Automobile pedal structure parameter optimization design method and equipment

Abstract

The invention is suitable for the technical field of pedal structure optimization, and particularly relates to an automobile pedal structure parameter optimization design method and device, and the automobile pedal structure parameter optimization design method comprises the steps: obtaining working condition information; analyzing according to the working condition information to obtain spatio-temporal characteristic information; wherein the spatio-temporal characteristic information is used for reflecting the change conditions of stress and contact pressure of the automobile pedal at different times; analyzing according to the spatio-temporal characteristic information to obtain an optimization target; and performing analysis according to the spatial-temporal feature information and the optimization target to obtain optimization parameters. By means of the method, the mechanical performance of the automobile pedal under different working conditions can be mastered, then the structure is optimized in a targeted mode, and the strength and stability of the pedal are improved.

Description

Technical Field

[0001] This application belongs to the field of pedal structure optimization technology, and in particular relates to a method and device for optimizing the structural parameters of automotive pedals. Background Technology

[0002] As a key component of vehicle human-machine interaction, the structure of car pedals directly affects driving safety, comfort, and durability.

[0003] In related technologies, traditional automotive pedal optimization typically involves using software such as ANSYS and engineers' experience to gradually adjust structural parameters. This approach fails to adequately consider the impact of automotive pedals in actual use during structural optimization, leading to inaccurate optimization parameters. Over-reliance on experience, on the other hand, results in excessively long optimization cycles. Summary of the Invention

[0004] This application provides a method and device for optimizing the structural parameters of automotive pedals, which can solve the problem of inaccurate optimization parameters caused by insufficient consideration of the impact of automotive pedals in actual use during structural optimization.

[0005] In a first aspect, embodiments of this application provide a method for optimizing the structural parameters of an automobile pedal, including: Obtain operating condition information; wherein, the operating condition information is used to reflect the state of the vehicle foot pedal when a rated pressure is applied to the pedal. Based on the analysis of the operating condition information, spatiotemporal characteristic information is obtained; wherein, the spatiotemporal characteristic information is used to reflect the changes in stress and contact pressure of the car pedal at different times; Based on the spatiotemporal feature information, an optimization target is obtained; wherein, the optimization target is used to reflect the structure of the car pedal that needs to be optimized; Based on the spatiotemporal feature information and the optimization objective, the optimization parameters for the car pedal structure are obtained.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The method for optimizing the structural parameters of automotive foot pedals provided in this application first obtains operating condition information reflecting the state of the foot pedal when a rated pressure is applied. Then, based on the operating condition information, spatiotemporal characteristic information reflecting the changes in stress and contact pressure of the foot pedal at different times is obtained. Next, based on the spatiotemporal characteristic information, optimization targets reflecting the structure of the foot pedal requiring optimization are obtained. Finally, based on the spatiotemporal characteristic information and the optimization targets, the optimized structural parameters of the automotive foot pedal are obtained. This method, by analyzing the operating condition information of the foot pedal when a rated pressure is applied, simulates and analyzes the dynamic load conditions of the foot pedal in actual use, enabling the understanding of the mechanical performance of the foot pedal under different operating conditions, and thus allowing for targeted structural optimization to improve the strength and stability of the foot pedal.

[0007] Secondly, embodiments of this application provide a system for optimizing the structural parameters of an automotive pedal, including: An acquisition module is used to acquire operating condition information; wherein, the operating condition information is used to reflect the state of the car pedal when a rated pressure is applied to the pedal. The first analysis module is used to analyze the working condition information to obtain spatiotemporal characteristic information; wherein, the spatiotemporal characteristic information is used to reflect the changes in stress and contact pressure of the car pedal at different times; The second analysis module is used to analyze the spatiotemporal feature information to obtain the optimization target; wherein the optimization target is used to reflect the structure of the car pedal that needs to be optimized; The third analysis module is used to analyze the spatiotemporal feature information and the optimization objective to obtain the optimization parameters of the car pedal structure.

[0008] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any one of the first aspects above.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of the first aspects above.

[0010] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the automotive pedal structure parameter optimization design method described in any of the first aspects above.

[0011] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating the method for optimizing the structural parameters of an automotive foot pedal according to an embodiment of this application. Figure 2 This is a schematic diagram illustrating the implementation process of the automotive pedal structural parameter optimization design method provided in this application embodiment; Figure 3 This is a schematic diagram of the automotive pedal structural parameter optimization design system provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0014] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0015] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0016] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0017] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determination" or "if the described condition or event is detected" may be interpreted, depending on the context, as "once determination," "in response to determination," "once the described condition or event is detected," or "in response to the detection of the described condition or event."

[0018] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0019] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0020] As a key component of vehicle human-machine interaction, the structure of car pedals directly affects driving safety, comfort, and durability.

[0021] In related technologies, traditional automotive pedal optimization typically involves using software such as ANSYS and engineers' experience to gradually adjust structural parameters. This approach fails to adequately consider the impact of automotive pedals in actual use during structural optimization, leading to inaccurate optimization parameters. Over-reliance on experience, on the other hand, results in excessively long optimization cycles.

[0022] To address the aforementioned problems, this application provides a method and apparatus for optimizing the structural parameters of automotive foot pedals. The method involves acquiring operating condition information reflecting the state of the automotive foot pedal when a rated pressure is applied; analyzing this information to obtain spatiotemporal characteristic information reflecting the changes in stress and contact pressure of the foot pedal at different times; further analyzing this spatiotemporal characteristic information to obtain optimization targets for the structure of the foot pedal requiring optimization; and finally, analyzing both the spatiotemporal characteristic information and the optimization targets to obtain the optimized structural parameters for the automotive foot pedal. This method, by analyzing the operating condition information of the automotive foot pedal when a rated pressure is applied, simulates and analyzes the dynamic load conditions of the foot pedal in actual use, enabling the understanding of the mechanical performance of the foot pedal under different operating conditions, and thus allowing for targeted structural optimization to improve the strength and stability of the foot pedal.

[0023] The automotive pedal structure parameter optimization design method provided in this application embodiment can be applied to electronic devices. In this case, the electronic device is the executing subject of the automotive pedal structure parameter optimization design method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of electronic device.

[0024] For example, electronic devices can be mobile phones, tablets, wearable devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), desktop computers, smart screens, smart TVs, and other terminal devices; handheld devices with wireless communication capabilities; computing devices or other processing devices connected to a wireless modem; IoT terminals; computers; laptops; handheld communication devices; handheld computing devices; satellite wireless devices; customer premises equipment (CPEs); and / or other devices used for communication over wireless systems, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved Public Land Mobile Networks (PLMNs).

[0025] To better understand the automotive pedal structure parameter optimization design method provided in this application embodiment, the specific implementation process of the automotive pedal structure parameter optimization design method provided in this application embodiment will be described by way of example below.

[0026] Figure 1 and Figure 2 This illustration shows a schematic flowchart of a method for optimizing the structural parameters of an automotive pedal according to an embodiment of this application. The method includes: S100, acquire operating condition information; wherein, the operating condition information is used to reflect the state of the vehicle's foot pedal when a rated pressure is applied to the pedal.

[0027] As is understandable, car pedals mainly consist of two parts: the pedal itself and the connecting arm. The pedal is the part that directly contacts the driver's foot, and its surface usually has anti-slip textures (such as raised particles or stripes). The connecting arm is used to connect the pedal to the vehicle's control system. The rated pressure is a standard value that simulates the pressure that the pedal experiences during normal operation in actual driving. Operating condition information can be collected in a laboratory by simulating actual pedal operation. This involves applying a rated pressure to the car pedal and then monitoring its condition using a detection device. This device could include a stress detection device and a contact pressure detection device. The stress detection device measures the stress at various locations on the pedal. For example, a strain gauge can be used; by attaching strain gauges to key locations on the pedal, the resistance changes when the pedal is under stress. By measuring this change in resistance, the strain at the corresponding location can be accurately calculated, and the stress value can be derived based on the material's mechanical properties. Alternatively, a fiber Bragg grating sensor can be used. Utilizing the optical properties of optical fibers, the center wavelength of the fiber Bragg grating shifts under stress. By monitoring this wavelength shift, the stress information of the pedal can be obtained in real time and accurately. The contact pressure detection device measures the contact pressure and contact range of the car pedal. For example, the contact pressure detection device could be a pressure-sensitive thin-film sensor. By attaching the sensor to the pedal surface, when an object applying rated pressure contacts the pedal and applies pressure, the resistance or capacitance value of the pressure-sensitive thin-film sensor changes according to the pressure magnitude. By measuring and analyzing these electrical parameters, the contact pressure value at each point on the pedal surface can be accurately obtained. The contact pressure detection device can also be a piezoelectric sensor array. When subjected to pressure, it generates an electrical signal whose charge is proportional to the pressure magnitude. Multiple piezoelectric sensors can be arranged in a specific array on the pedal surface, each sensor corresponding to a specific detection area, and so on, but are not limited to these.

[0028] S200 analyzes the operating condition information to obtain spatiotemporal characteristic information; among which, the spatiotemporal characteristic information is used to reflect the changes in stress and contact pressure of the car pedal at different times.

[0029] It is understandable that during actual use, the internal stress distribution and contact pressure between the car pedal and the shoe sole dynamically change over time and with variations in the driver's pedal pressure. For example, spatiotemporal feature information can be obtained by analyzing the operating conditions of the car pedal at different speeds under rated pressure. This involves obtaining the operating conditions at different speeds and then simultaneously extracting spatial geometric features and dynamic load features using a multiphysics coupling model of the pedal. Alternatively, the operating conditions information can be input into a learning model, which outputs the corresponding spatiotemporal feature information, and so on, but is not limited to these methods. The learning model is trained using multiple sets of training data, each set of which includes both operating conditions information and spatiotemporal feature information.

[0030] In one possible implementation, in step S200, spatiotemporal characteristic information is obtained by analyzing the operating condition information, including: S210, based on the first working condition information, analyze the first spatiotemporal characteristics to obtain the first spatiotemporal features; wherein, the first spatiotemporal features are used to reflect the changes in stress and contact pressure of the car pedal at different times when the pressure speed of the rated pressure is the first preset speed.

[0031] It can be understood that the first preset speed is a pre-set pressure application speed, which is set by simulating the pedal speed when a driver rapidly depresses the car's foot pedal. The first operating condition is the state of the car's foot pedal when the rated pressure is applied to the pedal at the first preset speed. By integrating the first operating condition at each point in time during the pressure application process, the first spatiotemporal characteristic is formed.

[0032] S220, based on the second working condition information, analyze the second working condition to obtain the second spatiotemporal characteristics; wherein, the second spatiotemporal characteristics are used to reflect the changes in stress and contact pressure of the car pedal at different times when the pressure speed of the rated pressure is the second preset speed, and the first preset speed is greater than the second preset speed.

[0033] It can be understood that the second preset speed is also a pre-set pressure application speed, which is set by simulating the pedal speed set when a driver slowly presses the car pedal. The second operating condition is the state of the car pedal when the rated pressure is applied to the pedal at the second preset speed. By integrating the second operating condition at each point in time during the pressure application process, the second spatiotemporal characteristic is formed.

[0034] S230, based on the first and second spatiotemporal features, spatiotemporal feature information is obtained through analysis.

[0035] It can be understood that the first and second spatiotemporal features obtain data on the changes in stress and contact pressure of the car's pedal over time under two different pressure application conditions: rapid and slow pedal depressing. By comprehensively analyzing these two features, spatiotemporal feature information can be obtained. For example, feature extraction can be performed on the first and second spatiotemporal features separately to obtain stress and pressure features, respectively. Then, the stress features of the spatiotemporal feature information can be obtained by analyzing the stress features of the first and second spatiotemporal features, and the pressure features of the spatiotemporal feature information can be obtained by analyzing the pressure features of the first and second spatiotemporal features. Alternatively, the first and second spatiotemporal features can be input into a learning model, and the learning model can output the corresponding spatiotemporal feature information, and so on, but are not limited to these methods.

[0036] This setup, by analyzing the spatiotemporal characteristics under different pressure speeds and then synthesizing them to obtain overall spatiotemporal characteristic information, can simulate the diverse pedal usage scenarios more closely resembling real-world driving situations. It can more comprehensively reflect the pedal's performance under various possible conditions. Based on this characteristic information, subsequent optimization of the pedal structure can fully consider the needs of different usage scenarios, ensuring that the optimized pedal possesses better mechanical performance and stability under various driving operations. This improves the pedal's applicability and reliability, thereby enhancing the safety and comfort of driving.

[0037] In one possible implementation, in step S230, spatiotemporal feature information is obtained by analyzing the first and second spatiotemporal features, including: S231, feature extraction is performed based on the first spatiotemporal characteristics to obtain the first stress change feature vector and the first contact pressure feature vector.

[0038] It can be understood that the first stress change feature vector refers to the vector extracted from the stress data of the car pedal at different times under rated pressure and pressure speed at a first preset speed, which is included in the first spatiotemporal feature, and can characterize key information about stress changes. This key information includes the stress value at different time points, the rate of stress change, and the trend of stress change. Through specific mathematical algorithms and data processing methods, this information is quantified and integrated, and presented in vector form. The first contact pressure feature vector refers to the vector extracted from the contact pressure data of the car pedal at different times under rated pressure and pressure speed at a first preset speed, which is included in the first spatiotemporal feature, and can characterize key information about pressure changes.

[0039] S232, feature extraction is performed based on the second spatiotemporal characteristics to obtain the second stress change feature vector and the second contact pressure feature vector.

[0040] It can be understood that the second stress change feature vector refers to the vector extracted from the stress data of the car pedal at different times under rated pressure and pressure speed at a second preset speed, which is included in the second spatiotemporal feature, and can characterize key information about stress change. The second contact pressure feature vector refers to the vector extracted from the contact pressure data of the car pedal at different times under rated pressure and pressure speed at a second preset speed, which is included in the second spatiotemporal feature, and can characterize key information about pressure change.

[0041] S233, the first stress change feature vector and the second stress change feature vector are weighted and fused to obtain the stress feature of spatiotemporal feature information, and the first contact pressure feature vector and the second contact pressure feature vector are weighted and fused to obtain the pressure feature of spatiotemporal feature information.

[0042] It can be understood that the first stress change feature vector and the second stress change feature vector represent the characteristics of the foot pedal stress change under different pressure loading rates. For example, different weights can be assigned to the two vectors based on the actual influence of different pressure loading rates, and then the weighted vectors can be added together to obtain stress features that comprehensively reflect the foot pedal stress change under different pressure loading rates. Similarly, the contact pressure feature vector can be processed in a similar way to obtain pressure features. Alternatively, the first change feature vector, the second stress change feature vector, the first contact pressure feature vector, and the second contact pressure feature vector can be input into the learning model, and the learning model can then output the spatiotemporal feature information of stress features and pressure features, etc., but not limited to these methods.

[0043] This setup, by weighting and fusing the stress and contact pressure feature vectors under different working conditions, can fully consider the differences in the impact of different pressure loading speeds on actual use, and more accurately reflect the stress and contact pressure variation characteristics of the pedal under actual complex working conditions. This provides a more accurate data foundation for subsequent pedal structure optimization based on these features, and improves the accuracy and reliability of the optimization scheme.

[0044] In one possible implementation, in step S233, the first stress change feature vector and the second stress change feature vector are weighted and fused to obtain the stress feature of spatiotemporal feature information; the first contact pressure feature vector and the second contact pressure feature vector are weighted and fused to obtain the pressure feature of spatiotemporal feature information, including: S2331, obtain the first actual occurrence probability of the first preset speed and the second actual occurrence probability of the second preset speed; wherein, the actual occurrence probability refers to the frequency of the preset speed in actual driving conditions.

[0045] It is understandable that in actual use, the probability of different pressure loading speeds varies, and the degree of influence on the stress characteristics of the pedal also differs. For example, the driving environment may affect the probability of different pressure loading speeds occurring, such as the difference between urban roads and off-road roads, or the difference between male and female users. For instance, big data analytics can be used to obtain the first and second actual occurrence probabilities; sensors can also be installed on the car's pedals to continuously monitor and record the speed data of the driver's pedal presses over a certain period. After a period of data accumulation, this data can be categorized and organized to statistically determine the frequency of occurrence of the first and second preset speeds, thereby obtaining the corresponding actual occurrence probabilities, and so on, but not limited to these methods.

[0046] S2332, based on the first actual occurrence probability and the second actual occurrence probability, the first weight and the second weight are obtained; wherein, the first weight is used to reflect the influence degree of the first stress change characteristic vector, and the second weight is used to reflect the influence degree of the second stress change characteristic vector.

[0047] It's understandable that the higher the actual probability of occurrence, the higher the corresponding weight. The first and second actual probabilities of occurrence are normalized so that their sum is 1, and then used as the first and second weights, respectively.

[0048] S2333, the first stress change feature vector is weighted according to the first weight, and the second stress change feature vector is weighted according to the second weight. The summation yields the stress feature of the spatiotemporal feature information.

[0049] It can be understood that the stress characteristic = first weight × first stress change characteristic vector + second weight × second stress change characteristic vector.

[0050] S2334, Stability analysis is performed based on the first contact pressure feature vector and the second contact pressure feature vector to obtain the third weight and the fourth weight; wherein, the third weight is used to reflect the influence of the first contact pressure feature vector, and the fourth weight is used to reflect the influence of the second contact pressure feature vector.

[0051] Understandably, stability analysis can be evaluated by calculating indicators such as the degree of fluctuation and dispersion of vector data. Vectors with smaller fluctuations and lower dispersion indicate that the corresponding contact pressure changes are relatively stable, i.e., the better the stability. The better the stability, the lower the corresponding weight value.

[0052] S2335, the first contact pressure feature vector is weighted according to the third weight, and the second contact pressure feature vector is weighted according to the fourth weight. The summation yields the pressure feature of the spatiotemporal feature information.

[0053] It can be understood that the pressure feature = third weight × first contact pressure feature vector + fourth weight × second contact pressure feature vector.

[0054] This setup, by considering the actual occurrence probability and the stability of the feature vectors to determine the weights for weighted fusion, can more scientifically fuse stress and contact pressure feature vectors under different working conditions. This makes the stress and pressure features of the obtained spatiotemporal feature information more consistent with the force conditions of the pedal in actual driving, providing a more realistic basis for subsequent pedal structure optimization based on these features. This helps to improve the quality and effect of pedal structure optimization and enhance the reliability and stability of the pedal in actual use.

[0055] S300 analyzes spatiotemporal characteristic information to obtain optimization targets; among them, optimization targets are used to reflect the structure of the car pedal that needs to be optimized.

[0056] It is understandable that spatiotemporal characteristic information comprehensively displays the stress and contact pressure changes of a car's pedals at different times. Through in-depth analysis of this information, combined with the pedal's design requirements, safety standards, and performance demands in actual use, it is possible to identify structural weaknesses or unreasonable aspects of the pedal. For example, stress concentration areas may lead to premature fatigue damage of the pedal, and uneven contact pressure distribution may affect the driver's operating experience and safety. Identifying these structural parts or problems requiring improvement as optimization targets provides direction for subsequent optimization design.

[0057] For example, spatiotemporal feature information can be used to find abnormal regions or structures, and the influence between all abnormal regions or structures can be analyzed to obtain the structure that needs to be optimized, which is the optimization target; or the spatiotemporal feature information can be input into the learning model, and the learning model can output the corresponding optimization target, and so on, but not limited to these.

[0058] In one possible implementation, in step S300, the optimization objective is obtained by analyzing the spatiotemporal feature information, including: S310, Analyze the stress characteristics based on the spatiotemporal characteristic information to obtain at least one stress anomaly point; wherein, the stress anomaly point is used to reflect the location where the stress changes abruptly.

[0059] Understandably, stress characteristics meticulously record the stress changes of the pedal under different operating conditions over time. By analyzing this data and applying data analysis algorithms and relevant mechanical theories, points where stress values ​​suddenly change significantly can be identified; these points are stress anomaly points. For example, in a stress-over-time curve, locations where the slope suddenly increases or decreases may be stress anomaly points. These stress anomaly points are often areas in the pedal structure that are subject to complex stresses and are prone to problems; accurate identification of them helps in discovering potential structural risks in the pedal.

[0060] S320, based on the analysis of stress anomaly points, the first optimization objective is obtained.

[0061] It can be understood that the primary optimization objective refers to the structure that requires adjustment, reinforcement, or replacement of materials. The location corresponding to the stress variation point is the primary optimization objective.

[0062] In one possible implementation, in step S320, an analysis is performed based on the stress anomaly point to obtain a first optimization objective, including: S321a, when the number of stress anomaly points is one, the stress anomaly point is identified as the first optimization objective.

[0063] It is understandable that when there is only one stress anomaly point, that point is the location where the stress problem of the pedal is most prominent. Due to its uniqueness, its impact on the structural performance of the pedal is relatively clear and critical. Directly identifying it as the primary optimization target allows for targeted optimization of this critical part, effectively solving potential stress-related problems of the pedal and improving the structural strength and reliability of the pedal.

[0064] S321b, when the number of stress anomaly points is at least two, the analysis is performed based on multiple stress anomaly points to obtain the primary target and secondary target; among them, the primary target is used to reflect the stress anomaly point that appears first among multiple stress anomaly points, and the secondary target is used to reflect other stress anomaly points other than the primary target.

[0065] It's understandable that when multiple stress anomaly points exist, their order of occurrence and stress changes may differ. The first stress anomaly point to appear is often the location where the pedal first exhibits abnormalities during stress application, significantly impacting subsequent stress distribution and structural stability; therefore, it is identified as the primary target. Other stress anomaly points, while also presenting problems, are identified as secondary targets due to their later impact and order of occurrence. This distinction allows for prioritizing the areas with the greatest impact on the pedal structure during optimization, enabling a more rational arrangement of optimization order and focus.

[0066] S322b calculates the positional correlation between secondary targets and primary targets. When the positional correlation is less than the preset correlation, the corresponding secondary target is marked as the primary target. The positional correlation reflects the degree of structural correlation between stress anomaly points.

[0067] It is understandable that the positional correlation reflects the degree of closeness between two stress anomaly points in the pedal structure. A higher positional correlation indicates a closer structural proximity or stronger stress transfer between two points; conversely, a lower correlation indicates a greater distance between them. When the positional correlation between a secondary target and a primary target is less than the preset correlation, it means that although the secondary target occurs later in the stress anomaly time, it is structurally relatively independent of the primary target. Its own stress problems may have a significant individual impact on the pedal structure. Therefore, it is marked as a primary target to receive sufficient attention during the optimization process and ensure the overall safety of the pedal structure.

[0068] S323b, the primary objective is determined as the first optimization objective.

[0069] Understandably, after analyzing and screening multiple stress anomaly points, the primary targets identified were the most critical and prominent parts of the pedal stress problem. Designating these as the first optimization target allows for improvements to be made to these key parts first during the optimization process, effectively addressing the main stress issues of the pedal, improving its structural performance and reliability, and laying the foundation for subsequent optimization work.

[0070] This setup, which employs different treatment methods for different numbers of stress anomaly points, allows for a more reasonable prioritization of optimization objectives. It ensures that stress issues that have the greatest impact on the structural performance of the pedal are addressed first during the optimization process, thereby improving optimization efficiency, ensuring the safety and stability of the pedal in actual use, and extending the pedal's service life.

[0071] S330 analyzes the pressure characteristics based on spatiotemporal information to obtain the extreme points of contact pressure; among them, the extreme points of contact pressure are used to reflect the maximum value of the contact pressure of the car pedal and the corresponding position.

[0072] It is understandable that the spatiotemporal pressure characteristics record the changes in contact pressure of the foot pedal under different times and operating conditions. By processing and analyzing this data, the maximum contact pressure and the corresponding pedal position are identified; these points are the contact pressure extreme points. For example, in the data sequence of contact pressure changes over time, the maximum value is searched, and its coordinate position on the pedal is determined, thus obtaining the contact pressure extreme points. These extreme points reflect the areas where the foot pedal experiences the greatest pressure when in contact with the shoe sole during actual use.

[0073] S340, the contact pressure extreme point that is greater than the preset extreme point is identified as the second optimization target.

[0074] It's understandable that a preset extreme point is a reference value for contact pressure determined based on factors such as the design requirements of the pedal, ergonomic principles, and practical usage experience. This preset extreme point can be manually input, obtained from a pedal database, or other methods, but is not limited to these. The pedal database contains the structural parameters and preset extreme points for various car pedal models. This data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After acquisition, the collected data is organized, classified, and archived, useful information and patterns are extracted, and the relevant data is saved to the database to form the pedal database. Structural parameters cover the shape, dimensions, and other parameters corresponding to the pedal and supporting structure of the car pedal.

[0075] When the extreme contact pressure point exceeds the preset extreme value, it indicates that the contact pressure at that location is too high, which may cause discomfort to the driver's foot or accelerate the wear of the pedal surface material. Identifying these extreme contact pressure points exceeding the preset extreme value as secondary optimization targets allows for targeted optimization of these areas with excessive pressure, improving the user experience and durability of the pedals.

[0076] This setting, by determining the extreme point of contact pressure and comparing it with the preset extreme point, can accurately identify areas on the pedal with excessively high contact pressure as optimization targets. This helps improve the comfort and durability of the pedal, enhances the driver's experience of using the pedal, and also reduces the risk of pedal damage caused by excessive pressure, thus lowering maintenance costs.

[0077] S400 analyzes spatiotemporal characteristic information and optimization objectives to obtain the optimized parameters for the automotive pedal structure.

[0078] It is understandable that the spatiotemporal characteristic information details the stress and contact pressure variations of the pedal under different operating conditions, while the optimization objective clearly identifies the structural parts of the pedal that need improvement. Combining this information with relevant mechanical calculation methods, optimization algorithms, and engineering experience, the structural parameters of the pedal are adjusted and optimized to determine the parameter values ​​that can improve pedal performance and meet design requirements. These parameter values ​​are the automotive pedal structural optimization parameters. For example, based on the location and characteristics of stress anomaly points and extreme contact pressure points, parameters such as the pedal thickness and material distribution are adjusted to reduce stress concentration and excessive contact pressure.

[0079] In one possible implementation, in step S400, analysis is performed based on spatiotemporal feature information and optimization objectives to obtain the optimization parameters for the automobile pedal structure, including: S410, Analyze according to the optimization objective to obtain the pre-optimization parameter set; wherein, the pre-optimization parameter set is used to reflect the set of all structural parameters corresponding to the optimization objective.

[0080] It's understandable that optimization objectives clearly define the structural parts and performance issues that need improvement in automotive pedals, and these structural changes often involve adjusting multiple structural parameters. Through in-depth analysis of each optimization objective, combined with the structural design principles and mechanical properties of automotive pedals, all related parameters that may need adjustment are identified. For example, if the optimization objective is to address stress concentration at a stress anomaly point, it might involve parameters such as the thickness of the pedal in that area, the elastic modulus of the material, and the layout of reinforcing ribs; if the optimization objective is to reduce the pressure at the extreme contact pressure point, it might be related to parameters such as the shape of the pedal surface, the coefficient of friction of the material, and the stiffness of the supporting structure. Summarizing all these parameters related to the optimization objective forms a pre-optimization parameter set, which provides a comprehensive parameter range for subsequent precise determination of the optimization parameters.

[0081] S420, based on spatiotemporal characteristic information and pre-optimized parameter set analysis, obtains the optimized parameters for the car pedal structure.

[0082] It is understandable that by analyzing the stress and contact pressure distribution in the spatiotemporal feature information, combined with mechanical principles and engineering experience, we can determine which parameters are most critical to solving the problems in the optimization objective, and how to adjust these parameters to effectively improve the performance of the pedal. For example, if the spatiotemporal feature information shows severe stress concentration in a certain area, and the pre-optimization parameter set includes the pedal thickness parameter, through mechanical calculations and simulation analysis, it can be determined that increasing the pedal thickness in that area can effectively reduce stress concentration. Therefore, the adjusted thickness can be included as part of the optimization parameters. For instance, the pre-optimization parameter set can be selectively filtered based on the actual situation reflected by the spatiotemporal feature information to identify parameters that truly improve pedal performance and meet the optimization objective requirements, and the specific values ​​of these parameters can be determined as the optimization parameters. Alternatively, the spatiotemporal feature information and the pre-optimization parameter set can be input into a learning model, which then outputs the corresponding optimization parameters, and so on, but are not limited to these methods.

[0083] This setup, by comprehensively considering spatiotemporal characteristics and pre-optimized parameter sets, allows for more targeted determination of optimization parameters, avoiding blind parameter adjustments and improving optimization efficiency. This method fully utilizes the actual stress and contact pressure variations of the pedal, as well as all possible parameters related to the optimization objective, ensuring that the determined optimization parameters better match the actual performance requirements of the pedal. This contributes to improving the accuracy and effectiveness of pedal structure optimization. In one possible implementation, in step S420, analysis is performed based on spatiotemporal feature information and a pre-optimized parameter set to obtain the optimized parameters for the car pedal structure, including: S421, Based on the spatiotemporal feature information and the pre-optimized parameter set, a matching analysis is performed to obtain the final optimized parameter set; wherein, the final optimized parameter set is used to reflect the set of parameters that need to be optimized in the optimization objective.

[0084] Matching analysis can be understood as associating and evaluating each parameter in the pre-optimized parameter set with spatiotemporal characteristic information. Based on the optimization objective, it determines which parameters directly affect the improvement of pedal stress and contact pressure. For example, for the optimization objective of stress anomaly points, if the spatiotemporal characteristic information indicates that the stress concentration at that point is due to insufficient local stiffness, the material elastic modulus and structural shape parameters in the pre-optimized parameter set may be closely related to improving this problem. After analysis and screening, these parameters that have a significant impact on the optimization objective are selected to form the final optimized parameter set.

[0085] S422, construct a function-mapping model of stress characteristics, pressure characteristics and final optimized parameter set, and use optimization algorithm to iteratively optimize the parameters in the final optimized parameter set to obtain the optimized parameters of the car pedal structure; wherein, the objective function of the function-mapping model is the stress threshold corresponding to the stress characteristics and the pressure threshold corresponding to the pressure characteristics.

[0086] It's understandable that stress and pressure thresholds are pre-set values. These can be manually input, retrieved from a pedal database, or other methods, but are not limited to these. Stress and pressure characteristics directly reflect the mechanical performance of the pedal, while the final optimized parameter set is the set of parameters that need to be adjusted. Constructing a function-mapping model establishes the mathematical relationship between stress and pressure characteristics and the final optimized parameter set. This model describes how changes in these parameters affect stress and pressure. Stress and pressure thresholds are used as objective functions because there exists an acceptable range for stress and pressure in pedal design, i.e., stress and pressure thresholds. The optimization algorithm continuously adjusts the parameter values ​​in the final optimized parameter set, performing multiple iterative calculations while satisfying the objective function. Each iteration adjusts the parameters in a direction that brings stress and pressure closer to the thresholds until an optimal set of parameter values ​​is found. This set of parameter values ​​is the optimized parameter set for the automotive pedal structure. For example, by using a genetic algorithm, the parameters in the final optimized parameter set are randomly combined and mutated by simulating the natural evolution process. The objective function value is continuously calculated, and the parameter combination that makes the objective function value optimal is selected. This is the optimization parameter of the car pedal structure, so as to optimize the stress and pressure performance of the pedal.

[0087] This setup, through constructing a function-mapping model and employing an optimization algorithm for iterative optimization, can accurately find the optimal parameters that meet the stress and pressure performance requirements of the pedal. This method, based on mathematical models and algorithms, is more scientific and accurate than empirical judgment. It fully considers the complex relationship between stress, pressure, and structural parameters, enabling the optimized pedal to achieve best performance in terms of stress and pressure, improving the pedal's reliability and safety, and also enhancing the technical level and scientific rigor of pedal structural optimization.

[0088] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0089] Corresponding to the automotive pedal structure parameter optimization design method described in the above embodiments, this application also provides an automotive pedal structure parameter optimization design system, the various modules of which can implement the various steps of the automotive pedal structure parameter optimization design method. Figure 3 The diagram shows a structural block diagram of the automotive pedal structural parameter optimization design system provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0090] Reference Figure 3 The automotive pedal structure parameter optimization design system includes: The acquisition module is used to acquire operating condition information; wherein, the operating condition information is used to reflect the state of the car pedal when the rated pressure is applied to the pedal.

[0091] The first analysis module is used to analyze the working condition information to obtain spatiotemporal characteristic information; among which, the spatiotemporal characteristic information is used to reflect the changes in stress and contact pressure of the car pedal at different times.

[0092] The second analysis module is used to analyze spatiotemporal feature information to obtain optimization targets; among them, the optimization targets are used to reflect the structure of the car pedal that needs to be optimized.

[0093] The third analysis module is used to analyze spatiotemporal feature information and optimization objectives to obtain the optimization parameters of the car pedal structure.

[0094] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0095] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described module division is merely an example. In practical applications, the above functions can be assigned to different modules as needed, that is, the internal structure of the system can be divided into different modules to complete all or part of the functions described above. The modules in the embodiments can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0096] This application also provides an electronic device. Figure 4 This is a schematic diagram of the structure of an electronic device 6 provided in an embodiment of this application. Figure 4 As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown in the image), at least one memory 61 ( Figure 4(Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, it causes the electronic device 6 to implement the steps in any of the above embodiments of the automotive pedal structure parameter optimization design method, or causes the electronic device 6 to implement the functions of each module in the above system embodiments.

[0097] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the electronic device 6.

[0098] The electronic device 6 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. This electronic device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0099] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0100] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 61 may be an external storage device of the electronic device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 6. Furthermore, the memory 61 may include both internal and external storage units of the electronic device 6. The memory 61 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0101] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0102] This application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the steps in any of the above method embodiments.

[0103] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0104] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] In the embodiments provided in this application, it should be understood that the disclosed electronic devices and systems can be implemented in other ways. For example, the above-described embodiment of the automotive pedal structure parameter optimization design system is merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0107] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing the design of a structure parameter of an automobile running board, characterized in that, The method comprises: acquiring working condition information, wherein the working condition information is used to reflect a state of the automobile foot pedal when a rated pressure is applied to a pedal of the automobile foot pedal; analyzing the working condition information to obtain space-time characteristic information, wherein the space-time characteristic information is used to reflect a change of stress and contact pressure of the automobile foot pedal at different times; analyzing the space-time characteristic information to obtain an optimization target, wherein the optimization target is used to reflect a structure of the automobile foot pedal that needs to be optimized; analyzing the space-time characteristic information and the optimization target to obtain a structure optimization parameter of the automobile foot pedal.

2. The method of claim 1, wherein The analyzing the working condition information to obtain space-time characteristic information comprises: analyzing a first working condition of the working condition information to obtain a first space-time characteristic, wherein the first space-time characteristic is used to reflect a change of stress and contact pressure of the automobile foot pedal at different times when a pressure speed of the rated pressure is a first preset speed; analyzing a second working condition of the working condition information to obtain a second space-time characteristic, wherein the second space-time characteristic is used to reflect a change of stress and contact pressure of the automobile foot pedal at different times when the pressure speed of the rated pressure is a second preset speed, and the first preset speed is greater than the second preset speed; analyzing the first space-time characteristic and the second space-time characteristic to obtain space-time characteristic information.

3. The method of claim 2, wherein the step of optimizing the parameters of the automotive foot pedal structure is performed by using a genetic algorithm. The analyzing the first space-time characteristic and the second space-time characteristic to obtain space-time characteristic information comprises: performing feature extraction on the first space-time characteristic to obtain a first stress change feature vector and a first contact pressure feature vector; performing feature extraction on the second space-time characteristic to obtain a second stress change feature vector and a second contact pressure feature vector; performing weighted fusion on the first stress change feature vector and the second stress change feature vector to obtain a stress feature of the space-time characteristic information; performing weighted fusion on the first contact pressure feature vector and the second contact pressure feature vector to obtain a pressure feature of the space-time characteristic information.

4. The method of claim 3, wherein the step of optimizing the parameters of the automobile foot pedal structure is performed by using a genetic algorithm. The performing weighted fusion on the first stress change feature vector and the second stress change feature vector to obtain the stress feature of the space-time characteristic information, and the performing weighted fusion on the first contact pressure feature vector and the second contact pressure feature vector to obtain the pressure feature of the space-time characteristic information comprises: obtaining a first actual occurrence probability of the first preset speed and a second actual occurrence probability of the second preset speed, wherein the actual occurrence probability refers to a frequency of a preset speed occurring in an actual driving working condition; analyzing the first actual occurrence probability and the second actual occurrence probability to obtain a first weight and a second weight, wherein the first weight is used to reflect an influence degree of the first stress change feature vector, and the second weight is used to reflect an influence degree of the second stress change feature vector; performing weighting on the first stress change feature vector according to the first weight, performing weighting on the second stress change feature vector according to the second weight, and summing to obtain the stress feature of the space-time characteristic information.

5. The method of claim 3, wherein the step of optimizing the parameters of the automobile foot pedal structure is performed by using a genetic algorithm. The weighted fusion of the first contact pressure feature vector and the second contact pressure feature vector obtains a pressure feature of the space-time feature information, and the method comprises: According to the first contact pressure feature vector and the second contact pressure feature vector, a stability analysis is performed to obtain a third weight and a fourth weight; wherein the third weight is used to reflect the influence degree of the first contact pressure feature vector, and the fourth weight is used to reflect the influence degree of the second contact pressure feature vector; According to the third weight, the first contact pressure feature vector is weighted, and according to the fourth weight, the second contact pressure feature vector is weighted, and the sum is obtained to obtain the pressure feature of the space-time feature information.

6. The method of claim 3, wherein the step of optimizing the parameters of the automobile foot pedal structure is performed by using a genetic algorithm. According to the space-time feature information, an analysis is performed to obtain an optimization target, which comprises: According to the stress feature of the space-time feature information, at least one stress anomaly point is obtained; wherein the stress anomaly point is used to reflect the position where the stress mutates; According to the stress anomaly point, a first optimization target of the optimization target is obtained; According to the pressure feature of the space-time feature information, a contact pressure extreme point is obtained; wherein the contact pressure extreme point is used to reflect the maximum value and the corresponding position of the contact pressure of the pedal of the automobile running board; The contact pressure extreme point greater than the preset extreme point is confirmed as a second optimization target of the optimization target.

7. The method of claim 6, wherein the step of optimizing the parameters of the automotive foot pedal structure is performed by using a genetic algorithm. According to the stress anomaly point, a first optimization target of the optimization target is obtained, which comprises: When the number of the stress anomaly points is one, the stress anomaly point is confirmed as the first optimization target of the optimization target; When the number of the stress anomaly points is at least two, according to a plurality of the stress anomaly points, a main target and a secondary target are obtained; wherein the main target is used to reflect the stress anomaly point that appears first in the plurality of stress anomaly points, and the secondary target is used to reflect the other stress anomaly points except the main target; The position correlation degree of the secondary target and the main target is calculated, and when the position correlation degree is less than a preset correlation degree, the corresponding secondary target is marked as the main target; wherein the position correlation degree is used to reflect the correlation degree of the structure between the stress anomaly points; The main target is determined as the first optimization target of the optimization target.

8. The method of claim 5, wherein the step of optimizing the parameters of the automobile foot pedal structure is performed by using a genetic algorithm. According to the space-time feature information and the optimization target, an automobile running board structure optimization parameter is obtained, which comprises: According to the optimization target, a pre-optimization parameter group is obtained; wherein the pre-optimization parameter group is used to reflect the set of all structure parameters corresponding to the optimization target; According to the space-time feature information and the pre-optimization parameter group, an automobile running board structure optimization parameter is obtained.

9. The method for optimizing the structural parameters of an automotive foot pedal as described in claim 8, characterized in that, According to the space-time feature information and the pre-optimization parameter group, an optimization parameter is obtained, which comprises: According to the space-time feature information and the pre-optimization parameter group, a matching analysis is performed to obtain a final optimization parameter group; wherein the final optimization parameter group is used to reflect the set of parameters that need to be optimized in the optimization target; A function-mapping model of the stress feature, the pressure feature and the final optimization parameter group is constructed, and an optimization algorithm is used to iteratively optimize parameters in the final optimization parameter group to obtain an automobile pedal structure optimization parameter; wherein a target function of the function-mapping model is a stress threshold corresponding to the stress feature and a pressure threshold corresponding to the pressure feature.

10. An electronic device, comprising: A computer program product comprising a memory, a processor, and a computer program stored in the memory and loadable into the processor, the processor implementing the method according to any one of claims 1 to 9 when executing the computer program.

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