Optimization method of railway freight carriage heavy side door reset device

Abstract

The invention provides an optimization method of a railway freight carriage heavy side door resetting device, which comprises the steps that real-time working condition data are acquired through a sensor system deployed on a railway freight carriage side door, and the working condition data comprise weight distribution and opening angle change information of a heavy vehicle door; performing multi-source information fusion processing on the working condition data by adopting a data integration method to obtain a comprehensive state model containing mechanical characteristics and motion characteristics; if the accuracy of the motion track does not reach a preset threshold value, iteratively adjusting the motion control parameters, fusing the force transmission data to regenerate a motion curve, and obtaining an accurate reset motion sequence; and based on the device performance indexes, evaluating the stability of the whole system by adopting a verification method, and generating a final optimization report.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an optimized method for a heavy-duty side door reset device for railway freight cars. Background Technology

[0002] In the research field of railway freight car side door reset devices, related technologies play a crucial role in ensuring cargo transportation safety and improving operational efficiency. The reset device of the car side door directly affects the stability and safety of the door after frequent opening and closing. If the reset is not timely or accurate, it may lead to cargo leakage or transportation accidents. Therefore, developing efficient and reliable reset technology has become a key focus that cannot be ignored in the industry.

[0003] However, existing reset devices on the market often reveal shortcomings when dealing with complex operating conditions. Many solutions lack the design adaptability to different car side door sizes, weights, and opening angles, resulting in unstable reset performance in practical applications. This is especially true when facing heavy doors or extreme environments, where the device is prone to insufficient force or jamming. This limitation makes it difficult for existing technology to meet the high adaptability and high reliability requirements of modern railway freight transport.

[0004] Focusing on the core technical challenges, the resetting mechanism presents particularly significant challenges in terms of force transmission and motion control. The primary issue lies in how to effectively amplify force through structural design to complete the resetting action of a heavy-duty door with relatively small driving force. If this problem remains unresolved, it will further lead to inaccurate movement trajectories of the device under different operating conditions, making it difficult to guarantee the stability of the door's position after resetting. For example, in some heavy-duty freight cars, the side doors are heavy and have varying opening angles. The resetting mechanism needs to provide sufficiently large force within a limited space while ensuring a smooth movement process to avoid the door failing to close completely or requiring repeated adjustments due to trajectory deviations.

[0005] Therefore, how to amplify the force by optimizing the device structure under limited driving force, while ensuring the accuracy of the motion trajectory and the smoothness of the reset process, has become a key problem that urgently needs to be solved in this research. Solving this problem is not only related to improving the performance of the device, but also directly affects the safety and efficiency of operating the side doors of railway freight cars. Summary of the Invention

[0006] This invention provides an optimized method for the reset device of the heavy-duty side door of a railway freight car, mainly comprising: Real-time operating data is collected by a sensor system deployed on the side doors of railway freight cars. The operating data includes information on the weight distribution and opening angle changes of heavy doors. The working condition data is processed by multi-source information fusion using a data integration method to obtain a comprehensive state model that includes mechanical and kinematic characteristics; Based on the comprehensive state model, the force transmission process is simulated and calculated using mechanical analysis methods. The stress distribution of the heavy-duty door under different opening angles is analyzed to determine the key stress points in the force transmission path. If the key force points show insufficient force transmission, the structural parameters of the reset device are adjusted by structural optimization methods, and the lever ratio and spring stiffness are optimized for the force transmission path to generate an improved structural design scheme. Motion control parameters are extracted from the structural design scheme, a reset motion curve is generated using a path planning method, multiple motion trajectories are simulated for the change of the opening angle, and the accuracy of the motion trajectory is determined to reach a preset threshold. If the accuracy of the motion trajectory does not reach the preset threshold, the motion control parameters are iteratively adjusted, and the motion curve is regenerated by fusing the force transmission data to obtain an accurate reset motion sequence. Based on the reset motion sequence, adaptive simulation tests are conducted under complex environments to verify the response behavior of the heavy-duty door under extreme opening angles and determine the performance indicators of the improved device. Based on the performance indicators of the device, a verification method is used to evaluate the overall system stability and generate a final optimization report.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses an optimization method for the reset device of a heavy-duty side door of a railway freight car. Addressing the problem of inaccurate reset motion and poor overall stability caused by insufficient force transmission at different opening angles of heavy-duty doors, the method collects real-time operating data, including weight distribution and opening angle variation information, using sensors deployed on the side door. A comprehensive state model is obtained through multi-source information fusion processing using a data integration method. Based on this model, a mechanical analysis method is applied to simulate the force transmission process and analyze stress distribution to determine key stress points. If force transmission is insufficient, a structural optimization method is used to adjust the lever ratio and spring stiffness of the reset device to generate an improved structural design. Motion control parameters are extracted from this design, and a path planning method is used to generate a reset motion curve and simulate multiple motion trajectories. If the accuracy does not reach a threshold, the parameters are iteratively adjusted, and the force transmission data is fused to regenerate a precise reset motion sequence. Based on this sequence, adaptive simulation tests are conducted under complex environments to verify the response behavior under extreme opening angles and evaluate overall stability, generating a final optimization report. This achieves precise control and improved stability of the heavy-duty door reset process, thereby improving the safety and efficiency of railway freight transport. Attached Figure Description

[0008] Figure 1 This is a flowchart of an optimized method for a heavy-duty side door reset device for a railway freight car according to the present invention.

[0009] Figure 2 This is a schematic diagram of an optimized method for a heavy-duty side door reset device for a railway freight car according to the present invention.

[0010] Figure 3 This is another schematic diagram of an optimized method for a heavy-duty side door reset device for a railway freight car according to the present invention. Detailed Implementation

[0011] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] like Figure 1-3 The optimized method for the reset device of the heavy-duty side door of a railway freight car in this embodiment may specifically include: S101. Real-time operating data is collected by a sensor system deployed on the side door of a railway freight car. The operating data includes information on the weight distribution and opening angle changes of the heavy-duty doors.

[0013] Real-time operating data, including load distribution information and opening angle change information, is acquired from the side doors of railway freight cars via a sensor system, resulting in initial data records. Based on these initial data records, a Kalman filter is used to denoise the load distribution and opening angle change information, yielding clean data. If the load distribution information in the clean data exceeds a preset threshold, an anomaly marker is triggered, and the corresponding opening angle change information is recorded, resulting in clean data marked as anomalous. For the marked anomalous clean data, its corresponding timestamp and location information are obtained, and preliminary classification is performed according to preset rules, resulting in classified anomalous data groups. Based on these classified anomalous data groups, a support vector machine algorithm is used to perform pattern recognition on the load distribution and opening angle change information to determine the specific type and priority value of the anomalous data. The priority values ​​of the anomalous data are sorted using numerical calculation tools to obtain high-priority anomalous data, which is then cross-validated with railway freight operation logs to determine the correlation between the anomalous data and the operating status. Based on the cross-validated high-priority anomalous data, corresponding status monitoring records are generated, and the final handling action is determined according to the safety management requirements of the car side doors.

[0014] For example, in the scenario of monitoring the side doors of railway freight cars, acquiring real-time operating data through a sensor system is fundamental to the entire process. Sensors can be installed at key locations on the side door to collect load distribution information and changes in opening angle. Suppose that in a monitoring session, the sensor records a load distribution of 800 kg for a side door during operation, while the preset threshold range is 500 to 700 kg, and the side door opening angle changes by 15 degrees. This constitutes the initial data record. This data acquisition method can reflect the status of the side door in real time, providing a reliable basis for subsequent analysis.

[0015] In one possible implementation, a Kalman filter is used to eliminate noise interference that may occur during sensor acquisition in the initial data recording denoising process. For example, the load distribution value in the original data may fluctuate to 850 kg in a short period of time due to vibration. After smoothing by the Kalman filter, the final clean data is 810 kg, which is close to the true value. This denoising method can improve data accuracy and ensure the accuracy of subsequent anomaly detection.

[0016] Specifically, if the load distribution information in the cleaning data exceeds the threshold range, such as 810 kg exceeding the 700 kg upper limit mentioned above, the system will trigger an anomaly flag and record the corresponding opening angle change information as 15 degrees, thus marking the cleaning data as abnormal. This anomaly flagging mechanism helps to quickly locate potential problems and reduce safety hazards.

[0017] For example, for cleaning data marked as abnormal, the system obtains its timestamp and location information. Assuming the abnormality occurred at 10:00 AM at kilometer 50 of a railway section, and is initially classified using preset rules, the abnormality might be categorized as "overload," resulting in a group of abnormal data. This classification method facilitates subsequent targeted analysis and improves processing efficiency.

[0018] In one possible implementation, a support vector machine (SVM) algorithm is used to perform pattern recognition on the classified abnormal data sets, analyzing the correlation between load distribution information and opening angle changes. For example, by training the model using historical data, it is found that exceeding load limits is highly correlated with opening angles exceeding 10 degrees, classifying the anomaly as "side door not closed tightly causing load anomaly," and assigning it a priority value of 8 (out of 10). This method can accurately identify the cause of the anomaly, providing a scientific basis for handling it.

[0019] Specifically, after sorting the priority values ​​of the abnormal data, anomalies with a priority value of 8 were classified as high-priority anomalies. Cross-validation using railway freight operation logs revealed that the carriage speed at the time was 80 km / h, meeting the conditions for an anomaly and verifying the correlation between the anomaly and the operational status. This verification method enhances the credibility of the judgment.

[0020] For example, based on high-priority anomaly data obtained through cross-validation, status monitoring records are generated. Addressing the safety management needs of the carriage side doors, the final action is determined to be "immediately notify maintenance personnel to check the side door status and conduct emergency repairs at the next station." This approach can promptly eliminate safety risks and ensure the smooth operation of railway freight. Through this process, from data collection to final action, a complete monitoring and processing chain is formed, significantly improving the intelligence and safety of carriage side door management.

[0021] S102. The working condition data is processed by multi-source information fusion using a data integration method to obtain a comprehensive state model that includes mechanical and kinematic characteristics.

[0022] Raw operating condition data is obtained from multiple data sources to generate an initial dataset containing a preset number of categories of information. For this initial dataset, a data cleaning method is used to remove redundant and abnormal records, resulting in a cleaned standard dataset. Based on the standard dataset, a classification algorithm is applied to divide the data into a first subset related to mechanical properties and a second subset related to motion properties. If the information completeness of the first and second subsets reaches a preset threshold, feature extraction is performed on both subsets to obtain a set of key indicators for mechanical and motion properties. For these key indicator sets, the correlation between mechanical and motion properties is calculated to determine the feature combination of the comprehensive state. By standardizing the feature combination, a unified format comprehensive state description is generated, constructing the final state model structure. A pre-established validation dataset is used to perform a consistency check on the state model structure to determine whether the output meets the expected standards, thus obtaining the final comprehensive state model.

[0023] For example, in the field of monitoring the operating conditions of railway freight car side doors, during the process of acquiring raw operating condition data from multiple data sources, real-time information about the door load and opening / closing status can be collected using various sensors installed on the car side doors, such as pressure sensors and angle sensors. Assuming that during a freight transport operation, the sensors collect data once per minute, forming an initial dataset containing load and angle values, where the load value may fluctuate between 0 and 5000 kg, and the angle value varies between 0 and 90 degrees. This data originates from different cars and operating environments, and the information covers multiple dimensions such as static load, dynamic load, and angle offset.

[0024] For example, data cleaning methods for the initial dataset can remove abnormal records by setting a threshold range. Assuming the normal range for load values ​​is set to 100 to 4000 kg, if a record shows a load value of 4500 kg, it is marked as abnormal and removed. Simultaneously, for missing data, interpolation can be used to complete the data by using data from before and after the missing data points, ensuring that the cleaned standard dataset is complete and free of redundancy.

[0025] For example, when applying classification algorithms to divide data into subsets, we can classify load-related mechanical characteristic data into the first subset and angle-related motion characteristic data into the second subset based on the physical properties of the data. Suppose that in a single monitoring session, the first subset contains 1000 load records and the second subset contains 800 angle records. Classification algorithms ensure that the two types of data do not overlap, facilitating subsequent analysis.

[0026] For example, regarding the assessment of information completeness, if the preset threshold is 90%, and the completeness of the first and second data subsets in actual monitoring reaches 95% and 92% respectively, then feature extraction can be performed. The extracted key indicators may include the average and peak load, as well as the frequency and magnitude of angle changes, forming a comprehensive set of key indicators to lay the foundation for subsequent analysis.

[0027] For example, when calculating the correlation between mechanical and kinematic characteristics, statistical methods can be used to analyze the synchronicity between load changes and angle changes. If, during a certain operating period, it is observed that the frequency of angle changes also increases with the increase in load, it can be inferred that there is a strong correlation between the two, thus determining the characteristic combination of the overall state, such as the combination of peak load and frequency of angle changes.

[0028] For example, standardization of feature combinations can unify data with different dimensions into a range of 0 to 1, facilitating subsequent modeling. Assuming the original peak load is 3000 kg, it becomes 0.6 after standardization; and the original angle change frequency is 5 times per minute, it becomes 0.5 after standardization, ultimately generating a unified format for the comprehensive state description.

[0029] For example, when constructing the state model structure and performing consistency checks, historical operational data can be used as a validation dataset. If the validation results show that the model output matches the actual state by more than 85%, the model is considered to meet the expected standards and can be used in actual monitoring. This approach can effectively improve the reliability of state monitoring and provide data support for the safety management of the carriage side doors.

[0030] S103. Based on the comprehensive state model, the force transmission process is simulated and calculated using mechanical analysis methods. The stress distribution of the heavy-duty door under different opening angles is analyzed to determine the key stress points in the force transmission path.

[0031] State data of a heavy-duty vehicle door at different opening angles are acquired, and a mechanical analysis framework for the state data is constructed. Numerical simulation is used to obtain preliminary force transmission distribution results. First stress distribution data at different opening angles is extracted from the force transmission distribution results. Finite element analysis is performed on the first stress distribution data to determine the first stress concentration region corresponding to each angle. Based on the data of the first stress concentration region, the direction of the first force transmission path is analyzed, and the locations of key stress points in the path are identified. If the local stress data of the key stress points exceeds a preset threshold, the stress points are marked, resulting in a first set of marked stress points. The stability of the first force transmission path is analyzed for the first set of marked stress points. If the number of marked stress points in the path exceeds a preset threshold, the path is adjusted, and the force distribution of the adjusted path is determined. Based on the adjusted path force distribution, stress transmission characteristics of each segment are extracted, and the feature data is integrated to determine the optimization direction of the first force transmission path. Using the optimization direction, an adjustment scheme for the force transmission path of the heavy-duty vehicle door at different opening angles is generated, resulting in the final force distribution data.

[0032] For example, when analyzing the state data of a heavy-duty vehicle door at different opening angles, we can understand the force changes of the door at different angles from the perspective of mechanical behavior. The opening angle of a heavy-duty vehicle door may range from 0 degrees to 90 degrees, and the torque and shear force borne by the door's hinges and supporting structure differ at each angle. Assuming that at 30 degrees, the gravity component of the door exerts a large vertical force on the hinge, while at 60 degrees, the torque effect is more significant, by constructing a mechanical analysis framework for the state data, we can initially identify the main force directions at each angle, providing a basis for subsequent numerical simulations.

[0033] For example, based on the force transmission distribution results obtained from numerical simulation methods, software tools can be used to model the car door structure. By inputting material parameters such as the elastic modulus and yield strength of steel, the simulation shows that the force transmission distribution near the lower hinge of the car door exhibits a concentrated trend at a 45-degree opening angle. This distribution result intuitively reflects the force transmission law, laying the foundation for extracting the first stress distribution data.

[0034] For example, when performing finite element analysis to determine the first stress concentration region, the focus can be on the state when the car door is opened to 75 degrees. Analysis reveals that the stress value near the hinge connection reaches its peak, approximately 1.5 times the standard value. This area is identified as a stress concentration region, and its impact on the overall structure requires further investigation. Such analysis helps identify potential fatigue failure points.

[0035] For example, when analyzing the path of the first force transmission, it can be observed that at a 60-degree opening angle, the force is transmitted from the hinge along the edge of the door outwards, with the point near the latch experiencing greater force. By identifying the location of key stress points, it was found that the local stress at this point exceeded the preset threshold of 120 MPa, thus marking it as a key focus. This marking method provides a precise target for subsequent optimization.

[0036] For example, when analyzing the stability of a path based on the first set of marked stress points, if the number of marked points on a path exceeds three, it indicates insufficient stability of the path. This can be addressed by adjusting the hinge installation angle, changing from the original vertical installation to a slight tilt of 5 degrees, redistributing the stress and reducing stress concentration at key points. The adjusted path exhibits a more uniform stress distribution, which helps extend the lifespan of the car door.

[0037] For example, when extracting stress transfer characteristics from the adjusted path, it can be observed that the stress value of each segment gradually decreases from the hinge to the edge, indicating that the transfer efficiency is higher in the middle section. After integrating these characteristic data, it is determined that the optimization direction should focus on material strengthening in the middle section to improve the overall force transfer efficiency.

[0038] For example, when generating force transmission path adjustment schemes for different opening angles, it can be proposed to add auxiliary support structures and optimize hinge design at two key angles of 30 degrees and 60 degrees, respectively, to ensure that the force distribution data is controlled within a safe range at each angle. The final force distribution data provides a reliable reference for subsequent design improvements, significantly enhancing the durability and safety of the door structure.

[0039] S104. If the key force point shows insufficient force transmission, the structural parameters of the reset device are adjusted by structural optimization method, and the lever ratio and spring stiffness are optimized for the force transmission path to generate an improved structural design scheme.

[0040] Analytical data of key stress points is acquired, and the data is processed to determine the specific location and degree of insufficient force transmission, resulting in a first stress analysis result. Key nodes of the force transmission path are extracted from the first stress analysis result, and a path finding algorithm is used to determine the lever ratio deviation in the path. If the deviation exceeds a preset threshold, the lever ratio is adjusted to obtain first ratio data. Based on the first ratio data, the current parameter of the spring stiffness is acquired, and the matching degree between the parameter and the force transmission path is analyzed. If the matching degree is lower than a preset standard, the spring stiffness is adjusted using an optimization algorithm to determine the first stiffness parameter. Based on the first stiffness parameter and the first ratio data, a parameter adjustment scheme for the reset device is generated. Combined with preset structural constraints, a first design improvement scheme is obtained. Key parameters are extracted from the first design improvement scheme, and the improvement scheme is verified using a stress simulation tool to determine whether the simulation results meet the force transmission requirements. If not, the key parameters are iteratively adjusted to obtain a second parameter combination. Based on the second parameter combination, complete improved design data is generated, and a second design scheme that conforms to force transmission path optimization is output.

[0041] For example, when analyzing data from critical stress points, one can begin by examining the stress state of a heavy-duty vehicle door at different opening angles to understand the specific location and extent of insufficient force transmission. Suppose that when the door is opened to 45 degrees, the stress data near the hinge is significantly lower than in other areas. By comparing this with design standard values, it can be found that the force transmission efficiency at this location is only 60% of the expected value, thus identifying this as a critical area of ​​insufficient force transmission. This analytical approach helps to accurately pinpoint problem areas and provides data support for subsequent improvements.

[0042] In one possible implementation, the extraction of key nodes in the force transmission path can be achieved by identifying lever ratio deviations through a path finding algorithm. For example, if the path analysis reveals a lever ratio of 1:1.5 at a certain node, while the design standard is 1:2, the deviation reaches 25%, exceeding the preset threshold of 10%. In this case, the lever ratio can be adjusted to be closer to the standard value, for example, to 1:1.8, thereby reducing losses during force transmission and improving the overall stability of the path.

[0043] For example, in analyzing the matching degree between spring stiffness parameters and force transmission paths, we can first obtain the current spring stiffness value, assuming it to be 500 N / m. Simulations reveal that the ideal value is 600 N / m, resulting in a matching degree of only 83%, lower than the preset standard of 90%. Through optimization algorithms, the stiffness can be gradually adjusted to approach the ideal value, for example, ultimately determined to be 590 N / m, forming the first stiffness parameter. This adjustment helps ensure that energy loss during force transmission is minimized.

[0044] In one possible implementation, when generating the parameter adjustment scheme for the reset device, the first stiffness parameter and lever ratio data can be combined, taking into account structural constraints such as door weight limitations and spatial layout. Assuming the door weighs 200 kg and the maximum allowable spring size is 0.5 m, the adjustment scheme needs to optimize parameter configuration within this range, ultimately forming the first design improvement scheme. This approach can meet the needs of practical application scenarios.

[0045] For example, when verifying an improved design using a stress simulation tool, key parameters can be input, such as the adjusted lever ratio of 1:1.8 and a stiffness value of 590 N / m, to simulate the force distribution of the car door at different angles. If the simulation results show that the force is still uneven at a certain angle, such as high stress at the hinge at 60 degrees, the parameters need to be iteratively adjusted to form a second parameter combination. This verification method can effectively identify potential problems and ensure the feasibility of the design.

[0046] In one possible implementation, when generating complete improved design data for the second parameter combination, all optimized parameters can be integrated. For example, the final lever ratio can be set at 1:1.9, and the stiffness value at 600 N / m. These parameters, combined with constraints on the door material and installation location, form a second design scheme. This scheme can comprehensively improve the efficiency of the force transmission path while ensuring structural stability, providing a reliable basis for the design optimization of heavy-duty doors. Through the above multi-faceted analysis and adjustments, shortcomings in the force transmission process can be significantly improved, laying a solid foundation for subsequent practical applications.

[0047] S105. Extract motion control parameters from the structural design scheme, generate a reset motion curve using a path planning method, simulate multiple motion trajectories for the change of the opening angle, and determine whether the accuracy of the motion trajectory reaches a preset threshold.

[0048] Motion control parameters are obtained from the structural design scheme. These parameters are then categorized and organized using data processing tools to obtain a preliminary control parameter set. Based on this preliminary control parameter set, a path planning algorithm is used to model the reset motion, generating initial motion curve data. For this initial motion curve data, combined with the range of opening angle changes, multiple sets of motion trajectory datasets are generated using simulation tools. Trajectory deviation values ​​are extracted from these datasets, and their matching with a preset threshold is assessed. If the deviation exceeds the threshold, the initial motion curve data is locally adjusted to obtain an optimized trajectory dataset. Using this optimized dataset, trajectory stability indices are obtained for each angle change. Whether these indices meet preset conditions is determined. If not, the parameters of the path planning algorithm are fine-tuned to determine the final trajectory data. Based on the final trajectory data, a complete reset motion control scheme is generated, determining the execution order and parameter configuration of each motion control step to obtain the final motion control output.

[0049] For example, when obtaining motion control parameters from structural design schemes.

[0050] Understandably, motion control parameters typically include the movement speed, angle range, and displacement data of key nodes of the reset device. Suppose that in the design of a reset device, the initial parameters show an opening angle range of 0 to 45 degrees and a movement speed of 2 degrees per second. By classifying and organizing these parameters using data processing tools, they can be divided into static parameters such as angle range and dynamic parameters such as movement speed, forming a preliminary set of control parameters and providing a clear data foundation for subsequent modeling.

[0051] For example, when using a path planning algorithm to model the reset motion for a preliminary set of control parameters, initial motion curve data can be generated through piecewise linear interpolation. Assuming the motion is divided into five key nodes during the angle transition from 0 to 45 degrees, with different angle changes and velocity requirements at each node, the generated initial curve data can reflect the smoothness requirements of the reset process. This approach helps ensure the controllability of the motion trajectory.

[0052] For example, when generating multiple motion trajectory datasets using simulation tools by combining the range of opening angle changes, different angle change step sizes can be set, such as 5 degrees, 10 degrees, etc., to generate corresponding trajectory data. By comparing these datasets, the impact of the angle change step size on trajectory smoothness can be discovered, providing a reference for subsequent optimization.

[0053] For example, when extracting trajectory deviation values ​​and comparing them with a preset threshold, assuming the preset threshold is 0.5 degrees, and the deviation value of a certain set of data is 0.8 degrees, exceeding the threshold range, local adjustments can be made to the initial motion curve data. For example, adding transition points at nodes with large deviations can effectively reduce the deviation and improve motion accuracy.

[0054] For example, when obtaining trajectory stability indicators under each angle change, stability can be judged by analyzing the jitter amplitude of the trajectory at different angles. If the jitter amplitude exceeds the preset standard of 0.2 degrees at 30 degrees, the parameters of the path planning algorithm are fine-tuned, such as adjusting the speed weight, to ultimately determine trajectory data that meets the stability requirements. This adjustment can significantly improve the reliability of the reset device under complex operating conditions.

[0055] For example, when generating a complete reset motion control scheme based on the final trajectory data, the execution sequence of each motion control step can be clearly defined. This includes first initializing the angle, then gradually adjusting to the target position, while simultaneously configuring the velocity and acceleration parameters for each step. This meticulous parameter configuration ensures the smoothness and accuracy of the reset motion, guaranteeing the overall performance of the device.

[0056] For example, when determining the final motion control output, all parameters can be integrated into a complete set of control instructions, covering all motion details from start to finish. This approach not only improves the execution efficiency of the control scheme but also facilitates subsequent debugging and optimization.

[0057] S106. If the accuracy of the motion trajectory does not reach the preset threshold, the motion control parameters are iteratively adjusted, and the motion curve is regenerated by fusing the force transmission data to obtain an accurate reset motion sequence.

[0058] Motion trajectory data is acquired from sensors, and deviation analysis is performed on the motion trajectory data against a preset threshold to obtain an initial deviation assessment result. Based on the initial deviation assessment result, a Kalman filter is used to adjust the motion control parameters, determining the adjusted parameter set. For the adjusted parameter set, real-time force transmission data is acquired, and a correlation between the force transmission data and the motion control parameters is constructed using linear regression to obtain an output deviation result. If the output deviation result exceeds a preset range, a matching value between the motion control parameters and the force transmission data is calculated using cosine similarity to obtain an optimized parameter combination. Based on the optimized parameter combination, the motion curve is reconstructed to generate corresponding reset motion sequence data. By comparing the reset motion sequence data with the motion trajectory data, it is determined whether the accuracy requirements are met, and the final motion sequence is determined. Using the final motion sequence, the motion control execution command is updated to complete the trajectory reset.

[0059] For example, in acquiring motion trajectory data from sensors, high-precision gyroscopes and accelerometers can be used to collect real-time displacement and angle change data of the device. Suppose that in a robotic arm reset scenario, the sensors collect 100 sets of data per second, recording the robotic arm's position coordinates and rotation angle in three-dimensional space. This data provides the foundation for subsequent deviation analysis, ensuring the comprehensiveness and real-time nature of the data.

[0060] For example, to analyze the deviation between motion trajectory data and a preset threshold, an ideal trajectory can be set as a benchmark, such as the robotic arm moving in a straight line when resetting, with a maximum allowable deviation of 0.5 mm. By comparing the actual collected data with the ideal trajectory, an initial deviation assessment result is obtained. If the average deviation of a certain trajectory segment is 0.8 mm, exceeding the preset threshold, it indicates that further adjustments are needed.

[0061] For example, when using a Kalman filter to adjust motion control parameters, it can be understood as smoothing noise in sensor data through a filtering algorithm to optimize velocity and acceleration parameters. Assuming the original velocity parameter is 2 m / s, after filtering, it is adjusted to 1.8 m / s to reduce errors caused by jitter. This method effectively improves parameter stability.

[0062] For example, for acquiring real-time force transmission data and performing linear regression analysis, resistance data during the movement of a robotic arm can be collected using force sensors, such as the resistance experienced by a joint during resetting being 10 Newtons. By using linear regression, the relationship between resistance and speed parameters can be analyzed, leading to the conclusion that the speed should be appropriately reduced when resistance increases, thereby optimizing the output deviation results.

[0063] For example, if the output deviation exceeds the preset range, cosine similarity calculation can be used to evaluate the degree of matching between motion control parameters and force transmission data. If the calculation shows a matching value of 0.85, which is lower than the preset 0.9, then the parameter combination needs to be adjusted, such as reducing the acceleration value from 0.5 m / s² to 0.3 m / s², to improve the matching degree.

[0064] For example, when reconstructing the motion curve and generating reset motion sequence data, a smooth reset path can be designed based on the optimized parameter combination. Assuming the robotic arm needs to return from a 30-degree angle to a 0-degree angle, the new motion curve will adjust the speed in segments to ensure a natural transition at each stage and avoid abrupt changes.

[0065] For example, by comparing the reset motion sequence data with the original trajectory data, the accuracy is determined. If a certain trajectory deviation is found to be 0.6 mm, exceeding the 0.5 mm standard, further fine-tuning of parameters is required until the requirements are met. This iterative verification method ensures the reliability of the final motion sequence.

[0066] For example, when updating motion control execution instructions, the final motion sequence can be converted into specific control signals, such as setting the robotic arm to adjust its angle every 0.1 seconds to ensure a smooth reset process. This refined instruction approach improves execution accuracy and lays the foundation for subsequent operations.

[0067] S107. Based on the reset motion sequence, conduct adaptive simulation tests under complex environments to verify the response behavior of the heavy-duty door under extreme opening angles and determine the performance indicators of the improved device.

[0068] A heavy-duty vehicle door model is constructed using a virtual simulation platform. Reset motion data of the heavy-duty vehicle door under a preset number of environmental conditions is obtained, and its performance at different opening angles is recorded to obtain a preliminary response characteristic dataset. Based on this preliminary response characteristic dataset, a support vector machine algorithm is used to classify the behavior at extreme angles, determining the classification results of the heavy-duty vehicle door's response characteristics under specific environmental conditions. For the classification results, the correlation data between extreme angles and opening angles is obtained, the stability of the reset motion is calculated, and key environmental factors affecting the response characteristics are identified. If the key environmental factors exceed a preset threshold, the parameters of the device improvement scheme are adjusted, and the adjusted behavior is simulated and tested using the virtual simulation platform to obtain optimized performance index data. Based on the optimized performance index data, the changing trend of the heavy-duty vehicle door's response characteristics at extreme angles is analyzed, and multi-scenario simulation tests are conducted to determine the final adaptive performance results. Based on the final adaptive performance results, the matching degree between environmental adaptability and complex environments is analyzed, the stability of the heavy-duty vehicle door at different opening angles is determined, and comprehensive performance evaluation data is obtained.

[0069] For example, when constructing a heavy-duty vehicle door model, a virtual simulation platform can be used to simulate its performance under different environmental conditions. Assuming a cold environment with a temperature set at -30 degrees Celsius and humidity at 80%, the platform records the door's reset motion data at different opening angles, such as 30 degrees, 60 degrees, and 90 degrees. The results show that the reset speed decreases at low temperatures, and the preliminary response characteristic dataset exhibits a significant delay trend. This data acquisition method helps identify the impact of the environment on door behavior, laying the foundation for subsequent analysis.

[0070] For example, when using a support vector machine (SVM) algorithm to classify behavior under extreme angles on a preliminary response characteristic dataset, the performance of a 90-degree opening angle in strong winds can be categorized separately. Assuming a wind speed of 15 meters per second, the data shows significant resistance when the door resets, and the classification results indicate that the response characteristics are somewhat unstable under such conditions. This classification method can clearly distinguish behavioral patterns under different conditions, providing a basis for further optimization.

[0071] For example, when analyzing the correlation data between extreme angles and opening angles, the stability of the reset motion within the 60-90 degree range can be considered. Suppose simulation tests show that when the opening angle exceeds 80 degrees, the stability of the reset motion decreases by approximately 20%, with key environmental factors such as wind speed and temperature identified as the main influencing factors. This correlation analysis helps to pinpoint areas for improvement.

[0072] For example, if key environmental factors exceed preset thresholds, such as wind speeds exceeding 10 meters per second, the damping parameters of the door device can be adjusted, and the adjusted performance can be simulated using a virtual simulation platform. Assuming the damping coefficient is increased by 15%, test results show that the reset time is shortened by 0.5 seconds, and performance data is optimized. This method of parameter adjustment and simulation testing can effectively improve the device's adaptability to complex environments.

[0073] For example, when analyzing the response characteristics of heavy-duty vehicle doors at extreme angles based on optimized performance data, a simulated rainstorm environment can be used. Assuming a rainfall of 50 mm per hour, the data shows that the door's reset stability when opened at 90 degrees is improved by 10%. Through multi-scenario testing, the final adaptive performance results are more comprehensive. This multi-scenario verification ensures the reliability of the door under various conditions.

[0074] For example, when analyzing the adaptability and compatibility with complex environments, the focus can be on high-temperature environments. Assuming a temperature of 45 degrees Celsius, the stability of the door can be tested at different opening angles, such as 45 degrees and 90 degrees. Comprehensive performance evaluation data shows that the deviation in the reset motion at high temperatures is only 5%, indicating a high degree of compatibility. This analytical approach provides data support for door design, ensuring stable performance in various environments. Through these multi-faceted examples and analyses, a complete logical chain is formed, from model building to data classification, parameter adjustment, and multi-scenario testing. Each link supports the others, jointly verifying the adaptability of heavy-duty doors under different environments and angles. This systematic approach provides a reliable basis for subsequent design improvements, while simultaneously enhancing the stability and reliability of the device in practical applications.

[0075] S108. Based on the performance indicators of the device, the overall system stability is evaluated using a verification method, and a final optimization report is generated.

[0076] Step 1: Acquire various performance indicators of the device. Use pre-established monitoring tools to collect key parameters during operation, obtaining initial performance monitoring records. Step 2: Based on the initial performance monitoring records, use data processing methods to classify and organize the collected parameters, determining the categorized parameter set. Step 3: Based on the categorized parameter set, use a system verification process to compare operational stability item by item. If a parameter exceeds a preset threshold, it is marked as an anomaly, resulting in an anomaly list. Step 4: For the anomaly list, use stability testing tools to perform in-depth analysis of the marked anomalies, identifying potential stability vulnerabilities. Step 5: Based on the potential stability vulnerabilities, use indicator analysis methods to define the impact range of the overall assessment, obtaining the distribution data of the impact range. Step 6: Based on the distribution data of the impact range, use the process design module to generate corresponding adjustment strategy records, determining the final optimization solution. Step 7: Based on the final optimization solution, use document organization tools to integrate all analysis results, generating a complete evaluation basis document.

[0077] The above embodiments are merely one of the preferred embodiments of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but solve the same technical problem as the present invention, should be included within the scope of protection of the present invention.

Claims

1. An optimized method for resetting a heavy-duty side door of a railway freight car, characterized in that, The method includes: Real-time operating data is collected by a sensor system deployed on the side doors of railway freight cars. The operating data includes information on the weight distribution and opening angle changes of heavy doors. The working condition data is processed by multi-source information fusion using a data integration method to obtain a comprehensive state model that includes mechanical and kinematic characteristics; Based on the comprehensive state model, the force transmission process is simulated and calculated using mechanical analysis methods. The stress distribution of the heavy-duty door under different opening angles is analyzed to determine the key stress points in the force transmission path. If the key force points show insufficient force transmission, the structural parameters of the reset device are adjusted by structural optimization methods, and the lever ratio and spring stiffness are optimized for the force transmission path to generate an improved structural design scheme. Motion control parameters are extracted from the structural design scheme, a reset motion curve is generated using a path planning method, multiple motion trajectories are simulated for the change of the opening angle, and the accuracy of the motion trajectory is determined to reach a preset threshold. If the accuracy of the motion trajectory does not reach the preset threshold, the motion control parameters are iteratively adjusted, and the motion curve is regenerated by fusing the force transmission data to obtain an accurate reset motion sequence. Based on the reset motion sequence, adaptive simulation tests are conducted under complex environments to verify the response behavior of the heavy-duty door under extreme opening angles and determine the performance indicators of the improved device. Based on the performance indicators of the device, a verification method is used to evaluate the overall system stability and generate a final optimization report.

2. The optimized method for the reset device of the heavy-duty side door of a railway freight car according to claim 1, characterized in that, The system collects real-time operating data through a sensor system deployed on the side doors of railway freight cars. This operating data includes information on the weight distribution and opening angle changes of the heavy-duty doors, including: Real-time operating condition data is acquired from the side door of the railway freight car using a sensor system. The operating condition data includes load distribution information and opening angle change information to obtain initial data records. Based on the initial data record, a Kalman filter is used to denoise the load distribution information and the opening angle change information to obtain clean data. If the load distribution information in the cleaning data exceeds the preset threshold range, an anomaly marker is triggered, and the corresponding opening angle change information is recorded to obtain the cleaning data marked as abnormal. For the cleaning data marked as abnormal, obtain its corresponding timestamp and location information, perform preliminary classification according to preset rules, and obtain the classified abnormal data group; Based on the classified abnormal data groups, the support vector machine algorithm is used to perform pattern recognition on the load distribution information and opening angle change information to determine the specific type and priority value of the abnormal data. The priority values ​​of the abnormal data are sorted using numerical calculation tools to obtain high-priority abnormal data. Cross-validation is then performed using railway freight operation logs to determine the correlation between the abnormal data and the operational status. Based on the high-priority abnormal data obtained after cross-validation, corresponding status monitoring records are generated, and the final handling action is determined according to the safety management requirements of the carriage side doors.

3. The optimized method for the reset device of the heavy-duty side door of a railway freight car according to claim 1, characterized in that, The process employs a data integration method to fuse multi-source information from the operating condition data, resulting in a comprehensive state model that includes both mechanical and kinematic characteristics, comprising: Raw operating condition data is obtained from multiple data sources to generate an initial dataset containing a preset number of types of information; For the initial dataset, a data cleaning method is used to remove redundant and abnormal records, resulting in a cleaned standard dataset; Based on the standard dataset, a classification algorithm is applied to divide the data into a first subset of data related to mechanical properties and a second subset of data related to motion properties; If the information completeness of the first data subset and the second data subset reaches a preset threshold, then feature extraction is performed on the first data subset and the second data subset to obtain a set of key indicators of mechanical and kinematic properties. For the set of key indicators, the correlation between mechanical properties and kinematic properties is calculated to determine the characteristic combination of the comprehensive state; By standardizing the feature combinations, a unified format comprehensive state description is generated, and the final state model structure is constructed. The state model structure is tested for consistency using a pre-established validation dataset to determine whether the output meets the expected standards, thus obtaining the final integrated state model.

4. The optimized method for the reset device of the heavy-duty side door of a railway freight car according to claim 1, characterized in that, Based on the comprehensive state model, mechanical analysis methods are applied to simulate and calculate the force transmission process. The stress distribution of the heavy-duty vehicle door at different opening angles is analyzed to determine the key stress points in the force transmission path, including: Acquire state data of heavy-duty vehicle doors at different opening angles, construct a mechanical analysis framework for the state data, and use numerical simulation methods to obtain preliminary force transmission distribution results; Extract the first stress distribution data under different opening angles from the force transmission distribution results, perform finite element calculations on the first stress distribution data, and determine the first stress concentration region corresponding to each angle. Based on the data from the first stress concentration region, analyze the direction of the first force transmission path and identify the location of key stress points in the path; If the local stress data of the key stress point exceeds a preset threshold, the stress point is marked to obtain a first set of marked stress points; For the first set of marked force points, the stability of the first force transmission path is analyzed. If the number of marked force points in the path exceeds a preset threshold, the path is adjusted, and the force distribution of the adjusted path is determined. Based on the adjusted path force distribution, extract the stress transmission characteristics of each segment, integrate the feature data, and determine the optimization direction of the first force transmission path; Based on the optimization direction, a force transmission path adjustment scheme is generated for the heavy-duty vehicle door under different opening angles, and the final force distribution data is obtained.

5. An optimized method for a heavy-duty side door reset device for railway freight cars according to claim 1, characterized in that, If the critical force-bearing point shows insufficient force transmission, the structural parameters of the reset device are adjusted through structural optimization methods. The lever ratio and spring stiffness are optimized for the force transmission path to generate an improved structural design scheme, including: Analytical data of key stress points are obtained, and the analytical data is processed to determine the specific location and degree of insufficient force transmission, thereby obtaining the first stress analysis result; The key nodes of the force transmission path are extracted from the first force analysis results. The lever ratio deviation in the path is determined by the path finding algorithm. If the deviation exceeds a preset threshold, the lever ratio is adjusted to obtain the first ratio data. For the first ratio data, the current parameter of the spring stiffness is obtained, and the matching degree between the parameter and the force transmission path is analyzed. If the matching degree is lower than the preset standard, the spring stiffness is adjusted by the optimization algorithm to determine the first stiffness parameter. Based on the first stiffness parameter and the first proportional data, a parameter adjustment scheme for the reset device is generated, and combined with the preset structural constraints, a first design improvement scheme is obtained. Key parameters are extracted from the first design improvement scheme, and the improvement scheme is verified by a force simulation tool to determine whether the simulation results meet the force transmission requirements. If not, the key parameters are iteratively adjusted to obtain the second parameter combination. For the second parameter combination, complete improved design data is generated, and a second design scheme that optimizes the force transmission path is output.

6. An optimized method for a heavy-duty side door reset device for railway freight cars according to claim 1, characterized in that, The process of extracting motion control parameters from the structural design scheme, generating a reset motion curve using a path planning method, simulating multiple sets of motion trajectories for the change in the opening angle, and determining whether the accuracy of the motion trajectory reaches a preset threshold includes: Motion control parameters are obtained from the structural design scheme, and data processing tools are used to classify and organize the motion control parameters to obtain a preliminary set of control parameters; Based on the aforementioned set of preliminary control parameters, a path planning algorithm is used to model the reset motion and generate initial motion curve data; Based on the initial motion curve data and the range of change of the opening angle, multiple sets of motion trajectory datasets are generated using simulation tools. Trajectory deviation values ​​are extracted from the multiple sets of motion trajectory datasets. The matching status of the trajectory deviation values ​​with a preset threshold is determined. If the trajectory deviation value exceeds the preset threshold, the initial motion curve data is locally adjusted to obtain an optimized trajectory dataset. Using the optimized trajectory dataset, obtain the trajectory stability index under each angle change, determine whether the stability index meets the preset conditions, and if not, fine-tune the parameters of the path planning algorithm to determine the final trajectory data. Based on the final trajectory data, a complete reset motion control scheme is generated, the execution order and parameter configuration of each motion control link are determined, and the final motion control output is obtained.

7. An optimized method for a heavy-duty side door reset device for railway freight cars according to claim 1, characterized in that, If the accuracy of the motion trajectory does not reach a preset threshold, the motion control parameters are iteratively adjusted, and the motion curve is regenerated by fusing the force transmission data to obtain an accurate reset motion sequence, including: Motion trajectory data is acquired from sensors, and deviation analysis is performed between the motion trajectory data and a preset threshold to obtain an initial deviation evaluation result. Based on the initial deviation assessment results, the motion control parameters are adjusted using a Kalman filter to determine the adjusted parameter set. For the adjusted parameter set, real-time force transmission data is obtained, and the correlation between the force transmission data and the motion control parameters is constructed through linear regression to obtain the output deviation result; If the output deviation result exceeds the preset range, the matching value between the motion control parameters and the force transmission data is calculated by cosine similarity to obtain the optimized parameter combination; Based on the optimized parameter combination, the motion curve is reconstructed to generate the corresponding reset motion sequence data; By comparing the reset motion sequence data with the motion trajectory data, it is determined whether the accuracy requirements are met, and the final motion sequence is determined. Using the final motion sequence, update the motion control execution instructions to complete the trajectory reset.

8. An optimized method for a heavy-duty side door reset device for railway freight cars according to claim 1, characterized in that, The process involves conducting adaptive simulation tests under complex environments based on the reset motion sequence, verifying the response behavior of the heavy-duty door at extreme opening angles, and determining the performance indicators of the improved device, including: A heavy-duty vehicle door model is constructed using a virtual simulation platform. The reset motion data of the heavy-duty vehicle door under a preset number of different environments is obtained. The performance of the door at different opening angles is recorded to obtain a preliminary response characteristic dataset. Based on the preliminary response characteristic dataset, the support vector machine algorithm is used to classify the behavior under extreme angles, and the classification result of the response characteristics of the heavy-duty door under specific environmental conditions is determined. Based on the classification results of the response characteristics, obtain the correlation data between the extreme angle and the opening angle, calculate the stability of the reset motion, and determine the key environmental factors affecting the response characteristics; If the key environmental factors exceed the preset threshold, the parameters of the device improvement scheme are adjusted, and the adjusted behavior is simulated and tested through the virtual simulation platform to obtain optimized performance index data. Based on the optimized performance index data, the response characteristics of the heavy-duty door under extreme angles are analyzed, and multi-scenario simulation tests are conducted on the changes to determine the final adaptive performance results. Based on the final adaptive performance results, the degree of matching between environmental adaptability and complex environment is analyzed, the stability of the heavy-duty vehicle door under different opening angles is judged, and comprehensive performance evaluation data is obtained.

9. An optimized method for a heavy-duty side door reset device for a railway freight car according to claim 1, characterized in that, Based on the performance indicators of the device, a verification method is used to evaluate the overall system stability and generate a final optimization report, including: Step 1: Obtain various performance indicators of the device, and collect key parameters during operation using pre-established monitoring tools to obtain initial performance monitoring records; Step 2: Based on the initial performance monitoring records, data processing methods are used to classify and organize the collected parameters to determine the parameter set after classification; Step 3: Based on the classified parameter set, the system verification process is used to compare the operational stability item by item. If a parameter exceeds the preset threshold, it is marked as an anomaly, and an anomaly list is obtained. Step 4: For the list of anomalies, use stability testing tools to perform in-depth analysis on the marked anomalies to identify potential stability vulnerabilities; Step 5: Based on the potential stability risks mentioned above, use index analysis methods to define the scope of impact of the overall assessment and obtain the distribution data of the scope of impact; Step Six: Based on the distribution data of the affected area, generate corresponding adjustment strategy records through the process design module to determine the final optimization solution; Step 7: Based on the final optimization scheme, integrate all analysis results using a document organization tool to generate a complete evaluation basis document.

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