A device and method for detecting the durability of a horn button press on a steering wheel

Abstract

The application provides a kind of automobile steering wheel horn button pressing endurance detection device and its detection method, by based on the mechanism characteristics of the pressing mechanism and driving mechanism of automobile steering wheel horn button, set the pressing detection parameter of automobile steering wheel horn button, ensure that the detection condition is highly matched with the real use scene, improve the accuracy and reliability of detection result, based on the pressing detection parameter, control driving mechanism drives pressing mechanism to press automobile steering wheel horn button, pressing control module automatically completes pressing action by program driving mechanism, without manual intervention, the performance parameters in the pressing process of automobile steering wheel horn button are collected, the performance data is obtained, the full cycle performance data chain is formed, the potential defects are found in advance, the pressing endurance is detected and evaluated based on the performance data, the detection report is obtained, the performance abnormal point can be accurately positioned, help research and development or production department to quickly trace fault reason, optimize design process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of horn button detection, and particularly relates to a durability detection device for a horn button of an automobile steering wheel and a detection method thereof. BACKGROUND

[0002] In the daily use of an automobile, the horn button of a steering wheel is an important component for a driver to communicate with the outside world through sound warning, and its use frequency is extremely high. With the continuous development of the automobile industry, the reliability and durability of automobile parts are increasingly required, and the press durability of the horn button of the automobile steering wheel is directly related to driving safety and user experience.

[0003] At present, the existing durability detection device for the horn button of the automobile steering wheel has some deficiencies. On the one hand, the traditional detection device is mostly simple in structure and low in automation, and needs frequent manual participation, such as manual control of the frequency and force of the pressing action, which not only increases the working strength of the operator, but also leads to low detection efficiency, and manual operation cannot guarantee the consistency and accuracy of the detection parameters, affecting the reliability of the detection results. On the other hand, the existing detection device is usually designed for a specific model of the horn button of the automobile steering wheel, and has poor universality. When different specifications and different types of horn buttons need to be detected, the entire detection device needs to be replaced or a large amount of debugging work needs to be done, increasing the detection cost and time cost. In addition, some detection devices cannot accurately collect and record various performance parameters of the horn button, such as the pressing stroke, the pressing force, the response time, etc., during the detection process, which is not conducive to the comprehensive and in-depth analysis and evaluation of the durability of the horn button.

[0004] Therefore, it is urgent to design a durability detection device for the horn button of the automobile steering wheel with high automation, strong universality and accurate detection, and a detection method thereof, to meet the actual needs of the automobile industry for durability detection of the horn button. SUMMARY

[0005] The present application provides a durability detection device for the horn button of the automobile steering wheel and a detection method thereof, to solve the problems raised in the background art.

[0006] A durability detection device for the horn button of the automobile steering wheel, comprising:

[0007] A parameter setting module is configured to set the pressing detection parameters of the horn button of the automobile steering wheel based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the horn button of the automobile steering wheel.

[0008] A pressing control module is configured to control the driving mechanism to drive the pressing mechanism to press the horn button of the automobile steering wheel based on the pressing detection parameters.

[0009] The data acquisition module is configured to acquire performance data of the performance parameters of the pressing process of the horn button of the steering wheel of the automobile.

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

[0011] Preferably, the parameter setting module comprises:

[0012] The scanning unit is configured to scan the pressing area of the horn button of the steering wheel of the automobile based on the micro laser scanner, obtain pressing three-dimensional point cloud data, and acquire multiple sets of driving three-dimensional point cloud data of the driving mechanism in a dynamic situation.

[0013] The feature extraction unit is configured 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, and obtain initial mechanism features.

[0014] The feature supplement unit is configured to supplement the initial mechanism features based on material attribute parameters of the pressing mechanism and the driving mechanism, and obtain mechanism features.

[0015] The atlas acquisition unit is configured to acquire a knowledge graph of vehicle type-structure-parameter-test result determined based on historical durability detection data.

[0016] The judgment unit is configured to compare the mechanism features and other related parameters with the knowledge graph, and determine whether there is an atlas feature with a similarity greater than a preset similarity.

[0017] If yes, the pressing detection parameters for the horn button of the steering wheel of the automobile are determined based on the atlas feature.

[0018] Otherwise, the pressing detection parameters for the horn button of the steering wheel of the automobile are determined based on the mechanism features and a machine learning prediction model.

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

[0020] Preferably, in the judgment unit, the pressing detection parameters for the horn button of the steering wheel of the automobile are determined based on the mechanism features and a machine learning prediction model, and the determination comprises:

[0021] The simulation unit is configured to input the mechanism features into simulation software, and obtain a virtual button model based on principles of material science and kinematics.

[0022] The setting unit is configured to acquire historical setting parameters matched with the virtual button model based on historical durability detection data, and divide the historical setting parameters into a plurality of setting parameter groups based on the frequency of occurrence of the historical setting parameters in the historical durability detection data;

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

[0024] The compensation determination unit is configured to acquire change information of a material expansion coefficient and humidity influence with temperature change from the historical durability detection data, and establish a dynamic environmental compensation strategy based on the change information;

[0025] The correction determination unit is configured to determine a dynamic correction strategy between a material fatigue parameter and a test parameter based on the material properties of the horn button of the automobile steering wheel;

[0026] The pressing simulation unit is configured to input the virtual button model into the machine learning prediction model to obtain initial pressing detection parameters, simulate and predict according to the initial pressing detection parameters, and collect temperature parameters and material parameters in the simulation process, and adjust the initial pressing detection parameters in real time based on the dynamic environmental compensation strategy and the dynamic correction strategy to obtain final pressing detection parameters.

[0027] Preferably, the correction determination unit comprises:

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

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

[0030] Preferably, the pressing control module comprises:

[0031] The signal determination unit is configured to determine a control signal of the driving mechanism based on the pressing detection parameters.

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

[0033] Preferably, the data acquisition module comprises:

[0034] The collection unit is configured to collect sensor parameters during pressing of the horn button of the steering wheel of the automobile to obtain sensing parameters.

[0035] The data processing unit is configured to process the sensing parameters to obtain performance data.

[0036] Preferably, the data processing unit comprises:

[0037] The cleaning unit is configured to clean the sensing parameters to obtain intermediate data.

[0038] The standardization unit is configured to standardize the intermediate data to obtain the performance data.

[0039] Preferably, the durability detection module comprises:

[0040] The data division unit is configured to divide the performance data into multiple dimensions according to data types to obtain data groups in each dimension, and perform time alignment on the data groups in the multiple dimensions based on sensor collection time and transmission time to obtain target data groups.

[0041] The model establishment unit is configured to determine evaluation indexes of each evaluation dimension based on historical durability detection data and in combination with machine learning, and establish separate evaluation models.

[0042] The weight allocation unit is configured to divide the detection process into an early stage, a middle stage and a late stage, determine influence of each evaluation dimension on each stage based on historical durability detection data, and determine dimension weights of each evaluation dimension in each stage based on the influence.

[0043] The weight allocation unit is further configured to determine index weights of each evaluation index based on influence of each evaluation index on each stage in the evaluation dimension.

[0044] The model fusion unit is configured to perform weighted fusion on all separate evaluation models based on the dimension weights and the index weights to obtain comprehensive evaluation models in each stage.

[0045] The result analysis unit is configured to input performance data of different detection time periods into the comprehensive evaluation models multiple times to obtain time sequence detection results, and obtain a pressing durability attenuation curve of the horn button of the steering wheel of the automobile based on the time sequence detection results.

[0046] The report generation unit is configured to generate a whole-cycle detection result report based on the time sequence detection results, generate a durability prediction evaluation report based on the pressing durability attenuation curve, integrate the whole-cycle detection result report and the durability prediction evaluation report, and obtain a final detection report.

[0047] Preferably, the model fusion unit comprises:

[0048] a weighting unit configured to weight all individual evaluation models based on dimension weights to obtain weighted models, and weight the weighted models based on index weights to obtain weighted individual evaluation models;

[0049] a fusion unit configured 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 horn button of an automobile steering wheel, comprising:

[0051] S1: based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the horn button of the automobile steering wheel, setting pressing detection parameters for the horn button of the automobile steering wheel;

[0052] S2: based on the pressing detection parameters, controlling the driving mechanism to drive the pressing mechanism to press the horn button of the automobile steering wheel;

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

[0054] S4: detecting and evaluating the pressing durability based on the performance data to obtain a detection report.

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

[0056] By setting the pressing detection parameters for the horn button of the automobile steering wheel based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the horn button of the automobile steering wheel, the detection conditions are highly matched with the real use scene, 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 horn button of the automobile steering wheel, the pressing control module automatically completes the pressing action through the program driving mechanism without manual intervention, compared with the traditional manual test, the detection efficiency can be greatly improved, the performance parameters during the pressing process of the horn button of the automobile steering wheel are collected to obtain performance data, forming a full-cycle performance data chain, not only can the failure of the button be judged, but also the performance degradation trend can be analyzed to find potential defects in advance, the pressing durability is detected and evaluated based on the performance data to obtain a detection report, the performance abnormal point can be accurately located, which helps the R&D or production department to quickly trace the fault cause and optimize the design process.

[0057] Other features and advantages of the present application will be further described in the following specification, and some will become apparent from the specification, or will be learned by practice of the present application. The purposes and other advantages of the present application can be realized and obtained by the structures specifically pointed out in this application document.

[0058] The technical solutions of the present application will be further described in detail below with the help of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0059] The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and are meant to explain the present application and are not intended to limit the application.

[0060] Figure 1 It is a structural diagram of a kind of automobile steering wheel horn button press endurance detection device in the embodiment of the application;

[0061] Figure 2 It is a structural diagram of the parameter setting module described in the embodiment of the application;

[0062] Figure 3 It is a flow chart of a kind of automobile steering wheel horn button press endurance detection method in the embodiment of the application. DETAILED DESCRIPTION

[0063] The preferred embodiments of the application are described below in conjunction with the accompanying drawings, it should be understood that the preferred embodiments described here are only used to illustrate and explain the application, and not to limit the application.

[0064] Example 1:

[0065] The embodiment of the application provides a kind of automobile steering wheel horn button press endurance detection device, as shown in Figure 1 It includes:

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

[0067] Press control module, for controlling driving mechanism to drive press mechanism to press automobile steering wheel horn button based on the press detection parameter;

[0068] Data acquisition module, for collecting performance parameter in the process of pressing automobile steering wheel horn button, to obtain performance data;

[0069] Endurance detection module, for detecting and evaluating press endurance based on performance data, to obtain detection report.

[0070] In this embodiment, the automobile steering wheel to be detected is installed on the steering wheel fixing mechanism, the position and angle of the steering wheel are adjusted, so that the horn button is directly below the press mechanism, to ensure that the press mechanism can accurately aim at the horn button.

[0071] In this embodiment, the press detection parameter includes press frequency, press strength, 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 for collecting various performance parameters of the horn button during the pressing process in real time, including the contact resistance, the trigger force change, the rebound time, the wear amount, the response time, the horn sounding state and the like.

[0073] In this embodiment, the mechanism characteristics of the pressing mechanism and the driving mechanism are, for example, the pressing stroke, the trigger force, the material characteristics and the like.

[0074] In this embodiment, by flexibly adjusting the pressing detection parameters, the structural differences of the horn button of the steering wheel of different brands and models of automobiles can be adapted, one machine can be used for multiple purposes, the equipment procurement cost of the enterprise is reduced, and the universality of the detection device is improved.

[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 and the like.

[0076] The beneficial effects of the above design scheme are as follows: based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the horn button of the steering wheel of an automobile, the pressing detection parameters of the horn button of the steering wheel of an automobile are set, the detection conditions are highly matched with the real use scene, the accuracy and reliability of the detection result are improved, based on the pressing detection parameters, the driving mechanism is controlled to drive the pressing mechanism to press the horn button of the steering wheel of an automobile, the pressing control module automatically completes the pressing action by driving the mechanism through a program, without manual intervention, compared with the traditional manual test, the detection efficiency can be greatly improved, the performance parameters in the pressing process of the horn button of the steering wheel of an automobile are collected, the performance data is obtained, the whole cycle performance data chain is formed, not only whether the button is invalid can be judged, but also the performance degradation trend can be analyzed, potential defects can be found in advance, the pressing durability is detected and evaluated based on the performance data, a detection report is obtained, the performance abnormal point can be accurately located, the fault reason can be quickly traced back for the research and development or production department, and the design process is optimized.

[0077] Embodiment 2:

[0078] Based on the basis of embodiment 1, the present embodiment provides a pressing durability detection device for a horn button of a steering wheel of an automobile, as shown in Figure 2 The parameter setting module comprises:

[0079] The scanning unit is used for scanning the pressing area of the horn button of the steering wheel of an automobile based on a miniature laser scanner, obtaining pressing three-dimensional point cloud data, and collecting multiple frames of driving three-dimensional point cloud data under dynamic conditions;

[0080] The feature extraction unit is used for automatically extracting initial mechanism characteristics based on a deep learning algorithm, from the pressing three-dimensional point cloud data and the multiple groups of driving three-dimensional point cloud data.

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

[0082] a graph obtaining unit configured to obtain a knowledge graph of vehicle model-structure-parameter-test result determined based on historical durability detection data;

[0083] a judging unit configured to compare the mechanism feature and other related parameters with the knowledge graph to determine whether there is a graph feature with a similarity greater than a preset similarity;

[0084] If yes, the pressing detection parameter for the horn button of the automobile steering wheel is determined based on the graph feature;

[0085] Otherwise, the pressing detection parameter for the horn button of the automobile steering wheel is determined based on the mechanism feature and in combination with a machine learning prediction model;

[0086] an updating unit configured to update the knowledge graph in real time based on the pressing detection parameter determined by the machine learning prediction model.

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

[0088] In this embodiment, based on the principles of material mechanics and kinematics, a parameter mapping formula can be developed, for example, F_dynamic=F_static x (1+k v 2 ), wherein F_dynamic represents the dynamic force, F_static represents the static trigger force, v represents the pressing speed, and k represents the spring stiffness.

[0089] In this embodiment, the three-dimensional point cloud data of the pressing area obtained by the micro laser scanner can accurately restore the micron-level geometric features such as the surface curvature of the button, the key cap stroke, and the trigger point position, thereby providing a high-precision physical model basis for subsequent parameter calculation.

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

[0091] In this embodiment, unlike the traditional static database, the knowledge graph of this scheme supports real-time updating and dynamic reasoning, and can adapt to the changing vehicle model design and material innovation.

[0092] In this embodiment, the material elastic modulus, Poisson's ratio, fatigue limit, and other attribute parameters are combined with geometric features, so that the parameter setting is more in line with the physical law.

[0093] The beneficial effects of the above design scheme are: by comparing the mechanism characteristics and other related parameters with the knowledge graph, it is determined whether there is a graph feature with a similarity greater than a preset similarity; if so, based on the graph feature, the pressing detection parameter of the automobile steering wheel horn button is determined; the efficiency of parameter setting is improved through the pre-determined knowledge graph, otherwise, based on the mechanism characteristics, a machine learning prediction model is combined to determine the pressing detection parameter of the automobile steering wheel horn button, ensuring the accuracy of the set pressing detection parameter, realizing accurate parameter setting, reducing material waste and equipment loss caused by excessive testing, and combining three-dimensional scanning, physical simulation and AI algorithm in the process of determining the button feature, realizing full-link automation from the physical object to the virtual model and then to the test parameter, breaking through the traditional parameter setting method based on empirical formula, realizing the conversion of the traditional parameter setting process relying on manual experience into a quantifiable, traceable and evolving intelligent decision system, and having wide engineering application prospects.

[0094] Embodiment 3:

[0095] Based on the basis of embodiment 2, the application provides an automobile steering wheel horn button pressing endurance detection device, in the judgment unit, based on the mechanism characteristics, a machine learning prediction model is combined to determine the pressing detection parameter of the automobile steering wheel horn button, comprising:

[0096] The simulation unit is used for inputting the mechanism characteristics into the simulation software, and obtaining a virtual button model based on the principles of material science and kinematics;

[0097] The setting unit is used for obtaining historical setting parameters matched with the virtual button model based on historical endurance detection data, and dividing the historical setting parameters into a plurality of setting parameter groups based on the frequency of occurrence of the historical setting parameters in the historical endurance detection data;

[0098] The model training unit is used for training a plurality of pre-training prediction models based on each setting parameter group and its historical detection result, determining the model weight based on the frequency of occurrence, fusing the plurality of pre-training prediction models based on the model weight, and obtaining a machine learning prediction model;

[0099] The compensation determination unit is used for obtaining change information of the material expansion coefficient and the humidity influence with temperature change from the historical endurance detection data, and establishing a dynamic environment compensation strategy based on the change information;

[0100] The correction determination unit is used for determining a dynamic correction strategy between the material fatigue parameter and the test parameter based on the material properties of the automobile steering wheel horn button;

[0101] The pressing simulation unit is used for inputting the virtual button model into a machine learning prediction model to obtain initial pressing detection parameters, determining the initial pressing detection parameters, simulating and predicting according to the initial pressing detection parameters, collecting temperature parameters and material parameters in the simulation process, and adjusting the initial pressing detection parameters in real time based on a dynamic environment compensation strategy and a dynamic correction strategy to obtain final pressing detection parameters.

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

[0103] In this embodiment, parameter groups are set by frequency clustering division to avoid one-sidedness of a single historical parameter. For example, when a parameter appears in historical data with a frequency of >70%, the weight is automatically increased, making the new parameter setting closer to the industry optimal practice.

[0104] In this embodiment, the frequency of occurrence of each set of setting parameters 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 fused, and each model focuses on the prediction of different numerical dimensions to improve the prediction accuracy of the model, especially when dealing with complex structures.

[0106] In this embodiment, the model weights are dynamically allocated based on the frequency of occurrence of historical data, so that the detection device can quickly adjust the decision strategy when facing new materials or structures. For example, when detecting a new type of silicone button, the training model that is good at handling elastic materials automatically increases its weight to 0.6, and the weight of the traditional metal model decreases to 0.3.

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

[0108] In this embodiment, the dynamic correction strategy realizes the simulation of performance degradation after long-term use in advance.

[0109] The beneficial effects of the above design scheme are: by determining the pressing detection parameters of the horn button of the automobile steering wheel based on the mechanism characteristics and combining the machine learning prediction model, the prediction accuracy is improved, the physical law and data-driven method are deeply combined, the theoretical rationality of parameter setting is guaranteed, and the actual effectiveness is improved through historical experience and real-time feedback, realizing intelligent and dynamic generation of the durability detection parameters of the horn button of the automobile steering wheel.

[0110] Embodiment 4:

[0111] Based on the basis of embodiment 3, the embodiment of the application provides a kind of automobile steering wheel horn button press endurance detection device, it is characterized in that, the correction determination unit, comprising:

[0112] First correction unit, for determining the material fatigue parameter of automobile steering wheel horn button, when the material fatigue parameter is greater than the first preset fatigue value, based on the difference of material fatigue parameter and the first preset fatigue value, determine test force increasing value;

[0113] Second correction unit, for when material fatigue parameter is greater than the second preset fatigue value, based on the difference of material fatigue parameter and the second preset fatigue value, determine test cycle extension value.

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

[0115] The beneficial effects of the above design scheme are: by determining the material fatigue parameter of automobile steering wheel horn button, when the material fatigue parameter is greater than the first preset fatigue value, based on the difference of material fatigue parameter and the first preset fatigue value, determine test force increasing value, when material fatigue parameter is greater than the second preset fatigue value, based on the difference of material fatigue parameter and the second preset fatigue value, determine test cycle extension value, realize the establishment of dynamic correction strategy.

[0116] Embodiment 5:

[0117] Based on the basis of embodiment 1, the embodiment of the application provides a kind of automobile steering wheel horn button press endurance detection device, and the press control module includes:

[0118] Signal determination unit, for determining the control signal of drive mechanism based on the press detection parameter;

[0119] Drive unit, for controlling drive mechanism to drive press mechanism to press automobile steering wheel horn button according to the control signal.

[0120] The beneficial effects of the above design scheme are: by controlling drive mechanism to drive press mechanism to press automobile steering wheel horn button according to the control signal, press control module is automatically completed by program drive mechanism press action, without manual intervention, compared with traditional manual test, can greatly improve detection efficiency.

[0121] Embodiment 6:

[0122] Based on the basis of embodiment 1, the embodiment of the application provides a kind of automobile steering wheel horn button press endurance detection device, and the data acquisition module includes:

[0123] The acquisition unit is configured to acquire sensor parameters during pressing of the horn button of the steering wheel of the automobile to obtain sensing parameters.

[0124] The data processing unit is configured to process the sensing parameters to obtain performance data.

[0125] The beneficial effects of the above design scheme are as follows: the sensor parameters during pressing of the horn button of the steering wheel of the automobile are acquired to obtain sensing parameters, the sensing parameters are processed to obtain performance data, and a full-cycle performance data chain is formed, so that it is not only possible to determine whether the button is invalid, but also possible to analyze the performance attenuation trend and find potential defects in advance.

[0126] Embodiment 7

[0127] Based on the basis of Embodiment 6, the present embodiment provides a durability detection device for pressing of a horn button of a steering wheel of an automobile, and the data processing unit comprises:

[0128] The cleaning unit is configured to clean data of the sensing parameters to obtain intermediate data.

[0129] The standardization unit is configured to standardize the intermediate data to obtain performance data.

[0130] The beneficial effects of the above design scheme are as follows: the data of the sensing parameters are cleaned to obtain intermediate data, and the intermediate data are standardized to obtain performance data, so that the quality of the obtained performance data is ensured, and high-quality data basis is provided for performance data analysis.

[0131] Embodiment 8

[0132] Based on the basis of Embodiment 1, the present embodiment provides a durability detection device for pressing of a horn button of a steering wheel of an automobile, and the durability detection module comprises:

[0133] The data division unit is configured to divide the performance data into multiple dimensions according to data types to obtain data groups in each dimension, and perform time alignment on the data groups in the multiple dimensions based on sensor acquisition time and transmission time to obtain a target data group.

[0134] The model establishment unit is configured to determine evaluation indexes of each evaluation dimension based on historical durability detection data and in combination with machine learning, and establish a separate evaluation model.

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

[0136] The weight distribution unit is further configured to determine an index weight of each evaluation index based on an influence of each evaluation index on each stage under the evaluation dimension;

[0137] The model fusion unit is configured to perform weighted fusion on all the single evaluation models based on the dimension weight and the index weight to obtain a comprehensive evaluation model at each stage;

[0138] The result analysis unit is configured to input performance data of different detection periods into the comprehensive evaluation model multiple times to obtain a time sequence detection result, and obtain a press durability decay curve of the steering wheel horn button based on the time sequence detection result.

[0139] The report generation unit is configured to generate a whole-cycle detection result report based on the time sequence detection result, generate a durability prediction evaluation report based on the press durability decay curve, and integrate the whole-cycle detection result report and the durability prediction evaluation report to obtain a final detection 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 performance, electrical performance, material characteristics, and comprehensive life. The evaluation indexes corresponding to the mechanical performance are trigger force decay rate, rebound time change rate, stroke loss rate, etc. The evaluation indexes corresponding to the electrical performance are, for example, contact resistance fluctuation range, conduction reliability, signal attenuation degree. The evaluation indexes corresponding to the material characteristics are, for example, surface wear depth, crack propagation rate, hardness change, etc. The evaluation indexes corresponding to the comprehensive life are, for example, residual life prediction based on Weibull distribution and failure probability curve.

[0142] In this embodiment, the six types of data types, i.e., mechanical, electrical, acoustic, etc., are divided, covering the mechanical response, electrical characteristics, material degradation, and environmental impact of the pressing process, and breaking through the limitation of traditional detection focusing on only a single index.

[0143] In this embodiment, based on the alignment of the sensor collection time and the transmission time, the time sequence misalignment problem caused by the sampling rate difference of multiple sources (such as a pressure sensor 1000 Hz vs. a vibration sensor 2000 Hz) is solved.

[0144] In this embodiment, the dimension weight is dynamically allocated according to the detection stage, which conforms to the actual decay law of the press durability. For example:

[0145] Early stage (first 10,000 presses): mechanical performance (trigger force, stroke) weight accounts for 60% (main factor is structure grinding)

[0146] Middle stage (10-50 thousand times): Electrical performance (contact resistance, conduction reliability) weight increases to 50% (main factor is contact oxidation)

[0147] Late stage (after 50 thousand times): Material properties (wear depth, crack propagation) weight accounts for 70% (main factor is material fatigue).

[0148] In this embodiment, the attenuation curve is generated by inputting different period data multiple times, realizing the leap from single-point detection to full-cycle tracking.

[0149] In this embodiment, unlike traditional fixed weight evaluation, the detection stage is first associated with dimension and index weight, which is more in line with the nonlinear attenuation law of press durability.

[0150] The beneficial effects of the above design scheme are: the durability detection module realizes precise evaluation and prediction of the press durability of the automobile steering wheel horn button through multi-dimensional data fusion, dynamic weight distribution and full-cycle time sequence analysis.

[0151] Embodiment 9:

[0152] Based on the basis of embodiment 8, the embodiment of the application provides a press durability detection device for an automobile steering wheel horn button, and the model fusion unit comprises:

[0153] The weighting unit is configured to weight all individual evaluation models based on the dimension weight to obtain a weighted model, and weight the weighted model based on the index weight to obtain a weighted individual evaluation model.

[0154] The fusion unit is configured 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 the dimension weight to obtain a weighted model, weighting the weighted model based on the index weight to obtain a weighted individual evaluation model, and fusing all weighted individual evaluation models to obtain a comprehensive evaluation model at each stage, the model for the press durability detection of the automobile steering wheel horn button is realized.

[0156] Embodiment 10:

[0157] The embodiment of the application provides a press durability detection method for an automobile steering wheel horn button, as shown in Figure 3 The method comprises the following steps:

[0158] S1: based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the automobile steering wheel horn button, setting the press detection parameters of the automobile steering wheel horn button;

[0159] S2: based on the pressing detection parameters, controlling the driving mechanism to drive the pressing mechanism to press the horn button of the automobile steering wheel;

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

[0161] S4: detecting and evaluating the pressing durability based on the performance data to obtain a detection report.

[0162] In this embodiment, the automobile steering wheel to be detected is installed on the steering wheel fixing mechanism, the position and angle of the steering wheel are adjusted, and the horn button is directly below the pressing mechanism to ensure that the pressing mechanism can accurately aim at the horn button.

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

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

[0165] In this embodiment, the mechanism characteristics of the pressing mechanism and the driving mechanism are, for example, pressing stroke, trigger force, material characteristics, etc.

[0166] In this embodiment, by flexibly adjusting the pressing detection parameters, the structural differences of the horn buttons of the steering wheels of different brands and models of automobiles can be adapted, one machine can be used for multiple purposes, the enterprise equipment procurement cost is reduced, and the universality of the detection device is improved.

[0167] In this embodiment, the detection and evaluation of the pressing durability based on the performance data are, for example, threshold comparison, trend fitting, life prediction model, etc.

[0168] The beneficial effects of the above design scheme are: through the mechanism characteristics of the pressing mechanism and the driving mechanism based on the automobile steering wheel horn button, the pressing detection parameters of the automobile steering wheel horn button are set, the detection conditions are highly matched with the real use scene, the accuracy and reliability of the detection result are improved, based on the pressing detection parameters, the driving mechanism is controlled to drive the pressing mechanism to press the automobile steering wheel horn button, the pressing control module automatically completes the pressing action through the program driving mechanism, without manual intervention, compared with the traditional manual test, the detection efficiency can be greatly improved, the performance parameters in the pressing process of the automobile steering wheel horn button are collected, the performance data is obtained, the whole cycle performance data chain is formed, not only whether the button is invalid can be judged, but also the performance attenuation trend can be analyzed, potential defects can be found in advance, the performance data is used for detecting and evaluating the pressing durability, the detection report is obtained, the performance abnormal point can be accurately positioned, the fault reason can be quickly traced back for the research and development or production department, and the design process is optimized.

[0169] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A device for testing the durability of a car steering wheel horn button press, characterized in that, The method comprises the following steps: A parameter setting module is configured to set a pressing detection parameter of a horn button of a steering wheel of a vehicle based on mechanism characteristics of a pressing mechanism and a driving mechanism of the horn button; A pressing control module is configured to control the driving mechanism to drive the pressing mechanism to press the horn button of the steering wheel of the vehicle based on the pressing detection parameter; A data acquisition module is configured to acquire performance parameters during pressing of the horn button of the steering wheel of the vehicle to obtain performance data; A durability detection module is configured to detect and evaluate pressing durability based on the performance data to obtain a detection report, which comprises: A data division unit is configured to divide the performance data into multiple dimensions according to data types to obtain data groups in each dimension, and to perform time alignment on the data groups in the multiple dimensions based on sensor acquisition time and transmission time to obtain target data groups; A model establishment unit is configured to determine evaluation indexes of each evaluation dimension based on historical durability detection data and machine learning, and to establish separate evaluation models; A weight allocation unit is configured to divide a detection process into an early stage, a middle stage and a late stage, to determine an influence of each evaluation dimension on each stage based on historical durability detection data, and to determine a dimension weight of each evaluation dimension in each stage based on the influence; The weight allocation unit is further configured to determine an index weight of each evaluation index based on an influence of each evaluation index on each stage in the evaluation dimension; A model fusion unit is configured to perform weighted fusion on all separate evaluation models based on the dimension weight and the index weight to obtain a comprehensive evaluation model in each stage; A result analysis unit is configured to input performance data in different detection periods into the comprehensive evaluation model multiple times to obtain time sequence detection results, and to obtain a pressing durability decay curve of the horn button of the steering wheel of the vehicle based on the time sequence detection results; A report generation unit is configured to generate a full-cycle detection result report based on the time sequence detection results, to generate a durability prediction evaluation report based on the pressing durability decay curve, and to integrate the full-cycle detection result report and the durability prediction evaluation report to obtain a final detection report.

2. The device according to claim 1, wherein The parameter setting module comprises: A scanning unit is configured to scan a pressing area of the horn button of the steering wheel of the vehicle based on a micro laser scanner to obtain pressing three-dimensional point cloud data, and to perform multi-frame acquisition on the driving mechanism in a dynamic situation to obtain multiple sets of driving three-dimensional point cloud data; A feature extraction unit is configured to perform automatic feature extraction on 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 characteristics; A feature supplement unit is configured to supplement the initial mechanism characteristics based on material attribute parameters of the pressing mechanism and the driving mechanism to obtain mechanism characteristics; A graph obtaining unit is configured to obtain a knowledge graph of vehicle type-structure-parameter-test result determined based on historical durability detection data; A judgment unit is configured to compare the mechanism characteristics and other related parameters with the knowledge graph to determine whether there is a graph feature with a similarity greater than a preset similarity; If yes, the pressing detection parameter of the horn button of the steering wheel of the vehicle is determined based on the graph feature. Otherwise, based on the mechanism characteristics, a press detection parameter of a horn button of a steering wheel of a vehicle is determined in combination with a machine learning prediction model; An updating unit is configured to update the knowledge graph in real time based on the press detection parameter determined by the machine learning prediction model.

3. The device according to claim 2, wherein In the determining unit, based on the mechanism characteristics, a press detection parameter of a horn button of a steering wheel of a vehicle is determined in combination with a machine learning prediction model, including: A simulation unit is configured to input the mechanism characteristics into simulation software, and obtain a virtual button model based on principles of material science and kinematics; A setting unit is configured to obtain historical setting parameters matched with the virtual button model based on historical durability detection data, and divide the historical setting parameters into a plurality of setting parameter groups based on frequencies of the historical setting parameters in the historical durability detection data; A model training unit is configured to train a plurality of pre-training prediction models based on each setting parameter group and its historical detection result, determine model weights based on the frequencies, fuse the plurality of pre-training prediction models based on the model weights, and obtain a machine learning prediction model; A compensation determining unit is configured to obtain change information of a material expansion coefficient and a humidity influence with temperature change from the historical durability detection data, and establish a dynamic environment compensation strategy based on the change information; A correction determining unit is configured to determine a dynamic correction strategy between a material fatigue parameter and a test parameter based on material properties of the horn button of the steering wheel of the vehicle; A press simulation unit is configured to input the virtual button model into the machine learning prediction model to obtain an initial press detection parameter, simulate and predict according to the initial press detection parameter, collect temperature parameters and material parameters in the simulation process, and adjust the initial press detection parameter in real time based on the dynamic environment compensation strategy and the dynamic correction strategy to obtain a final press detection parameter.

4. The device according to claim 3, wherein The correction determining unit includes: A first correction unit is configured to determine a material fatigue parameter of the horn button of the steering wheel of the vehicle, and determine a test force increase value based on a difference between the material fatigue parameter and a first preset fatigue value when the material fatigue parameter is greater than the first preset fatigue value; A second correction unit is configured to determine a test period extension value based on a difference between the material fatigue parameter and a second preset fatigue value when the material fatigue parameter is greater than the second preset fatigue value.

5. The device for detecting durability of pressing of a horn button of a steering wheel of a vehicle according to claim 1, characterized in that, The press control module includes: A signal determining unit is configured to determine a control signal of a driving mechanism based on the press detection parameter; A driving unit is configured to control the driving mechanism to drive a pressing mechanism to press the horn button of the steering wheel of the vehicle according to the control signal.

6. The device for detecting durability of pressing of a horn button of a steering wheel of a vehicle according to claim 1, characterized in that, The data collection module includes: A collection unit is configured to collect sensor parameters in a pressing process of the horn button of the steering wheel of the vehicle to obtain sensing parameters; A data processing unit is configured to process the sensing parameters to obtain performance data.

7. The device according to claim 6, wherein The data processing unit includes: A cleaning unit is configured to clean the sensing parameters to obtain intermediate data; A standardization unit is configured to standardize the intermediate data to obtain the performance data.

8. The device according to claim 1, wherein The model fusion unit includes: The weighting unit is configured to perform model weighting on all the individual evaluation models based on dimension weights to obtain weighted models, and perform weighting on the weighted models based on index weights to obtain weighted individual evaluation models; The fusion unit is configured to fuse all the weighted individual evaluation models to obtain comprehensive evaluation models at each stage.

9. A method for detecting the durability of pressing of a horn button of a steering wheel of an automobile, particularly for use in the durability detection device for pressing of a horn button of a steering wheel of an automobile according to claim 1, characterized by, The method comprises the following steps: S1: based on the mechanism characteristics of the pressing mechanism and the driving mechanism of the automobile steering wheel horn button, set the pressing detection parameters of the automobile steering wheel horn button; S2: based on the pressing detection parameters, control the driving mechanism to drive the pressing mechanism to press the automobile steering wheel horn button; S3: collect performance parameters during the pressing process of the automobile steering wheel horn button to obtain performance data; S4: detect and evaluate the pressing endurance based on the performance data to obtain a detection report.

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