State sensing device and state sensing method based on heavy-duty truck wheelset stress monitoring

Abstract

The invention discloses a state sensing device and a state sensing method based on heavy-duty truck wheelset stress monitoring, which can be applied to the field of railway vehicles. The device comprises a full-bridge circuit which comprises a strain gauge pasted on a wheel set spoke plate and is used for measuring a strain signal of the wheel set spoke plate; the GNSS antenna is used for receiving satellite signals to obtain speed and position information of wheels; the acquisition module is coupled to the full-bridge circuit and is used for supplying power to the full-bridge circuit, acquiring the strain signals output by the full-bridge circuit and acquiring the speed and position information of the wheels obtained by the GNSS antenna; the calculation module is coupled to the acquisition module and is used for identifying the state of the wheel and / or the track based on the strain signal and the speed and position information of the wheel; and the battery is coupled to the acquisition module and the calculation module and is used for supplying power to the acquisition module and the calculation module.

Description

Technical Field

[0001] This application relates to the field of rail vehicle technology, specifically to a state sensing device and method based on stress monitoring of heavy-duty freight car wheelsets. Background Technology

[0002] Currently, heavy-duty freight car wheelsets do not have onboard monitoring capabilities; their condition is mainly monitored through trackside equipment. Summary of the Invention

[0003] To address at least some of the aforementioned technical problems, embodiments of this application provide a state sensing device and state sensing method based on stress monitoring of heavy-duty truck wheelsets.

[0004] This application provides a state sensing device based on heavy-duty truck wheelset stress monitoring, including:

[0005] A full-bridge circuit includes four strain gauges attached to the spokes of a wheelset, the full-bridge circuit being used to measure the strain signal of the spokes of the wheelset;

[0006] GNSS antennas are used to receive satellite signals to obtain the speed and position information of the wheels;

[0007] The acquisition module is coupled to the full-bridge circuit and the GNSS antenna. The acquisition module is used to acquire the strain signal output by the full-bridge circuit and to acquire the speed and position information of the wheel obtained by the GNSS antenna.

[0008] A calculation module, coupled to the acquisition module, is used to identify the state of the wheel and / or track based on the wheel speed and position information obtained from the strain signal and the GNSS antenna.

[0009] The battery is coupled to the acquisition module and the computing module respectively, and is used to power the acquisition module and the computing module. The acquisition module is also used to power the full-bridge circuit.

[0010] This application embodiment also provides a state sensing method based on stress monitoring of heavy-duty truck wheelsets, and based on the state sensing device for stress monitoring of heavy-duty truck wheelsets described in the above embodiments, the method includes:

[0011] Based on the strain signals of the wheelset spokes and the speed and position information of the wheel obtained by the GNSS antenna, the state of the wheel and / or track is identified.

[0012] In some embodiments, identifying the state of the wheel and / or track based on the wheel speed and position information obtained by the GNSS antenna from the strain signal of the wheelset spokes includes:

[0013] Based on the strain signal of the wheelset spokes, the wheel rotation frequency is calculated by spectral Fourier analysis;

[0014] The speed of the wheel is calculated based on the wheel rotation frequency and the wheel diameter;

[0015] The first state of the wheel is determined based on the wheel's speed and the wheel's GNSS speed.

[0016] In some embodiments, determining the first state of the wheel based on the wheel's speed and the wheel's GNSS speed includes:

[0017] If the difference between the GNSS speed of the wheel and the speed of the wheel is greater than a first threshold, and the speed of the wheel is less than a second threshold, then it is determined that the wheel has abnormally locked up, wherein the second threshold is less than the first threshold.

[0018] In some embodiments, identifying the state of the wheel and / or track based on the wheel speed and position information obtained by the GNSS antenna from the strain signal of the wheelset spokes includes:

[0019] The polygonal shape of the wheel is identified based on the strain signal of the spokes of the wheelset.

[0020] In some embodiments, identifying the wheel polygon based on the strain signal of the wheelset spokes includes:

[0021] Fourier spectrum analysis is performed on the strain signal of the wheelset spokes. If a frequency with an energy amplitude exceeding A% of the wheel speed frequency amplitude occurs outside the wheel speed frequency, and this frequency is an integer multiple of the wheel speed frequency, it is identified as a wheel polygon, where A≥10.

[0022] In some embodiments, identifying the state of the wheel and / or track based on the wheel speed and position information obtained by the GNSS antenna from the strain signal of the wheelset spokes includes:

[0023] Based on the strain signals of the wheelset spokes, wheel tread damage is identified.

[0024] In some embodiments, identifying wheel tread damage based on the strain signal of the wheelset spokes includes:

[0025] Fourier spectrum analysis was performed on the strain signal of the wheelset spokes to obtain the wheel rotation frequency H;

[0026] The maximum absolute values ​​of the time-domain peak and valley values ​​are statistically calculated within one cycle 1 / H of the strain signal. If the amplitude of the maximum absolute value exceeds 50% of the amplitude of the wheel rotation frequency H, and the maximum absolute value appears continuously with an interval of 1 / H, it is identified as wheel tread damage.

[0027] In some embodiments, identifying the state of the wheel and / or track based on the wheel speed and position information obtained by the GNSS antenna from the strain signal of the wheelset spokes includes:

[0028] Track condition is identified and analyzed by abnormally large strain signals, and track defects are identified by combining wheel position information obtained from GNSS antennas.

[0029] This application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the methods described in any of the above embodiments.

[0030] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0031] This application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0032] This application provides a state sensing device and method based on stress monitoring of heavy-duty freight car wheelsets. By integrating a battery, full-bridge circuit, GNSS antenna, acquisition module, and computing module onto the freight car wheelset, it monitors and analyzes wheel stress in real time, achieving intelligent monitoring of the wheelset's own state and / or track state. This upgrades ordinary heavy-duty freight car wheelsets into intelligent heavy-duty freight car wheelsets, improving the safety of heavy-duty freight car operation. The upgrade work is minimal and cost-effective, and this technology has a promising market prospect. Its promotion can improve the intelligence level of heavy-duty freight cars and provide technical and data support for the safety of heavy-duty railways. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0034] Figure 1This is a schematic diagram of the structure of a state sensing device for monitoring the stress of wheelsets of heavy-duty trucks, provided in an embodiment of this application.

[0035] Figure 2 This is a schematic diagram of a full-bridge circuit provided in an embodiment of this application.

[0036] Figure 3 This is a schematic diagram of the bonding position of a strain gauge on a wheelset spoke, provided in an embodiment of this application.

[0037] Figure 4 This is a schematic diagram of the strain signal acquired by the acquisition module in one embodiment of this application.

[0038] Figure 5 This is a flowchart illustrating a state perception method based on stress monitoring of heavy-duty truck wheelsets provided in an embodiment of this application.

[0039] Figure 6 This is a partial flowchart illustrating a state perception method based on stress monitoring of heavy-duty truck wheelsets provided in an embodiment of this application.

[0040] Figure 7 This application provides a state sensing device for monitoring the stress of heavy-duty truck wheelsets, which collects the time-domain waveform and spectrum of dynamic stress of the wheelsets, and uses them to calculate the wheel rotation frequency.

[0041] Figure 8 This application provides a state sensing device for monitoring the stress of heavy-duty truck wheelsets, which collects dynamic stress time-domain waveforms and spectrum diagrams of heavy-duty truck wheelsets to identify wheel polygons.

[0042] Figure 9 This is a partial flowchart illustrating a state perception method based on stress monitoring of heavy-duty truck wheelsets provided in an embodiment of this application.

[0043] Figure 10 This application provides a state sensing device for monitoring the stress of heavy-duty truck wheelsets, which collects dynamic stress time-domain waveforms of the wheelsets and is used to identify wheel tread damage.

[0044] Figure 11 This application provides a state sensing device for monitoring the stress of heavy-duty truck wheelsets, which collects dynamic stress time-domain waveforms of the wheelsets and is used to identify track conditions.

[0045] Figure 12 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and their descriptions are used to explain this application, but are not intended to limit this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily arranged.

[0047] The terms “first,” “second,” etc., used in this document are not intended to specifically refer to order or sequence, nor are they used to limit this application; they are merely used to distinguish elements or operations described using the same technical terms.

[0048] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0049] The term "and / or" as used in this document includes any or all of the items mentioned.

[0050] Figure 1 This is a schematic diagram of the structure of a state sensing device for monitoring the stress of wheelsets of heavy-duty trucks, provided in an embodiment of this application. Figure 1 As shown in the figure, an embodiment of this application provides a state sensing device 100 based on heavy-duty truck wheelset stress monitoring, comprising:

[0051] The full-bridge circuit 10 includes four strain gauges attached to the wheelset spokes, and the full-bridge circuit is used to measure the strain signal of the wheelset spokes;

[0052] GNSS antenna 20 is used to receive satellite signals to obtain the speed and position information of the wheels;

[0053] The acquisition module 30 is coupled to the full-bridge circuit 10 to acquire the strain signal output by the full-bridge circuit 10, and to acquire the speed and position information of the wheel obtained by the GNSS antenna 20.

[0054] The calculation module 40 is coupled to the acquisition module 30. The calculation module 40 is used to identify the state of the wheel and / or the track based on the wheel speed and position information obtained by the strain signal and the GNSS antenna 20.

[0055] Battery 50 is coupled to the acquisition module 30 and the calculation module 40 respectively, and is used to power the acquisition module 30 and the calculation module 40. The acquisition module 30 is also used to power the full-bridge circuit 10.

[0056] Specifically, strain gauges are attached to the spokes of the wheelset to form a full-bridge circuit. The strain signal is measured, and as the wheel rotates one revolution, the strain signal becomes a periodic sine and cosine signal. For example, a full-bridge circuit would look like this: Figure 2As shown, the four strain gauges 1, 1F, 2, and 2F in the full-bridge circuit 10 can be attached to the wheelset spokes in the following positions: Figure 3 As shown. Figure 4 This is a schematic diagram of the strain signal acquired by the acquisition module in one embodiment of this application.

[0057] The full-bridge circuit 10 can specifically be a high-resistance strain gauge full-bridge, employing a low-power sensor (strain gauge); the battery 50 can be a high-density, high-capacity battery; furthermore, a low-power signal acquisition module 30 and a calculation module 40 can also be used. The GNSS antenna 20 receives satellite signals to obtain the speed and position information of the wheel, the position information specifically including the latitude and longitude information of the wheel.

[0058] The state sensing device based on heavy-duty freight car wheelset stress monitoring provided in this application integrates a battery, full-bridge circuit, GNSS antenna, acquisition module, and computing module onto the freight car wheelset. It monitors wheel stress and performs real-time analysis using the wheel speed and position information obtained from the GNSS antenna, achieving intelligent monitoring of the wheelset's own state and / or track state. This upgrades ordinary heavy-duty freight car wheelsets into intelligent heavy-duty freight car wheelsets, improving the safety of heavy-duty freight car operation. The upgrade work is minimal and cost-effective, and the technology has a promising market prospect. Its widespread adoption can improve the intelligence level of heavy-duty freight cars and provide technical and data support for the safety of heavy-duty railways.

[0059] Based on the same inventive concept, this application also provides a state sensing method based on heavy-duty truck wheelset stress monitoring, and a state sensing device based on heavy-duty truck wheelset stress monitoring as described in the above embodiments.

[0060] Figure 5 This is a flowchart illustrating a state-sensing method based on stress monitoring of heavy-duty truck wheelsets provided in an embodiment of this application. Figure 5 As shown in the embodiment of this application, a state perception method based on stress monitoring of heavy-duty truck wheelsets is provided, including:

[0061] S1. Based on the strain signal of the wheelset spokes and the speed and position information of the wheel obtained by the GNSS antenna, identify the state of the wheel and / or track.

[0062] The state perception method based on heavy-haul freight car wheelset stress monitoring provided in this application embodiment monitors wheel stress and combines it with real-time analysis of wheel speed and position information obtained from GNSS antennas to achieve intelligent monitoring of the wheelset's own state and / or track state. This upgrades ordinary heavy-haul freight car wheelsets into intelligent heavy-haul freight car wheelsets, improving the safety of heavy-haul freight car operation. The upgrade work is minimal and cost-effective, and this technology has a promising market prospect. Its widespread adoption can improve the intelligence level of heavy-haul freight cars and provide technical and data support for the safety of heavy-haul railways.

[0063] like Figure 6 As shown, in some embodiments, identifying the state of the wheel and / or track based on the strain signal of the wheelset spokes and the wheel speed and position information obtained from the GNSS antenna includes:

[0064] S11. Based on the strain signal of the wheelset spokes, the wheel rotation frequency is calculated by spectral Fourier analysis;

[0065] S12. The speed of the wheel is calculated based on the wheel rotation frequency and the wheel diameter;

[0066] S13. Determine the first state of the wheel based on the speed of the wheel and the GNSS speed of the wheel.

[0067] Specifically, an acquisition module is used to acquire sinusoidal and cosine strain signals (see...). Figure 7 Using edge computing technology, the dominant frequency H (wheel rotation frequency) is calculated through spectrum FFT (Fast Fourier Transform), and the wheel speed V is also calculated. wheel .

[0068] Speed ​​calculation formula: Vwheel = D * π * H * 3.6

[0069] In the formula, D is the wheel diameter, in meters (m).

[0070] H is the dominant frequency obtained from the spectrum calculation, in Hz;

[0071] V wheel Speed, unit: km / h.

[0072] Then, the calculated velocity signal V wheel With GNSS (Global Positioning System) velocity signal V gnss Compare the results to determine if the wheels are abnormally locked.

[0073] In some embodiments, determining the first state of the wheel based on the wheel's speed and the wheel's GNSS speed includes: if the difference between the wheel's GNSS speed and the wheel's speed is greater than a first threshold and the wheel's speed is less than a second threshold, then determining that the wheel has abnormally locked up, wherein the second threshold is less than the first threshold.

[0074] For example, if V gnss -V wheel >10km / h, and V wheel If the speed is less than 5 km / h, it is determined that the wheels have locked up abnormally due to braking.

[0075] In some embodiments, identifying the state of the wheel and / or track based on the strain signal of the wheelset spokes and the speed and position information of the wheel obtained by the GNSS antenna includes: identifying the wheel polygon based on the strain signal of the wheelset spokes.

[0076] Wheel polygons refer to the wheels of rail transit vehicles such as trains and subways. They should be perfect circles, but during use, they gradually lose their roundness and become irregular "polygonal" shapes.

[0077] In some embodiments, the identification of wheel polygons based on the strain signal of the wheelset spokes and the speed and position information of the wheel obtained by the GNSS antenna includes: performing Fourier spectrum analysis on the strain signal of the wheelset spokes; if a frequency with an energy amplitude exceeding A% of the wheel rotation frequency amplitude occurs outside the wheel rotation frequency, and the frequency is an integer multiple of the wheel rotation frequency, it is identified as a wheel polygon, where A≥10.

[0078] Specifically, the periodic sine and cosine carrier signals are analyzed to identify wheel polygons based on the periodicity of the signal. For example, FFT spectral analysis is performed on the signal. If a frequency H2, whose energy amplitude exceeds 10% of the dominant frequency amplitude and is an integer multiple of H, is consistently present outside the wheel rotation frequency H, it is identified as a wheel polygon. (See [link to relevant documentation]). Figure 8 .

[0079] In some embodiments, identifying the state of the wheel and / or track based on the strain signal of the wheelset spokes and the wheel speed and position information obtained from the GNSS antenna includes: identifying wheel tread damage based on the strain signal of the wheelset spokes. Wheel tread damage refers to various forms of damage and defects on the rolling surface (i.e., "tread") of the train wheel in contact with the rail.

[0080] like Figure 9As shown, in some embodiments, identifying wheel tread damage based on the strain signal of the wheelset spokes and the wheel speed and position information obtained from the GNSS antenna includes:

[0081] S14. Perform Fourier spectrum analysis on the strain signal of the wheelset spokes to obtain the wheel rotation frequency H;

[0082] S15. Statistically calculate the maximum absolute value of the time domain peak and valley values ​​within one cycle 1 / H of the strain signal. If the amplitude of the maximum absolute value exceeds 50% of the amplitude of the wheel rotation frequency H, and the maximum absolute value appears continuously with an interval of 1 / H, it is identified as wheel tread damage.

[0083] Specifically, periodic sinusoidal and cosine wave carrier signals are analyzed to identify wheel tread damage based on periodic signals at fixed angular positions. For example, the wheel rotational speed frequency H is calculated, and the absolute maximum values ​​of the time-domain peak and trough values ​​are statistically calculated within one period (1 / H) of the strain signal. If the amplitude of the absolute maximum value exceeds 50% of the main frequency amplitude, and the absolute maximum values ​​appear consecutively with an interval of 1 / H, then it is identified as wheel tread damage. See [link to relevant documentation]. Figure 10 .

[0084] In some embodiments, identifying the state of the wheel and / or track based on the strain signal of the wheelset spokes and the speed and position information of the wheel obtained by the GNSS antenna includes: identifying and analyzing the track state through abnormally large strain signals, and identifying track defects by combining the wheel position information obtained by the GNSS antenna.

[0085] Specifically, by performing correlation analysis on the strain signals and GNSS positioning signals, the track condition is identified and analyzed through abnormally large strain signals. Combined with GNSS latitude and longitude positioning signals, track defects are identified. For example, by calculating the absolute values ​​of 10 consecutive peaks and troughs, calculating the average value, and then calculating the deviation of the absolute value of a single peak or trough from the average value, if the deviation is greater than twice the average value, it is judged as an abnormal track condition and identified as track defect. See [link to relevant documentation]. Figure 11 .

[0086] In some embodiments, the computing module 3 can also send the identified wheel status and track defects to a ground server via 4G / 5G mobile internet.

[0087] It is evident that the state sensing device and sensing method based on heavy-duty freight car wheelset stress monitoring provided in this application can enrich the content of safety monitoring for heavy-duty freight cars and heavy-duty lines, improve the safety monitoring technology level of heavy-duty trains, ensure the safe operation of heavy-duty freight cars, have a good market prospect, and can generate good economic and social benefits.

[0088] Figure 12 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of this application, as shown below. Figure 12 As shown, the electronic device may include a processor 301, a communications interface 302, a memory 303, and a communication bus 304, wherein the processor 301, the communications interface 302, and the memory 303 communicate with each other via the communication bus 304. The processor 301 may call logical instructions in the memory 303 to execute the methods described in any of the above embodiments.

[0089] Furthermore, the logical instructions in the aforementioned memory 303 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can perform the methods provided in the above-described method embodiments.

[0091] This embodiment provides a computer-readable storage medium storing a computer program that causes the computer to perform the methods provided in the above-described method embodiments.

[0092] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0093] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0097] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A state sensing device based on stress monitoring of heavy-duty truck wheelsets, characterized in that, include: A full-bridge circuit includes four strain gauges attached to the spokes of a wheelset, the full-bridge circuit being used to measure the strain signal of the spokes of the wheelset; GNSS antennas are used to receive satellite signals to obtain the speed and position information of the wheels; The acquisition module is coupled to the full-bridge circuit and the GNSS antenna. The acquisition module is used to acquire the strain signal output by the full-bridge circuit and to acquire the speed and position information of the wheel obtained by the GNSS antenna. A calculation module, coupled to the acquisition module, is used to identify the state of the wheel and / or track based on the wheel speed and position information obtained from the strain signal and the GNSS antenna. The battery is coupled to the acquisition module and the computing module respectively, and is used to power the acquisition module and the computing module. The acquisition module is also used to power the full-bridge circuit.

2. A state perception method based on stress monitoring of heavy-duty truck wheelsets, characterized in that, Based on the state sensing device for monitoring wheel set stress of heavy-duty trucks as described in claim 1, the method includes: Based on the strain signals of the wheelset spokes and the speed and position information of the wheel obtained by the GNSS antenna, the state of the wheel and / or track is identified.

3. The method according to claim 2, characterized in that, The wheel speed and position information obtained by the GNSS antenna based on the strain signal of the wheelset spokes, identifying the state of the wheel and / or track, includes: Based on the strain signal of the wheelset spokes, the wheel rotation frequency is calculated by spectral Fourier analysis; The speed of the wheel is calculated based on the wheel rotation frequency and the wheel diameter; The first state of the wheel is determined based on the wheel's speed and the wheel's GNSS speed.

4. The method according to claim 3, characterized in that, Determining the first state of the wheel based on its speed and GNSS speed includes: If the difference between the GNSS speed of the wheel and the speed of the wheel is greater than a first threshold, and the speed of the wheel is less than a second threshold, then it is determined that the wheel has abnormally locked up, wherein the second threshold is less than the first threshold.

5. The method according to claim 2, characterized in that, The wheel speed and position information obtained by the GNSS antenna based on the strain signal of the wheelset spokes, identifying the state of the wheel and / or track, includes: The polygonal shape of the wheel is identified based on the strain signal of the spokes of the wheelset.

6. The method according to claim 5, characterized in that, The identification of the wheel polygon based on the strain signal of the wheelset spokes includes: Fourier spectrum analysis is performed on the strain signal of the wheelset spokes. If a frequency with an energy amplitude exceeding A% of the wheel speed frequency amplitude occurs outside the wheel speed frequency, and this frequency is an integer multiple of the wheel speed frequency, it is identified as a wheel polygon, where A≥10.

7. The method according to claim 2, characterized in that, The wheel speed and position information obtained by the GNSS antenna based on the strain signal of the wheelset spokes, identifying the state of the wheel and / or track, includes: Based on the strain signals of the wheelset spokes, wheel tread damage is identified.

8. The method according to claim 7, characterized in that, The method of identifying wheel tread damage based on the strain signal of the wheelset spokes includes: Fourier spectrum analysis was performed on the strain signal of the wheelset spokes to obtain the wheel rotation frequency H; The maximum absolute values ​​of the time-domain peak and valley values ​​are statistically calculated within one cycle 1 / H of the strain signal. If the amplitude of the maximum absolute value exceeds 50% of the amplitude of the wheel rotation frequency H, and the maximum absolute value appears continuously with an interval of 1 / H, it is identified as wheel tread damage.

9. The method according to claim 2, characterized in that, The wheel speed and position information obtained by the GNSS antenna based on the strain signal of the wheelset spokes, identifying the state of the wheel and / or track, includes: Track condition is identified and analyzed by abnormally large strain signals, and track defects are identified by combining wheel position information obtained from GNSS antennas.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, implements the steps of the method as described in any one of claims 2 to 9.

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