A train strong wind adjustment control method and device, medium and train

Abstract

The application discloses a train strong wind adjustment control method and device, medium and train, relates to the field of rail trains, and solves the problem of low real-time performance of monitoring environmental strong wind changes. A vehicle-mounted wind measuring device is used to monitor the wind field in a certain range in front of the running direction of the train, and the real-time monitoring of the whole global continuous wind field around the train, including the wind speed and direction, obtains safety indexes such as the derailment coefficient and the wheel load reduction rate of a section of line in front, and is used for finding the strong wind condition that may affect the driving safety of the vehicle in advance, guiding the train to evaluate the maximum running speed according to the wind field near the line, and improving the line operation efficiency.

Description

Technical Field

[0001] This application relates to the field of rail trains, and in particular to a method, device and medium for adjusting and controlling high winds in trains, and a train. Background Technology

[0002] High-speed trains, being long objects running close to the ground, experience complex airflow around them due to air viscosity during high-speed operation on the track, resulting in significant aerodynamic loads acting on the train's surface. When there are strong ambient winds around the track, these winds exert pressure on one side of the train. If this pressure exceeds a certain threshold, it reduces the train's operational stability and can lead to derailment. Therefore, to ensure the safe operation of trains, certain speed limits for operation in strong winds have been implemented.

[0003] Currently, there are two main methods for obtaining information about windy conditions along railway lines: one is local weather forecasts, which plan train speeds based on predicted weather changes over a certain period; the other is ultrasonic wind speed and direction monitoring sensors along the railway line. The former has a certain degree of uncertainty because weather forecasts predict wind field values ​​over a large area, while the latter, being a single-point measurement, generally requires processing with a moving average over a certain period to eliminate the randomness of sudden gusts, and necessitates the deployment of numerous sensors at regular intervals along the railway line. This means that trains currently rely primarily on gale warnings from meteorological stations to predict wind changes. Consequently, train stoppages due to sudden gusts are frequent, even on subway trains, significantly reducing the operational efficiency of the line.

[0004] Therefore, how to solve the problem of low real-time performance in monitoring changes in strong winds is a technical problem that urgently needs to be solved by researchers in this field. Summary of the Invention

[0005] The purpose of this application is to provide a train wind adjustment control method, device, medium, and train to solve the problem of low real-time performance in monitoring environmental wind changes.

[0006] To solve the above-mentioned technical problems, this application provides a train high wind adjustment control method, including:

[0007] Based on the vehicle-mounted wind measurement equipment, wind field information within a preset distance range ahead of the train in the direction of travel is obtained;

[0008] Based on the wind field information, aerodynamic load data under the current operating line conditions are obtained;

[0009] Based on the aerodynamic load data and current vehicle operating parameters, the safety indicators of the train under the current operating conditions are obtained through a pre-established real vehicle track model.

[0010] A train speed limit adjustment strategy for strong winds is generated based on the aforementioned safety indicators.

[0011] As an optional solution, in the above-mentioned train wind adjustment control method, the on-board wind measuring device is a wind measuring system based on the coherent optical Doppler effect; the on-board wind measuring device includes one or more laser emitters for emitting laser beams and measuring the Doppler frequency shift caused by aerosol particles; and one or more photosensitive receivers for receiving the reflected laser beams and converting them into electrical signals;

[0012] Correspondingly, the acquisition of wind field information within a preset distance range ahead of the train's direction of travel based on the vehicle-mounted wind measurement equipment includes:

[0013] The laser emitter is controlled to emit a laser beam, which is then collimated by a lens system and taken into the atmospheric environment.

[0014] The laser beam reflected back from aerosol particles in the atmosphere is received by a photosensitive receiver and converted into an electrical signal.

[0015] The wind field information is determined by calculating the Doppler frequency shift based on the electrical signal.

[0016] As an optional solution, in the above-mentioned train wind adjustment control method, obtaining aerodynamic load data under the current operating line conditions based on the wind field information includes:

[0017] The simulated aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds were obtained through wind tunnel testing.

[0018] A multi-parameter database of train shape, vehicle speed, wind speed, and aerodynamic load is established based on the simulated aerodynamic load data.

[0019] The aerodynamic load data corresponding to the real-time wind field information of the current vehicle is determined using the multi-parameter database.

[0020] As an optional solution, the above-mentioned train wind adjustment control method further includes, before simulating the aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds through wind tunnel testing:

[0021] Acquire actual aerodynamic load data of trains with different shapes under different operating speeds and wind speeds;

[0022] The aerodynamic load data of trains with different shapes under different operating speeds and wind speeds were simulated through wind tunnel tests.

[0023] The accuracy of the wind tunnel test was verified based on the actual aerodynamic load data and the experimental aerodynamic load data.

[0024] When the accuracy of the wind tunnel test meets the preset conditions, the process proceeds to the step of simulating the aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds through wind tunnel testing.

[0025] As an optional solution, in the above-mentioned train wind adjustment control method, determining the aerodynamic load data of the vehicle under the current wind field information through the multi-parameter database includes:

[0026] A mapping model of train shape, vehicle speed, ambient wind speed and aerodynamic load is established based on the multi-parameter database.

[0027] The aerodynamic load data corresponding to the real-time wind field information of the current vehicle is determined based on the mapping model.

[0028] As an optional solution, in the above-mentioned train high wind adjustment control method, based on the aerodynamic load data and current vehicle operating parameters, the safety indicators of the train under the current operating line conditions are obtained through a pre-established actual vehicle track model, including:

[0029] A multi-rigid-body vehicle system dynamics model is established based on the train model and train route described above.

[0030] The train's safety indicators are obtained by using a pre-established real-vehicle track model based on the aerodynamic load data and current vehicle operating parameters; wherein, the safety indicators include one or more of the following: wheel load reduction rate, derailment coefficient, and wheel axle lateral force.

[0031] As an optional solution, in the above-mentioned train wind speed control method, the step of generating a train wind speed limit adjustment strategy based on the safety indicators includes:

[0032] Determine whether a speed limit is necessary based on the aforementioned safety indicators;

[0033] If so, then the speed limit conditions, speed limit method, speed reduction response time, and speed limit recovery conditions are generated based on the aforementioned safety indicators.

[0034] As an optional solution, the above-mentioned train wind adjustment and control method also includes:

[0035] Collect one or more meteorological data points, including temperature, humidity, and air pressure, in the train's operating environment using onboard environmental monitoring equipment;

[0036] Meteorological influencing factors are generated based on the meteorological data;

[0037] The aerodynamic load data are adjusted according to the meteorological influencing factors.

[0038] To solve the above-mentioned technical problems, this application provides a train high wind adjustment control device, comprising:

[0039] The data acquisition module is used to acquire wind field information within a preset distance range ahead of the train's direction of travel, based on the onboard wind measurement equipment.

[0040] The aerodynamic load analysis module is used to obtain aerodynamic load data under the current operating line conditions based on the wind field information.

[0041] The safety index analysis module is used to obtain the safety index of the train under the current operating line conditions based on the aerodynamic load data and the current vehicle operating parameters, through a pre-established real vehicle line model.

[0042] The speed limit adjustment module is used to generate a train speed limit adjustment strategy for strong winds based on the safety indicators.

[0043] To solve the above-mentioned technical problems, this application provides a train high wind adjustment control device, comprising:

[0044] Memory, used to store computer programs;

[0045] A processor is used to implement the steps of the above-described train wind adjustment control method when executing the computer program.

[0046] To solve the above-mentioned technical problems, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned train wind adjustment control method.

[0047] To solve the above-mentioned technical problems, this application provides a train including the aforementioned train wind adjustment control device.

[0048] The train wind adjustment and control method provided in this application includes: acquiring wind field information within a preset distance range ahead of the train in the direction of travel using onboard wind measuring equipment; obtaining aerodynamic load data under the current operating line conditions based on the wind field information; obtaining the train's safety indicators under the current operating line conditions based on the aerodynamic load data and current vehicle operating parameters through a pre-established real-vehicle line model; and generating a train wind speed limit adjustment strategy based on the safety indicators. By monitoring the wind field within a certain range ahead of the train in the direction of travel using onboard wind measuring equipment, and by real-time monitoring of the continuous wind field around the train (including wind speed and direction), safety indicators such as the derailment coefficient and wheel load reduction rate of a section of track ahead are obtained through analysis. This allows for the early detection of wind conditions that may affect vehicle safety, guiding the train to assess its maximum operating speed based on the wind field near the track, and improving track operating efficiency.

[0049] In addition, this application also provides a device, medium, and train, which correspond to the above-mentioned train wind adjustment and control method and have the same effect. Attached Figure Description

[0050] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments 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.

[0051] Figure 1 This embodiment provides a flowchart of a train high wind adjustment control method;

[0052] Figure 2 A structural diagram of a train high wind adjustment control device provided in an embodiment of this application;

[0053] Figure 3 This is a structural diagram of another train wind adjustment control device provided in an embodiment of this application. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0055] The core of this application is to provide a method, device, medium, and train for adjusting and controlling high winds in trains.

[0056] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] High-speed trains, being long and close to the ground, experience complex airflow around them at high speeds on the track due to air viscosity, resulting in significant aerodynamic loads on the train's surface. When strong ambient winds blow around the track, these winds exert pressure on one side of the train. If this pressure exceeds a certain threshold, it reduces the train's stability and can lead to derailment. Therefore, to ensure safe train operation, certain speed limits for operation in strong winds have been implemented. Take high-speed trains as an example:

[0058] When the ambient wind speed is 0 m / s ≤ 15 m / s, the maximum operating speed of the train is 350 km / h;

[0059] When the ambient wind speed is 15 m / s < 0 m / s, the maximum operating speed of the train is 300 km / h;

[0060] When the ambient wind speed is less than 20 m / s and less than or equal to 25 m / s, the maximum operating speed of the train is 200 km / h.

[0061] When the ambient wind speed is 25 m / s < 30 m / s, the maximum operating speed of the train is 120 km / h;

[0062] When the ambient wind speed is less than 30 m / s, trains are strictly prohibited from entering the wind zone.

[0063] Currently, there are two main methods for obtaining information about windy weather conditions along railway lines: one is local weather forecasts, which are used to plan train speeds based on predicted weather changes over a certain period; the other is ultrasonic wind speed and direction monitoring sensors along the railway line. The former has a certain degree of uncertainty because weather forecasts predict wind field values ​​over a large area, while the latter, being a single-point measurement, generally requires processing with a moving average over a certain period to eliminate the randomness of tests such as sudden gusts, and necessitates the deployment of a large number of sensors at regular intervals along the railway line.

[0064] As a result, train services are currently operating primarily based on gale warnings from the weather station. Consequently, train cancellations due to sudden gusts of wind are frequent on subway trains, significantly reducing the line's operational efficiency.

[0065] To address the aforementioned problems, this embodiment provides a method for controlling high wind speeds in trains, such as... Figure 1 As shown, it includes:

[0066] S11: Based on the vehicle-mounted wind measurement equipment, obtain wind field information within a preset distance range ahead of the train in the direction of travel;

[0067] S12: Obtain aerodynamic load data under the current operating line conditions based on wind field information;

[0068] S13: Based on aerodynamic load data and current vehicle operating parameters, the safety indicators of the train under the current operating conditions are obtained through a pre-established actual vehicle track model.

[0069] S14: Generate train speed limit adjustment strategy based on safety indicators.

[0070] Wind field information is acquired by using onboard anemometers to monitor wind field information within a preset distance ahead of the train, including wind speed and direction. The onboard anemometer module may employ the coherent light Doppler effect principle, measuring wind speed and direction by analyzing the Doppler frequency shift of reflected light through laser emission and reception. Real-time monitoring of wind field information provides accurate data for subsequent aerodynamic load calculations, ensuring train operation safety.

[0071] Aerodynamic load data for rail trains primarily refers to pressure changes caused by airflow during train operation, especially when passing through tunnels or at high speeds. Specifically, aerodynamic load data is calculated based on real-time wind field information. This implementation does not limit the specific calculation method; for example, it can be obtained through preset mapping relationships, preset calculation formulas, or simulation experiments using wind tunnel testing. Accurate prediction of aerodynamic loads provides crucial parameters for train safety assessment and helps reduce train accidents caused by wind factors.

[0072] Safety index assessment: Combining aerodynamic load data and vehicle operating parameters, and using a real-vehicle track model, the safety indices of the train are calculated, such as derailment coefficient, wheel load reduction rate, lateral force, and overturning coefficient. This assesses the train's operational safety under current wind speed and track conditions, providing data support for developing speed-limiting strategies.

[0073] The generated speed limit adjustment strategy is transmitted to the train control system in real time, automatically adjusting the train's operating speed to ensure safety. Automated control reduces human error and improves the speed and accuracy of response to sudden strong winds.

[0074] Specifically, wind field information, aerodynamic load prediction results, safety indicators, and speed limit adjustment strategies are displayed on the train control console display module, while the information is transmitted to the train control center for centralized monitoring and dispatching. This enhances the driver's understanding of the current operating environment and improves decision-making quality; the control center can monitor multiple trains globally and optimize overall operating strategies.

[0075] To improve the accuracy and adaptability of system predictions, the system performance will continue to improve over time, better serving the safe operation of trains.

[0076] The train wind adjustment control method provided in this embodiment includes: acquiring wind field information within a preset distance range ahead of the train's direction of travel using onboard wind measuring equipment; obtaining aerodynamic load data under the current operating line conditions based on the wind field information; obtaining the train's safety indicators under the current operating line conditions based on the aerodynamic load data and current vehicle operating parameters through a pre-established real-vehicle line model; and generating a train wind-induced speed limit adjustment strategy based on the safety indicators. By using onboard wind measuring equipment to monitor the wind field within a certain range ahead of the train's direction of travel, and real-time monitoring of the continuous wind field around the train (including wind speed and direction), safety indicators such as the derailment coefficient and wheel load reduction rate of a section of track ahead are obtained through analysis. This allows for the early detection of wind conditions that may affect vehicle safety, guiding the train to assess its maximum operating speed based on the wind field near the track, and improving track operating efficiency.

[0077] According to the above embodiments, specifically, in the above train wind adjustment control method, the on-board wind measuring device is a wind measuring system based on the coherent optical Doppler effect; the on-board wind measuring device includes one or more laser emitters for emitting laser beams and measuring the Doppler frequency shift caused by aerosol particles; and one or more photosensitive receivers for receiving the reflected laser beams and converting them into electrical signals;

[0078] Correspondingly, based on the onboard wind measurement equipment, wind field information within a preset distance range ahead of the train's direction of travel is obtained, including:

[0079] The laser emitter is controlled to emit a laser beam, which is then collimated by a lens system and taken into the atmosphere.

[0080] The laser beam reflected back from aerosol particles in the atmosphere is received by a photosensitive receiver and converted into an electrical signal.

[0081] Wind field information is determined by calculating the Doppler frequency shift based on the electrical signal.

[0082] The Doppler effect describes how the frequency of a signal received by an observer changes when there is relative motion between the wave source and the observer. If the wave source moves towards the observer, the frequency increases; if the wave source moves away from the observer, the frequency decreases. Since the speed of light in the atmosphere is approximately equal to its speed in a vacuum, c ≈ 3 × 10⁻⁶. 8 Since wind speed changes are generally within 50 m / s, directly observing the Doppler frequency shift caused by wind speed changes is practically impossible. Therefore, interference fringe scanning with the same source beam is used to achieve equivalent measurement.

[0083] A laser emitter emits a high-frequency laser beam with high monochromaticity and coherence, suitable for measuring the Doppler effect. A lens system collimates the laser beam, ensuring it is emitted into the atmosphere as a parallel beam. The collimated beam is then emitted into the atmosphere in front of the train, interacting with aerosol particles. These aerosol particles (such as dust and water droplets) scatter the incident laser beam. As these particles move relative to the laser emitter (i.e., due to wind), a Doppler shift occurs.

[0084] A photosensitive receiver receives the reflected laser beam and converts the optical signal into an electrical signal. These electrical signals contain frequency variation information due to the Doppler effect. The received electrical signal is analyzed by a signal processing system to calculate the Doppler frequency shift. The magnitude of the Doppler frequency shift is proportional to the wind speed; therefore, the wind speed can be determined by measuring the frequency shift. Based on the calculated Doppler frequency shift, combined with the emission angle and direction of the laser beam, the wind speed and direction within a preset distance range ahead of the train can be determined, thus obtaining wind field information.

[0085] Lasers possess high monochromaticity and coherence, making Doppler shift measurements highly accurate. The system can monitor wind speed and direction in real time, providing instant wind field information for train operations. Lasers have excellent penetrating power and can operate in various weather conditions, including nighttime and bright light environments. By adjusting the power of the laser emitter and the sensitivity of the receiver, wind field information can be measured at longer distances.

[0086] According to the above embodiments, specifically, in the above train wind adjustment control method, obtaining aerodynamic load data under the current operating line conditions based on wind field information includes:

[0087] The simulated aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds were obtained through wind tunnel testing.

[0088] A multi-parameter database of train shape, speed, wind speed, and aerodynamic load was established based on simulated aerodynamic load data.

[0089] By using a multi-parameter database, the aerodynamic load data corresponding to the real-time wind field information of the current vehicle is determined.

[0090] In a controlled wind tunnel environment, different train shapes are tested to simulate aerodynamic loads under various vehicle operating speeds and wind conditions. Wind tunnel testing can provide accurate aerodynamic load data under safe and repeatable conditions, providing a reliable foundation for simulation and database establishment.

[0091] Based on simulated aerodynamic load data, a multi-parameter database was established, encompassing the relationship between train shape, speed, wind speed, and aerodynamic load. This database can quickly retrieve and provide aerodynamic load predictions under specific conditions, offering real-time data support for train operation safety assessments.

[0092] By using real-time wind field information and combining it with a multi-parameter database, aerodynamic load data under current vehicle operating conditions can be determined. Real-time determination of aerodynamic loads enables the train control system to react quickly based on current conditions, improving the safety and reliability of train operation.

[0093] Furthermore, before simulating the aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds through wind tunnel testing, the following steps are also included:

[0094] Acquire actual aerodynamic load data of trains with different shapes under different operating speeds and wind speeds;

[0095] The aerodynamic load data of trains with different shapes under different operating speeds and wind speeds were simulated through wind tunnel tests.

[0096] The accuracy of the wind tunnel test was verified based on actual aerodynamic load data and experimental aerodynamic load data.

[0097] When the accuracy of the wind tunnel test meets the preset conditions, the next step is to simulate the aerodynamic load data of different train shapes under different vehicle operating speeds and different wind speeds through wind tunnel testing.

[0098] Under actual operating conditions, aerodynamic load tests were conducted on trains with different shapes, and real-world aerodynamic load data were collected under different operating speeds and wind speeds. This provides benchmark data for actual operating environments, offering a basis for verification in subsequent wind tunnel tests and simulations.

[0099] In laboratory or real-world operating environments, wind tunnel tests are used to collect simulated aerodynamic load data of trains under different track and wind speed conditions. This data provides the foundation for subsequent simulations and database establishment.

[0100] The aerodynamic load data obtained from wind tunnel tests are compared and analyzed with aerodynamic load data collected during actual operation to verify the accuracy of the wind tunnel tests and ensure the reliability of the wind tunnel test results.

[0101] Based on the verification results, the accuracy of the wind tunnel tests is evaluated to ensure they meet the preset technical conditions and error range. Wind tunnel data is only used when the results are within acceptable error range, ensuring the quality and reliability of the simulation data. Wind tunnel tests are used to simulate the aerodynamic loads on trains under different operating speeds and wind conditions. The simulated aerodynamic load data is integrated into a multi-parameter database and correlated with parameters such as train shape, speed, and wind speed for easy real-time querying and application. This generates aerodynamic load data covering a wide range of conditions, providing comprehensive support for establishing the multi-parameter database.

[0102] The aerodynamic load data obtained from wind tunnel tests are compared with the simulation values ​​to verify the accuracy of the simulation model. This step ensures the reliability of the simulation results and provides quality assurance for subsequent simulated aerodynamic load data.

[0103] Furthermore, by using a multi-parameter database, the aerodynamic load data of the current vehicle under the current wind field information is determined, including:

[0104] A mapping model of train shape, speed, ambient wind speed and aerodynamic load is established based on a multi-parameter database.

[0105] The aerodynamic load data corresponding to the real-time wind field information of the current vehicle is determined based on the mapping model.

[0106] Based on the aerodynamic load data obtained from simulations, a database containing multiple parameters such as train shape, speed, wind speed, and aerodynamic loads is established. This database can provide rapid querying and reference for the aerodynamic loads of the train under different conditions. Using the train shape, speed, ambient wind speed, and aerodynamic load data from the multi-parameter database, a mapping model is established between them. This model can describe the relationship between these parameters and aerodynamic loads. The mapping model provides a method to quickly predict aerodynamic loads based on train characteristics and environmental conditions, providing accurate reference data for train control.

[0107] The mapping model is calibrated and validated using actual operational data or wind tunnel test data to ensure its predictive accuracy. Ensuring the mapping model provides reliable predictions under various conditions enhances the reliability of the train control system. Real-time wind field information collected by onboard anemometers, including wind speed, wind direction, and possible ambient temperature, is integrated into the mapping model. This real-time updated wind field information enables the mapping model to provide accurate aerodynamic load predictions under current conditions.

[0108] By combining actual and simulation data, the accuracy of aerodynamic load prediction has been improved. Simulations of different train shapes can provide guidance for train design, reducing the impact of aerodynamic loads on train operation safety. Accurate aerodynamic load data helps in developing more rational train operation strategies to adapt to different wind speeds and track conditions. A multi-parameter database provides more comprehensive data support for train safety assessment, helping to promptly identify and address potential safety risks.

[0109] According to the above embodiments, specifically, in the above train high wind adjustment control method, based on aerodynamic load data and current vehicle operating parameters, the safety indicators of the train under the current operating line conditions are obtained through a pre-established actual vehicle line model, including:

[0110] A dynamic model of a multi-rigid-body vehicle system is established based on the current train model and train operation route;

[0111] The safety indicators of the train are obtained by using a pre-established actual track model based on aerodynamic load data and current vehicle operating parameters. Among them, the safety indicators include one or more of the following: wheel load reduction rate, derailment coefficient, and wheel axle lateral force.

[0112] Based on the train model and operating route, a detailed multi-rigid-body dynamics model is established to simulate the train's physical behavior under various operating conditions. An accurate mathematical model is provided to simulate the train's dynamic response during actual operation, including its response to aerodynamic loads. Real-time aerodynamic load data obtained from onboard anemometers and a multi-parameter database are integrated into the dynamics model to ensure it reflects the actual stress conditions on the train under current environmental conditions. Current operating parameters of the train, such as speed, weight, and train formation, are input into the dynamics model to consider their impact on the train's dynamic behavior, thereby improving the accuracy of model predictions.

[0113] Using a dynamic model, the safety indicators of the train under current aerodynamic loads and operating parameters are calculated, such as wheel load reduction rate, derailment coefficient, and wheel axle lateral force.

[0114] Wheel load reduction rate refers to the proportion of reduced wheel-rail contact force due to aerodynamic loads during train operation, and is an important indicator for assessing train operational stability. Similarly, a dynamic model is used to calculate the train's derailment coefficient. The derailment coefficient is the ratio of the lateral force to the vertical force between the train's wheelset and the track; exceeding a certain threshold may lead to derailment. The lateral force on the wheel axle is calculated; this is the lateral force caused by aerodynamic loads, which may cause the train to deviate from the track or affect its smooth operation.

[0115] The safety of the train under current operating conditions is assessed by comprehensively considering indicators such as wheel load reduction rate, derailment coefficient, and lateral force on wheel axles. These indicators will be used to generate train speed limit adjustment strategies for high winds.

[0116] By employing precise dynamic models and real-time aerodynamic load data, train safety under high wind conditions can be assessed more accurately. The assessment results of safety indicators are fed back to the train control system in real time, automatically adjusting train speed or taking other necessary measures. This improves the train's response speed to environmental changes, reduces human reaction time, and enhances safety. Analyzing dynamic models of different train types and lines enhances the train's adaptability to various environmental conditions. Under extreme weather conditions such as high winds, train safety can be quickly assessed, and corresponding measures, such as speed limits or stopping, can be taken to ensure the safety of passengers and the train.

[0117] According to the above embodiments, specifically, in the above train wind adjustment control method, generating a train wind speed limit adjustment strategy based on safety indicators includes:

[0118] Determine whether a speed limit is necessary based on safety indicators;

[0119] If so, then generate speed limit conditions, speed limit method, speed reduction response time, and speed limit recovery conditions based on safety indicators.

[0120] First, the system assesses the train's current operating status based on safety indicators such as wheel load reduction rate, derailment coefficient, and lateral force on wheel axles. If these indicators exceed preset safety thresholds, the system determines that a speed limit needs to be imposed. Specific conditions for the speed limit are then determined, such as specific wind speed, wind direction, or aerodynamic load levels. These conditions will serve as the basis for triggering the speed limit. Based on the train's safety indicators and operating status, an appropriate speed limiting method is selected. This may include linear deceleration, immediate deceleration, or phased deceleration. The time required for the train to reduce from its current speed to the speed limit is determined. This time should be sufficient for the train to decelerate smoothly while ensuring safety. Under what conditions can the train's normal operating speed be restored? This may be based on a reduction in wind speed, a decrease in aerodynamic load, or improvements in other safety indicators.

[0121] Once the speed limit conditions, methods, reduction response time, and recovery conditions are determined, the system will automatically or with driver assistance implement the speed limit strategy. During the implementation of the speed limit strategy, train safety indicators and wind field information are continuously monitored. The speed limit strategy is adjusted based on real-time data to adapt to changes in the wind field.

[0122] By employing speed-limiting strategies based on real-time safety indicators, the safety risks of train operation under high wind conditions can be significantly reduced. A reasonable speed-limiting strategy can minimize the impact on train operating efficiency while ensuring safety. Rapid speed reduction response time helps to take swift action when strong winds suddenly intensify, reducing potential accident risks. Dynamically adjusting the speed-limiting strategy based on real-time wind field information and train status allows trains to better adapt to constantly changing environmental conditions. Precise control of train speed allows for more efficient allocation of railway resources, such as track usage and train scheduling.

[0123] This method enables intelligent and dynamic management of train speed under high wind conditions, ensuring the safety and efficiency of train operation.

[0124] According to the above embodiments, specifically, the above-mentioned train high wind adjustment control method further includes:

[0125] Collect one or more meteorological data points, including temperature, humidity, and air pressure, in the train's operating environment using onboard environmental monitoring equipment;

[0126] Meteorological influencing factors are generated based on meteorological data;

[0127] Adjust aerodynamic load data based on meteorological influence factors.

[0128] Onboard environmental monitoring equipment is used to collect meteorological data such as temperature, humidity, and air pressure in the train's operating environment in real time. This data is crucial for understanding the meteorological conditions of the train's environment. Based on the collected meteorological data, meteorological influencing factors are generated. These factors may include the effects of temperature on air density, humidity on air viscosity, and air pressure on air pressure differences, etc.

[0129] This study analyzes how meteorological factors affect the aerodynamic characteristics of trains. For example, changes in temperature affect air density, thus influencing the magnitude of aerodynamic loads; changes in humidity may affect aerodynamic drag and lift; and changes in air pressure may affect the pressure difference between the inside and outside of the train. Based on these meteorological factors, aerodynamic load data obtained through wind tunnel tests or simulations are adjusted. This adjustment can be achieved by modifying parameters in the dynamic model or by directly adjusting aerodynamic load values ​​in a multi-parameter database.

[0130] Using the adjusted aerodynamic load data, the train's wheel load reduction rate, derailment coefficient, and wheel axle lateral force, among other safety indicators, are recalculated. This step ensures that the safety indicators reflect actual weather conditions. Based on the updated safety indicators, a high-wind speed limit adjustment strategy for the train is generated or updated. This ensures that the speed limit strategy takes into account the impact of weather conditions, making it more accurate and safer.

[0131] Implement updated speed limit policies and continuously monitor meteorological data and train operation status to ensure safe train operation under changing weather conditions.

[0132] Taking meteorological factors into account can improve the accuracy of aerodynamic load prediction, making train operation safer. It enables trains to adapt to different weather conditions, reducing safety risks caused by weather changes. Optimizing operational strategies: Adjusting aerodynamic load data based on meteorological influencing factors can optimize train operation strategies and improve operational efficiency. It also provides more comprehensive data support for train drivers or automatic control systems, helping to make more accurate operational decisions.

[0133] The above embodiments have described the train high wind adjustment control method in detail. This application also provides embodiments corresponding to the train high wind adjustment control device. It should be noted that this application describes the embodiments of the device from two perspectives: one is based on the functional modules, and the other is based on the hardware.

[0134] From the perspective of functional modules Figure 2 A structural diagram of a train high wind adjustment control device provided in this application embodiment is shown below. Figure 2 As shown, a train high wind adjustment control device includes:

[0135] The data acquisition module 21 is used to acquire wind field information within a preset distance range ahead of the train in the direction of train operation based on the on-board wind measurement equipment;

[0136] The aerodynamic load analysis module 22 is used to obtain aerodynamic load data under the current operating line conditions based on wind field information;

[0137] The safety index analysis module 23 is used to obtain the safety index of the train under the current operating line conditions based on the aerodynamic load data and the current vehicle operating parameters through a pre-established real vehicle line model.

[0138] Speed ​​limit adjustment module 24 is used to generate train speed limit adjustment strategies for strong winds based on safety indicators.

[0139] The train wind adjustment control device provided in this embodiment includes: a data acquisition module 21, used to acquire wind field information within a preset distance range ahead of the train's running direction based on onboard wind measuring equipment; an aerodynamic load analysis module 22, used to obtain aerodynamic load data under the current operating line conditions based on the wind field information; a safety index analysis module 23, used to obtain the train's safety index under the current operating line conditions based on the aerodynamic load data and current vehicle operating parameters, through a pre-established real-vehicle line model; and a speed limit adjustment module 24, used to generate a train wind speed limit adjustment strategy based on the safety index. By monitoring the wind field within a certain range ahead of the train's running direction using onboard wind measuring equipment, and by real-time monitoring of the continuous wind field around the train, including wind speed and direction, and by analyzing the data, safety indicators such as the derailment coefficient and wheel load reduction rate of the preceding section of the line are obtained. This allows for the early detection of wind conditions that may affect vehicle operating safety, guiding the train to assess its maximum operating speed based on the wind field near the line, thereby improving line operating efficiency.

[0140] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0141] Figure 3 A structural diagram of another train high wind adjustment control device provided in the embodiments of this application is shown below. Figure 3 As shown, the train high wind adjustment control device includes: a memory 30 for storing computer programs;

[0142] The processor 31 is used to execute a computer program to implement the steps of the method for obtaining user operation habit information as described in the above embodiment (train wind adjustment control method).

[0143] The train wind adjustment control device provided in this embodiment may include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0144] The processor 31 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 31 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 31 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 31 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0145] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 30 is used to store at least the following computer program 301, which, after being loaded and executed by the processor 31, is capable of implementing the relevant steps of the train high-wind adjustment control method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, and the storage method may be temporary or permanent storage. The operating system 302 may include Windows, Unix, Linux, etc. The data 303 may include, but is not limited to, the data involved in implementing the train high-wind adjustment control method.

[0146] In some embodiments, the train high wind adjustment control device may further include a display screen 32, an input / output interface 33, a communication interface 34, a power supply 35, and a communication bus 36.

[0147] Those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the train wind adjustment control device and may include more or fewer components than shown.

[0148] The train wind adjustment control device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method: train wind adjustment control method.

[0149] This application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above embodiment of the train high wind adjustment control method.

[0150] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they 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 all or 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 executes all or part of the steps of the methods 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.

[0151] The computer-readable storage medium provided in this embodiment stores a computer program thereon. When the processor executes the program, the following method can be implemented: a train wind adjustment control method.

[0152] Finally, this application also provides a train including the aforementioned train wind adjustment control device. By using onboard wind measuring equipment to monitor the wind field within a certain range ahead of the train's direction of travel, and by real-time monitoring of the continuous wind field around the train, including wind speed and direction, safety indicators such as the derailment coefficient and wheel load reduction rate of the preceding track section are obtained through analysis. This allows for the early detection of strong wind conditions that may affect vehicle safety, guiding the train to assess its maximum operating speed based on the wind field near the track, thereby improving track operating efficiency.

[0153] The foregoing has provided a detailed description of the train high-wind adjustment control method, device, medium, and train provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0154] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for adjusting and controlling high winds on trains, characterized in that, include: Based on the vehicle-mounted wind measurement equipment, wind field information within a preset distance range ahead of the train's running direction is obtained, and the wind field information includes wind speed and wind direction; Based on the wind field information, aerodynamic load data under the current operating line conditions are obtained; Based on the aerodynamic load data and current vehicle operating parameters, the safety indicators of the train under the current operating line conditions are obtained through a pre-established multi-rigid-body vehicle system dynamic model. The safety indicators include one or more of the following: wheel load reduction rate, derailment coefficient, and wheel axle lateral force. The multi-rigid-body vehicle system dynamic model is established based on the current train model and the train operating line. A train speed limit adjustment strategy for strong winds is generated based on the aforementioned safety indicators; The step of obtaining aerodynamic load data under the current operating line conditions based on the wind field information includes: The simulated aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds were obtained through wind tunnel testing. A multi-parameter database of train shape, vehicle speed, wind speed, and aerodynamic load is established based on the simulated aerodynamic load data. The aerodynamic load data corresponding to the real-time wind field information of the current vehicle is determined using the multi-parameter database.

2. The train high wind adjustment and control method according to claim 1, characterized in that, The vehicle-mounted wind measurement device includes: one or more laser emitters for emitting laser beams and measuring the Doppler frequency shift caused by aerosol particles; and one or more photosensitive receivers for receiving the reflected laser beams and converting them into electrical signals. Correspondingly, the acquisition of wind field information within a preset distance range ahead of the train's direction of travel based on the vehicle-mounted wind measurement equipment includes: The laser emitter is controlled to emit a laser beam, which is then collimated by a lens system and taken into the atmospheric environment. The laser beam reflected back from aerosol particles in the atmosphere is received by a photosensitive receiver and converted into an electrical signal. The wind field information is determined by calculating the Doppler frequency shift based on the electrical signal.

3. The train high wind adjustment and control method according to claim 1, characterized in that, Before simulating the aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds through wind tunnel testing, the following also includes: Acquire actual aerodynamic load data of trains with different shapes under different operating speeds and wind speeds; The aerodynamic load data of trains with different shapes under different operating speeds and wind speeds were simulated through wind tunnel tests. The accuracy of the wind tunnel test was verified based on the actual aerodynamic load data and the experimental aerodynamic load data. When the accuracy of the wind tunnel test meets the preset conditions, the process proceeds to the step of simulating the aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds through wind tunnel testing.

4. The train high wind adjustment and control method according to claim 3, characterized in that, The step of determining the aerodynamic load data of the vehicle under the current wind field information using the multi-parameter database includes: A mapping model of train shape, vehicle speed, ambient wind speed and aerodynamic load is established based on the multi-parameter database. The aerodynamic load data corresponding to the real-time wind field information of the current vehicle is determined based on the mapping model.

5. The train high wind adjustment and control method according to claim 1, characterized in that, The process of generating a train speed limit adjustment strategy based on the safety indicators includes: Determine whether a speed limit is necessary based on the aforementioned safety indicators; If so, then the speed limit conditions, speed limit method, speed reduction response time, and speed limit recovery conditions are generated based on the aforementioned safety indicators.

6. The train high wind adjustment control method according to claim 2, characterized in that, Also includes: Collect one or more meteorological data points, including temperature, humidity, and air pressure, in the train's operating environment using onboard environmental monitoring equipment; Meteorological influencing factors are generated based on the meteorological data; The aerodynamic load data are adjusted according to the meteorological influencing factors.

7. A train high-wind adjustment control device, characterized in that, include: The data acquisition module is used to acquire wind field information within a preset distance range ahead of the train's running direction based on the vehicle-mounted wind measurement equipment. The wind field information includes wind speed and wind direction. The aerodynamic load analysis module is used to obtain aerodynamic load data under the current operating line conditions based on the wind field information. The safety index analysis module is used to obtain the safety index of the train under the current operating line conditions based on the aerodynamic load data and the current vehicle operating parameters through a pre-established multi-rigid-body vehicle system dynamic model; wherein, the safety index includes one or more of the following: wheel load reduction rate, derailment coefficient, and wheel axle lateral force; wherein, the multi-rigid-body vehicle system dynamic model is established based on the current train model and train operating line; The speed limit adjustment module is used to generate a train speed limit adjustment strategy for strong winds based on the safety indicators. The step of obtaining aerodynamic load data under the current operating line conditions based on the wind field information includes: The simulated aerodynamic load data of different train shapes under different vehicle operating speeds and wind speeds were obtained through wind tunnel testing. A multi-parameter database of train shape, vehicle speed, wind speed, and aerodynamic load is established based on the simulated aerodynamic load data. The aerodynamic load data corresponding to the real-time wind field information of the current vehicle is determined using the multi-parameter database.

8. A train high wind adjustment control device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the train high wind adjustment control method as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the train high wind adjustment control method as described in any one of claims 1 to 6.

10. A train, characterized in that, Includes the train wind adjustment control device as described in claim 8.

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