Wheel abrasion loss determination method and device, computer equipment and storage medium

Abstract

The invention relates to a wheel abrasion loss determination method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a running scene sequence corresponding to a current running path of a wheel; according to working conditions under multiple dimensions and current wheel parameters of the wheels, which are respectively included in each operation scene in the operation scene sequence, respectively determining respective wheel abrasion loss of the wheels under each sub-road section; fusion is carried out based on the respective wheel abrasion loss of each operation scene, and the stage abrasion loss of the wheel under the current operation path is obtained; when it is determined that the iteration ending condition is not met, the current wheel parameters are updated according to the stage abrasion loss, and the next running path of the current running path is obtained; and updating the next running path to the current running path, and carrying out the next wheel abrasion loss iterative calculation until an iteration ending condition is met, so as to obtain the accumulated abrasion loss of the wheel. By adopting the method, the wheel abrasion loss can be accurately determined.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining wheel wear. Background Technology

[0002] Heavy-haul railways represent a crucial direction for freight railway development. These railways place significant demands on locomotive carrying capacity, characterized by high axle load, high traction, and high wheel tread contact stress. Furthermore, the complex conditions of heavy-haul railway lines, including some sections with poor track conditions, exacerbate wheel wear and fatigue damage. Particularly on curved sections or lines with steep gradients, severe wheel-rail vibrations significantly impact vehicle stability and reliability, directly jeopardizing safe operation. This also greatly increases the difficulty and cost of locomotive and rolling stock maintenance for railway departments.

[0003] During the operation of heavy-haul locomotives, the wheels are critical components, and their wear condition directly affects the safety of train operation and the life of the tracks. Traditional detection methods rely on manual periodic measurements or offline detection systems based on non-contact sensors such as optical and ultrasonic sensors, which suffer from large subjective errors, resulting in low accuracy in determining wheel wear. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can accurately determine wheel wear in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for determining wheel wear, including:

[0006] Obtain the sequence of operating scenarios corresponding to the current running path of the wheel. The sequence of operating scenarios includes the operating scenarios that are arranged in order and correspond to each sub-segment of the current running path. The operating scenarios include the working conditions of the wheel in multiple dimensions during the running process of the corresponding sub-segment.

[0007] Based on the working conditions under multiple dimensions and the current wheel parameters of each operating scenario in the operating scenario sequence, the wheel wear amount of each wheel under each sub-road segment is determined.

[0008] The wheel wear amount of each operating scenario is fused to obtain the stage wear amount of the wheel under the current operating path.

[0009] If the iteration termination condition is not met, the current wheel parameters are updated based on the stage wear amount, and the next running path of the current running path is obtained.

[0010] Update the next running path to the current running path, and return to the step of obtaining the running scene sequence corresponding to the current running path of the wheel, until the iteration end condition is met, and obtain the cumulative wear of the wheel according to the stage wear of each running path.

[0011] Secondly, this application also provides a wheel wear determination device, comprising:

[0012] The running scenario sequence acquisition module is used to acquire the running scenario sequence corresponding to the current running path of the wheel. The running scenario sequence includes the running scenarios arranged in order and corresponding to each sub-segment in the current running path. The running scenario includes the working conditions of the wheel in multiple dimensions during the running process of the corresponding sub-segment.

[0013] The sub-segment wear determination module is used to determine the wheel wear amount of each wheel in each sub-segment based on the working conditions under multiple dimensions and the current wheel parameters of each operating scenario in the operating scenario sequence.

[0014] The stage wear determination module is used to fuse the wheel wear amounts based on each operating scenario to obtain the stage wear amount of the wheel under the current operating path.

[0015] The wheel parameter update module is used to update the current wheel parameters based on the stage wear amount when it is determined that the iteration termination condition is not met, and to obtain the next running path of the current running path.

[0016] The cumulative wear determination module is used to update the next running path to the current running path and return to the step of obtaining the running scene sequence corresponding to the current running path of the wheel until the iteration end condition is met, and obtain the cumulative wear of the wheel according to the stage wear of each running path.

[0017] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the method provided in the first aspect above.

[0018] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the first aspect above.

[0019] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the method provided in the first aspect above.

[0020] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining wheel wear amount obtain a sequence of operating scenarios corresponding to the current operating path of the wheel. Based on the operating conditions under multiple dimensions included in each operating scenario in the sequence of operating scenarios and the current wheel parameters, the wheel wear amount of each wheel under each sub-road segment is determined. The wheel wear amounts of each operating scenario are fused to obtain the stage wear amount of the wheel under the current operating path. If the iteration termination condition is not met, the current wheel parameters are updated according to the stage wear amount, and the next operating path of the current operating path is obtained. The next operating path is updated to the current operating path to determine the stage wear amount of the next operating path. This process continues until the iteration termination condition is met, and the cumulative wear amount of the wheel is obtained based on the stage wear amount of each operating path. In the process of determining wheel wear, the working conditions under multiple dimensions included in each running scenario sequence can be comprehensively and meticulously simulated for complex working conditions under different sub-segments. If the iteration termination condition is not met, the current wheel parameters are updated based on the stage wear to determine the stage wear of the next running path until the iteration termination condition is met. This can accurately simulate the wear change process of the wheel during continuous operation. By accumulating the stage wear of multiple running paths, the cumulative wear of the wheel can be obtained, which can capture the nonlinear cumulative effect of wheel wear development throughout the entire running cycle, thereby improving the accuracy of wheel wear determination. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is an application environment diagram of the wheel wear determination method in one embodiment;

[0023] Figure 2 This is a flowchart illustrating a method for determining wheel wear in one embodiment;

[0024] Figure 3 This is a schematic diagram of the process for determining wheel wear in one embodiment;

[0025] Figure 4 This is a flowchart illustrating the method for determining wheel wear in another embodiment;

[0026] Figure 5 This is a schematic diagram illustrating the prediction and extrapolation of wheel wear in one embodiment;

[0027] Figure 6 This is a structural block diagram of a wheel wear determination device in one embodiment;

[0028] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0030] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0031] The wheel wear determination method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0032] The user can send the sequence of operating scenarios corresponding to the current operating path to the server 104 via terminal 102. The sequence of operating scenarios can include sequentially arranged operating scenarios corresponding to each sub-segment of the current operating path. Each operating scenario includes the working conditions of the wheel under multiple dimensions during its operation in the corresponding sub-segment. After obtaining the sequence of operating scenarios, server 104 can determine the wheel wear amount of each wheel under each sub-segment based on the working conditions under multiple dimensions included in each operating scenario and the current wheel parameters. The wheel wear amounts of each operating scenario are fused to obtain the stage wear amount of the wheel under the current operating path. If the iteration termination condition is not met, the current wheel parameters are updated according to the stage wear amount, and the next operating path is obtained. The next operating path is updated to the current operating path to determine the stage wear amount of the next operating path. This process continues until the iteration termination condition is met, and the cumulative wear amount of the wheel is obtained based on the stage wear amount of each operating path. Server 104 can send the cumulative wear amount of the wheel to terminal 102 to provide feedback to the user.

[0033] In some embodiments, the method for determining wheel wear can also be implemented independently by terminal 102 or server 104. For example, terminal 102 can directly perform wheel wear determination based on the running scenario sequence. Alternatively, server 104 can obtain the running scenario sequence corresponding to the current running path of the wheel from the data storage system and perform wheel wear determination based on the running scenario sequence.

[0034] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0035] In one exemplary embodiment, such as Figure 2 As shown, a method for determining wheel wear is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1Taking the server in the example, the explanation includes the following steps 202 to 210. Wherein:

[0036] Step 202: Obtain the sequence of operating scenarios corresponding to the current operating path of the wheel. The sequence of operating scenarios includes the operating scenarios that are arranged in order and correspond to each sub-segment of the current operating path. The operating scenarios include the working conditions of the wheel in multiple dimensions during the operation of the corresponding sub-segment.

[0037] Wheels can be found in various vehicles. In locomotives, for example, wheels and axles form wheelsets through interference fits, which are core components of the bogie, directly bearing the locomotive's weight and track impact loads. During use, friction, load, and environmental factors cause gradual wear and tear on the wheel material. Researching the evolution of wheel wear, prediction methods, and related influencing factors is crucial for ensuring the long-term safe operation of locomotive systems under wheel wear conditions. Building upon this research, further investigation into the wear evolution of wheels under complex operating environments influenced by multiple factors can define the causal factors of wheel failures in heavy-haul railway locomotives from a complex system perspective. This provides a more scientific and objective theoretical basis for wheel wear reduction analysis, ultimately improving locomotive turnaround rates, reducing maintenance costs, and enhancing the overall safety and efficiency of heavy-haul railway operations.

[0038] The current operating path can be the path where wheel wear needs to be determined, i.e., the wear amount of the wheel needs to be determined after traversing this operating path. The operating scenario sequence is constructed for the current operating path and is used to describe the operating environment of the current operating path. The operating scenario sequence can include multiple sequentially arranged operating scenarios, each corresponding to a sub-segment within the current operating path; that is, each operating scenario describes the operating conditions of the corresponding sub-segment within the current operating path. The current operating path can be divided into multiple sub-segments according to the wheel's shape, and each sub-segment can be described by a corresponding operating scenario, indicating the operating conditions of the wheel running on that sub-segment. For example, the current operating path can be 10 kilometers long and can be divided into 5 sub-segments, such as sub-segment A (0-2 km), sub-segment B (2-4 km), sub-segment C (4-6 km), sub-segment D (6-8 km), and sub-segment E (8-10 km). The operating scenario encompasses the working conditions of the wheels during their movement along a corresponding sub-segment, across multiple dimensions. These conditions may include track conditions, weather conditions, load information, train formation, and driver operation information. For instance, the current operating path might be divided into five sub-segments: A, B, C, D, and E. Each sub-segment can correspond to an operating scenario, which describes the operating conditions of that sub-segment using the working conditions across multiple dimensions. For example, for sub-segment A, its operating scenario could include five dimensions: a straight track, snowy weather, a load of 20,000 tons, an 800-meter train formation, and an acceleration of 15 meters per second.

[0039] For example, the server can determine the wheel whose wear needs to be calculated and determine the wheel's current running path to calculate the wear of the wheel after running through the current running path. The server can determine a sequence of running scenarios corresponding to the current running path. The sequence of running scenarios can include multiple running scenarios arranged in sequence. Each running scenario can be constructed for a sub-segment of the current running path. Specifically, each running scenario can include the working conditions of the wheel under multiple dimensions during its operation in the corresponding sub-segment. In some embodiments, the multiple dimensions in the running scenario can be configured according to actual needs, such as, but not limited to, various dimensions such as track conditions, weather conditions, load information, formation mode, and driver operation information. In some embodiments, the global running path of the wheel can be divided into multiple running paths. The wear of each running path can be determined iteratively to gradually determine the cumulative wear of the vehicle after running through the global running path. When iteratively determining the wear of each running path segment, the server can determine the current running path for this iteration and obtain the sequence of running scenarios corresponding to the current running path.

[0040] Step 204: Based on the working conditions under multiple dimensions and the current wheel parameters of each running scenario in the running scenario sequence, determine the wheel wear amount of each wheel under each sub-road segment.

[0041] The current wheel parameters can include the wheel's attribute information at the time of this wear determination, such as the wheel's geometric parameters, material parameters, and other information. Wheel wear is the wear amount corresponding to a sub-section, that is, the wear amount of the wheel after traveling through the sub-section.

[0042] Optionally, the server can obtain the current wheel parameters of the wheels. Based on the operating conditions under multiple dimensions and the current wheel parameters included in each operating scenario sequence, the server can determine the wheel wear amount corresponding to each sub-segment. In some embodiments, the multiple dimensions in the operating scenario can be configured according to actual needs, such as, but not limited to, various dimensions including track conditions, weather conditions, load information, train formation, and driver operation information. Track conditions can affect the wheel-rail contact geometry and creep force; weather conditions can change the wheel-rail interface friction characteristics; load information affects the wheel-rail normal pressure; train formation can affect the vehicle body mass and dynamics; and driver operation information can change the creep speed and contact state. Therefore, different operating conditions under multiple dimensions will cause different wheel wear amounts. In some embodiments, for each operating scenario in the operating scenario sequence, the server can traverse the operating scenarios and determine the wheel wear amount of the corresponding sub-segment for each operating scenario by combining the current wheel parameters.

[0043] Step 206: The wheel wear amount of each operating scenario is fused to obtain the stage wear amount of the wheel under the current operating path.

[0044] The stage wear amount is the wear amount of the wheel under the current running path, which can be obtained by fusing the wheel wear amounts determined by each running scenario in the running scenario sequence corresponding to the current running path. Optionally, the running scenario sequence corresponding to the current running path includes multiple running scenarios arranged in sequence. Each running scenario is used to simulate the corresponding sub-segment in the current running path. For each running scenario, the corresponding wheel wear amount can be determined, thereby obtaining the wheel wear amount after running in each sub-segment of the current running path. The server can fuse the wheel wear amounts of each running scenario, such as by weighting and fusing the wheel wear amounts of each running scenario according to the weight of each sub-segment, thereby obtaining the stage wear amount of the wheel under the current running path.

[0045] Step 208: If the iteration termination condition is not met, update the current wheel parameters according to the stage wear amount and obtain the next running path of the current running path.

[0046] The iteration termination condition is used to determine whether the calculation of wheel wear needs to be terminated. If the termination condition is met, the iteration can end, and the wheel wear amount can be determined. If the termination condition is not met, the next running path can be obtained for iteration to calculate wheel wear. The iteration termination condition can be flexibly set according to actual needs, such as the cumulative running path of multiple wear calculation iterations reaching a path threshold, the cumulative wear of multiple wear calculation iterations reaching a wear threshold, the stage wear of the current wear calculation iteration reaching a wear threshold, or the number of wear calculation iterations reaching a number threshold.

[0047] For example, the server can obtain a pre-set iteration termination condition. After obtaining the stage wear amount of the wheel under the current running path, it determines whether the iteration termination condition is met. If the iteration termination condition is not met, it indicates that the next wear amount iteration calculation will be performed. The server can then update the current wheel parameters based on the stage wear amount. For example, the server can update the wheel's geometric parameters based on the stage wear amount and obtain the next running path of the current running path for the next wear amount iteration calculation. In some embodiments, when it is determined that the iteration termination condition is met, the server can directly obtain the cumulative wear amount of the wheel based on the stage wear amount of each running path, thereby obtaining the cumulative wear amount of the wheel after running through each running path.

[0048] Step 210: Update the next running path to the current running path, and return to the step of obtaining the running scene sequence corresponding to the current running path of the wheel, until the iteration end condition is met, and obtain the cumulative wear of the wheel according to the stage wear of each running path.

[0049] Optionally, after obtaining the next running path of the current running path, the server can update the next running path to the current running path and return to the step of obtaining the running scene sequence corresponding to the current running path of the wheel, thereby determining the stage wear amount for the next running path. When the iteration termination condition is met, the server can obtain the cumulative wear amount of the wheel based on the stage wear amount of each running path that has been iteratively calculated. In some embodiments, when the iteration termination condition is met, the server can determine the stage wear amount of each running path that has been iteratively calculated and sum the stage wear amounts to obtain the cumulative wear amount of the wheel. For example, the server performs K iterations of wear amount calculation for the wheel sequentially, that is, the server uses K running paths to perform iterative wear amount calculation for the wheel sequentially. After performing the K+1th iteration of wear amount calculation using the K+1th running path, if the iteration termination condition is met, the server can obtain the stage wear amount of each of the K+1th iterations of wear amount calculation, and the server can sum the K+1 stage wear amounts to obtain the cumulative wear amount of the wheel. In some embodiments, the wheel replacement cycle can be predicted based on the cumulative wear of the wheel (e.g., maintenance is required when the cumulative wear reaches 2mm), and maintenance strategies can be optimized, such as prioritizing the replacement of the wheel flange rather than the entire wheel.

[0050] In the above method for determining wheel wear, a sequence of operating scenarios corresponding to the current running path of the wheel is obtained. Based on the working conditions under multiple dimensions and the current wheel parameters of each operating scenario in the sequence, the wheel wear of each wheel under each sub-segment is determined. The wheel wear of each operating scenario is fused to obtain the stage wear of the wheel under the current running path. If the iteration termination condition is not met, the current wheel parameters are updated according to the stage wear, and the next running path is obtained. The next running path is updated to the current running path to determine the stage wear of the next running path. This process continues until the iteration termination condition is met, and the cumulative wear of the wheel is obtained based on the stage wear of each running path. In the process of determining wheel wear, the working conditions under multiple dimensions included in each running scenario sequence can be comprehensively and meticulously simulated for complex working conditions under different sub-segments. If the iteration termination condition is not met, the current wheel parameters are updated based on the stage wear to determine the stage wear of the next running path until the iteration termination condition is met. This can accurately simulate the wear change process of the wheel during continuous operation. By accumulating the stage wear of multiple running paths, the cumulative wear of the wheel can be obtained, which can capture the nonlinear cumulative effect of wheel wear development throughout the entire running cycle, thereby improving the accuracy of wheel wear determination.

[0051] In one exemplary embodiment, such as Figure 3 As shown, the process for determining wheel wear involves steps 302 to 308, which determine the wheel wear amount for each sub-road segment based on the operating conditions under multiple dimensions and the current wheel parameters of each operating scenario in the operating scenario sequence. Wherein:

[0052] Step 302: Obtain the current wheel parameters.

[0053] The current wheel parameters are those used in this wear calculation iteration, and may include geometric and material parameters. The wear of a wheel during operation is related not only to the operating conditions across multiple dimensions of the scenario but also to its own wheel parameters. Wear during operation also affects these parameters, such as changing geometric parameters, which in turn influences wear. Therefore, the wheel parameters are updated after each wear calculation iteration. Optionally, the server can obtain the current wheel parameters for this wear calculation iteration. These parameters can be updated based on the stage wear from the previous iteration.

[0054] Step 304: For each operating scenario in the operating scenario sequence, determine the track conditions, weather conditions, load information, formation method, and driver operation information for the target operating scenario.

[0055] These dimensions include track conditions, weather conditions, load information, train formation, and driver operation information. Different operating conditions under each dimension will have different wear effects on the wheels. For example, the server can traverse each operating scenario in the sequence of operating scenarios. For each operating scenario, the server can determine the track conditions, weather conditions, load information, train formation, and driver operation information, that is, determine the operating conditions under each dimension, such as track conditions, weather conditions, load information, train formation, and driver operation information.

[0056] Step 306: Based on the current wheel parameters, track conditions, weather conditions, load information, train formation, and driver operation information, determine the wheel wear amount for the target operating scenario.

[0057] Here, wheel wear is calculated based on the operating conditions under various dimensions of the operational process, combined with the current wheel parameters. Each operational scenario in the operational scenario sequence may include different wheel parameters, track conditions, weather conditions, load information, train formation, and driver operation information. Different wheel wear amounts can be determined by combining the current wheel parameters. For example, for each operational scenario, the server can calculate the corresponding wheel wear amount based on the track conditions, weather conditions, load information, train formation, driver operation information, and current wheel parameters. In some embodiments, the server can pre-build a wheel wear amount determination model and input the current wheel parameters, track conditions, weather conditions, load information, train formation, and driver operation information into the wear amount determination model to calculate the corresponding wheel wear amount.

[0058] Step 308: Based on the wheel wear amount of each operating scenario, obtain the wheel wear amount of each wheel under each sub-road segment.

[0059] Optionally, each operating scenario can have its corresponding wheel wear amount determined separately. The server can obtain the wheel wear amount of each wheel under each sub-road segment based on the wheel wear amount of each operating scenario. For example, for the i-th operating scenario in the sequence of operating scenarios, it can correspond to the i-th sub-road segment in the current operating path. That is, the i-th operating scenario can include the working conditions of the wheel under multiple dimensions during the operation of the wheel under the i-th sub-road segment. The server can determine the wheel wear amount of the i-th operating scenario based on the track conditions, weather conditions, load information, formation mode, driver operation information and current wheel parameters of the i-th operating scenario. The server can determine the wheel wear amount of the i-th operating scenario as the wheel wear amount of the wheel under the i-th sub-road segment. After traversing each operating scenario, the server can obtain the wheel wear amount of each wheel under each sub-road segment.

[0060] In this embodiment, the server determines the wheel wear amount for each operating scenario based on the track conditions, weather conditions, load information, train formation, and driver operation information, combined with the current wheel parameters. This yields the wheel wear amount for each sub-segment. By comprehensively and meticulously simulating complex operating conditions under different sub-segments using track conditions, weather conditions, load information, train formation, and driver operation information, the accuracy of wheel wear amount calculation for each sub-segment is ensured, thereby improving the accuracy of wheel wear amount determination.

[0061] In an exemplary embodiment, the wheel wear amount for a given operating scenario is determined based on current wheel parameters, track conditions, weather conditions, load information, train formation, and driver operation information. This includes: determining the normal contact pressure and sliding distance based on track conditions, weather conditions, load information, train formation, driver operation information, and current wheel parameters; and obtaining the wheel wear amount for the given operating scenario based on the normal contact pressure, sliding distance, and current wheel parameters.

[0062] The normal contact pressure is the perpendicular force acting on the wheel-rail contact surface, and the sliding distance is the cumulative path length of relative sliding within the wheel-rail contact patch. For example, the server can determine the normal contact pressure and sliding distance based on track conditions, weather conditions, load information, train formation, driver operation information, and current wheel parameters. Track conditions affect the wheel-rail contact geometry and creep force, altering the wheel-rail contact point position and lateral force. Specifically, they affect the contact ellipse semi-axis, lateral creep force, and spin angular velocity. Small-radius curves cause changes in the contact ellipse semi-axis, thus affecting the sliding distance and consequently the wear volume. Weather conditions alter the wheel-rail interface friction characteristics, specifically affecting the coefficient of friction. A lower coefficient of friction results in a corresponding decrease in creep force but an expansion of the sliding area, leading to an increase in the sliding distance and thus an increase in the wear volume. Load information affects the wheel-rail normal pressure; specifically, increased load increases the vehicle mass, increasing the spring preload and consequently the normal contact pressure. The train formation indirectly affects the vehicle's mass and dynamics, thus influencing the sliding distance; while driver input changes creep speed and contact state, also affecting the sliding distance. Wheel position parameters within the current wheel parameters lead to different spring preload distributions, thus affecting the normal contact pressure; wheel profile geometry within the current wheel parameters affects creep force distribution, thus influencing the sliding distance.

[0063] The server can determine the normal contact pressure and sliding distance by combining track conditions, weather conditions, load information, train formation, driver operation information, and current wheel parameters. It can then combine these factors to obtain the wheel wear amount for a specific operating scenario. For example, the server can combine the normal contact pressure, sliding distance, and material hardness information and wheel geometry parameters from the current wheel parameters to obtain the wheel wear amount for that specific operating scenario.

[0064] In this embodiment, the server can determine the normal contact pressure and sliding distance based on track conditions, weather conditions, load information, train formation, driver operation information, and current wheel parameters. It can also combine the normal contact pressure, sliding distance, and current wheel parameters to obtain the wheel wear amount. This allows for comprehensive and detailed simulation of complex working conditions under different sub-sections, ensuring the accuracy of wheel wear amount calculation for each sub-section and thus improving the accuracy of wheel wear amount determination.

[0065] In an exemplary embodiment, the wheel wear amount for a given operating scenario is obtained based on the normal contact pressure, sliding distance, and current wheel parameters, including: determining the wear volume based on the material hardness information in the normal contact pressure, sliding distance, and current wheel parameters; and obtaining the wheel wear amount for the given operating scenario based on the wear volume and the wheel geometry parameters in the current wheel parameters.

[0066] Wear volume is the volume of wheel material lost due to wear failure, and is a direct quantitative indicator of wear evolution. Wheel geometric parameters can include the wheel rolling circle radius at arc coordinates and the length of the segment divided along the wheel-rail profile curve. The wheel rolling circle radius dynamically changes with the profile position, and the arc coordinates are the curve length parameters along the wheel profile curve (profile), used to accurately locate the specific position of the wheel-rail contact point on the wheel profile. Wheel geometric parameters can also include the average circumference length of the wheel profile segment and the segment length divided along the wheel-rail profile curve. Optionally, the server can determine the wheel wear volume by comprehensively considering the normal contact pressure, sliding distance, and material hardness information in the current wheel parameters, and calculate the wheel wear amount for the specific operating scenario based on the wear volume and the wheel geometric parameters in the current wheel parameters.

[0067] In this embodiment, the server determines the wear volume by combining the normal contact pressure, sliding distance, and wheel material hardness information, and obtains the wheel wear amount based on the wear volume and wheel geometric parameters. The wear amount can be calculated by combining the wheel material hardness information and wheel geometric parameters, which improves the accuracy of wheel wear amount determination.

[0068] In an exemplary embodiment, the wheel wear amount of each operating scenario is fused to obtain the stage wear amount of the wheel under the current operating path, including: determining the weight factor of each operating scenario, the weight factor being matched with the sub-road segment corresponding to the operating scenario; and weighting and fusing the wheel wear amount of each operating scenario according to the weight factor of each operating scenario to obtain the stage wear amount of the wheel under the current operating path.

[0069] The weighting factors can be pre-configured for each operating scenario, and each weighting factor is matched with its corresponding sub-segment. For example, the weighting factor can be determined based on the proportion of the length of the sub-segment corresponding to the operating scenario in the current operating path. For instance, the server can obtain the weighting factors configured for the operating scenario. The weighting factor for the operating scenario can be determined based on the proportion of the length of the sub-segment corresponding to the operating scenario, which can be calculated as the ratio of the length of the sub-segment to the total length of the current operating path. The server can weight and fuse the wheel wear amounts of each operating scenario according to their respective weighting factors to obtain the stage wear amount of the wheel under the current operating path. In some embodiments, the weighting factors for each operating scenario can also be determined through a pre-trained weight configuration model. The weight configuration model can be trained based on historical wheel wear data. The weight configuration model can configure weights based on the weather conditions, load information, formation method, and driver operation information of the input operating scenario, thereby outputting the weighting factors specific to the operating scenario.

[0070] In this embodiment, the server can perform weighted fusion of the wheel wear amounts of each operating scenario based on the weight factors of each operating scenario. This can further improve the reliability of the stage wear amount and thus improve the accuracy of the wheel wear amount determination.

[0071] In an exemplary embodiment, if it is determined that the iteration termination condition is not met, updating the current wheel parameters according to the stage wear amount includes: updating the wheel geometry parameters in the current wheel parameters according to the stage wear amount if it is determined that the iteration termination condition is not met; wherein, the iteration termination condition includes at least one of the following: the stage wear amount reaches a first wear amount threshold, the cumulative running path obtained based on the current running path and the previous running path reaches a path length threshold, or the sum of the stage wear amount and the stage wear amount of the previous running path reaches a second wear amount threshold.

[0072] The first wear threshold, path length threshold, or second wear threshold can be pre-configured according to actual needs. The first wear threshold is used to determine the end of the iteration based on the stage wear of the running scenario, the path length threshold is used to determine the end of the iteration based on the cumulative running path length of each running path, and the second wear threshold is used to determine the end of the iteration based on the cumulative wear of each stage of each running path.

[0073] Optionally, the server obtains a pre-set iteration termination condition. This condition may include the stage wear amount reaching a first wear threshold. Specifically, the server can compare the stage wear amount of the current running scenario with the pre-set first wear threshold to determine if the stage wear amount is greater than or equal to the first wear threshold. If so, the iteration termination condition is satisfied. In some embodiments, the iteration termination condition may include the cumulative running path obtained from the current running path and previous running paths reaching a path length threshold. The previous running path can be the running path calculated in previous iterations. The cumulative running path can be obtained by summing the current running path and previous running paths. The server can determine the length of the cumulative running path and compare it with a path length threshold to determine if the length of the cumulative running path is greater than or equal to the path length threshold. If so, the iteration termination condition is satisfied. In some embodiments, the iteration termination condition can be that the sum of the stage wear amount and the stage wear amount of the previously run path reaches a second wear amount threshold. The specific server can determine the sum of the stage wear amount of the current run path and the stage wear amount of the previously run path, and compare the obtained wear amount sum with the second wear amount threshold to determine whether the stage wear amount is greater than or equal to the second wear amount threshold. If so, it can be determined that the iteration termination condition is met at this time.

[0074] When it is determined that the iteration termination condition is not met, the server can update the wheel geometry parameters in the current wheel parameters according to the stage wear amount of the current running path, so as to perform subsequent wear amount iteration calculation processing based on the updated wheel geometry parameters.

[0075] In this embodiment, the server can determine the end of the iteration based on the stage wear amount of the current running path, the cumulative running path, or the sum of the stage wear amount of the current running path and the stage wear amount of the previous running path. If the iteration end condition is not met, the wheel geometry parameters in the current wheel parameters can be updated in a timely manner during the wear amount iteration calculation process, ensuring the reliability and accuracy of the wheel geometry parameters, which is beneficial to improving the accuracy of wheel wear amount determination.

[0076] This application also provides an application scenario in which the above-described method for determining wheel wear is applied. Specifically, the method for determining wheel wear is applied in this scenario as follows:

[0077] In heavy-haul locomotive applications, heavy-haul railways place high demands on locomotive load capacity, resulting in heavy axle loads, high traction forces, and high wheel tread contact stress. Researching the evolution of wheel wear, prediction methods, and related influencing factors is crucial for ensuring the long-term safe operation of locomotive systems under wheel wear conditions. Building upon this research, further investigation into the wear evolution of wheels under complex operating environments influenced by multiple factors will allow for the definition of the causal factors of wheel failures in heavy-haul railway locomotives from a complex system perspective. This will provide a more scientific and objective theoretical basis for wheel wear reduction analysis, ultimately improving locomotive turnaround rates, reducing maintenance costs, and enhancing the overall safety and efficiency of heavy-haul railway operations.

[0078] Wheel-rail wear is not caused by a single factor, but rather by a series of complex mechanisms. Many factors influence the wear of heavy-duty freight car wheels, including vehicle system parameters, track system parameters, and operating conditions. However, a systematic study of these factors is currently lacking, as is research on wheel wear of heavy-duty freight cars under non-ideal conditions. In terms of locomotive wheel-rail wear calculation technology, it is still based on variable operating conditions constrained by single influencing factors. Research on the theory and technology of complex operating systems of heavy-duty locomotives is still in its early stages, and there is a lack of theoretical and technical support for understanding the impact of the operating environment on the failure of key components of heavy-duty locomotives.

[0079] Based on this, this application provides a method for calculating wheel wear of heavy-duty locomotives under complex environments. First, based on the structural characteristics of heavy-duty locomotives, a multi-body dynamics simulation model of the locomotive is established. Second, from the perspective of complex operating systems, the coupling mechanism between locomotive wheelsets and the operating environment is revealed. The locomotive operating environment is deconstructed into five dimensions of operating conditions: track conditions, weather conditions, load information, train formation, and driver operation information. The locomotive's operating route is then divided into multiple operating scenarios, each containing various operating condition data, forming a set of complex operating scenarios (i.e., a sequence of operating scenarios). Next, the wheel-rail contact parameters under different scenarios are calculated using a wheel-rail rolling contact algorithm. Wheel wear is then calculated using a wheel wear model to obtain the wheel wear amount under different scenarios, thus determining the stage wear amount corresponding to each operating path. Finally, the stage wear amount under different operating scenarios is calculated to obtain the cumulative wheel wear amount, thereby realizing the cumulative calculation of wheel wear of heavy-duty locomotives operating in a complex environment. This improves the calculation level of locomotive wheel wear evolution under different scenario combinations and ensures the long-term safe operation of the locomotive system under wheel wear conditions.

[0080] In this embodiment, as Figure 4 The diagram shown is a flowchart of the wheel wear determination method provided in this application in a heavy-duty locomotive application scenario, specifically including the following steps:

[0081] Step 401: Based on the inherent properties of the heavy-duty locomotive, establish a multi-body dynamics simulation model of the locomotive, which includes wheelsets, car body, springs, and axle boxes.

[0082] Specifically, the server can create a wheelset subsystem based on the locomotive's own attributes, and set the geometric and inertia parameters of the wheelset, as well as its longitudinal and vertical positions in the coordinate system. Several rigid bodies are introduced into the model, including springs, axle box tie rods, wheelset axle boxes, shock absorbers, and the car body. The mass and moment of inertia of each rigid body are set, and the rotational hinges of the rigid bodies relative to the wheelsets are defined. Spring force elements are added to the locomotive's multibody dynamics model. These spring force elements are related to the car body's mass and positional distance, and the spring preload of the front wheelset can be obtained from the static equilibrium equations. Where G is the mass of the vehicle body, which determines the vertical force between the wheelset and the track; g is the acceleration due to gravity; L1 is the longitudinal position of the first wheelset; and L2 is the longitudinal position of the second wheelset. Due to the different forces acting on the front and rear wheelsets, the static equation for the rear wheelset is: .in, The static vertical force, representing the vehicle body mass *f*, is transmitted to the wheelset through the suspension system; it is the initial normal force at the wheel-rail contact point. The spring preload is calculated using static equilibrium equations. This vertical force, transmitted to the wheel-rail contact point, becomes the normal contact pressure. A higher spring preload results in a higher wheel-rail vertical force, increasing the normal contact pressure and consequently increasing the wear volume. Furthermore, as a core parameter of the suspension system, the spring preload also affects wheel-rail vibration characteristics (such as wheel-rail impact and creep behavior), thus influencing the slip distance.

[0083] Step 402 involves deconstructing the locomotive's operating environment into five categories of operating conditions: track conditions, weather conditions, load information, train formation, and driver operation information. The locomotive's operating line is then divided into multiple scenarios, each containing various operating condition data, forming a complex set of operating scenarios.

[0084] Specifically, the server can decompose five external conditions—A. track conditions, B. weather conditions, C. load information, D. train formation, and E. driver operation information—into five independent operating conditions in the actual locomotive operating line. The decomposition of the locomotive operating environment into five operating conditions is based on an analysis and classification of factors influencing locomotive wheel wear.

[0085] Furthermore, regarding influencing factor A, track conditions, when the train travels on curves with small radii, the wheels are subjected to lateral forces, resulting in greater lateral friction between the wheel flange and the track. This exacerbates wear between the wheel and rail. Therefore, track conditions, as one of the operating conditions categorized within the operating environment, can be determined based on the locomotive's LKJ (Train Operation Monitoring and Recording). Different track conditions are designed based on parameters of the train operation monitoring and recording device (Device). For factor B, weather conditions, wind, sand, rain, and snow affect the wheel-rail interface friction coefficient, and different friction coefficients have different effects on the interaction between the wheel and rail. Therefore, weather conditions are considered one of the operating conditions in the operating environment, and different weather conditions can be designed based on the locomotive's environmental monitoring data. For factor C, load information, a heavy load increases the pressure between the wheel and the rail, increasing friction and thus accelerating wheel wear. Therefore, load information is considered one of the operating conditions in the operating environment. For factor D, train formation, different formations may cause different pressures on the wheels during operation, affecting wheel-rail wear. Therefore, train formation is considered one of the operating conditions in the operating environment. For factor E, driver operation information, different traction or braking operations affect the friction between the wheelset tread and the rail. Therefore, driver operation information is considered one of the operating conditions in the operating environment.

[0086] For each working condition, it is divided into different working condition states. Track condition A is divided into straight sections and curved sections with different radii, with a total of S1 states; weather condition B is divided into rain, snow, temperature, oil pollution, and sandstorm, with a total of S2 states; load information C can be divided into different heavy-load train load capacities, with a total of S3 states; formation method D is divided into different formation lengths, with a total of S4 states; driver operation information E is divided into traction and braking operations, with a total of S5 states for its specific circumstances.

[0087] Each scenario represents a segment of a route under a complex operating environment. This segment consists of different states of five external conditions: track conditions, weather conditions, load information, train formation, and driver operation information. Different states form different sets of complex operating scenarios, i.e., different sequences of operating scenarios. Each set of complex operating scenarios represents a specific complex scenario, such as {A1, B1, C1, D1, E1}... which can be combined into... A set of complex operational scenarios with different combinations, namely Number of scenarios. For example Figure 5As shown, for locomotives on different operating paths, corresponding operating scenario sequences can be configured. Each operating scenario sequence can include different configuration scenarios (i.e., operating scenarios), thereby decomposing each operating path into multiple operating scenarios. Each operating scenario can be used to describe the corresponding sub-segment in the operating path.

[0088] Step 403: Calculate wheel-rail contact parameters for different scenarios using the wheel-rail rolling contact algorithm.

[0089] Specifically, in locomotive wheel wear calculations, the wheel-rail rolling contact algorithm can be used to calculate the distribution of creep force, creep rate, and adhesion zone within the contact patch, thereby analyzing the changes in wheel wear. Assuming the normal contact stress is distributed in a semi-elliptical pattern along the wheel rolling direction, the creep force distribution in the x and y directions is as follows:

[0090] (1)

[0091] in, The creep force component is located in the x-direction. Let G be the creep force component in the y-direction; G is the wheel-rail contact constant. , , is the Kaller creep coefficient; a and b are the major and minor axes of the wheel-rail contact ellipse; , , Let a and b be the functions corresponding to a and b, which are functions related to the size of the contact ellipse; The creep velocity (i.e., the relative sliding velocity between the wheel and the rail) is in the x and y directions. It is the spin angular velocity (representing the rotational motion of the wheel-rail contact point). The sliding distance can be determined based on the calculation results of the creep force distribution.

[0092] Step 404: Calculate wheel wear using the wheel wear model to obtain the wheel wear amount under different scenarios.

[0093] Specifically, in locomotive wheel wear calculations, a wheel-rail wear model based on wheel-rail normal force and contact patch slip is used to qualitatively analyze and predict the evolution of the wheel-rail profile. Specifically, the wheel wear volume can be calculated using the wheel-rail wear model:

[0094] (2)

[0095] Where V is the wear volume. is the volumetric wear coefficient, N is the normal contact pressure, S is the sliding distance, and H is the material hardness.

[0096] Furthermore, the wear depth W at any point on the wheel profile can be expressed as follows:

[0097] (3)

[0098] Where V is the wear volume; s is the arc coordinate, which is the curve length parameter along the wheel profile curve (surface), used to accurately locate the specific position of the wheel-rail contact point on the wheel profile; R is the wheel rolling circle radius. It is the radius of the wheel rolling circle at arc coordinate 𝑠 (which changes dynamically with the contour position); is the circumference of the wheel at arc coordinate 𝑠; h is the length of the segment divided along the contour curve of the wheel-rail profile. Formula (3) can calculate the wear depth at any point on the wheel-rail contact surface (located by arc coordinate 𝑠), and analyze the wear spatial distribution through arc coordinate 𝑠, which is suitable for complex working conditions.

[0099] Furthermore, assuming that the wear of the wheel is uniform along the circumference and remains axisymmetric, the wear depth of the wheel profile can be expressed as:

[0100] (4)

[0101] Where L is the average circumference length of the wheel in the profile section. By assuming that the wear of the wheel is absolutely uniform along the circumference (axisymmetric) and that the profile is always rotationally symmetric, the calculation of local radii can be avoided. Using the average perimeter L directly can significantly reduce computational complexity. Formula (4) relies on the axisymmetric assumption and is suitable for simple scenarios with uniform wear.

[0102] Step 405: Calculate the wheel wear in each scenario, accumulate the wear amounts to obtain the wheel wear of the heavy-duty locomotive under complex operating conditions.

[0103] Specifically, a complex operating environment consists of five different states of external conditions: track conditions, weather conditions, load conditions, train formation, and driver operation. These different states form different sets of complex operating scenarios, and each set of complex operating scenarios (a sequence of operating scenarios) represents a specific complex scenario. For example... Figure 5 As shown, each running path can correspond to a running scenario sequence. The running scenario sequence can include multiple running scenarios arranged in sequence, each corresponding to a sub-segment in the current running path. The running scenario includes the working conditions of the wheels in multiple dimensions during the running process of the corresponding sub-segment.

[0104] Furthermore, an iterative wear equation is established within the wheel wear calculation model, and the wear of the wheel profile is predicted using the parallel discretization method. This involves dividing a complex operating environment into multiple road segments based on specific complex scenarios. For each road segment, the wheel wear depth is summarized based on the operating mileage of a specified wear step and the weighting factors considered. After calculating the wheel wear amount under each complex scenario, the wear amount is accumulated.

[0105] Among them, a dimensionless weighting factor is introduced. Weighting factor used to determine the ratio of locomotive external conditions to wheel wear. The value is the ratio of the track length to the rail bus length in each specific complex scenario. The depth of wheel wear in the i-th specific complex scenario is... The weighting factor is ,like Figure 5 As shown, after superimposing n scene road segments, the wheel wear depth is... The wheel profile (wheel geometry parameters) is updated based on the cumulative wear depth to obtain the worn wheel profile. A new wheel-rail profile is generated through one iteration, and the newly generated profile is used as the initial profile for the next calculation. By using the maximum wear amount or vehicle operating mileage as the iteration condition for wheel wear, the wheel wear amount of the locomotive in each scenario and the wheel wear amount under a complex operating environment can be obtained. In this way, the cumulative wear value of locomotive wheels under different scenario combinations can be predicted and extrapolated.

[0106] The wheel wear determination method provided in this application addresses key issues such as the lack of research on influencing factors of wheel wear in heavy-duty freight cars, including vehicle system parameters, track system parameters, operating conditions, and driver operations, as well as the lack of research on wheel wear of heavy-duty freight cars under non-ideal conditions. By deconstructing the locomotive operating environment into five categories of operating conditions: track conditions, weather conditions, load information, formation mode, and driver operation information, and further dividing the locomotive operating line into multiple scenarios, a method for calculating the evolution of wheel wear of heavy-duty locomotives under different complex operating scenario sets is proposed for each scenario and the various operating condition data contained therein. This method supports the state assessment and wear prediction of heavy-duty locomotive wheelsets based on specific complex operating scenarios, ensuring the long-term safe operation of the locomotive system under wheel wear conditions.

[0107] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0108] Based on the same inventive concept, this application also provides a wheel wear determination device for implementing the wheel wear determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more wheel wear determination device embodiments provided below can be found in the limitations of the wheel wear determination method described above, and will not be repeated here.

[0109] In one exemplary embodiment, such as Figure 6 As shown, a wheel wear determination device 600 is provided, including: a running scene sequence acquisition module 602, a sub-segment wear determination module 604, a stage wear determination module 606, a wheel parameter update module 608, and a cumulative wear determination module 610, wherein:

[0110] The running scenario sequence acquisition module 602 is used to acquire the running scenario sequence corresponding to the current running path of the wheel. The running scenario sequence includes running scenarios arranged in sequence and corresponding to each sub-segment in the current running path. The running scenario includes the working conditions of the wheel in multiple dimensions during the running process of the corresponding sub-segment.

[0111] The sub-segment wear determination module 604 is used to determine the wheel wear amount of the wheel in each sub-segment based on the working conditions under multiple dimensions included in each of the operating scenarios in the operating scenario sequence and the current wheel parameters of the wheel.

[0112] The stage wear determination module 606 is used to fuse the wheel wear amounts based on each of the aforementioned operating scenarios to obtain the stage wear amount of the wheel under the current operating path.

[0113] The wheel parameter update module 608 is used to update the current wheel parameters according to the stage wear amount when it is determined that the iteration end condition is not met, and to obtain the next running path of the current running path.

[0114] The cumulative wear determination module 610 is used to update the next running path to the current running path and return to the step of obtaining the running scene sequence corresponding to the current running path of the wheel until the iteration end condition is met, and obtain the cumulative wear of the wheel according to the stage wear of each running path.

[0115] In some embodiments, the sub-segment wear determination module 604 is further configured to acquire the current wheel parameters of the wheel; for each operating scenario in the operating scenario sequence, determine the track conditions, weather conditions, load information, formation mode, and driver operation information of the targeted operating scenario; determine the wheel wear amount of the targeted operating scenario based on the current wheel parameters, the track conditions, the weather conditions, the load information, the formation mode, and the driver operation information; and obtain the wheel wear amount of the wheel in each sub-segment according to the wheel wear amount of each operating scenario.

[0116] In some embodiments, the sub-section wear determination module 604 is further configured to determine the normal contact pressure and sliding distance based on the track conditions, the weather conditions, the load information, the formation mode, the driver operation information, and the current wheel parameters; and to obtain the wheel wear amount for the target operating scenario based on the normal contact pressure, the sliding distance, and the current wheel parameters.

[0117] In some embodiments, the sub-section wear determination module 604 is further configured to determine the wear volume based on the normal contact pressure, the sliding distance, and the material hardness information in the current wheel parameters; and to obtain the wheel wear amount for the target operating scenario based on the wear volume and the wheel geometry parameters in the current wheel parameters.

[0118] In some embodiments, the stage wear determination module 606 is further configured to determine the weight factor of each of the operating scenarios, wherein the weight factor is matched with the sub-road segment corresponding to the operating scenario; and to perform weighted fusion of the wheel wear of each of the operating scenarios according to the weight factor of each of the operating scenarios to obtain the stage wear of the wheel under the current operating path.

[0119] In some embodiments, the wheel parameter update module 608 is further configured to update the wheel geometry parameters in the current wheel parameters according to the stage wear amount if it is determined that the iteration termination condition is not met; wherein the iteration termination condition includes at least one of the following: the stage wear amount reaches a first wear amount threshold, the cumulative running path obtained based on the current running path and the previous running path reaches a path length threshold, or the sum of the stage wear amount and the stage wear amount of the previous running path reaches a second wear amount threshold.

[0120] Each module in the aforementioned wheel wear determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0121] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores various data involved in the wheel wear determination method. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a wheel wear determination method.

[0122] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0124] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0125] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0127] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0128] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0129] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining wheel wear, characterized in that, The method includes: Obtain the sequence of operating scenarios corresponding to the current operating path of the wheel. The sequence of operating scenarios includes the operating scenarios arranged in order and corresponding to each sub-segment of the current operating path. The operating scenarios include the working conditions of the wheel in multiple dimensions during the operation of the corresponding sub-segment. Based on the working conditions under multiple dimensions included in each of the operating scenarios in the operating scenario sequence and the current wheel parameters of the wheel, the wheel wear amount of the wheel in each of the sub-road segments is determined respectively. The wheel wear amount of each of the aforementioned operating scenarios is fused to obtain the stage wear amount of the wheel under the current operating path. If it is determined that the iteration termination condition is not met, the current wheel parameters are updated according to the stage wear amount, and the next running path of the current running path is obtained. The next running path is updated to the current running path, and the process returns to the step of obtaining the running scene sequence corresponding to the current running path of the wheel, until the iteration end condition is met, and the cumulative wear of the wheel is obtained according to the stage wear of each running path.

2. The method according to claim 1, characterized in that, The step of determining the wheel wear amount of each wheel in each sub-road segment based on the working conditions under multiple dimensions included in each of the operating scenarios in the operating scenario sequence and the current wheel parameters of the wheel includes: Obtain the current wheel parameters of the wheel; For each operational scenario in the operational scenario sequence, determine the track conditions, weather conditions, load information, formation method, and driver operation information for that operational scenario. Based on the current wheel parameters, track conditions, weather conditions, load information, formation mode, and driver operation information, the wheel wear amount for the target operating scenario is determined. Based on the wheel wear amount of each of the aforementioned operating scenarios, the wheel wear amount of each wheel under each of the aforementioned sub-road segments is obtained.

3. The method according to claim 2, characterized in that, The determination of wheel wear for the specific operating scenario based on the current wheel parameters, track conditions, weather conditions, load information, train formation, and driver operation information includes: Based on the track conditions, weather conditions, load information, formation method, driver operation information, and current wheel parameters, the normal contact pressure and sliding distance are determined. The wheel wear amount for the target operating scenario is obtained based on the normal contact pressure, the sliding distance, and the current wheel parameters.

4. The method according to claim 3, characterized in that, The step of obtaining the wheel wear amount for the specific operating scenario based on the normal contact pressure, the sliding distance, and the current wheel parameters includes: The wear volume is determined based on the normal contact pressure, the sliding distance, and the material hardness information in the current wheel parameters; The wheel wear amount for the target operating scenario is obtained based on the wear volume and the wheel geometry parameters in the current wheel parameters.

5. The method according to claim 1, characterized in that, The process of fusing the wheel wear data based on each of the aforementioned operating scenarios to obtain the stage wear data of the wheel under the current operating path includes: Determine the weighting factor for each of the aforementioned operating scenarios, and match the weighting factor with the sub-road segment corresponding to the operating scenario; According to the weighting factors of each of the aforementioned operating scenarios, the wheel wear amounts of each of the aforementioned operating scenarios are weighted and fused to obtain the stage wear amount of the wheel under the current operating path.

6. The method according to any one of claims 1 to 5, characterized in that, The step of updating the current wheel parameters based on the stage wear amount when it is determined that the iteration termination condition is not met includes: If it is determined that the iteration termination condition is not met, the wheel geometry parameters in the current wheel parameters are updated according to the stage wear amount; The iteration termination condition includes at least one of the following: the stage wear amount reaches a first wear amount threshold, the cumulative running path obtained based on the current running path and the previous running path reaches a path length threshold, or the sum of the stage wear amount and the stage wear amount of the previous running path reaches a second wear amount threshold.

7. A device for determining wheel wear, characterized in that, The device includes: The running scenario sequence acquisition module is used to acquire the running scenario sequence corresponding to the current running path of the wheel. The running scenario sequence includes running scenarios arranged in sequence, each corresponding to a sub-segment in the current running path. The running scenario includes the working conditions of the wheel in multiple dimensions during the running process of the corresponding sub-segment. The sub-segment wear determination module is used to determine the wheel wear amount of the wheel in each sub-segment based on the working conditions under multiple dimensions included in each of the operating scenarios in the operating scenario sequence and the current wheel parameters of the wheel. The stage wear determination module is used to fuse the wheel wear amounts based on each of the aforementioned operating scenarios to obtain the stage wear amount of the wheel under the current operating path. The wheel parameter update module is used to update the current wheel parameters according to the stage wear amount when it is determined that the iteration termination condition is not met, and to obtain the next running path of the current running path. The cumulative wear determination module is used to update the next running path to the current running path and return to the step of obtaining the running scene sequence corresponding to the current running path of the wheel until the iteration end condition is met, and to obtain the cumulative wear of the wheel according to the stage wear of each running path.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images