Automobile steering wheel horn button pressing durability detection device and detection method thereof

Abstract

According to the automobile steering wheel horn button pressing durability detection device and the detection method thereof provided by the invention, the pressing detection parameters of the automobile steering wheel horn button are set based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the automobile steering wheel horn button, so that the detection condition is ensured to be highly matched with a real use scene; based on the pressing detection parameters, the driving mechanism is controlled to drive the pressing mechanism to press the horn button of the automobile steering wheel, and the pressing control module automatically completes the pressing action through the program driving mechanism without manual intervention; performance parameters in the pressing process of the automobile steering wheel horn button are collected, performance data are obtained, a full-periodic energy data chain is formed, potential defects are found in advance, pressing durability is detected and evaluated based on the performance data, a detection report is obtained, performance abnormal points can be accurately positioned, and the detection accuracy is improved. And research and development or production departments are helped to quickly trace fault reasons and optimize the design process.

Description

Technical Field

[0001] The present invention relates to the technical field of horn button detection, and in particular to a device and method for detecting the pressing durability of a car steering wheel horn button. Background Art

[0002] In daily car use, the steering wheel horn button is a crucial component for drivers to communicate with the outside world through audible warnings, and it is used very frequently. With the continuous development of the automotive industry, the reliability and durability requirements of automotive components are becoming increasingly stringent. The pressing durability of the steering wheel horn button is directly related to driving safety and user experience.

[0003] Currently, existing automobile steering wheel horn button pressing durability testing devices have some shortcomings. On the one hand, most traditional testing devices have simple structures and low levels of automation, requiring frequent human intervention, such as manual control of the frequency and strength of the pressing action. This not only increases the workload of the operator, but also leads to low detection efficiency. Manual operation makes it difficult to ensure the consistency and accuracy of the detection parameters, affecting the reliability of the test results. On the other hand, existing testing devices are usually designed for specific models of automobile steering wheel horn buttons and have poor versatility. When it is necessary to test horn buttons of different specifications and types, it is often necessary to replace the entire testing device or perform a lot of debugging work, which increases the cost and time of testing. In addition, some testing devices are unable to accurately collect and record the various performance parameters of the horn button, such as pressing stroke, pressing force, response time, etc., in real time during the testing process, which is not conducive to a comprehensive and in-depth analysis and evaluation of the durability performance of the horn button.

[0004] Therefore, there is an urgent need to design a car steering wheel horn button pressing durability detection device and detection method with high degree of automation, strong versatility and accurate detection to meet the actual needs of the automotive industry for horn button durability detection. Summary of the Invention

[0005] The present invention provides a device and method for detecting the durability of a car steering wheel horn button press, which are used to solve the problems raised in the background technology.

[0006] A device for detecting the durability of a car steering wheel horn button press, comprising:

[0007] A parameter setting module, for setting a press detection parameter for the automobile steering wheel horn button based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the automobile steering wheel horn button;

[0008] A pressing control module, for controlling the driving mechanism to drive the pressing mechanism to press the horn button of the car steering wheel based on the pressing detection parameter;

[0009] A data acquisition module is used to collect performance parameters during the process of pressing the car steering wheel horn button to obtain performance data;

[0010] The durability detection module is used to detect and evaluate the pressing durability based on performance data and obtain a test report.

[0011] Preferably, the parameter setting module includes:

[0012] The scanning unit is used to scan the pressed area of ​​the car steering wheel horn button based on a micro laser scanner to obtain 3D point cloud data of the pressed area, and to perform multi-frame acquisition of the driving mechanism in a dynamic state to obtain multiple sets of 3D point cloud data of the driven mechanism;

[0013] A feature extraction unit is used to automatically extract features from the pressing three-dimensional point cloud data and the multiple sets of driving three-dimensional point cloud data based on a deep learning algorithm to obtain initial mechanism features;

[0014] a feature supplementing unit, configured to supplement the initial mechanism feature based on material property parameters of the pressing mechanism and the driving mechanism to obtain a mechanism feature;

[0015] A graph acquisition unit, used to acquire a knowledge graph of vehicle type-structure-parameters-test results determined based on historical durability test data;

[0016] A judgment unit, configured to compare the knowledge graph with the organizational characteristics and other relevant parameters to determine whether there are graph features with a comparison similarity greater than a preset similarity;

[0017] If so, determining a press detection parameter for a horn button on a vehicle steering wheel based on the graph features;

[0018] Otherwise, based on the mechanism characteristics and in combination with the machine learning prediction model, determining a press detection parameter for the car steering wheel horn button;

[0019] An updating unit is used to update the knowledge graph in real time based on the press detection parameters determined by the machine learning prediction model.

[0020] Preferably, the judgment unit determines the pressing detection parameters of the car steering wheel horn button based on the mechanism characteristics in combination with the machine learning prediction model, including:

[0021] A simulation unit, configured to input the mechanism characteristics into simulation software and obtain a virtual button model based on material science and kinematics principles;

[0022] a setting unit, configured to obtain historical setting parameters matching the virtual button model based on the historical durability detection data, and divide the historical setting parameters into a plurality of setting parameter groups based on the occurrence frequencies of the historical setting parameters in the historical durability detection data;

[0023] A model training unit is configured to train a plurality of pre-trained prediction models based on each set parameter group and its historical detection results, determine model weights based on the frequency of occurrence, and fuse the plurality of pre-trained prediction models based on the model weights to obtain a machine learning prediction model;

[0024] A compensation determination unit, configured to obtain information on changes in material expansion coefficient and humidity influence as they change with temperature from historical durability test data, and to establish a dynamic environmental compensation strategy based on the information;

[0025] A correction determination unit, for determining a dynamic correction strategy between material fatigue parameters and test parameters based on the material properties of the car steering wheel horn button;

[0026] The press simulation unit is used to input the virtual button model into the machine learning prediction model to obtain initial press detection parameters, determine the initial press detection parameters, perform simulation prediction according to the initial press detection parameters, and collect temperature parameters and material parameters during the simulation process, and adjust the initial press detection parameters in real time based on the dynamic environment compensation strategy and dynamic correction strategy to obtain the final press detection parameters.

[0027] Preferably, the correction determination unit includes:

[0028] a first correction unit, configured to determine a material fatigue parameter of the automobile steering wheel horn button, and when the material fatigue parameter is greater than a first preset fatigue value, determine a test force increase value based on a difference between the material fatigue parameter and the first preset fatigue value;

[0029] The second correction unit is configured to determine a test cycle extension value based on a difference between the material fatigue parameter and the second preset fatigue value when the material fatigue parameter is greater than a second preset fatigue value.

[0030] Preferably, the pressing control module includes:

[0031] a signal determination unit, configured to determine a control signal for a driving mechanism based on the pressure detection parameter;

[0032] The driving unit is used to control the driving mechanism to drive the pressing mechanism to press the car steering wheel horn button according to the control signal.

[0033] Preferably, the data acquisition module includes:

[0034] The acquisition unit is used to acquire sensor parameters during the process of pressing the horn button on the steering wheel of the car to obtain sensing parameters;

[0035] The data processing unit is used to process the sensed parameters to obtain performance data.

[0036] Preferably, the data processing unit includes:

[0037] A cleaning unit is used to clean the sensor parameters to obtain intermediate data;

[0038] The normalization unit is used to normalize the intermediate data to obtain performance data.

[0039] Preferably, the durability detection module includes:

[0040] A data partitioning unit is used to divide the performance data into multiple dimensions according to the data type, obtain the data group under each dimension, and perform time alignment on the data groups of multiple dimensions based on the sensor acquisition time and transmission time to obtain the target data group;

[0041] The model building unit is used to determine the evaluation indicators for each evaluation dimension based on historical durability test data and combine machine learning, and to establish a separate evaluation model;

[0042] A weight allocation unit is used to divide the detection process into an early stage, a middle stage, and a late stage, and determine the impact of each evaluation dimension on each stage based on historical durability detection data, and determine the dimension weight of each evaluation dimension in each stage based on the impact;

[0043] The weight allocation unit is further used to determine the indicator weight of each evaluation indicator based on the impact of each evaluation indicator on each stage under the evaluation dimension;

[0044] The model fusion unit is used to perform weighted fusion of all individual evaluation models based on dimension weights and indicator weights to obtain a comprehensive evaluation model at each stage;

[0045] A result analysis unit is used to input the performance data of different detection periods into the comprehensive evaluation model multiple times to obtain a time series detection result, and obtain a pressing durability attenuation curve of the automobile steering wheel horn button based on the time series detection result;

[0046] The report generation unit is used to generate a full-cycle test result report based on the time series test results, generate a durability prediction evaluation report based on the press durability attenuation curve, and integrate the full-cycle test result report and the durability prediction evaluation report to obtain the final test report.

[0047] Preferably, the model fusion unit includes:

[0048] A weighting unit is used to perform model weighting for all individual evaluation models based on dimension weights to obtain a weighted model, and to weight the weighted model based on indicator weights to obtain a weighted individual evaluation model;

[0049] The fusion unit is used to fuse all weighted individual evaluation models to obtain a comprehensive evaluation model at each stage.

[0050] A method for detecting the pressing durability of a car steering wheel horn button, comprising:

[0051] S1: setting a press detection parameter for the car steering wheel horn button based on the mechanism characteristics of the pressing mechanism and driving mechanism of the car steering wheel horn button;

[0052] S2: Based on the pressure detection parameter, controlling the driving mechanism to drive the pressing mechanism to press the horn button on the car steering wheel;

[0053] S3: collecting performance parameters of the car steering wheel horn button during the pressing process to obtain performance data;

[0054] S4: Test and evaluate the pressing durability based on the performance data and obtain a test report.

[0055] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0056] By setting the pressing detection parameters for the car steering wheel horn button based on the structural characteristics of the pressing mechanism and driving mechanism of the car steering wheel horn button, it is ensured that the detection conditions are highly matched with the actual usage scenarios, and the accuracy and reliability of the detection results are improved. Based on the pressing detection parameters, the driving mechanism is controlled to drive the pressing mechanism to press the car steering wheel horn button. The pressing control module automatically completes the pressing action through the program driving mechanism without human intervention. Compared with traditional manual testing, the detection efficiency can be greatly improved. The performance parameters of the car steering wheel horn button during pressing are collected to obtain performance data and form a full-cycle performance data chain. It can not only determine whether the button has failed, but also analyze the performance degradation trend and discover potential defects in advance. The pressing durability is tested and evaluated based on the performance data to obtain a test report, which can accurately locate performance anomalies and help R&D or production departments quickly trace the cause of the failure and optimize the design process.

[0057] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0058] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 This is a structural diagram of a device for detecting the durability of a car steering wheel horn button press according to an embodiment of the present invention;

[0061] Figure 2 is a structural diagram of the parameter setting module in an embodiment of the present invention;

[0062] Figure 3 The present invention is a flowchart of a method for detecting the durability of a car steering wheel horn button press according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0064] Example 1:

[0065] The embodiment of the present invention provides a device for detecting the durability of a car steering wheel horn button press. Figure 1 Shown, including:

[0066] A parameter setting module, for setting a press detection parameter for the automobile steering wheel horn button based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the automobile steering wheel horn button;

[0067] A pressing control module, for controlling the driving mechanism to drive the pressing mechanism to press the horn button of the car steering wheel based on the pressing detection parameter;

[0068] A data acquisition module is used to collect performance parameters during the process of pressing the car steering wheel horn button to obtain performance data;

[0069] The durability detection module is used to detect and evaluate the pressing durability based on performance data and obtain a test report.

[0070] In this embodiment, the steering wheel of the car to be tested is mounted on the steering wheel fixing mechanism, and the position and angle of the steering wheel are adjusted so that the horn button is directly below the pressing mechanism, ensuring that the pressing mechanism can be accurately aligned with the horn button.

[0071] In this embodiment, the press detection parameters include press frequency, press force, press stroke, etc.

[0072] In this embodiment, the detection module is arranged on the pressing mechanism or the steering wheel fixing mechanism, and is used to collect various performance parameters of the horn button during the pressing process in real time, including contact resistance, trigger force change, rebound time, wear amount, response time, horn sound status, etc.

[0073] In this embodiment, the mechanical characteristics of the pressing mechanism and the driving mechanism include, for example, pressing stroke, triggering force, material properties, and the like.

[0074] In this embodiment, by flexibly adjusting the press detection parameters, it is possible to adapt to the structural differences of steering wheel horn buttons of cars of different brands and models, achieve multi-purpose use of one device, reduce the equipment procurement cost of the enterprise, and improve the versatility of the detection device.

[0075] In this embodiment, the pressing durability is detected and evaluated based on the performance data, such as threshold comparison, trend fitting, life prediction model, etc.

[0076] The beneficial effects of the above design scheme are: by setting the pressing detection parameters of the car steering wheel horn button based on the structural characteristics of the pressing mechanism and the driving mechanism of the car steering wheel horn button, ensuring that the detection conditions are highly matched with the actual usage scenarios, and improving the accuracy and reliability of the detection results; based on the pressing detection parameters, the driving mechanism is controlled to drive the pressing mechanism to press the car steering wheel horn button; the pressing control module automatically completes the pressing action through the program driving mechanism without manual intervention, which can greatly improve the detection efficiency compared with traditional manual testing; the performance parameters of the car steering wheel horn button during the pressing process are collected to obtain performance data and form a full-cycle performance data chain, which can not only determine whether the button has failed, but also analyze the performance degradation trend and discover potential defects in advance; the pressing durability is tested and evaluated based on the performance data to obtain a test report, which can accurately locate performance anomalies, help R&D or production departments quickly trace the cause of the failure, and optimize the design process.

[0077] Example 2:

[0078] Based on Example 1, the present invention provides a device for detecting the durability of a car steering wheel horn button press. Figure 2 As shown, the parameter setting module includes:

[0079] The scanning unit is used to scan the pressed area of ​​the car steering wheel horn button based on a micro laser scanner to obtain 3D point cloud data of the pressed area, and to perform multi-frame acquisition of the driving mechanism in a dynamic state to obtain multiple sets of 3D point cloud data of the driven mechanism;

[0080] A feature extraction unit is used to automatically extract features from the pressing three-dimensional point cloud data and the multiple sets of driving three-dimensional point cloud data based on a deep learning algorithm to obtain initial mechanism features;

[0081] a feature supplementing unit, configured to supplement the initial mechanism feature based on material property parameters of the pressing mechanism and the driving mechanism to obtain a mechanism feature;

[0082] A graph acquisition unit, used to acquire a knowledge graph of vehicle type-structure-parameters-test results determined based on historical durability test data;

[0083] A judgment unit, configured to compare the knowledge graph with the organizational characteristics and other relevant parameters to determine whether there are graph features with a comparison similarity greater than a preset similarity;

[0084] If so, determining a press detection parameter for a horn button on a vehicle steering wheel based on the graph features;

[0085] Otherwise, based on the mechanism characteristics and in combination with the machine learning prediction model, determining a press detection parameter for the car steering wheel horn button;

[0086] An updating unit is used to update the knowledge graph in real time based on the press detection parameters determined by the machine learning prediction model.

[0087] In this embodiment, the material property parameters include elastic modulus and Poisson's ratio.

[0088] In this embodiment, a parameter mapping formula can be developed based on the principles of material mechanics and kinematics, such as F_dynamic=F_static×(1+k·v 2 ), where F_dynamic represents the dynamic force, F_static is the static trigger force, v is the pressing velocity, and k is the spring stiffness.

[0089] In this embodiment, the three-dimensional point cloud data of the pressed area acquired by the micro laser scanner can accurately restore micron-level geometric features such as the button surface curvature, keycap travel, and trigger point position, providing a high-precision physical model basis for subsequent parameter calculations.

[0090] In this embodiment, by acquiring dynamic three-dimensional point cloud data of the driving mechanism in multiple frames, dynamic characteristics such as motor speed fluctuations, transmission component clearances, and spring deformation hysteresis can be analyzed, breaking through the limitations of traditional static detection and more realistically reflecting the mechanical environment in actual use.

[0091] In this embodiment, unlike traditional static databases, the knowledge graph of this solution supports real-time updates and dynamic reasoning, and can adapt to ever-changing vehicle design and material innovations.

[0092] In this embodiment, material property parameters such as elastic modulus, Poisson's ratio, fatigue limit, etc. are combined with geometric features to make parameter settings more consistent with physical laws.

[0093] The beneficial effects of the above design scheme are: by comparing the mechanism characteristics and other relevant parameters with the knowledge graph, it is determined whether there is a graph feature with a comparison similarity greater than a preset similarity; if so, based on the graph feature, the press detection parameters for the car steering wheel horn button are determined; the efficiency of parameter setting is improved through the predetermined knowledge graph, otherwise, based on the mechanism characteristics, combined with the machine learning prediction model, the press detection parameters for the car steering wheel horn button are determined to ensure the accuracy of the set press detection parameters, and achieve precise parameter setting, thereby reducing material waste and equipment loss caused by excessive testing, and combining three-dimensional scanning, physical simulation and AI algorithms in the process of determining button characteristics to achieve full-link automation from physical objects to virtual models to test parameters, breaking through the traditional parameter setting method based on empirical formulas, and realizing the transformation of the traditional parameter setting process that relies on manual experience into a quantifiable, traceable and evolvable intelligent decision-making system, which has broad engineering application prospects.

[0094] Example 3:

[0095] Based on Example 2, an embodiment of the present invention provides a device for detecting the durability of a car steering wheel horn button press. The judgment unit determines the pressure detection parameters of the car steering wheel horn button based on the mechanism characteristics and in combination with a machine learning prediction model, including:

[0096] A simulation unit, configured to input the mechanism characteristics into simulation software and obtain a virtual button model based on material science and kinematics principles;

[0097] a setting unit, configured to obtain historical setting parameters matching the virtual button model based on the historical durability detection data, and divide the historical setting parameters into a plurality of setting parameter groups based on the occurrence frequencies of the historical setting parameters in the historical durability detection data;

[0098] A model training unit is configured to train a plurality of pre-trained prediction models based on each set parameter group and its historical detection results, determine model weights based on the frequency of occurrence, and fuse the plurality of pre-trained prediction models based on the model weights to obtain a machine learning prediction model;

[0099] A compensation determination unit, configured to obtain information on changes in material expansion coefficient and humidity influence as they change with temperature from historical durability test data, and to establish a dynamic environmental compensation strategy based on the information;

[0100] A correction determination unit, for determining a dynamic correction strategy between material fatigue parameters and test parameters based on the material properties of the car steering wheel horn button;

[0101] The press simulation unit is used to input the virtual button model into the machine learning prediction model to obtain initial press detection parameters, determine the initial press detection parameters, perform simulation prediction according to the initial press detection parameters, and collect temperature parameters and material parameters during the simulation process, and adjust the initial press detection parameters in real time based on the dynamic environment compensation strategy and dynamic correction strategy to obtain the final press detection parameters.

[0102] In this embodiment, the mechanism characteristics are input into the simulation software to generate a virtual model, and the principles of materials science and kinematics are combined to achieve accurate simulation of the mechanical behavior of the button.

[0103] In this embodiment, frequency clustering is used to divide and set parameter groups, avoiding the one-sidedness of a single historical parameter. For example, when a parameter appears more than 70% of the time in historical data, its weight is automatically increased, making the new parameter setting closer to industry best practices.

[0104] In this embodiment, the frequency of occurrence of each set parameter group is different, and the higher the frequency of occurrence, the greater the weight of the corresponding training model.

[0105] In this embodiment, multiple pre-trained models are integrated, and each model focuses on the prediction of a different numerical dimension, thereby improving the model prediction accuracy, which is particularly advantageous when processing complex structures.

[0106] In this embodiment, model weights are dynamically assigned based on the frequency of occurrence of historical data, so that the detection device can quickly adjust its decision-making strategy when faced with new materials or structures. For example, when detecting new silicone buttons, the weight of the training model that is good at processing elastic materials is automatically increased to 0.6, and the weight of the traditional metal model is reduced to 0.3.

[0107] In this embodiment, a dynamic environmental compensation strategy is established to adjust the test parameters in real time. For example, when the ambient temperature rises from 25°C to 40°C, the pressing stroke is automatically increased by 2.3% to compensate for thermal expansion. For every 10% increase in humidity, the contact force threshold is reduced by 1.5N to offset the influence of the surface water film.

[0108] In this embodiment, the dynamic correction strategy is used to simulate the performance degradation after long-term use in advance.

[0109] The beneficial effects of the above design scheme are: by combining the characteristics of the mechanism with the machine learning prediction model, the pressing detection parameters of the car steering wheel horn button are determined, the accuracy of regular prediction is improved, and the physical laws and data-driven methods are deeply combined, which not only ensures the theoretical rationality of the parameter setting, but also improves the actual effectiveness through historical experience and real-time feedback, and realizes the intelligent and dynamic generation of the durability detection parameters of the car steering wheel horn button.

[0110] Example 4:

[0111] Based on Example 3, an embodiment of the present invention provides a device for detecting the durability of a car steering wheel horn button press, characterized in that the correction determination unit includes:

[0112] a first correction unit, configured to determine a material fatigue parameter of the automobile steering wheel horn button, and when the material fatigue parameter is greater than a first preset fatigue value, determine a test force increase value based on a difference between the material fatigue parameter and the first preset fatigue value;

[0113] The second correction unit is configured to determine a test cycle extension value based on a difference between the material fatigue parameter and the second preset fatigue value when the material fatigue parameter is greater than a second preset fatigue value.

[0114] In this embodiment, the second preset fatigue value is greater than the first preset fatigue value.

[0115] The beneficial effect of the above design scheme is: by determining the material fatigue parameter of the car steering wheel horn button, when the material fatigue parameter is greater than the first preset fatigue value, the test force increase value is determined based on the difference between the material fatigue parameter and the first preset fatigue value; when the material fatigue parameter is greater than the second preset fatigue value, the test cycle extension value is determined based on the difference between the material fatigue parameter and the second preset fatigue value, thereby realizing the establishment of a dynamic correction strategy.

[0116] Example 5:

[0117] Based on Example 1, this embodiment of the present invention provides a device for detecting the durability of a car steering wheel horn button press, wherein the press control module includes:

[0118] a signal determination unit, configured to determine a control signal for a driving mechanism based on the pressure detection parameter;

[0119] The driving unit is used to control the driving mechanism to drive the pressing mechanism to press the car steering wheel horn button according to the control signal.

[0120] The beneficial effect of the above design scheme is: by controlling the driving mechanism to drive the pressing mechanism to press the car steering wheel horn button according to the control signal, the pressing control module automatically completes the pressing action through the program driving mechanism without human intervention, which can greatly improve the detection efficiency compared with traditional manual testing.

[0121] Example 6:

[0122] Based on Example 1, this embodiment of the present invention provides a device for detecting the durability of a car steering wheel horn button press, wherein the data acquisition module includes:

[0123] The acquisition unit is used to acquire sensor parameters during the process of pressing the horn button on the steering wheel of the car to obtain sensing parameters;

[0124] The data processing unit is used to process the sensed parameters to obtain performance data.

[0125] The beneficial effect of the above design scheme is: by collecting sensor parameters during the process of pressing the car steering wheel horn button, obtaining sensing parameters, processing the sensing parameters, obtaining performance data, and forming a full-cycle performance data chain, it can not only determine whether the button is invalid, but also analyze the performance degradation trend and discover potential defects in advance.

[0126] Example 7:

[0127] Based on Example 6, this embodiment of the present invention provides a device for detecting the durability of a car steering wheel horn button press, wherein the data processing unit includes:

[0128] A cleaning unit is used to clean the sensor parameters to obtain intermediate data;

[0129] The normalization unit is used to normalize the intermediate data to obtain performance data.

[0130] The beneficial effects of the above design scheme are: by cleaning the sensor parameters, obtaining intermediate data, standardizing the intermediate data, obtaining performance data, ensuring the quality of the obtained performance data, and providing a high-quality data basis for performance data analysis.

[0131] Example 8:

[0132] Based on Example 1, this embodiment of the present invention provides a device for detecting the durability of a car steering wheel horn button press, wherein the durability detection module includes:

[0133] A data partitioning unit is used to divide the performance data into multiple dimensions according to the data type, obtain the data group under each dimension, and perform time alignment on the data groups of multiple dimensions based on the sensor acquisition time and transmission time to obtain the target data group;

[0134] The model building unit is used to determine the evaluation indicators for each evaluation dimension based on historical durability test data and combine machine learning, and to establish a separate evaluation model;

[0135] A weight allocation unit is used to divide the detection process into an early stage, a middle stage, and a late stage, and determine the impact of each evaluation dimension on each stage based on historical durability detection data, and determine the dimension weight of each evaluation dimension in each stage based on the impact;

[0136] The weight allocation unit is further used to determine the indicator weight of each evaluation indicator based on the impact of each evaluation indicator on each stage under the evaluation dimension;

[0137] The model fusion unit is used to perform weighted fusion of all individual evaluation models based on dimension weights and indicator weights to obtain a comprehensive evaluation model at each stage;

[0138] A result analysis unit is used to input the performance data of different detection periods into the comprehensive evaluation model multiple times to obtain a time series detection result, and obtain a pressing durability attenuation curve of the automobile steering wheel horn button based on the time series detection result;

[0139] The report generation unit is used to generate a full-cycle test result report based on the time series test results, generate a durability prediction evaluation report based on the press durability attenuation curve, and integrate the full-cycle test result report and the durability prediction evaluation report to obtain the final test report.

[0140] In this embodiment, the data types include mechanical data types, electrical data types, acoustic data types, vibration data types, image data types, and environmental data types.

[0141] In this embodiment, the evaluation dimensions include mechanical properties, electrical properties, material properties and comprehensive life. The evaluation indicators corresponding to the mechanical properties are trigger force attenuation rate, rebound time change rate, stroke loss rate, etc. The evaluation indicators corresponding to the electrical performance are, for example, contact resistance fluctuation range, conduction reliability, signal attenuation, and the evaluation indicators corresponding to the material properties are, for example, surface wear depth, crack propagation rate, hardness change, etc. The evaluation indicators corresponding to the comprehensive life are, for example, remaining life prediction based on Weibull distribution and failure probability curve.

[0142] In this embodiment, the data types are divided into six categories, including mechanics, electricity, and acoustics, covering the mechanical response, electrical characteristics, material degradation, and environmental impact of the pressing process, breaking through the limitation of traditional testing that only focuses on a single indicator.

[0143] In this embodiment, based on the alignment of sensor acquisition time and transmission time, the timing misalignment problem caused by sampling rate differences of multi-source data (such as 1000 Hz for pressure sensor vs. 2000 Hz for vibration sensor) is solved.

[0144] In this embodiment, the dimension weights are dynamically assigned according to the detection stage, which conforms to the actual attenuation law of pressing durability. For example:

[0145] Early stage (first 10,000 presses): Mechanical performance (trigger force, travel) accounts for 60% (the dominant factor is structural running-in)

[0146] Mid-term stage (10,000-50,000 times): The weight of electrical performance (contact resistance, conduction reliability) increases to 50% (the dominant factor is contact oxidation)

[0147] Late stage (after 50,000 times): Material properties (wear depth, crack extension) account for 70% of the weight (the dominant factor is material fatigue).

[0148] In this embodiment, the attenuation curve is generated by inputting data of different time periods multiple times, thereby achieving the transition from single-point detection to full-cycle tracking.

[0149] In this embodiment, different from the traditional fixed weight evaluation, the detection stage is associated with the dimension and indicator weight for the first time, which is more in line with the nonlinear attenuation law of pressing durability.

[0150] The beneficial effect of the above design scheme is that the durability detection module realizes accurate evaluation and prediction of the durability of the car steering wheel horn button pressing through multi-dimensional data fusion, dynamic weight allocation and full-cycle timing analysis.

[0151] Example 9:

[0152] Based on Example 8, this embodiment of the present invention provides a device for detecting the durability of a car steering wheel horn button press, wherein the model fusion unit includes:

[0153] A weighting unit is used to perform model weighting for all individual evaluation models based on dimension weights to obtain a weighted model, and to weight the weighted model based on indicator weights to obtain a weighted individual evaluation model;

[0154] The fusion unit is used to fuse all weighted individual evaluation models to obtain a comprehensive evaluation model at each stage.

[0155] The beneficial effects of the above design scheme are: by weighting all individual evaluation models based on dimension weights to obtain a weighted model, weighting the weighted model based on the indicator weight to obtain a weighted individual evaluation model, fusing all weighted individual evaluation models to obtain a comprehensive evaluation model at each stage, realizing weighted fusion of dimension models according to stages, and providing an accurate model for the durability detection of automobile steering wheel horn button pressing.

[0156] Example 10:

[0157] The embodiment of the present invention provides a method for detecting the durability of a car steering wheel horn button press. Figure 3 Shown, including:

[0158] S1: setting a press detection parameter for the car steering wheel horn button based on the mechanism characteristics of the pressing mechanism and driving mechanism of the car steering wheel horn button;

[0159] S2: Based on the pressure detection parameter, controlling the driving mechanism to drive the pressing mechanism to press the horn button on the car steering wheel;

[0160] S3: collecting performance parameters of the car steering wheel horn button during the pressing process to obtain performance data;

[0161] S4: Test and evaluate the pressing durability based on the performance data and obtain a test report.

[0162] In this embodiment, the steering wheel of the car to be tested is mounted on the steering wheel fixing mechanism, and the position and angle of the steering wheel are adjusted so that the horn button is directly below the pressing mechanism, ensuring that the pressing mechanism can be accurately aligned with the horn button.

[0163] In this embodiment, the press detection parameters include press frequency, press force, press stroke, etc.

[0164] In this embodiment, the detection module is arranged on the pressing mechanism or the steering wheel fixing mechanism, and is used to collect various performance parameters of the horn button during the pressing process in real time, including contact resistance, trigger force change, rebound time, wear amount, response time, horn sound status, etc.

[0165] In this embodiment, the mechanical characteristics of the pressing mechanism and the driving mechanism include, for example, pressing stroke, triggering force, material properties, and the like.

[0166] In this embodiment, by flexibly adjusting the press detection parameters, it is possible to adapt to the structural differences of steering wheel horn buttons of cars of different brands and models, achieve multi-purpose use of one device, reduce the equipment procurement cost of the enterprise, and improve the versatility of the detection device.

[0167] In this embodiment, the pressing durability is detected and evaluated based on the performance data, such as threshold comparison, trend fitting, life prediction model, etc.

[0168] The beneficial effects of the above design scheme are: by setting the pressing detection parameters of the car steering wheel horn button based on the structural characteristics of the pressing mechanism and the driving mechanism of the car steering wheel horn button, ensuring that the detection conditions are highly matched with the actual usage scenarios, and improving the accuracy and reliability of the detection results; based on the pressing detection parameters, the driving mechanism is controlled to drive the pressing mechanism to press the car steering wheel horn button; the pressing control module automatically completes the pressing action through the program driving mechanism without manual intervention, which can greatly improve the detection efficiency compared with traditional manual testing; the performance parameters of the car steering wheel horn button during the pressing process are collected to obtain performance data and form a full-cycle performance data chain, which can not only determine whether the button has failed, but also analyze the performance degradation trend and discover potential defects in advance; the pressing durability is tested and evaluated based on the performance data to obtain a test report, which can accurately locate performance anomalies, help R&D or production departments quickly trace the cause of the failure, and optimize the design process.

[0169] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of this application document and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. A car steering wheel horn button pressing durability detection device, characterized in that: include: A parameter setting module, for setting a press detection parameter for the automobile steering wheel horn button based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the automobile steering wheel horn button; A pressing control module, for controlling the driving mechanism to drive the pressing mechanism to press the horn button of the car steering wheel based on the pressing detection parameter; A data acquisition module is used to collect performance parameters during the process of pressing the car steering wheel horn button to obtain performance data; The durability detection module is used to detect and evaluate the pressing durability based on performance data and obtain a test report.

2. The automobile steering wheel horn button pressing durability detection device according to claim 1, characterized in that: The parameter setting module includes: The scanning unit is used to scan the pressed area of ​​the car steering wheel horn button based on a micro laser scanner to obtain 3D point cloud data of the pressed area, and to perform multi-frame acquisition of the driving mechanism in a dynamic state to obtain multiple sets of 3D point cloud data of the driven mechanism; A feature extraction unit is used to automatically extract features from the pressing three-dimensional point cloud data and the multiple sets of driving three-dimensional point cloud data based on a deep learning algorithm to obtain initial mechanism features; a feature supplementing unit, configured to supplement the initial mechanism feature based on material property parameters of the pressing mechanism and the driving mechanism to obtain a mechanism feature; A graph acquisition unit, used to acquire a knowledge graph of vehicle type-structure-parameters-test results determined based on historical durability test data; A judgment unit, configured to compare the knowledge graph with the organizational characteristics and other relevant parameters to determine whether there are graph features with a comparison similarity greater than a preset similarity; If so, determining a press detection parameter for a horn button on a vehicle steering wheel based on the graph features; Otherwise, based on the mechanism characteristics and in combination with the machine learning prediction model, determining a press detection parameter for the car steering wheel horn button; An updating unit is used to update the knowledge graph in real time based on the press detection parameters determined by the machine learning prediction model.

3. The automobile steering wheel horn button pressing durability detection device according to claim 2, characterized in that: In the judgment unit, based on the mechanism characteristics and in combination with the machine learning prediction model, the pressing detection parameters of the car steering wheel horn button are determined, including: A simulation unit, configured to input the mechanism characteristics into simulation software and obtain a virtual button model based on material science and kinematics principles; a setting unit, configured to obtain historical setting parameters matching the virtual button model based on the historical durability detection data, and divide the historical setting parameters into a plurality of setting parameter groups based on the occurrence frequencies of the historical setting parameters in the historical durability detection data; A model training unit is configured to train a plurality of pre-trained prediction models based on each set parameter group and its historical detection results, determine model weights based on the frequency of occurrence, and fuse the plurality of pre-trained prediction models based on the model weights to obtain a machine learning prediction model; A compensation determination unit, configured to obtain information on changes in material expansion coefficient and humidity influence as they change with temperature from historical durability test data, and to establish a dynamic environmental compensation strategy based on the information; A correction determination unit, for determining a dynamic correction strategy between material fatigue parameters and test parameters based on the material properties of the car steering wheel horn button; The press simulation unit is used to input the virtual button model into the machine learning prediction model to obtain initial press detection parameters, determine the initial press detection parameters, perform simulation prediction according to the initial press detection parameters, and collect temperature parameters and material parameters during the simulation process, and adjust the initial press detection parameters in real time based on the dynamic environment compensation strategy and dynamic correction strategy to obtain the final press detection parameters.

4. The automobile steering wheel horn button pressing durability detection device according to claim 3, characterized in that: The correction determination unit includes: a first correction unit, configured to determine a material fatigue parameter of the automobile steering wheel horn button, and when the material fatigue parameter is greater than a first preset fatigue value, determine a test force increase value based on a difference between the material fatigue parameter and the first preset fatigue value; The second correction unit is configured to determine a test cycle extension value based on a difference between the material fatigue parameter and the second preset fatigue value when the material fatigue parameter is greater than a second preset fatigue value.

5. The automobile steering wheel horn button pressing durability detection device according to claim 1, characterized in that: The pressing control module includes: a signal determination unit, configured to determine a control signal for a driving mechanism based on the pressure detection parameter; The driving unit is used to control the driving mechanism to drive the pressing mechanism to press the car steering wheel horn button according to the control signal.

6. The automobile steering wheel horn button pressing durability detection device according to claim 1, characterized in that: The data acquisition module includes: The acquisition unit is used to acquire sensor parameters during the process of pressing the horn button on the steering wheel of the car to obtain sensing parameters; The data processing unit is used to process the sensed parameters to obtain performance data.

7. The automobile steering wheel horn button pressing durability detection device according to claim 6, characterized in that: The data processing unit includes: A cleaning unit is used to clean the sensor parameters to obtain intermediate data; The normalization unit is used to normalize the intermediate data to obtain performance data.

8. The automobile steering wheel horn button pressing durability detection device according to claim 1, characterized in that: The durability detection module includes: A data partitioning unit is used to divide the performance data into multiple dimensions according to the data type, obtain the data group under each dimension, and perform time alignment on the data groups of multiple dimensions based on the sensor acquisition time and transmission time to obtain the target data group; The model building unit is used to determine the evaluation indicators for each evaluation dimension based on historical durability test data and combine machine learning, and to establish a separate evaluation model; A weight allocation unit is used to divide the detection process into an early stage, a middle stage, and a late stage, and determine the impact of each evaluation dimension on each stage based on historical durability detection data, and determine the dimension weight of each evaluation dimension in each stage based on the impact; The weight allocation unit is further used to determine the indicator weight of each evaluation indicator based on the impact of each evaluation indicator on each stage under the evaluation dimension; The model fusion unit is used to perform weighted fusion of all individual evaluation models based on dimension weights and indicator weights to obtain a comprehensive evaluation model at each stage; A result analysis unit is used to input the performance data of different detection periods into the comprehensive evaluation model multiple times to obtain a time series detection result, and obtain a pressing durability attenuation curve of the automobile steering wheel horn button based on the time series detection result; The report generation unit is used to generate a full-cycle test result report based on the time series test results, generate a durability prediction evaluation report based on the press durability attenuation curve, and integrate the full-cycle test result report and the durability prediction evaluation report to obtain the final test report.

9. The automobile steering wheel horn button pressing durability detection device according to claim 8, characterized in that: The model fusion unit includes: A weighting unit is used to perform model weighting for all individual evaluation models based on dimension weights to obtain a weighted model, and to weight the weighted model based on indicator weights to obtain a weighted individual evaluation model; The fusion unit is used to fuse all weighted individual evaluation models to obtain a comprehensive evaluation model at each stage.

10. A method for detecting the durability of a car steering wheel horn button being pressed, specifically used in the device for detecting the durability of a car steering wheel horn button being pressed as claimed in claim 1, characterized in that: include: S1: setting a press detection parameter for the car steering wheel horn button based on the mechanism characteristics of the pressing mechanism and driving mechanism of the car steering wheel horn button; S2: Based on the pressure detection parameter, controlling the driving mechanism to drive the pressing mechanism to press the horn button on the car steering wheel; S3: collecting performance parameters of the car steering wheel horn button during the pressing process to obtain performance data; S4: Test and evaluate the pressing durability based on the performance data and obtain a test report.

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