding to the error wind speed signal caused by the shielding of the shielding object can be screened out. In addition, the signal data with intensity much higher than the atmospheric scattering signal intensity and abnormally widened spectrum width can be identified as obstacle signal and removed according to the judgment of the signal intensity and spectrum width corresponding to the original real-time radial wind speed.

[0063] According to an embodiment of the present disclosure, the signal-to-noise ratio can be a ratio of a power of an effective signal detected by the laser radar to a power of noise introduced by the laser radar. The signal-to-noise ratio in each axis beam direction can be determined by calculating the effective signal obtained after the emission of the laser beam in each axis beam direction and the noise introduced by the laser radar.

[0064] According to an embodiment of the present disclosure, the signal-to-noise ratio is compared with a signal-to-noise ratio threshold of the laser radar, and the signal-to-noise ratio greater than or equal to the signal-to-noise ratio threshold is determined as a target signal-to-noise ratio. The original real-time radial wind speed corresponding to the target signal-to-noise ratio is reassembled to form a target real-time radial wind speed.

[0065] According to an embodiment of the present disclosure, the preset measurement range includes a cross angle of the dual-axis and a distance from the front of the train head.

[0066] According to an embodiment of the present disclosure, the original real-time radial wind speed and the signal-to-noise ratio corresponding to the original real-time radial wind speed of the preset measurement range in front of the train head in the two axis beam directions of the laser radar are measured by the dual-axis cross-coherent Doppler wind lidar, comprising: based on the cross angle of the dual-axis, the emitted light generated by the dual-axis cross-coherent Doppler wind lidar is synchronously emitted into the atmosphere through the dual-axis to generate an atmospheric backscattering signal; the dual-axis cross-coherent Doppler wind lidar synchronously receives the atmospheric backscattering signal and performs optical mixing with the local oscillator light generated by the dual-axis cross-coherent Doppler wind lidar to obtain a difference frequency signal generated by the atmospheric backscattering signal and the local oscillator light; the difference frequency signal is subjected to frequency domain conversion and noise processing to obtain a target Doppler shift and a noise signal; according to the Doppler shift, the original real-time radial wind speed in the preset measurement range in front of the train head in the two axis beam directions of the laser radar is calculated; and according to the difference frequency signal and the noise signal, the signal-to-noise ratio corresponding to the original real-time radial wind speed is calculated.

[0067] According to an embodiment of the present disclosure, the preset measurement range includes a cross angle of the dual-axis, and the cross angle includes a first angle of a first cross-axis with respect to the train advancing direction and a second angle of a second cross-axis with respect to the train advancing direction, and the first angle and the second angle are equal.

[0068] According to an embodiment of the present disclosure, for example, Figure 4 A hardware diagram of the dual-axis cross-coherent Doppler wind lidar according to an embodiment of the present disclosure is schematically shown.

[0069] As Figure 4The double-axis beam direction of the double-axis cross-coherent Doppler wind measurement laser radar is the direction of the laser beams emitted from the two telescopes. The two telescopes can be two axes of laser beam emission, and the two telescopes have a certain angle, the angle of which is centered on the intersection point of the two telescopes, and the train advancing direction is the center line, and the included angle between the two telescopes and the center line is the intersection angle, and the two intersection angles are equal.

[0070] According to an embodiment of the present disclosure, along the two intersection angles of the two telescopes of the double-axis cross-coherent Doppler wind measurement laser radar and the center line, the laser beams generated by the laser radar are divided into signal light and local light, the signal light is modulated into pulsed light and then power amplified by an amplifier, and then emitted into the atmosphere based on the directions of the two telescope axes, respectively, to generate atmospheric backscattering signals. After receiving the two atmospheric backscattering signals, the laser radar synchronously receives the atmospheric backscattering signals. The two intersection angles are the first angle and the second angle, and both the first angle and the second angle can be represented as α, for example, α can be 30°,

[0071] According to an embodiment of the present disclosure, the double-axis cross-coherent Doppler wind measurement laser radar emits most of the generated emission light into the atmosphere to generate atmospheric backscattering signals, and a small part of the emission light is used as local light in the laser radar. After receiving the two atmospheric backscattering signals, the laser radar generates two difference frequency signals by mixing the two atmospheric backscattering signals with the local light.

[0072] According to an embodiment of the present disclosure, the two difference frequency signals are further digitized by the data acquisition system in the laser radar to generate raw data. The noise signal is obtained by curve fitting from the raw data, and the effective signal is obtained by subtracting the noise signal from the raw data. The signal center frequency position is obtained by performing frequency domain conversion, power spectrum estimation, and Gaussian fitting on the effective signal, thereby obtaining the Doppler shift and obtaining the original real-time radial wind speed obtained by the two axes. The corresponding signal-to-noise ratio is obtained by integrating the intensity ratio of the effective signal to the noise signal after the difference frequency signal is processed as described above.

[0073] According to an embodiment of the present disclosure, by using the two cross laser beams of the double-axis cross-coherent Doppler wind measurement laser radar and the coherent heterodyne detection technology, the two-dimensional horizontal wind vector (wind speed and wind direction) in the preset measurement range of the high-speed train during driving is directly measured with high precision, high resolution and non-contact, and the radial wind speed and wind direction of the high-speed train during operation are monitored in real time, thereby providing accurate data support for monitoring the train operation and overcoming the limitation of single-beam laser that can only measure the wind speed component along the line of sight direction (one-dimensional information).

[0074] According to an embodiment of the present disclosure, the target real-time radial wind speed in the two-axis beam directions is vector calculated respectively to obtain the wind speed component of the train perpendicular to the track direction, including: vector decomposing the target real-time radial wind speed in the first-axis beam direction according to the first angle to obtain a first decomposed component of the target real-time radial wind speed in the first-axis beam direction; vector decomposing the target real-time radial wind speed in the second-axis beam direction according to the second angle to obtain a second decomposed component of the target real-time radial wind speed in the second-axis beam direction; and vector synthesizing the first decomposed component and the second decomposed component to obtain the wind speed component of the train perpendicular to the track direction.

[0075] According to an embodiment of the present disclosure, for example, the target real-time radial wind speed in the first-axis beam direction is V1, the target real-time radial wind speed in the second-axis beam direction is V2, and the first angle and the second angle are both α; the target real-time radial wind speed V1 in the first-axis beam direction and the target real-time radial wind speed V1 are decomposed into a longitudinal wind speed u along the track direction and a crosswind wind speed v perpendicular to the track direction, and the crosswind wind speed v perpendicular to the track direction is calculated by using the following formula, specifically:

[0076] ;

[0077] ;

[0078] The vector calculation by using the above two formulas can obtain the wind speed component v perpendicular to the track direction and the longitudinal wind speed u along the track direction of the train.

[0079] According to an embodiment of the present disclosure, the real crosswind wind speed of the train perpendicular to the track direction is determined according to the wind speed component and the speed component of the train perpendicular to the track direction, including: performing a difference operation on the wind speed component and the speed component of the train perpendicular to the track direction to obtain a difference component; and determining the real crosswind wind speed of the train perpendicular to the track direction according to the difference component.

[0080] According to an embodiment of the present disclosure, the vector difference calculation can be performed on the wind speed component v perpendicular to the track direction and the speed component of the train perpendicular to the track direction obtained by the above calculation to obtain a difference component. The difference component represents the real crosswind wind speed component of the atmosphere after deducting the train running speed.

[0081] According to an embodiment of the present disclosure, the value of the difference component can be determined as the real crosswind wind speed value of the atmosphere during the train running.

[0082] According to an embodiment of the present disclosure, the crosswind warning of the train according to the real crosswind speed perpendicular to the track direction comprises: obtaining current real-time state information of the train; determining a dynamic crosswind warning threshold in a preset measurement range in front of the train head according to the current real-time state information of the train; and determining whether the train needs to be warned of crosswind according to the real crosswind speed perpendicular to the track direction and the dynamic crosswind warning threshold.

[0083] According to an embodiment of the present disclosure, the current real-time state information of the train comprises train current speed information, position information, train model identification, line environment state information in a preset range in front of the train head, and weather conditions.

[0084] According to an embodiment of the present disclosure, the line environment state information can be hills, highways, forests, viaducts, etc. The weather conditions can be typhoon, rainstorm, sunny day, etc.

[0085] According to an embodiment of the present disclosure, the dynamic crosswind warning threshold can be a critical wind speed value dynamically calculated according to the current real-time state of the train during the running of the train.

[0086] According to an embodiment of the present disclosure, the real crosswind speed value and the dynamic crosswind warning threshold are compared based on the calculation, and if the real crosswind speed value does not satisfy the relationship between the two, the train is warned of crosswind and a safety response mechanism is started.

[0087] According to an embodiment of the present disclosure, the dynamic crosswind warning threshold in the preset measurement range in front of the train head is determined according to the current real-time state information of the train, comprising: retrieving a critical crosswind speed and speed curve corresponding to the train model identification from a database according to the train model identification; calculating a critical crosswind speed theoretical value corresponding to the current speed information of the train from the critical crosswind speed and speed curve according to the current speed information of the train; determining a dynamic safety coefficient corresponding to the critical crosswind speed theoretical value corresponding to the current speed information of the train; determining a geographical type safety coefficient in the preset measurement range in front of the train head according to the current position information of the train and the preset measurement range in front of the train head; and determining the dynamic crosswind warning threshold in the preset measurement range in front of the train head according to the critical crosswind speed theoretical value, the dynamic safety coefficient, and the geographical type safety coefficient.

[0088] According to an embodiment of the present disclosure, the curves between the critical crosswind speed and the speed of different train models are different. The curve between the critical crosswind speed and the speed corresponding to the train model identification can be retrieved from the database based on different train model identifications.

[0089] According to an embodiment of the present disclosure, based on the obtained current train speed information, a corresponding critical crosswind speed theoretical value is determined from the above-mentioned critical crosswind speed and train speed curve. The critical crosswind speed theoretical value is a data obtained based on historical data, rather than real-time data.

[0090] According to an embodiment of the present disclosure, the dynamic safety factor represents a dynamic scaling factor of the additional buffer space reserved by the train based on the critical crosswind speed theoretical value. That is, a certain value can be floated above and below the critical crosswind speed theoretical value.

[0091] According to an embodiment of the present disclosure, the position information of the current train operation can be given to determine the line environment state information within a preset measurement range in front of the train head, such as hills, highways, forests, viaducts, etc., so as to determine the geographical type safety factor.

[0092] According to an embodiment of the present disclosure, the target critical crosswind speed theoretical value of the train can be determined based on the critical crosswind speed theoretical value and the dynamic safety factor, and the dynamic crosswind warning threshold within the preset measurement range in front of the train head can be determined based on the target critical crosswind speed theoretical value and the geographical safety factor.

[0093] According to an embodiment of the present disclosure, the above-mentioned method further comprises: in response to the real crosswind speed perpendicular to the track direction being greater than or equal to the dynamic crosswind warning threshold of different levels, performing a warning measure corresponding to the different levels on the train.

[0094] According to an embodiment of the present disclosure, determining which level of the dynamic crosswind warning threshold the calculated real crosswind speed perpendicular to the track direction is greater than or equal to, and taking a warning measure corresponding to the level for different levels of crosswind.

[0095] Figure 5 A schematic diagram of a train crosswind warning method according to an embodiment of the present disclosure is schematically shown.

[0096] As Figure 5As shown, a dual-axis cross-coherent Doppler wind measurement lidar is used to measure the original real-time radial wind speed and the signal-to-noise ratio (S501) corresponding to the original real-time radial wind speed in a preset measurement range in front of the train head in the two axial beam directions of the lidar, the signal-to-noise ratio corresponding to the original real-time radial wind speed that meets the signal-to-noise ratio threshold in each axial beam direction is determined as the target signal-to-noise ratio (S502) in each axial beam direction, and the original real-time radial wind speed corresponding to the target signal-to-noise ratio in each axial beam direction is determined as the target real-time radial wind speed (S503) in each axial beam direction. Perform vector calculation on the target real-time radial wind speed in the two axis beam directions respectively to obtain the wind speed component of the train perpendicular to the track direction S504; perform vector decomposition on the obtained train speed of the train running on the track to obtain the speed component of the train perpendicular to the track direction S505; perform difference operation on the wind speed component and the speed component of the train perpendicular to the track direction to obtain the difference component S506; determine the actual crosswind speed of the train perpendicular to the track direction according to the difference component S507; obtain the current real-time status information of the train S508; determine the dynamic crosswind warning threshold within the preset measurement range in front of the train head according to the current real-time status information of the train S509; judge whether the actual crosswind speed is greater than or equal to the dynamic crosswind warning thresholds of different levels S510; if not, do not issue a crosswind warning to the train and the train continues to travel S511; if so, issue a crosswind warning to the train and execute warning measures corresponding to different levels S512.

[0097] Figure 6 A block diagram of a train crosswind warning device according to an embodiment of the present disclosure is schematically shown.

[0098] like Figure 6 As shown, the device 600 includes: a wind speed acquisition module 610, a vector calculation module 620, a vector decomposition module 630, a wind speed determination module 640 and a crosswind warning module 650.

[0099] The wind speed acquisition module 610 is used to obtain the real-time radial wind speed of the target within a preset measurement range in front of the train head in the two-axis beam direction of the laser radar measured by the dual-axis cross-coherence Doppler wind measurement laser radar.

[0100] The vector calculation module 620 is used to perform vector calculation on the target real-time radial wind speed in the two axis beam directions respectively to obtain the wind speed component of the train perpendicular to the track direction.

[0101] The vector decomposition module 630 is used to perform vector decomposition on the acquired train speed of the train running on the track to obtain the speed component of the train perpendicular to the track.

[0102] The wind speed determination module 640 is configured to determine the real crosswind wind speed of the train in the direction perpendicular to the track according to the wind speed component and the speed component of the train in the direction perpendicular to the track.

[0103] The crosswind warning module 650 is configured to perform crosswind warning on the train according to the real crosswind wind speed of the train in the direction perpendicular to the track.

[0104] According to an embodiment of the present disclosure, the wind speed acquisition module 610 comprises a measurement sub-module, a target signal-to-noise ratio determination sub-module, and a wind speed first determination sub-module.

[0105] The measurement sub-module is configured to measure, by using the dual-axis cross-coherent Doppler wind lidar, the original real-time radial wind speed in a preset measurement range in front of the train head in two axial beam directions of the lidar and a signal-to-noise ratio corresponding to the original real-time radial wind speed.

[0106] The target signal-to-noise ratio determination sub-module is configured to determine, as a target signal-to-noise ratio in each axial beam direction, the signal-to-noise ratio corresponding to the original real-time radial wind speed in each axial beam direction that meets a signal-to-noise ratio threshold.

[0107] The wind speed first determination sub-module is configured to determine, as a target real-time radial wind speed in each axial beam direction, the original real-time radial wind speed corresponding to the target signal-to-noise ratio in each axial beam direction.

[0108] According to an embodiment of the present disclosure, the preset measurement range comprises a cross angle of the dual axes and a distance from the front of the train head.

[0109] According to an embodiment of the present disclosure, the measurement sub-module comprises a signal generation unit, a signal acquisition unit, a processing unit, a first calculation unit, and a second calculation unit.

[0110] The signal generation unit is configured to, based on the cross angle of the dual axes, synchronously emit, by the dual-axis cross-coherent Doppler wind lidar, the emitted light generated by the dual-axis cross-coherent Doppler wind lidar into the atmosphere to generate an atmospheric backscattering signal.

[0111] The signal acquisition unit is configured to synchronously receive, by the dual-axis cross-coherent Doppler wind lidar, the atmospheric backscattering signal and perform optical mixing with the local oscillator light generated by the dual-axis cross-coherent Doppler wind lidar to obtain a beat frequency signal generated by the atmospheric backscattering signal and the local oscillator light.

[0112] The processing unit is configured to perform frequency domain conversion and noise processing on the beat frequency signal to obtain a target Doppler shift and a noise signal.

[0113] The first calculation unit is configured to calculate, according to the Doppler shift, the original real-time radial wind speed in the preset measurement range in front of the train head in the two axial beam directions of the lidar.

[0114] The second calculation unit is configured to calculate a signal-to-noise ratio corresponding to the original real-time radial wind speed according to the difference frequency signal and the noise signal.

[0115] According to an embodiment of the present disclosure, the preset measurement range includes a crossing angle of the two axes, and the crossing angle includes a first angle of the first crossing axis with respect to the train advancing direction and a second angle of the second crossing axis with respect to the train advancing direction, and the first angle and the second angle are equal.

[0116] According to an embodiment of the present disclosure, the vector calculation module 620 includes a first obtaining sub-module, a second obtaining sub-module, and a third obtaining sub-module.

[0117] The first obtaining sub-module is configured to perform vector decomposition on the target real-time radial wind speed in the first axis beam direction according to the first angle to obtain a first decomposition component of the target real-time radial wind speed in the first axis beam direction.

[0118] The second obtaining sub-module is configured to perform vector decomposition on the target real-time radial wind speed in the second axis beam direction according to the second angle to obtain a second decomposition component of the target real-time radial wind speed in the second axis beam direction.

[0119] The third obtaining sub-module is configured to perform vector synthesis on the first decomposition component and the second decomposition component to obtain a wind speed component of the train perpendicular to the track direction.

[0120] According to an embodiment of the present disclosure, the wind speed determination module 640 includes a difference component operation sub-module and a wind speed second determination sub-module.

[0121] The difference component operation sub-module is configured to perform a difference operation on the wind speed component of the train perpendicular to the track direction and the speed component to obtain a difference value component.

[0122] The wind speed second determination sub-module is configured to determine a real crosswind wind speed of the train perpendicular to the track direction according to the difference value component.

[0123] According to an embodiment of the present disclosure, the crosswind early warning module 650 includes an obtaining sub-module, a threshold first determination sub-module, and an early warning determination sub-module.

[0124] The obtaining sub-module is configured to obtain current real-time state information of the train.

[0125] The threshold first determination sub-module is configured to determine a dynamic crosswind early warning threshold in a preset measurement range in front of the train head according to the current real-time state information of the train.

[0126] The early warning determination sub-module is configured to determine whether the train needs to be given a crosswind early warning according to the real crosswind wind speed of the train perpendicular to the track direction and the dynamic crosswind early warning threshold.

[0127] According to an embodiment of the present disclosure, the real-time state information of the train currently includes train current speed information, position information and train model identification.

[0128] According to an embodiment of the present disclosure, the threshold first determination submodule includes: a curve calling unit, a theoretical value calculation unit, a theoretical value calculation unit, a coefficient first determination unit, a coefficient second determination unit and a warning threshold determination unit.

[0129] The curve calling unit is configured to call a pre-stored critical crosswind speed and speed curve corresponding to the train model identification from a database according to the train model identification.

[0130] The theoretical value calculation unit is configured to calculate a critical crosswind speed theoretical value corresponding to the train current speed information from the critical crosswind speed and speed curve according to the train current speed information.

[0131] The coefficient first determination unit is configured to determine a dynamic safety coefficient corresponding to the critical crosswind speed theoretical value corresponding to the train current speed information, wherein the dynamic safety coefficient represents a dynamic scaling factor of an additional buffer space reserved by the train on the basis of the critical crosswind speed theoretical value.

[0132] The coefficient second determination unit is configured to determine a geographical type safety coefficient within a preset measurement range in front of the train head according to the train current position information and the preset measurement range in front of the train head.

[0133] The warning threshold determination unit is configured to determine a dynamic crosswind warning threshold within the preset measurement range in front of the train head according to the critical crosswind speed theoretical value, the dynamic safety coefficient and the geographical type safety coefficient.

[0134] According to an embodiment of the present disclosure, the device 600 further includes a measure execution module.

[0135] The measure execution module is configured to execute a warning measure corresponding to different levels on the train in response to the real crosswind speed of the train perpendicular to the track direction being greater than or equal to the dynamic crosswind warning threshold of different levels.

[0136] Any of the modules, sub-modules, units, sub-units, or at least part of any of them according to embodiments of the present disclosure can be implemented in one module. Any of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure can be split into multiple modules. Any of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure can be implemented at least in part as a hardware circuit, for example, a field-programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application-specific integrated circuit (ASIC), or any other reasonable way of hardware or firmware by integrating or packaging circuits, or in any one of software, hardware, and firmware, or in a proper combination of any of them. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure can be implemented at least in part as computer program modules, which can perform corresponding functions when the computer program modules are run.

[0137] For example, any of the wind speed obtaining module 610, the vector calculating module 620, the vector decomposing module 630, the wind speed determining module 640, and the crosswind warning module 650 can be combined in one module / unit / sub-unit, or any of them can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of the modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units, and implemented in one module / unit / sub-unit. According to embodiments of the present disclosure, at least one of the wind speed obtaining module 610, the vector calculating module 620, the vector decomposing module 630, the wind speed determining module 640, and the crosswind warning module 650 can be implemented at least in part as a hardware circuit, for example, a field-programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application-specific integrated circuit (ASIC), or any other reasonable way of hardware or firmware by integrating or packaging circuits, or in any one of software, hardware, and firmware, or in a proper combination of any of them. Alternatively, at least one of the wind speed obtaining module 610, the vector calculating module 620, the vector decomposing module 630, the wind speed determining module 640, and the crosswind warning module 650 can be implemented at least in part as computer program modules, which can perform corresponding functions when the computer program modules are run.

[0138] It should be noted that the train crosswind early warning device part in the embodiments of the present disclosure corresponds to the train crosswind early warning method part in the embodiments of the present disclosure, and the description of the train crosswind early warning device system part refers to the train crosswind early warning method part, which will not be repeated here.

[0139] The embodiments of the present disclosure also provide a train, which comprises a vehicle body, a dual-axis cross-coherent Doppler wind lidar, and an edge computing device.

[0140] The vehicle body is configured with a vehicle sensor inside for detecting vehicle operation data of the vehicle; the dual-axis cross-coherent Doppler wind lidar is carried on the vehicle body for measuring real-time radial wind speed of a target within a preset measurement range in front of the train head in the two-axis beam direction of the lidar; the edge computing device is configured to have a communication connection with the vehicle sensor and the dual-axis cross-coherent Doppler wind lidar respectively, and the edge computing device comprises one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0141] Figure 7 A block diagram of an edge computing device suitable for implementing the above-described method according to an embodiment of the present disclosure is schematically shown. Figure 7 The electronic device shown is merely an example and should not impose any limitation on the functions and use range of the embodiments of the present disclosure.

[0142] As Figure 7 shown, the electronic device 700 according to an embodiment of the present disclosure comprises a processor 701, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 702 or loaded into a random access memory (RAM) 703 from a storage portion 708. The processor 701 may, for example, include a general-purpose microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a special-purpose microprocessor (such as an application-specific integrated circuit (ASIC)), etc. The processor 701 can also include on-board memory for cache use. The processor 701 can include a single processing unit or multiple processing units for performing different actions of the method processes according to embodiments of the present disclosure.

[0143] In the RAM 703, various programs and data required for the operation of the electronic device 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other via the bus 704. The processor 701 performs various operations of the method flow according to the embodiments of the present disclosure by executing the programs in the ROM 702 and / or the RAM 703. It should be noted that the programs can also be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 can also perform various operations of the method flow according to the embodiments of the present disclosure by executing the programs stored in the one or more memories.

[0144] According to the embodiments of the present disclosure, the electronic device 700 can further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device 700 can further include one or more of the following components connected to the input / output (I / O) interface 705: an input part 706 including a keyboard, a mouse, and the like; an output part 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker, and the like; a storage part 708 including a hard disk, and the like; and a communication part 709 including a network interface card such as a LAN card, a modem, and the like. The communication part 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as necessary. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is installed in the storage part 708 as necessary.

[0145] According to the embodiments of the present disclosure, the method flow according to the embodiments of the present disclosure can be implemented as a computer software program. For example, the embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable storage medium, the computer program containing program codes for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above-described functions defined in the system of the embodiments of the present disclosure are performed. According to the embodiments of the present disclosure, the system, the device, the apparatus, the module, the unit, and the like described above can be implemented by computer program modules.

[0146] The present disclosure also provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments, or exist independently without being assembled into the device / apparatus / system. The above computer readable storage medium carries one or more programs, which when executed, implement the method according to the embodiments of the present disclosure.

[0147] According to the embodiments of the present disclosure, the computer readable storage medium can be a non-volatile computer readable storage medium. For example, it can include, but is not limited to: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device.

[0148] For example, according to the embodiments of the present disclosure, the computer readable storage medium can include the ROM 702 and / or the RAM 703 described above and / or one or more memories other than the ROM 702 and the RAM 703.

[0149] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program codes for executing the method provided by the embodiments of the present disclosure, and when the computer program product is run on an electronic device, the program codes are used to make the electronic device implement the train crosswind warning method provided by the embodiments of the present disclosure.

[0150] When the computer program is executed by the processor 701, the above functions defined in the system / apparatus of the embodiments of the present disclosure are performed. According to the embodiments of the present disclosure, the system, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0151] In one embodiment, the computer program can rely on tangible storage media such as optical storage media, magnetic storage media, etc. In another embodiment, the computer program can also be transmitted, distributed, downloaded and installed in the form of signals on a network medium, and be downloaded and installed through the communication part 709 and / or installed from the detachable medium 711. The program codes contained in the computer program can be transmitted by any appropriate network medium, including but not limited to wireless, wired, etc., or any suitable combination of the foregoing.

[0152] According to embodiments of the present disclosure, program code of the computer program for performing the methods provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages, including a high-level procedural and / or object-oriented programming language, and / or an assembly / machine language. Programming languages include, but are not limited to, Java, C++, python, "C" language, or the like. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on a remote computing device, or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0153] The computer program product of the present disclosure can be a computer program product, which is a machine-readable medium (media) having exact sequences of instructions, program, code segments, or computer instructions, which implement the methods provided by the embodiments of the present disclosure. Such a machine-readable medium can cause a processor or CPU to perform a particular process. The media and data storage can be device or equipment that is external to or internal to a computer system. Such computer program product can be a RAM (Random Access Memory), ROM (Read Only Memory), flash memory, or a combination of the above, etc. Where appropriate, computer program instructions can be stored on the memory of computer system before execution by the computer system. The computer program instructions can be implemented as program modules, such as routines, programs, objects, components, data structures, etc. that perform particular tasks.

[0154] The above describes the embodiments of the present disclosure. However, these embodiments are only for illustrative purposes, and are not intended to limit the scope of the present disclosure. Although each embodiment is described above separately, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present disclosure, those skilled in the art can make various alternatives and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A train crosswind warning method, characterized in that: The method comprises: Obtaining the real-time radial wind speed of a target within a preset measurement range in front of the train head in the two-axis beam direction of the dual-axis cross-coherence Doppler wind laser radar measured by the laser radar; Performing vector calculation on the target real-time radial wind speed in the two axis beam directions respectively to obtain the wind speed component of the train perpendicular to the track direction; Performing vector decomposition on the obtained train speed of the train running on the track to obtain a speed component of the train perpendicular to the track; determining a true crosswind speed of the train in a direction perpendicular to the track based on a wind speed component and a speed component of the train in a direction perpendicular to the track; A crosswind warning is issued to the train according to the actual crosswind speed of the train in a direction perpendicular to the track.

2. The method according to claim 1, characterized in that The target real-time radial wind speed in the two-axis beam direction of the dual-axis cross-coherence Doppler wind laser radar within the preset measurement range in front of the train head is measured by the following operations: Using the dual-axis cross-coherence Doppler wind measurement lidar to measure the original real-time radial wind speed within a preset measurement range in front of the train head in the two-axis beam direction of the lidar and the signal-to-noise ratio corresponding to the original real-time radial wind speed; Determine the signal-to-noise ratio corresponding to the original real-time radial wind speed that meets the signal-to-noise ratio threshold in each of the axial beam directions as the target signal-to-noise ratio in each of the axial beam directions; The original real-time radial wind speed corresponding to the target signal-to-noise ratio in each of the axial beam directions is determined as the target real-time radial wind speed in each of the axial beam directions.

3. The method according to claim 2, characterized in that The preset measurement range includes the intersection angle of the two axes and the distance from the front of the train head; The method of measuring the original real-time radial wind speed in the two-axis beam direction of the laser radar within a preset measurement range in front of the train head and the signal-to-noise ratio corresponding to the original real-time radial wind speed by using the dual-axis cross-coherence Doppler wind measurement laser radar includes: Based on the intersection angle of the two axes, the emission light generated by the two-axis cross-coherent Doppler wind laser radar is synchronously emitted into the atmosphere through the two axes to generate an atmospheric backscatter signal; The dual-axis cross-coherent Doppler wind measurement laser radar synchronously receives the atmospheric backscatter signal and optically mixes it with the local oscillation light generated by the dual-axis cross-coherent Doppler wind measurement laser radar to obtain a difference frequency signal generated by the atmospheric backscatter signal and the local oscillation light; Performing frequency domain conversion and noise processing on the difference frequency signal to obtain a target Doppler frequency shift and a noise signal; Calculating the original real-time radial wind speed within a preset measurement range in front of the train head in the two-axis beam direction of the laser radar according to the Doppler frequency shift; A signal-to-noise ratio corresponding to the original real-time radial wind speed is calculated according to the difference frequency signal and the noise signal.

4. The method according to claim 1, wherein The preset measurement range includes the intersection angle of the two axes, the intersection angle includes a first angle between the first intersection axis and the forward direction of the train, and a second angle between the second intersection axis and the forward direction of the train, the first angle and the second angle being equal; The performing vector calculation on the target real-time radial wind speed in the two axis beam directions respectively to obtain the wind speed component of the train perpendicular to the track direction includes: performing vector decomposition on the target real-time radial wind speed in the first-axis beam direction according to the first angle to obtain a first decomposition component of the target real-time radial wind speed in the first-axis beam direction; performing vector decomposition on the target real-time radial wind speed in the second axis beam direction according to the second angle to obtain a second decomposition component of the target real-time radial wind speed in the second axis beam direction; Perform vector synthesis on the first decomposition component and the second decomposition component to obtain a wind speed component of the train perpendicular to the track direction.

5. The method according to claim 1, wherein Determining the actual crosswind speed of the train in a direction perpendicular to the track based on the wind speed component and the speed component of the train in a direction perpendicular to the track includes: performing a difference operation on a wind speed component and a speed component of the train perpendicular to the track to obtain a difference component; The actual crosswind speed of the train in a direction perpendicular to the track is determined according to the difference component.

6. The method according to claim 1, characterized in that Providing a crosswind warning to the train according to the actual crosswind speed of the train in a direction perpendicular to the track, including: Get the current real-time status information of the train; Determining a dynamic crosswind warning threshold within a preset measurement range in front of the train head according to the current real-time status information of the train; Whether a crosswind warning is required for the train is determined according to the actual crosswind speed of the train in a direction perpendicular to the track and the dynamic crosswind warning threshold.

7. The method according to claim 6, characterized in that The current real-time status information of the train includes the current speed information, location information and train model identification of the train; Determining a dynamic crosswind warning threshold within a preset measurement range in front of the train head according to the current real-time status information of the train includes: According to the train model identification, a pre-stored critical crosswind speed and vehicle speed curve corresponding to the train model identification is retrieved from a database; Calculating a critical crosswind speed theoretical value corresponding to the current train speed information from the critical crosswind speed and speed curve according to the current train speed information; Determining a dynamic safety factor corresponding to a critical crosswind theoretical value corresponding to the current speed information of the train, wherein the dynamic safety factor represents a dynamic scaling factor for an additional buffer space reserved by the train based on the critical crosswind theoretical value; Determining a geographic type safety factor within the preset measurement range in front of the train head according to the current position information of the train and the preset measurement range in front of the train head; The dynamic crosswind warning threshold within a preset measurement range in front of the train locomotive is determined based on the critical crosswind speed theoretical value, the dynamic safety factor, and the geographical type safety factor.

8. The method according to claim 6, characterized in that The method further comprises: In response to the actual crosswind speed of the train in a direction perpendicular to the track being greater than or equal to the dynamic crosswind warning thresholds of different levels, warning measures corresponding to the different levels are executed on the train.

9. A train, characterized in that: include: a vehicle body, in which a vehicle sensor is disposed for detecting vehicle operation data of the vehicle; A dual-axis cross-coherence Doppler wind laser radar is mounted on the vehicle body and is used to measure the real-time radial wind speed of a target within a preset measurement range in front of the train head in the direction of the two-axis beam of the laser radar; An edge computing device is configured to have communication connections with the vehicle sensor and the dual-axis cross-coherence Doppler wind laser radar, respectively, and the edge computing device includes: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to implement the method according to any one of claims 1 to 8.

Images