Enhanced braking system for mountain rail vehicles

Abstract

The application discloses a mountain rail vehicle enhanced braking system and relates to the technical field of rail vehicle braking control. The system comprises the following steps: collecting a rail position vector and an arc length, calculating rail curvature and rail slope, combining a current vehicle speed with a target speed to solve a braking demand acceleration value, generating a slope parameter and a curve parameter; adjusting a wheel-rail hydraulic braking torque value according to a braking force distribution ratio, controlling a rack rail brake engagement depth and a vibration frequency through a rack rail action instruction, generating a wheel-rail system state report and a rack rail working log; extracting a slope parameter, an actual value of adhesion force and a rack rail engagement efficiency in a braking process from the wheel-rail system state report and the rack rail working log, and combining a terrain braking curve to perform closed-loop optimization. The application performs self-learning and self-adaptive adjustment on the braking process through closed-loop feedback and parameter optimization, so that the energy utilization efficiency and the service life of key components are improved while the braking efficiency is ensured.

Description

Technical Field

[0001] This invention relates to the field of rail vehicle braking control technology, and in particular to an enhanced braking system for mountain rail vehicles. Background Technology

[0002] With the continuous development of rail transit technology, the braking system of mountain rail vehicles, as a core subsystem ensuring driving safety, has received widespread attention for its technological evolution. Traditional braking control methods mainly rely on open-loop or semi-open-loop control based on vehicle speed, axle load, and basic track parameters. For example, they combine a pre-set track database with onboard sensors to achieve initial distribution of braking force. In recent years, with the advancement of multi-sensor fusion technology and dynamic modeling methods, some advanced systems have begun to introduce real-time environmental monitoring functions, such as using optical detection or infrared sensing to perform local assessment of wheel-rail contact status, in order to improve the adaptability of the braking process.

[0003] However, there is still room for further optimization in existing technologies. Particularly in terms of dynamic braking force distribution and environmental adaptability, current systems largely rely on static or offline calculated track parameters (such as gradient and curvature), failing to fully integrate real-time changes in wheel-rail contact conditions and external environmental factors (such as rail surface corrosion, wheel surface temperature distribution, and changes in track dielectric properties), resulting in insufficient accuracy in adhesion estimation. Due to the characteristics of mountainous track environments—continuous gradient changes, numerous small-radius curves, and complex climatic conditions—traditional methods struggle to accurately calculate braking demand acceleration, thus affecting the real-time performance and reliability of braking force distribution. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an enhanced braking system for mountain rail vehicles, which solves the problems of inaccurate adhesion estimation and insufficient dynamic adaptability of braking force distribution caused by the failure to integrate multi-source real-time data in complex environments in the prior art.

[0006] This invention provides an enhanced braking system for mountain rail vehicles, comprising,

[0007] The driving condition perception module collects the track position vector and arc length, calculates the track curvature and track slope, and combines the vehicle's current speed and target speed to calculate the braking acceleration value and generate slope parameters and curve parameters.

[0008] The multi-source information fusion module collects rail surface condition data, wheel surface temperature field distribution data, and track dielectric property data. After integrating slope parameters and curve parameters to correct the rail corrosion index and wheel surface temperature field data, it constructs adhesion safety boundary parameters and generates braking force distribution ratio and rack and pinion action commands.

[0009] The braking execution control module adjusts the wheel-rail hydraulic braking torque value according to the braking force distribution ratio, and controls the engagement depth and vibration frequency of the rack brake through rack action commands, generating a wheel-rail system status report and a rack work log.

[0010] The braking performance optimization module extracts gradient parameters, actual adhesion values, and gear meshing efficiency during braking from the wheel-rail system status report and gear working log, and performs closed-loop optimization by combining the terrain braking curve.

[0011] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0012] The specific steps for collecting the track position vector and arc length, and calculating the track curvature and slope are as follows.

[0013] Collect the three-dimensional coordinates of discrete points on the track profile to generate the track position vector;

[0014] The distance between adjacent points is obtained based on the orbital position vector, and the orbital arc length is generated by accumulating the distances segment by segment.

[0015] Based on the track position vector and track arc length, the track curvature is calculated using the three-point circle method, and the ratio of the change in height between adjacent points to the change in horizontal distance is calculated to generate the track slope.

[0016] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0017] The process involves combining the vehicle's current speed and target speed to calculate the braking acceleration requirement, generating gradient and curve parameters. The specific steps are as follows:

[0018] Collect the vehicle's current speed and combine it with the target speed to calculate the braking acceleration value;

[0019] Based on the braking demand acceleration value, combined with the track gradient and gradient classification standards, gradient parameters are generated, and combined with the track curvature and curve classification standards, curve parameters are generated.

[0020] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0021] The specific steps for collecting rail surface condition data, wheel surface temperature field distribution data, and track dielectric property data, and then correcting the rail corrosion index and wheel surface temperature field data by integrating gradient and curve parameters, are as follows.

[0022] Collect data on rail surface condition, wheel surface temperature field distribution, and track dielectric properties.

[0023] Based on the slope grade in the slope parameters, the corrosion degree index in the rail surface condition data is adjusted to generate the adjusted rail corrosion index.

[0024] By using the compensation coefficient in the curve parameters, the temperature gradient value in the wheel surface temperature field distribution data is adjusted to generate adjusted wheel surface temperature data.

[0025] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0026] The specific steps for constructing adhesion safety boundary parameters and generating braking force distribution ratios and gear track movement commands are as follows.

[0027] Construct adhesive safety boundary parameters to generate braking force distribution ratios and gear track movement commands;

[0028] Based on the adjusted rail corrosion index, adjusted wheel surface temperature data, and track dielectric properties data, the adhesion safety boundary parameters are calculated.

[0029] Based on the adhesion safety boundary parameters and the braking demand acceleration value, the braking force distribution ratio is calculated, and the gear train action command is obtained.

[0030] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0031] The specific steps for adjusting the wheel-rail hydraulic braking torque value according to the braking force distribution ratio are as follows.

[0032] Based on the braking force distribution ratio, the target braking pressure value is retrieved from the hydraulic system braking force distribution database, and combined with pressure monitoring data, the theoretical wheel-rail braking torque value is calculated.

[0033] Based on the target engagement depth parameter in the gear tooth movement command, the displacement adjustment command is obtained and the engagement state is verified by combining it with the actual displacement data, and a gear tooth engagement depth confirmation signal is generated.

[0034] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0035] The specific steps for controlling the engagement depth and vibration frequency of the gear brake via gear action commands are as follows:

[0036] Based on the target vibration frequency parameters in the gear track action command, the vibration state is verified by combining actual frequency data, and a gear track vibration frequency confirmation signal is obtained.

[0037] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0038] The specific steps for generating the wheel-rail system status report and the rack and pinion working log are as follows.

[0039] A wheel-rail system status report is prepared by combining theoretical braking torque values ​​with wheel surface temperature field distribution data.

[0040] Record the toothed rail engagement depth confirmation signal and the toothed rail vibration frequency confirmation signal.

[0041] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0042] The specific steps for extracting gradient parameters, actual adhesion values, and gear meshing efficiency during braking from the wheel-rail system status report and gear operating log are as follows.

[0043] Extract the actual value of braking torque, wheel surface temperature distribution characteristics and contact state parameters from the wheel-rail system status report, and obtain the gradient parameters and actual values ​​of adhesion during the braking process;

[0044] Calculate the gear meshing efficiency based on the actual values ​​of meshing depth, vibration frequency, stress distribution characteristics, and temperature distribution characteristics in the gear working log.

[0045] Based on the slope parameter, the actual value of adhesion, and the gear meshing efficiency, the track curvature compensation coefficient, adhesion safety boundary weight factor, and gear action response delay parameter that need to be adjusted are calculated.

[0046] As a preferred embodiment of the enhanced braking system for mountain rail vehicles described in this invention, wherein:

[0047] The closed-loop optimization based on the terrain braking curve involves the following specific steps.

[0048] The track curvature compensation coefficient, adhesion safety boundary weight factor, and toothed track motion response delay parameter are updated as control parameters.

[0049] Based on the updated control parameters, a closed-loop optimization is generated through the braking control cycle.

[0050] The beneficial effects of this invention are as follows: by dynamically integrating rail condition, wheel surface temperature and terrain parameters, the adhesion safety boundary is accurately constructed, realizing the intelligent coordinated distribution of braking force and rack movement, effectively improving braking safety and adaptability under complex mountain track conditions; and by using closed-loop feedback and parameter optimization to perform self-learning and adaptive adjustment of the braking process, thereby improving energy utilization efficiency and service life of key components while ensuring braking performance. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart for enhancing braking methods for mountain rail vehicles.

[0053] Figure 2 This is a flowchart for calculating track curvature and slope.

[0054] Figure 3 Flowchart for generating acceleration and parameters for braking requirements.

[0055] Figure 4 A flowchart for multi-source data fusion and correction. Detailed Implementation

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0059] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides an enhanced braking system for mountain rail vehicles, comprising the following steps:

[0060] The driving condition perception module is used to collect track position vectors and arc lengths, calculate track curvature and track slope, and combine the vehicle's current speed and target speed to calculate the braking acceleration value and generate slope parameters and curve parameters.

[0061] Collect the three-dimensional coordinates of discrete points on the track profile to generate the track position vector;

[0062] Furthermore, after acquiring the three-dimensional coordinates of the discrete points of the orbital position vector, the three-dimensional coordinates of each discrete point are regarded as a vector pointing from the origin of the spatial coordinate system to the discrete point. The vectors of the discrete points are then arranged according to the acquisition order to form the orbital position vector. All discrete points of the orbital position vector are processed in this way, ultimately resulting in a complete and ordered orbital position vector.

[0063] The distance between adjacent points is obtained based on the orbital position vector, and the orbital arc length is generated by accumulating the distances segment by segment.

[0064] Furthermore, the straight-line length between the corresponding endpoints of two adjacent track position vectors is calculated to obtain the distance between adjacent points. For example, if track position vector OP1 corresponds to endpoint P1 and track position vector OP2 corresponds to endpoint P2, then the straight-line distance between P1 and P2 is calculated as the first distance between adjacent points. Starting from the track's starting point, the distance between the first adjacent points is used as the initial segment of the track arc length. The distance between the second adjacent points is combined with the initial segment of the track arc length to obtain the accumulated track arc length up to the second point. Subsequent distances between adjacent points are combined with the previous accumulated track arc length in sequence until all distances between adjacent points have been processed, ultimately generating the complete track arc length from the track's starting point to its ending point.

[0065] Based on the track position vector and track arc length, the track curvature is calculated using the three-point circle method, and the ratio of the change in height to the change in horizontal distance between adjacent points is calculated to generate the track slope.

[0066] Furthermore, three consecutive points are selected from the track position vector, and the ratio of the change in height to the change in horizontal distance between adjacent points is calculated. For two adjacent points in the track position vector, the difference between the vertical coordinates of the latter point and the former point is calculated to obtain the change in height between adjacent points. The straight-line distance between the horizontal projection coordinates of the latter point and the former point is calculated to obtain the ratio of the change in horizontal distance. The ratio of the change in height to the change in horizontal distance between adjacent points is then calculated to obtain the track slope of the adjacent point segment.

[0067] The orbital curvature is calculated using the three-point circle method, and the expression is:

[0068] ;

[0069] in, θ represents the orbital curvature, and θ represents the angle between adjacent line segments. A sequence of spatial coordinates representing the bonding path;

[0070] Collect the vehicle's current speed and combine it with the target speed to calculate the braking acceleration value;

[0071] Furthermore, the current vehicle speed is obtained, and the target speed is determined. Based on the current vehicle speed value, the square difference of the target speed, and the distance determined by the spatial coordinate sequence of the braking distance, the theoretical speed value is derived through physical kinematic relationships. The theoretical speed value is combined with the component of gravitational acceleration in the direction of track slope angle, and then combined with the centripetal acceleration component determined by the square of the current vehicle speed value, track curvature, and reference radius of curvature. Finally, after adjusting the wheel-rail adhesion correction coefficient and the wheel surface temperature variable, the braking demand acceleration value is obtained.

[0072] It should be noted that the braking distance spatial coordinate sequence is a set of three-dimensional coordinates of discrete points on the track corresponding to the required braking distance, starting from the current vehicle position and moving forward along the track position vector.

[0073] The acceleration value required for braking is calculated using the following expression:

[0074] ;

[0075] in, This indicates the acceleration value required for braking. Indicates the vehicle's current speed. Indicates the target speed. Represents the spatial coordinate sequence of braking distance. Represents gravitational acceleration. Indicates the track slope angle. Indicates orbital curvature. Indicates the reference radius of curvature. This represents the wheel-rail adhesion correction factor. This represents the variable temperature of the wheel surface.

[0076] Based on the braking demand acceleration value, combined with the track gradient and gradient classification standards, gradient parameters are generated, and combined with the track curvature and curve classification standards, curve parameters are generated.

[0077] Furthermore, based on the braking demand acceleration value, combined with the track gradient and gradient grade classification standards, the calculated track gradient is compared with the gradient grade classification standards to determine the grade to which the track gradient belongs; combined with the track curvature and curve grade classification standards, the calculated track curvature value is compared with the curve grade classification standards to determine the grade to which the track curvature belongs; finally, gradient parameters and curve parameters containing gradient grade and curve grade are formed.

[0078] It should be noted that the specific source of the gradient classification standard is: based on the critical value study of the influence of gradient on traction and braking performance in wheel-rail dynamics theory, the track gradient value is divided into several levels; the specific source of the curve classification standard is: based on the results of curve passing performance in wheel-rail interaction theory, and referring to the classification method of curve radius in the track geometry and position maintenance standard, the track curvature value is divided into different levels according to the curve radius range.

[0079] The multi-source information fusion module is used to collect rail surface condition data, wheel surface temperature field distribution data and track dielectric property data. After integrating the slope parameters and curve parameters to correct the rail corrosion index and wheel surface temperature field data, it constructs the adhesion safety boundary parameters and generates the braking force distribution ratio and the rack and pinion action command.

[0080] Collect data on rail surface condition, wheel surface temperature field distribution, and track dielectric properties.

[0081] Furthermore, optical sensors and image acquisition devices are used to acquire rail surface image information, and rail surface condition data characterizing the degree of rail corrosion and pollution status are extracted from the rail surface image information; infrared thermometers are used to scan the wheelset tread to obtain temperature measurements at different locations on the wheel surface, forming wheel surface temperature field distribution data; and capacitive and microwave sensors are used to measure the dielectric properties between the rail surface and the sensors, obtaining track dielectric property data reflecting the degree of moisture and pollution components on the rail surface.

[0082] It should be noted that the rail corrosion index is generated by: acquiring images of the rail surface through a high-definition line scan camera installed on the bogie, analyzing the area ratio and color characteristics of rust spots on the rail surface using image processing algorithms, and combining the metal oxidation degree data detected by a spectral analyzer to generate the rail corrosion index data.

[0083] The wheel surface temperature field data is obtained by scanning the wheel tread of passing vehicles using an infrared thermal imager installed next to the track, acquiring temperature values ​​of different areas of the wheel surface through non-contact temperature measurement technology, and combining the wheel position information recorded by the rotary encoder to generate complete wheel surface temperature field data.

[0084] Based on the slope grade in the slope parameters, the corrosion degree index in the rail surface condition data is adjusted to generate the adjusted rail corrosion index.

[0085] Furthermore, based on the real-time acquired slope grade code, a predefined slope-adjustment coefficient mapping table is queried to obtain the corresponding adjustment coefficient value. The adjustment coefficient value is then combined with the original rail corrosion index to generate a rail corrosion index that has been weighted and corrected for the influence of slope, thereby more accurately reflecting the true risk level of corrosion on wheel-rail adhesion under specific slope conditions.

[0086] It should be noted that the predefined slope-adjustment coefficient mapping table converts the real-time acquired slope level into the corresponding adjustment coefficient. This mapping table is generated based on historical operational data, incorporating rail corrosion indices at different slopes, real-time measured wheel-rail adhesion coefficients, and Boolean labels indicating whether slippage or wheel spin occurred. Regression analysis is used to obtain the quantitative relationship between slope level, original corrosion index, and actual adhesion coefficient. This quantitative relationship directly calculates the slope's reinforcing weight on corrosion risk, ultimately generating the slope-adjustment coefficient mapping lookup table. The trained slope-specific adjustment coefficients are then fused with the real-time rail corrosion index to generate a slope-weighted rail corrosion index, thus accurately reflecting the true risk level of corrosion to adhesion under specific slope conditions.

[0087] By using the compensation coefficient in the curve parameters, the temperature gradient value in the wheel surface temperature field distribution data is adjusted to generate adjusted wheel surface temperature data.

[0088] Furthermore, the temperature gradient values ​​in the wheel surface temperature field distribution data are adjusted using the compensation coefficient in the curve parameters to determine the compensation coefficient values ​​included in the curve parameters. The temperature gradient values ​​at each measurement point in the wheel surface temperature field distribution data are combined with the compensation coefficient values ​​to generate adjusted temperature gradient values. Finally, the temperature at each location on the wheel surface is recalculated based on the adjusted temperature gradient values ​​to form adjusted wheel surface temperature data. By combining the adjusted temperature gradient values ​​generated by the compensation coefficients, the adjusted temperature values ​​are deduced.

[0089] Based on the adjusted rail corrosion index, adjusted wheel surface temperature data, and track dielectric properties data, the adhesion safety boundary parameters are calculated.

[0090] Furthermore, the baseline friction coefficient sequence and wheel-rail normal force values ​​are obtained; the adjusted wheel surface temperature data, track dielectric property data, real-time track curvature and slope angle are input into the correction rule library, and the correction rule library outputs temperature influence factor, dielectric influence factor, curvature influence factor and slope influence factor respectively; finally, the factors are comprehensively coupled with the baseline friction coefficient sequence and wheel-rail normal force values ​​to calculate the adhesion safety boundary parameters reflecting the current actual wheel-rail state.

[0091] The adhesive safety boundary parameters are calculated using the following expression:

[0092] ;

[0093] in, Indicates the adhesive safety boundary parameters. Represents the reference friction coefficient sequence. Indicates the normal force between the wheel and the rail. This represents the correction function for the wheel surface temperature field. This represents the correction function for the rail corrosion index. This represents the correction function for the track dielectric properties. This represents the orbital curvature correction function. This represents the track gradient correction function. This represents the wheel surface temperature distribution data. An index indicating the degree of rail corrosion. Indicates the dielectric properties of the track. Indicates orbital curvature. Indicates the track slope angle.

[0094] Based on the adhesion safety boundary parameters and the braking demand acceleration value, the braking force distribution ratio is calculated, and the gear train action command is obtained;

[0095] Furthermore, the braking demand acceleration value is combined with the vehicle mass to obtain the braking demand force; then the braking demand force is compared with the adhesion safety boundary parameter, and the braking force distribution ratio is determined by the allocation sensitivity coefficient in combination with the ratio of the rack braking efficiency coefficient, the real-time adhesion utilization rate and the adhesion utilization safety threshold; at the same time, according to the difference between the braking demand force and the adhesion safety boundary parameter, the corresponding rack action command is matched from the command library to obtain the rack action command containing the target meshing depth and target vibration frequency parameters.

[0096] It should be noted that the setting of the adhesion utilization safety threshold is based on training with historical operating data. A large number of samples of wheel-rail adhesion, normal force, wheel surface temperature, rail condition and corresponding adhesion utilization rate are collected. Through the correlation between these data features and driving safety, a critical value is automatically optimized. The adhesion utilization safety threshold must ensure that adhesion failure can be effectively avoided when the actual adhesion utilization rate is lower than this critical value under most operating conditions. Finally, it is determined and applied to real-time monitoring after repeated adjustments through cross-validation.

[0097] The formula for calculating the braking force distribution ratio is:

[0098] ;

[0099] in, This represents the sequence of braking force distribution ratios. Indicates the braking force required. Indicates the adhesive safety boundary parameters. Indicates the gear braking efficiency coefficient. Indicates real-time adhesion utilization. Indicates the adhesion utilization threshold. This represents the allocation sensitivity coefficient.

[0100] The braking execution control module is used to adjust the wheel-rail hydraulic braking torque value according to the braking force distribution ratio, and control the engagement depth and vibration frequency of the rack brake through rack action commands, and generate wheel-rail system status report and rack work log.

[0101] Based on the braking force distribution ratio, the target braking pressure value is retrieved from the hydraulic system braking force distribution database, and combined with pressure monitoring data, the theoretical wheel-rail braking torque value is calculated.

[0102] Furthermore, based on the calculated braking force distribution ratio, the corresponding target braking pressure value is searched in the hydraulic system braking force distribution database; real-time pressure monitoring data in the hydraulic circuit is read, and the target braking pressure value is compared and verified with the real-time pressure monitoring data to confirm the consistency between the pressure output and the target braking pressure value. Based on the verified target braking pressure value, the theoretical wheel-rail braking torque value is calculated using inherent parameters such as the hydraulic cylinder's effective area and the brake lever ratio.

[0103] It should be noted that the calculation process of the theoretical wheel-rail braking torque is as follows: using the verified target braking pressure value combined with the piston action area of ​​the hydraulic brake cylinder, the output thrust of the brake cylinder is obtained; the output thrust of the brake cylinder is then converted into a positive pressure acting on the brake caliper or brake shoe by the brake lever system and the inherent brake lever ratio; finally, this positive pressure is combined with the effective friction radius of the brake disc on the opposite side of the wheel to calculate the theoretical braking torque finally transmitted to the wheel axle.

[0104] Based on the target engagement depth parameter in the gear track action command, the displacement adjustment command is obtained and the engagement state is verified by combining the actual displacement data, and a gear track engagement depth confirmation signal is generated.

[0105] Furthermore, the gear track motion command is analyzed to extract the target engagement depth parameter; a corresponding displacement adjustment command is generated based on the target engagement depth parameter and sent to the displacement actuator of the gear track brake; then, the actual displacement data of the gear track brake is collected through a displacement sensor; the actual displacement data is compared with the target engagement depth parameter to verify whether the actual engagement state meets the requirements of the target engagement depth parameter; if the deviation between the actual displacement data and the target engagement depth parameter is within the allowable tolerance range, a gear track engagement depth confirmation signal indicating normal engagement state is generated; if the deviation exceeds the allowable tolerance range, a gear track engagement depth confirmation signal indicating abnormal engagement state is generated.

[0106] It should be noted that the process involves receiving the gear track motion command data stream, which is encoded in a structured format; identifying the command identifier field in the gear track motion command to locate the data segment containing the target engagement depth parameter; reading the numerical information from the target engagement depth parameter data segment according to the data length and type specified in the command protocol; and converting the read numerical information into the target engagement depth parameter expressed in engineering units.

[0107] The process of generating displacement adjustment commands is as follows: the received target engagement depth value is compared with the current position data fed back by the toothed brake displacement sensor to calculate the required displacement deviation; then, based on the response characteristics of the servo driver and the parameters of the mechanical transmission mechanism, the displacement deviation is converted into a specific pulse signal or analog voltage signal to form a control command for driving the motor to rotate forward or backward, and finally output to the displacement actuator.

[0108] The determination of the allowable tolerance range takes into account mechanical design tolerances, dynamic meshing safety requirements, and control accuracy. Based on the standard static tolerance base value of the mechanical fit clearance between the gear and the toothed rail, and then superimposed with the safety margin under dynamic working conditions such as train vibration and structural deformation, a displacement deviation range that can ensure reliable meshing without causing abnormal wear is finally determined through bench tests and dynamic simulation verification.

[0109] Based on the target vibration frequency parameters in the toothed rail action command, the vibration state is verified by combining actual frequency data, and a toothed rail vibration frequency confirmation signal is obtained.

[0110] Furthermore, based on the target vibration frequency parameter in the gear toothing action command, the vibration state is verified by combining actual frequency data. The gear toothing action command is analyzed, and the target vibration frequency parameter is extracted. The actual frequency data of the gear toothing brake is collected by a vibration sensor. The actual frequency data is compared with the target vibration frequency parameter to verify whether the actual vibration state meets the command requirements. If the deviation between the actual frequency data and the target vibration frequency parameter is within the allowable tolerance range, a gear toothing frequency confirmation signal indicating normal vibration state is generated. If the deviation exceeds the allowable tolerance range, a gear toothing vibration frequency confirmation signal indicating abnormal vibration state is generated.

[0111] A wheel-rail system status report is prepared by combining theoretical braking torque values ​​with wheel surface temperature field distribution data.

[0112] The theoretical wheel-rail braking torque values ​​obtained from the calculations are collected; the theoretical wheel-rail braking torque values ​​and the wheel surface temperature field distribution data are combined according to the report format; when combined, the theoretical wheel-rail braking torque values ​​are used as braking performance indicators, and the wheel surface temperature field distribution data are used as thermal load status indicators, together forming a complete wheel-rail system status report.

[0113] Record the toothed rail meshing depth confirmation signal and toothed rail vibration frequency confirmation signal, toothed rail stress distribution data and toothed rail temperature distribution data, and generate a toothed rail work log;

[0114] Furthermore, the toothed rail meshing depth confirmation signal, toothed rail vibration frequency confirmation signal, toothed rail stress distribution data, and toothed rail temperature distribution data are recorded in time stamp order; finally, all recorded data are integrated in log format to generate a complete toothed rail working log containing meshing status, vibration status, stress distribution, and temperature distribution information.

[0115] The braking performance optimization module is used to extract gradient parameters, actual adhesion values, and gear meshing efficiency during braking from wheel-rail system status reports and gear working logs, and to perform closed-loop optimization by combining them with terrain braking curves.

[0116] Extract the actual value of braking torque, wheel surface temperature distribution characteristics and contact state parameters from the wheel-rail system status report, and obtain the gradient parameters and actual values ​​of adhesion during the braking process;

[0117] Furthermore, the wheel-rail system status report is analyzed to extract the actual braking torque value from the braking performance index field; the wheel surface temperature distribution characteristics from the thermal load status index field; and the contact status parameters from the contact status parameter field. The gradient parameters calculated during braking are obtained; and the actual adhesion values ​​are retrieved from the adhesion monitoring records. Finally, a complete set of performance extraction results is obtained.

[0118] It should be noted that the process involves parsing the wheel-rail system status report, receiving the wheel-rail system status report data packet (which uses a fixed-format binary encoding structure), identifying the header identifier in the data packet to confirm the report type as a wheel-rail system status report, locating the specific offset position of the braking performance index field within the data packet (which contains the actual braking torque value), reading the raw data of the actual braking torque value according to the data type and byte length defined in the field, and converting the read raw data into the actual braking torque value expressed in engineering units using conversion rules.

[0119] Calculate the gear meshing efficiency based on the actual values ​​of meshing depth, vibration frequency, stress distribution characteristics, and temperature distribution characteristics in the gear working log.

[0120] Furthermore, the actual values ​​of engagement depth, vibration frequency, stress distribution characteristics, and temperature distribution characteristics are read from the gear track work log; the actual engagement depth is compared with the target engagement depth parameter to obtain the depth matching degree; the actual vibration frequency is compared with the target vibration frequency parameter to obtain the frequency matching degree; the gear track stress distribution data recorded in the gear track work log is obtained, which includes the stress values ​​at each measurement point on the tooth surface; the standard stress distribution data is read, which represents the stress distribution on the tooth surface under ideal working conditions; the stress values ​​at each measurement point of the gear track stress distribution data are compared point by point with the stress values ​​at the corresponding positions in the standard stress distribution data, and the stress difference value at each measurement point is calculated;

[0121] The stress deviation is obtained by averaging the absolute values ​​of the stress differences at all measurement points. The temperature distribution data of the toothed rail, recorded in the toothed rail work log, is obtained, including the temperature values ​​at each measurement point on the tooth surface. The allowable operating temperature range of the toothed rail is read, determined by the material properties. The temperature values ​​at each measurement point in the toothed rail temperature distribution data are compared with the upper limit of the allowable operating temperature range, and the temperature difference exceeding the upper limit at each measurement point is recorded. The temperature deviation is obtained by averaging the absolute values ​​of the temperature differences at all measurement points. The toothed rail meshing efficiency is calculated using the toothed rail meshing efficiency calculation formula, taking into account factors such as depth matching, frequency matching, stress deviation, temperature deviation, and action response delay time.

[0122] The formula for calculating the gear meshing efficiency is:

[0123] ;

[0124] in, This indicates the gear meshing efficiency. Indicates the degree of deep matching. Indicates the target engagement depth. Indicates the actual vibration frequency. Indicates the target vibration frequency. Indicates the amount of stress deviation. Indicates the temperature deviation. Indicates the action response delay time. , , The attenuation coefficients represent the effects of stress, temperature, and time. This indicates the maximum permissible operating temperature.

[0125] Based on the slope parameter, the actual value of adhesion force and the gear meshing efficiency, the track curvature compensation coefficient, adhesion safety boundary weight factor and gear action response delay parameter that need to be adjusted are calculated.

[0126] Furthermore, the slope parameters within the current calculation period are obtained; the track curvature calculation records corresponding to the same slope parameters in historical data are retrieved; the differences in track curvature calculation results under different slope parameter values ​​are compared; the degree of influence is determined based on the correspondence between slope parameter values ​​and track curvature changes; then, the difference between the actual adhesion value and the theoretical safety boundary is compared, and the correction range of the adhesion safety boundary weight factor is determined based on the magnitude of the difference; finally, the motion response performance is evaluated based on the gear meshing efficiency value, and the optimized value of the gear motion response delay parameter is determined based on the gear comprehensive performance deviation index.

[0127] It should be noted that the differences in the calculated track curvature under different slopes are achieved through the following steps: extract the calculated curvature values ​​and high-precision mapping reference values ​​of track sections from historical data according to slope level, and statistically analyze the average error and dispersion of each group; based on the statistical results, obtain a lookup table corresponding to the slope value and the curvature calculation error value, and determine the typical deviation range and correction direction of the calculated curvature value under a specific slope by looking up the table.

[0128] The change in track curvature is obtained by comparing the curvature calculation results within the current calculation period with the historical average curvature for the same slope grade.

[0129] The control parameters are updated based on the track curvature compensation coefficient, the adhesion safety boundary weight factor, and the gear track motion response delay parameter.

[0130] Furthermore, the track curvature compensation coefficient, the adhesive safety boundary weight factor, and the gear tooth action response delay parameter are written into the braking control parameter storage area; the track curvature compensation coefficient is used for real-time correction of the curvature value in subsequent track position vector processing, the adhesive safety boundary weight factor participates in the weighted evaluation when calculating subsequent adhesive safety boundary parameters, and the gear tooth action response delay parameter is applied to the timing adjustment when generating subsequent gear tooth action commands.

[0131] Based on the updated parameters, a closed-loop optimization is generated through the braking control cycle.

[0132] Furthermore, during the braking control cycle, the updated track curvature compensation coefficient, adhesive safety boundary weight factor, and gear tooth action response delay parameter are read from the braking control parameter storage area. The track curvature compensation coefficient is used to calculate and correct the curvature value in the track position vector processing stage, the adhesive safety boundary weight factor is used to perform weighted fusion in the adhesive safety boundary parameter calculation stage, and the gear tooth action response delay parameter is used to perform closed-loop optimization of gear tooth action command generation.

[0133] It should be noted that the generation of the braking control cycle depends on the timing triggering mechanism of the train operation control: based on fixed time intervals and fixed displacement intervals, the displacement signals provided by the trackside positioning device and the on-board odometer trigger periodic control tasks, thereby forming a continuously iterative braking control cycle.

[0134] In summary, this invention achieves intelligent and coordinated distribution of braking force and rack and pinion motion by dynamically integrating rail condition, wheel surface temperature and terrain parameters to accurately construct the adhesion safety boundary, effectively improving braking safety and adaptability under complex mountain track conditions; and by using closed-loop feedback and parameter optimization to perform self-learning and adaptive adjustment of the braking process, thereby improving energy utilization efficiency and the service life of key components while ensuring braking performance.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A system for enhancing braking of a mountain rail vehicle, characterized in that: The application relates to a rail vehicle brake system, which comprises a driving condition sensing module, a multi-source information fusion module, a brake execution control module and a brake efficiency optimization module. The driving condition sensing module is used for collecting track position vectors and arc lengths, calculating track curvatures and track slopes, and combining current vehicle speeds with target speeds to solve brake demand acceleration values, generate slope parameters and curve parameters. The multi-source information fusion module is used for collecting rail surface state data, wheel surface temperature field distribution data and track dielectric property data, correcting the rail corrosion index and the wheel surface temperature field data by fusing the slope parameters and the curve parameters, constructing adhesion force safety boundary parameters, generating brake force distribution ratios and rack rail action instructions, and generating wheel-rail system state reports and rack rail working logs. The brake execution control module is used for adjusting wheel-rail hydraulic brake torque values according to the brake force distribution ratios, controlling rack rail brake engagement depth and vibration frequency through the rack rail action instructions, and generating the wheel-rail system state reports and the rack rail working logs. The brake efficiency optimization module is used for extracting slope parameters, adhesion force actual values and rack rail engagement efficiencies in a brake process from the wheel-rail system state reports and the rack rail working logs, and combining the slope parameters with the adhesion force actual values and the rack rail engagement efficiencies to perform closed-loop optimization based on a terrain braking curve.

2. The alpine rail vehicle enhanced braking system of claim 1, wherein: The track position vectors and the arc lengths are collected, and the track curvatures and the track slopes are calculated, and the specific steps are as follows. Track profile discrete point three-dimensional coordinates are collected to generate track position vectors. The distances between adjacent points are obtained based on the track position vectors, and the track arc lengths are generated by piecewise accumulation. The track curvatures are calculated through a three-point circle determination method based on the track position vectors and the track arc lengths, and the track slopes are generated by calculating the ratio of the height variation amount to the horizontal distance variation amount of adjacent points.

3. The alpine rail vehicle enhanced braking system of claim 2, wherein: The current vehicle speeds are collected, and the brake demand acceleration values are calculated by combining the target speeds. The brake demand acceleration values are combined with the track slopes and slope grade division standards to generate the slope parameters, and the track curvatures are combined with curve grade division standards to generate the curve parameters. The specific steps of collecting the rail surface state data, the wheel surface temperature field distribution data and the track dielectric property data, and correcting the rail corrosion index and the wheel surface temperature field data by fusing the slope parameters and the curve parameters are as follows.

4. The alpine rail vehicle enhanced braking system of claim 3, wherein: The rail surface state data, the wheel surface temperature field distribution data and the track dielectric property data are collected. The corrosion degree index in the rail surface state data is adjusted according to the slope grade in the slope parameters to generate an adjusted rail corrosion index. The temperature gradient value in the wheel surface temperature field distribution data is adjusted through a compensation coefficient in the curve parameters to generate adjusted wheel surface temperature data. The specific steps of constructing the adhesion force safety boundary parameters, generating the brake force distribution ratios and the rack rail action instructions are as follows.

5. The alpine rail vehicle enhanced braking system of claim 4, wherein: The adhesion force safety boundary parameters are constructed to generate the brake force distribution ratios and the rack rail action instructions. The adhesion force safety boundary parameters are calculated based on the adjusted rail corrosion index, the adjusted wheel surface temperature data and the track dielectric property data. The brake force distribution ratios are calculated according to the adhesion force safety boundary parameters and the brake demand acceleration values, and the rack rail action instructions are obtained. The specific steps of adjusting the wheel-rail hydraulic brake torque values according to the brake force distribution ratios are as follows.

6. The alpine railcar enhanced braking system of claim 5, wherein: ​ According to the braking force distribution ratio, the target braking pressure value is queried from the hydraulic system braking force distribution database, and combined with the pressure monitoring data, the theoretical wheel rail braking torque value is calculated; According to the target engagement depth parameter in the rack rail action instruction, the displacement adjustment instruction is obtained combined with the actual displacement data to verify the engagement state, and a rack rail engagement depth confirmation signal is generated.

7. The alpine railcar enhanced braking system of claim 6, wherein: The rack rail engagement depth and vibration frequency are controlled by the rack rail action instruction, and the specific steps are as follows, Based on the target vibration frequency parameter in the rack rail action instruction, the vibration state is verified combined with the actual frequency data, and a rack rail vibration frequency confirmation signal is obtained.

8. The alpine rail vehicle enhanced braking system of claim 7, wherein: The wheel rail system state report and the rack rail working log are generated, and the specific steps are as follows, The theoretical braking torque value is combined with the wheel surface temperature field distribution data to generate the wheel rail system state report. Record the rack rail engagement depth confirmation signal and the rack rail vibration frequency confirmation signal.

9. The alpine rail vehicle enhanced braking system of claim 8, wherein: The slope parameter, the actual value of adhesion force and the rack rail engagement efficiency in the braking process are extracted from the wheel rail system state report and the rack rail working log, and the specific steps are as follows, The actual value of braking torque, the wheel surface temperature distribution characteristics and the contact state parameters are extracted from the wheel rail system state report, and the slope parameter and the actual value of adhesion force in the braking process are obtained; According to the actual value of engagement depth, the actual value of vibration frequency, the stress distribution characteristics and the temperature distribution characteristics in the rack rail working log, the rack rail engagement efficiency is calculated; Based on the slope parameter, the actual value of adhesion force and the rack rail engagement efficiency, the track curvature compensation coefficient, the adhesion force safety boundary weight factor and the rack rail action response delay parameter that need to be adjusted are calculated.

10. The alpine rail vehicle enhanced braking system of claim 9, wherein: The closed-loop optimization is carried out combined with the terrain braking curve, and the specific steps are as follows, According to the track curvature compensation coefficient, the adhesion force safety boundary weight factor and the rack rail action response delay parameter, the control parameter is updated; Based on the updated control parameter, the closed-loop optimization is generated through the braking control cycle.

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