Detection method and device for automobile skylight sealing strip

Abstract

The invention discloses a detection method and device for an automobile skylight sealing strip, and relates to the related technical field of sealing strip detection, and the method comprises the steps: constructing a sealing strip critical failure scene library; determining a to-be-detected automobile skylight sealing strip, reading user driving coverage information, executing failure scene adaptive adjustment, and generating an adaptive critical failure scene library; simulation is carried out through a closed simulation cabin, detection is carried out through a leakage sensor in a vehicle cabin, and a simulation detection data set is generated; sealing verification is carried out through the simulation detection data set, and prediction and reminding of sealing strip replacement are carried out in combination with a sealing verification result. The technical problems that in the prior art, a detection scene cannot fit the actual driving working condition of a user, and it is difficult to dynamically monitor the full life cycle performance change of the sealing strip, so that the potential failure risk cannot be warned in advance, and the sealing detection accuracy is poor are solved. The technical effects of improving the detection accuracy and realizing the early warning of the failure risk of the sealing strip are achieved.

Description

Technical Field

[0001] This application relates to the technical field of sealing strip testing, specifically to a testing method and apparatus for automotive sunroof sealing strips. Background Technology

[0002] The performance of automotive sunroof sealing strips directly affects a vehicle's waterproofing, dustproofing, sound insulation, and air leakage prevention. Long-term exposure to complex and changing environments exposes them to multiple challenges, including drastic temperature fluctuations, UV radiation, rain erosion, and mechanical vibration, making them prone to aging, deformation, and cracking. This can lead to problems such as water leakage and increased noise inside the vehicle. Traditional testing of automotive sunroof sealing strips relies on static performance tests and simulations under limited conditions, which cannot fully cover the extreme environments and complex scenarios encountered by users during actual driving. Different driving habits, regional climate conditions (such as hot and humid southern regions, cold and snowy northern regions, and windy and dusty northwest regions), and road conditions (urban congestion, highway driving, and bumpy mountain roads) all subject the sealing strips to varying degrees of stress and environmental effects. Furthermore, there is a lack of dynamic monitoring and prediction capabilities for the performance changes of the sealing strips throughout their entire lifecycle. Replacement is often only undertaken after obvious failure symptoms appear, failing to provide early warning of potential risks, resulting in increased maintenance costs and a decreased user experience.

[0003] Therefore, current technologies suffer from several technical problems: the detection scenarios cannot match actual driving conditions, it is difficult to dynamically monitor the performance changes of the sealing strip throughout its entire life cycle, resulting in the inability to provide early warnings of potential failure risks and poor accuracy in sealing detection. Summary of the Invention

[0004] This application provides a testing method and apparatus for automotive sunroof sealing strips, which solves the technical problems in the prior art where the testing scenarios cannot match the actual driving conditions of users, it is difficult to dynamically monitor the performance changes of the sealing strip throughout its entire life cycle, resulting in the inability to provide early warning of potential failure risks and poor sealing detection accuracy. It achieves the technical effect of improving detection accuracy and realizing early warning of sealing strip failure risks.

[0005] This application provides a method for detecting automotive sunroof sealing strips. The method includes: constructing a critical failure scenario library for the sealing strip; determining the automotive sunroof sealing strip to be inspected and reading the user's driving coverage information; starting from the critical failure scenario library, performing failure scenario adaptation adjustment to generate an adapted critical failure scenario library; simulating each scenario in the adapted critical failure scenario library through a pre-built closed simulation chamber and using a leakage sensor in the vehicle cabin for detection to generate a simulated detection dataset; performing a seal verification using the simulated detection dataset, and predicting and reminding users to replace the sealing strip based on the seal verification results.

[0006] In a possible implementation, the detection method for automotive sunroof sealing strips further includes the following processing: the leakage sensor includes a micro-pressure sensor and a humidity / water droplet sensing sensor.

[0007] In a possible implementation, the detection method for automotive sunroof sealing strips further includes the following steps: constructing a scene element matrix, including wind vector, rain vector, and vehicle driving posture vector; collecting historical sealing failure scenarios, assigning element values ​​to the scene element matrix as a reference, and constructing a scene feature matrix set; and generating the sealing strip critical failure scenario library using the scene feature matrix set.

[0008] In a possible implementation, the detection method for automotive sunroof sealing strips also performs the following processing: element assignment includes continuous quantitative assignment, continuous variable assignment, and instantaneous fluctuation assignment.

[0009] In a possible implementation, the detection method for automotive sunroof sealing strips also performs the following processing: the enclosed simulation chamber includes a multi-axis directional fan array, a spray rain curtain system, a dynamic attitude simulation platform, and a terminal controller.

[0010] In a possible implementation, the detection method for automotive sunroof sealing strips further includes the following steps: performing trigger probability analysis on each scenario in the sealing strip critical failure scenario library based on the user's driving coverage information, generating trigger probabilities for each scenario; adding first-class scenarios with probabilities greater than or equal to preset probabilities to the adaptive critical failure scenario library based on the trigger probabilities of each scenario; starting with second-class scenarios with probabilities less than preset probabilities, gradually reducing the feature values ​​of elements within the scenario based on a preset adjustment step size, and performing trigger probability analysis after each adjustment until the adjusted trigger probability is greater than or equal to the preset probability; adding the scenario at that time to the adaptive critical failure scenario library after passing the conflict verification with the first-class scenarios.

[0011] In a possible implementation, the detection method for the car sunroof sealing strip also performs the following processing: collecting a historical weather record dataset constrained by the user's driving coverage information; filtering the proportion of records in the historical weather record dataset whose weather feature similarity to each scenario is greater than or equal to a preset similarity threshold, and generating the trigger probability of each scenario.

[0012] In a possible implementation, the detection method for automotive sunroof sealing strips further performs the following processing: determining whether there is a sealing leak point based on the simulated detection dataset, and generating the sealing verification result; if the sealing verification result shows that there is a leak point, directly reminding the user to replace the sealing strip; if the sealing verification result shows that there is no leak point, predicting the sealing performance retention of the automotive sunroof sealing strip to be inspected, and reminding the user to replace the sealing strip based on the performance retention period.

[0013] In a possible implementation, the detection method for automotive sunroof sealing strips further includes the following processing: acquiring an image of the sealing strip to be inspected and comparing it with a sequence of standard sealing strip performance degradation images to generate a first prediction result; performing real-time material hardness acquisition on the automotive sunroof sealing strip to be inspected, performing elastic recovery analysis based on the real-time material hardness, and generating an elastic recovery level; optimizing the first prediction result with the elastic recovery level to generate a predicted sealing performance retention time.

[0014] This application also provides a detection device for automotive sunroof sealing strips, the device comprising: a scenario library construction module for constructing a critical failure scenario library for sealing strips; an adaptation adjustment module for determining the automotive sunroof sealing strip to be inspected and reading the user's driving coverage information, and performing failure scenario adaptation adjustment starting from the critical failure scenario library for sealing strips to generate an adaptation critical failure scenario library; a simulation detection module for simulating each scenario in the adaptation critical failure scenario library through a pre-built closed simulation chamber, and using a leakage sensor in the vehicle cabin for detection to generate a simulation detection dataset; and a seal verification module for performing seal verification using the simulation detection dataset, and predicting and reminding users to replace the sealing strip based on the seal verification results.

[0015] This application proposes a detection method and apparatus for automotive sunroof sealing strips, which aims to construct a critical failure scenario library for the sealing strips. The method involves identifying the sunroof sealing strip to be inspected and reading the user's driving coverage information, performing failure scenario adaptation adjustments, and generating an adapted critical failure scenario library. Simulations are conducted in a closed simulation chamber, and leak sensors within the vehicle cabin are used for detection, generating a simulated detection dataset. The simulated detection dataset is then used for seal verification, and the results are combined to predict and remind users of sealing strip replacement needs. This addresses the technical problems in existing technologies, such as detection scenarios failing to match actual user driving conditions, difficulty in dynamically monitoring the performance changes of the sealing strip throughout its lifecycle, resulting in an inability to provide early warnings of potential failure risks and poor accuracy in seal detection. The method achieves the technical effect of improving detection accuracy and providing early warnings of sealing strip failure risks. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a schematic flowchart of a testing method for automotive sunroof sealing strips provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the detection device for automotive sunroof sealing strips provided in an embodiment of this application.

[0019] Figure labeling: Scene library construction module 10, adaptation and adjustment module 20, simulation detection module 30, sealing verification module 40. Detailed Implementation

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or apparatuses. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides a method for detecting automotive sunroof sealing strips, such as... Figure 1 As shown, the method includes: Step S100: Construct a library of critical failure scenarios for sealing strips.

[0024] Furthermore, step S100 also includes step S110, constructing a scene element matrix, including wind vector, rain vector and vehicle driving posture vector; step S120, collecting historical sealing failure scenarios, using the scene element matrix as a reference, performing element assignment, and constructing a scene feature matrix set; step S130, generating the sealing strip critical failure scenario library using the scene feature matrix set.

[0025] Step S120 further includes performing element assignment, including continuous quantitative assignment, continuous variable assignment, and instantaneous fluctuation assignment.

[0026] Preferably, wind vector, rain vector, and vehicle driving posture vector are identified as scene elements to construct a scene element matrix. The wind vector includes variables such as wind speed, wind direction, and wind pressure. The continuous impact of strong winds on the sealing strip during high-speed driving, as well as the multi-angle pressure caused by sudden changes in wind direction, accelerates the fatigue and wear of the sealing strip. In the rain vector, different rainfall patterns such as slanting rain, heavy rain, and intermittent rain change the direction and scouring force of water flow, testing the waterproof sealing performance of the sealing strip. The vehicle driving posture vector includes changes in posture such as uphill, downhill, and side tilt, which not only alter the guiding path of water flow on the sunroof surface but also subject the sealing strip to uneven stress. Furthermore, historical sealing failure scenarios are collected from after-sales maintenance records of car manufacturers, user feedback, actual road tests, and simulation experiments. For example, records of sunroof sealing strip leaks under strong winds and rain at speeds exceeding 80 km / h, or instances of sealing strip deformation and wear when the vehicle tilts due to bumpy roads, are collected.

[0027] Preferably, each element is assigned a value based on the scene element matrix. This involves transforming the abstract scene into quantifiable and analyzable data through continuous quantitative assignment, continuous variable assignment, and instantaneous fluctuation assignment. Continuous quantitative assignment refers to assigning definite, fixed values ​​to the scene element attributes. Continuous variable assignment refers to assigning variable values ​​to the scene element attributes that change continuously within a range. Instantaneous fluctuation assignment is used to assign values ​​to the scene element attributes that change rapidly and irregularly within a very short time, in order to capture those special situations or sudden changes that occur instantly. Specifically, for scenarios with strong winds and rain (wind speed > 60 km / h), the wind speed element is continuously and quantitatively assigned, setting a specific numerical range greater than 60 km / h. For example, the wind speed is 65 km / h or 75 km / h, the wind direction angle is 135° (northwest wind), and the wind pressure is recorded as 2000 Pa and 2500 Pa. For sudden changes in wind direction (multi-angle impact), continuous variable assignment is used to describe the changes in wind direction between different angles. For instantaneous wind pressure fluctuations (such as high-speed vehicles passing by or wind tunnel effects), instantaneous fluctuation assignment is used to reflect the rapid changes in wind pressure in a short period of time.

[0028] Preferably, each element in both the rain vector and the vehicle driving attitude vector is assigned a value based on the actual scenario. Specifically, the value is assigned according to the rainfall amount per unit time. For example, the rainfall intensity of light rain may be between 1 and 10 mm / h, moderate rain is 10 to 25 mm / h, heavy rain is 25 to 50 mm / h, and torrential rain is greater than 50 mm / h. If it is intermittent rain, in addition to recording the rainfall intensity of each rainfall, the time interval between the intervals also needs to be recorded. For example, if each rainfall lasts for 30 minutes, the rainfall intensity is 30 mm / h, and the interval time is 1 hour, the rainfall angle is also recorded, which is the angle between the direction of the raindrops falling and the vertical direction. When there is no wind, the rainfall angle is close to 0°, that is, falling vertically. When there is strong wind, the rainfall angle will increase. The values ​​can be assigned based on the actual speed of the vehicle, or by using the slope angle of the road (uphill / downhill). Generally, the slope of urban roads is relatively small, around 1° to 3°; the slope of mountain roads may be larger, with uphill angles reaching 10° to 20°, and downhill angles similar. The values ​​can also be assigned by using the tilt angle of the car body relative to the horizontal plane during driving. Vehicles may tilt when turning, driving on bumpy roads, or encountering crosswinds. For example, if a vehicle is traveling at 60 km / h on a curve with a radius of 50 meters, the tilt angle of the vehicle body can be calculated to be 5° to 10° according to the centripetal force formula.

[0029] Preferably, a set of scene feature matrices is constructed based on the assignment of values ​​to each element under different scenarios. Each scene feature matrix represents a critical failure scenario. For example, "strong winds and rain (wind speed > 60 km / h), sudden wind direction changes (multi-angle impact), instantaneous wind pressure fluctuations, sloping rain, and vehicle uphill posture" are combined to form a scene feature matrix, which describes multiple scenarios that may lead to critical failure of the sealing strip under specific wind, rain, and vehicle driving posture conditions. Then, based on the set of scene feature matrices, the sealing strip failure mode corresponding to each scene feature matrix is ​​determined, that is, different combinations of wind, rain, and vehicle driving posture are determined to cause the sealing strip to reach a critical failure state, thereby forming a critical failure scenario library for sealing strips. By analyzing a large number of scenarios in the library, we can better understand the performance of sealing strips under various complex working conditions, and thus take corresponding measures to improve the reliability and durability of sealing strips.

[0030] Step S200: Determine the sunroof sealing strip of the car to be inspected and read the user's driving coverage information. Starting from the critical failure scenario library of the sealing strip, perform failure scenario adaptation adjustment to generate an adapted critical failure scenario library.

[0031] Preferably, sunroof sealing strips for different car models vary in material, structure, and installation method. First, determine the sunroof sealing strip of the car to be tested, and then collect relevant information about the user's daily driving, including but not limited to the driving area, road type, common driving environment, and driving habits. For example, whether the user mainly drives on urban roads or frequently drives on highways and rural roads; the climate characteristics of the driving area, whether it is a rainy area, a windy area, or an area with large temperature differences; and whether the user has driving habits such as rapid acceleration, sudden braking, or frequent lane changes.

[0032] Preferably, based on the user's driving coverage information, relevant scenarios are selected from the critical failure scenario library of the sealing strip. For example, if the user mainly drives in rainy areas, the focus is on rain-related scenarios in the library, such as sealing strip failure scenarios under different rain intensities, different raindrop sizes, and different rainfall angles. If the user frequently drives at high speeds on highways, the impact of wind on the sealing strip during high-speed driving is considered, such as strong winds and crosswinds.

[0033] Preferably, for the selected relevant scenarios, the parameters in the scenario are adjusted according to the user's driving information. For example, if the common wind speed in the user's area is 30~50km / h, and there is a strong wind scenario with a wind speed of 70km / h in the original scenario library, the wind speed parameter in that scenario is adjusted to a range that matches the user's actual driving environment. Other relevant parameters, such as wind pressure and the force of wind on the sealing strip, are also adjusted accordingly. At the same time, the elements in the vehicle driving posture vector are adjusted based on the user's driving habits, such as the impact of changes in the vehicle's posture during rapid acceleration or braking on the sealing strip.

[0034] Preferably, the system analyzes the user's driving coverage information to identify any special situations or unique driving scenarios not included in the original critical failure scenario library for the sunroof seals. For example, if a user frequently drives on rural roads with poor conditions, experiencing significant bumps and vibrations, the original scenario library is supplemented with bump and vibration-related scenarios, and corresponding parameters and failure modes are determined to more comprehensively adapt to the user's actual driving conditions. After failure scenario adaptation and adjustment, the adjusted and supplemented scenarios are organized to form a new adapted critical failure scenario library. This library more accurately reflects the various critical failure scenarios that the sunroof seals used by the user may face during actual driving, thereby better ensuring the sunroof seals have good sealing performance and reliability in the user's actual usage environment, reducing the risk of failure, and improving the user experience.

[0035] Furthermore, step S200 also includes step S210, which analyzes the trigger probability of each scenario in the critical failure scenario library of the sealing strip based on the user driving coverage information, and generates the trigger probability of each scenario; step S220, which adds the first type of scenario with a probability greater than or equal to a preset probability to the adaptation critical failure scenario library based on the trigger probability of each scenario; step S230, which starts with the second type of scenario with a probability less than the preset probability, and performs a gradual reduction adjustment of the feature value of the element in the scenario based on a preset adjustment step size, and performs a trigger probability analysis after each adjustment, until the adjusted trigger probability is greater than or equal to the preset probability, and adds the scenario at that time to the adaptation critical failure scenario library after the conflict verification with the first type of scenario passes.

[0036] Preferably, based on user driving coverage information, a trigger probability analysis is performed on each scenario in the critical failure scenario library for the sealing strip. The user driving coverage information includes driving area (e.g., urban, rural, mountainous), driving environment (e.g., high temperature, high humidity, windy conditions), and driving habits (e.g., frequency of rapid acceleration and braking, speed range). Specifically, the probability of each scenario in the critical failure scenario library occurring during actual user driving is analyzed. For example, if the user mainly drives in congested urban traffic, the trigger probability of scenarios related to high-speed driving may be low; while the trigger probability of scenarios related to frequent starts and stops and low-speed driving may be lower. The probability of triggering a scenario is likely to be high. Each scenario is assigned a trigger probability value, which represents the likelihood of the scenario actually occurring under the user's driving conditions. For example, for a scenario describing the failure of the sealing strip under the combined effects of strong winds (wind speed greater than 80 km / h) and heavy rain, if the user rarely drives under such extreme weather conditions, the trigger probability of this scenario may be set to a low value, such as 0.1 (i.e., 10% probability). For a scenario describing ordinary rainy days (moderate rain intensity) and normal driving speed, if the user's area frequently experiences rain and the driving speed matches the scenario setting, the trigger probability may be high, such as 0.6 (i.e., 60% probability).

[0037] Preferably, a preset probability, such as 0.4 (i.e. 40%), is configured based on actual needs and experience to filter out scenarios that are more likely to occur during user driving. Then, the trigger probability of each scenario is compared with the preset probability. If the trigger probability of a certain scenario is greater than or equal to the preset probability, it is classified as a first-class scenario, which has a high probability of occurrence in the user's actual driving and has a significant impact on the performance of the sealing strip. It is then added to the adaptation critical failure scenario library.

[0038] Preferably, for the second type of scenario where the trigger probability is less than the preset probability, the element feature values ​​within the scenario are adjusted according to a preset adjustment step size to better match the user's actual driving situation. The preset adjustment step size is a value pre-set to control the magnitude of change in the element feature values. For example, for a scenario describing strong winds (wind speed 100km / h) and large-angle crosswinds (crosswind angle 60°), the trigger probability is low. Assuming the preset adjustment step size is a 10km / h reduction in wind speed and a 10° reduction in crosswind angle, the wind speed is adjusted to 90km / h and the crosswind angle to 50°. Then, the trigger probability of the adjusted scenario is analyzed again based on the user's driving coverage information. This adjustment and analysis is repeated until the trigger probability of the adjusted scenario is greater than or equal to the preset probability. Finally, the scenarios whose trigger probabilities meet the requirements are compared with the first type of scenario for conflict verification. That is, it is checked whether there are any contradictions or unreasonable aspects between these scenarios. If the verification passes, it means that the scenario is logically reasonable and can fully reflect the situations that may occur during the user's driving process. It is then added to the adaptation critical failure scenario library. The final adaptation critical failure scenario library contains scenarios with different trigger probabilities, more accurately simulating the various working conditions that sunroof sealing strips may face during actual driving.

[0039] Furthermore, step S210 also includes step S211, collecting a historical weather record dataset based on the user's driving coverage information; step S212, filtering the proportion of records in the historical weather record dataset whose weather feature similarity to each scenario is greater than or equal to a preset similarity threshold, and generating the trigger probability of each scenario.

[0040] Preferably, the user's driving coverage information is used as a constraint. That is, the data collection range is determined based on the driving area (such as a specific city, region or different terrain area), driving time period (such as daytime, nighttime, different seasons, etc.), and driving route characteristics (such as urban roads, highways, mountain roads, etc.). Then, historical wind and rain weather data related to the user's driving coverage information are collected through meteorological monitoring data, professional meteorological databases, local meteorological station records, etc., including different weather characteristic parameters, such as wind speed, wind direction, rainfall intensity, rainfall duration, etc. For example, historical wind and rain weather records are collected, including wind speeds varying between 10 and 30 km / h on different dates, and rainfall intensities ranging from drizzle (rainfall intensity less than 10 mm / h) to moderate rain (rainfall intensity 10 to 25 mm / h), etc., to form a historical wind and rain weather record dataset.

[0041] Preferably, for each scenario in the adaptive critical failure scenario library, its weather characteristic parameters, such as wind speed range, wind direction angle, and rainfall intensity range, are defined. Then, each record in the historical wind and rain weather record dataset is compared with the weather characteristics of each scenario. Euclidean distance is used to calculate the similarity, and a preset similarity threshold is set, such as 0.6 (representing 60% similarity). Records with a similarity to the weather characteristics of each scenario greater than or equal to the preset similarity threshold are selected. Finally, the number of selected records is counted, and their proportion in the entire historical wind and rain weather record dataset is calculated. For example, if the historical wind and rain weather record dataset has 100 records, and 30 records meet the similarity requirement for the strong wind and rain scenario, then the record proportion for this scenario is 30%. This record proportion is used as the trigger probability of this scenario, that is, the probability of this strong wind and rain scenario occurring during actual driving is 30%. By analyzing and calculating each scenario in the adaptive critical failure scenario library, the trigger probability of each scenario is generated to facilitate performance evaluation and failure prediction of the sealing strip.

[0042] Step S300: Simulate each scenario in the adaptation critical failure scenario library using a pre-built closed simulation chamber, and use the leakage sensor in the cabin for detection to generate a simulation detection dataset.

[0043] Step S300 further includes the leakage sensor comprising a micro-pressure sensor and a humidity / water droplet sensing sensor.

[0044] Step S300 further includes the following: the enclosed simulation chamber includes a multi-axis directional fan array, a spray rain curtain system, a dynamic attitude simulation platform, and a terminal controller.

[0045] Preferably, each scenario in the adaptive critical failure scenario library is simulated through a pre-built closed simulation chamber. The closed simulation chamber includes a multi-axis variable-direction fan array, a spray rain curtain system, a dynamic attitude simulation platform, and a terminal controller. Specifically, the multi-axis variable-direction fan array can simulate winds of different directions and intensities, that is, change the wind direction in multiple dimensions, such as horizontal, vertical, and combinations of various angles, to simulate various wind conditions that may be encountered during actual driving. It can also adjust the intensity of the fans to meet the simulation requirements of wind in different scenarios, including from light breeze to strong wind. For example, to simulate a scenario of strong wind and rain, the multi-axis variable-direction fan array simulates a strong wind with a wind speed of 80km / h and a wind direction of 45° (northeast wind) to simulate the effect of strong wind on the sealing strip in reality.

[0046] Preferably, the spray rain curtain system is used to simulate different types of rainfall scenarios, including controlling parameters such as rain intensity, raindrop size, and rainfall angle. For example, it can simulate various rainfall states such as heavy rain, light rain, and slanting rain. Based on the rain vector settings in the adaptive critical failure scenario library, it can accurately simulate corresponding rainfall conditions, such as simulating a heavy rain scenario with a rain intensity of 100 mm / h, providing a realistic simulation environment for testing the sealing performance of the sealing strip under different rainfall conditions. The dynamic attitude simulation platform can simulate various postures of a car during driving, including uphill, downhill, side tilt, and bumps. That is, by changing parameters such as the platform's angle, vibration frequency, and amplitude, it can simulate the car's attitude changes under different road conditions. For example, it can simulate a car operating at an uphill angle of 15° and a side tilt angle of 10° to analyze the performance of the sunroof sealing strip under this posture. The terminal controller is the control core of the entire enclosed simulation chamber, used to coordinate the multi-axis variable-direction fan array, the spray rain curtain system, and the dynamic attitude simulation platform. Based on the scenario settings in the adaptive critical failure scenario library, it accurately controls the operating parameters of each device to ensure that the simulated scenario matches the actual situation.

[0047] Preferably, simulations are performed using a closed simulation chamber based on the parameters set for each scenario in the adaptive critical failure scenario library. For example, for a scenario set with a wind speed of 60 km / h, a rainfall intensity of 50 mm / h, and a vehicle tilt angle of 8°, the terminal controller controls a multi-axis directional fan array to generate a wind speed of 60 km / h, the spray rain curtain system simulates rainfall with a rainfall intensity of 50 mm / h, and the dynamic attitude simulation platform simulates the vehicle's tilt angle of 8°, thereby simulating the scenario; then, leakage sensors inside the vehicle cabin are used for detection.

[0048] Preferably, the leakage sensor includes a micro-pressure sensor and a humidity / water droplet sensing sensor. The micro-pressure sensor detects pressure changes inside the vehicle cabin. If the sunroof sealing strip has a sealing problem during the scenario simulation, it may cause changes in the pressure inside the vehicle cabin. The micro-pressure sensor can accurately detect and record the pressure change data. The humidity / water droplet sensing sensor detects humidity changes inside the vehicle cabin and whether water droplets are entering. If the sealing strip cannot seal effectively during a simulated rain scenario, and rainwater enters the vehicle cabin, the humidity / water droplet sensing sensor can detect the increase in humidity and the presence of water droplets, and record the relevant data, thereby judging the sealing performance of the sealing strip. By using the leakage sensor to detect each simulation scenario, pressure change data, humidity change data, and water droplet detection data inside the vehicle cabin are obtained. These sensor detection data are compiled into a simulation test dataset, which includes the performance data of the sunroof sealing strip under different scenarios. By analyzing the data from different scenarios in the simulation test dataset, the sealing performance of the sealing strip under different working conditions can be determined, the scenarios and factors that are prone to causing sealing strip failure can be identified, and the design of the sealing strip can be improved to enhance its reliability.

[0049] Step S400: Perform seal verification using the simulated detection dataset, and predict and remind users to replace the sealing strip based on the seal verification results.

[0050] Step S400 further includes step S410, determining whether there is a sealing leak point based on the simulated detection dataset, and generating the sealing verification result; step S420, if the sealing verification result shows that there is a leak point, directly reminding the user to replace the sealing strip; step S430, if the sealing verification result shows that there is no leak point, predicting the sealing performance of the sunroof sealing strip under inspection, and reminding the user to replace the sealing strip based on the performance maintenance cycle.

[0051] Preferably, a simulated test dataset is used for seal verification. This dataset includes data collected by leakage sensors inside the vehicle cabin during simulations of various scenarios from a library of adaptive critical failure scenarios in a closed simulation chamber. Specifically, the simulated test data is analyzed to determine whether there are abnormal pressure fluctuations (such as sudden increases or decreases in pressure), abnormal increases in humidity, or the detection of water droplets inside the cabin when simulating different scenarios. Based on the data analysis results, it is determined whether there is a seal leak. If there are abnormal pressure changes inside the cabin, such as a sudden increase in pressure exceeding the normal range during a simulated strong wind scenario, or if the humidity / water droplet sensing sensor detects water droplets entering the cabin, a seal leak is identified. Conversely, if all data are within the normal range, the pressure inside the cabin is stable, the humidity does not increase abnormally, and no water droplets are detected, no seal leak is identified. The judgment results are then compiled into a seal verification result to clearly indicate whether there is a seal leak in the sunroof sealing strip of the vehicle under test during the simulated test.

[0052] Preferably, when the sealing test results show a leak, it indicates that the sunroof sealing strip of the car under test cannot effectively seal under simulated actual working conditions, and there is a sealing performance problem. This may lead to water leakage, air leakage, etc. in actual use, affecting the comfort and safety of the vehicle. In this case, a reminder message for sealing strip replacement will be sent directly to the user. The message may include relevant information such as the model and specifications of the sealing strip, as well as the necessity and urgency of replacing the sealing strip, so that it can be replaced in time to solve the sealing leakage problem.

[0053] Preferably, when the sealing test results show no leakage points, it indicates that the sunroof sealing strip under test has good sealing performance in the current simulated scenario. However, considering that the sealing strip is affected by various factors (such as environmental factors, vehicle driving vibration, etc.) during actual use, its sealing performance will gradually decline over time. Therefore, based on the simulated test dataset, relevant material performance data, and actual usage experience, the sealing performance of the sealing strip is predicted. For example, the wear and aging rate of the sealing strip under different scenarios are analyzed to predict the time range within which the sealing strip can maintain good sealing performance under normal use. This determines the performance maintenance period, that is, the length of time the sealing strip can maintain effective sealing under normal use. For example, if the prediction result shows that the sealing strip can maintain good sealing performance for 3 years under normal use, then 3 years is the performance maintenance period. Then, based on the performance maintenance period, a reminder message for sealing strip replacement is sent to the user. The reminder content includes not only the performance maintenance period and the expected replacement time, but also a suggestion to regularly check the sealing strip within the performance maintenance period, so as to promptly detect potential sealing performance degradation problems and prepare for replacement in advance, thereby ensuring the sealing performance of the vehicle sunroof and the overall performance of the vehicle.

[0054] Furthermore, step S430 also includes step S431, comparing the image of the sunroof sealing strip to be inspected with a sequence of standard sealing strip performance degradation images to generate a first prediction result; step S432, performing real-time material hardness acquisition on the sunroof sealing strip to be inspected, performing elastic recovery analysis based on the real-time material hardness, and generating an elastic recovery level; step S433, optimizing the first prediction result with the elastic recovery level to generate a predicted sealing performance retention time.

[0055] Preferably, an image acquisition device (such as an industrial camera) is used to photograph the sunroof sealing strip to be inspected, acquiring an image of its current state and clearly displaying the appearance, shape, surface texture, and other characteristics of the sealing strip. Through experimental or actual use data collection, images of a standard sealing strip at different stages of use and different degrees of degradation are established, forming a standard sealing strip performance degradation image sequence. This sequence records various appearance changes of the sealing strip from a brand-new state to gradual aging and performance decline. The image of the sunroof sealing strip to be inspected is then compared with the standard sealing strip performance degradation image sequence, i.e., the similarity between the image of the sealing strip to be inspected and each image in the standard sequence is compared. The most similar standard image is determined, thereby judging the current performance degradation stage of the sealing strip to be inspected, and finally generating a first prediction result, i.e., a preliminary prediction of the possible duration of the sealing performance of the sunroof sealing strip to be inspected.

[0056] Preferably, a hardness testing device (such as a Shore hardness tester) is used to perform multiple real-time hardness measurements at different locations on the material of the sunroof sealing strip under test, obtaining relatively accurate hardness data. Then, based on the real-time material hardness data and combined with the theory of material elasticity, the ability of the sealing strip to recover its original shape after being deformed by a certain external force is analyzed, i.e., elastic recovery capability. By evaluating and generating a level that can quantify the elastic recovery capability, it is used as the elastic recovery level to reflect the current performance status of the sealing strip material. Finally, the first prediction result is optimized based on the elastic recovery level. Specifically, if the elastic recovery level is high, it indicates that the performance of the sealing strip material is good, and it may be able to maintain the sealing performance better in actual use, so the sealing performance retention time in the first prediction result is appropriately extended. Conversely, if the elastic recovery level is low, it indicates that the elasticity of the sealing strip material has decreased, and it may affect the sealing effect more quickly. In this case, the sealing performance retention time in the first prediction result is shortened. The final predicted sealing performance retention time can more accurately reflect the time that the sunroof sealing strip under test can maintain an effective seal in actual use, thereby allowing for a more reasonable arrangement of the sealing strip replacement time and ensuring good sealing performance of the sunroof.

[0057] In the above text, refer to Figure 1 A method for testing automotive sunroof sealing strips according to embodiments of the present invention is described in detail. Next, reference will be made to... Figure 2 A detection device for automotive sunroof sealing strips is described according to an embodiment of the present invention.

[0058] The detection device for automotive sunroof sealing strips according to embodiments of the present invention addresses the technical problems in the prior art where the detection scenarios cannot match actual driving conditions, making it difficult to dynamically monitor performance changes throughout the sealing strip's lifecycle, resulting in an inability to provide early warnings of potential failure risks and poor accuracy in sealing detection. The device achieves the technical effect of improving detection accuracy and providing early warnings of sealing strip failure risks. Figure 2 As shown, the testing device for automotive sunroof sealing strips includes: a scene library construction module 10, an adaptation and adjustment module 20, a simulation testing module 30, and a sealing verification module 40.

[0059] The scenario library construction module 10 is used to construct a critical failure scenario library for the sealing strip; the adaptation adjustment module 20 is used to determine the sunroof sealing strip to be inspected and read the user's driving coverage information, and perform failure scenario adaptation adjustment starting from the critical failure scenario library for the sealing strip to generate an adaptation critical failure scenario library; the simulation detection module 30 is used to simulate each scenario in the adaptation critical failure scenario library through a pre-built closed simulation chamber, and use the leakage sensor in the cabin to detect it, generating a simulation detection dataset; the sealing verification module 40 is used to perform sealing verification based on the simulation detection dataset, and predict and remind the user to replace the sealing strip based on the sealing verification results.

[0060] The specific configuration of the simulation detection module 30 will be described in detail below. The simulation detection module 30 further includes: the leakage sensor includes a micro-pressure sensor and a humidity / water droplet sensing sensor.

[0061] The specific configuration of the scenario library construction module 10 will be described in detail below. The scenario library construction module 10 further includes: constructing a scenario element matrix, including wind vector, rain vector and vehicle driving posture vector; collecting historical sealing failure scenarios, and using the scenario element matrix as a basis, performing element assignment to construct a scenario feature matrix set; and generating the critical failure scenario library of the sealing strip using the scenario feature matrix set.

[0062] The specific configuration of the scene library construction module 10 will be described in detail below. The scene library construction module 10 further includes: performing element assignment, including continuous quantitative assignment, continuous variable assignment, and instantaneous fluctuation assignment.

[0063] The specific configuration of the simulation detection module 30 will be described in detail below. The simulation detection module 30 further includes: the enclosed simulation chamber includes a multi-axis directional fan array, a spray rain curtain system, a dynamic attitude simulation platform, and a terminal controller.

[0064] The specific configuration of the adaptation adjustment module 20 will be described in detail below. The adaptation adjustment module 20 further includes: performing trigger probability analysis on each scenario in the sealing strip critical failure scenario library based on the user driving coverage information, generating trigger probabilities for each scenario; adding first-type scenarios with probabilities greater than or equal to preset probabilities to the adaptation critical failure scenario library based on the trigger probabilities of each scenario; starting with second-type scenarios with probabilities less than preset probabilities, gradually reducing the feature values ​​of elements within the scenario based on a preset adjustment step size, and performing trigger probability analysis after each adjustment, until the adjusted trigger probability is greater than or equal to the preset probability; adding the scenario at that time to the adaptation critical failure scenario library after passing the conflict verification with the first-type scenario.

[0065] The specific configuration of the adaptation and adjustment module 20 will be described in detail below. The adaptation and adjustment module 20 further includes: collecting a historical weather record dataset based on the user's driving coverage information; filtering the proportion of records in the historical weather record dataset whose weather feature similarity to each scenario is greater than or equal to a preset similarity threshold, and generating the trigger probability of each scenario.

[0066] The specific configuration of the sealing verification module 40 will be described in detail below. The sealing verification module 40 further includes: determining whether a sealing leak exists based on the simulated test dataset, and generating the sealing verification result; if the sealing verification result shows a leak, directly reminding the user to replace the sealing strip; if the sealing verification result shows no leak, predicting the sealing performance retention of the sunroof sealing strip under inspection, and reminding the user to replace the sealing strip based on the performance retention period.

[0067] The specific configuration of the sealing verification module 40 will be described in detail below. The sealing verification module 40 further includes: acquiring and comparing the image of the sunroof sealing strip to be inspected with a sequence of standard sealing strip performance degradation images to generate a first prediction result; performing real-time material hardness acquisition on the sunroof sealing strip to be inspected, performing elastic recovery analysis based on the real-time material hardness, and generating an elastic recovery level; and optimizing the first prediction result using the elastic recovery level to generate a predicted sealing performance retention time.

[0068] The detection device for automotive sunroof sealing strips provided in this embodiment of the invention can execute the detection method for automotive sunroof sealing strips provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0069] Although this application makes various references to certain modules in the apparatus according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not intended to limit the scope of protection of this invention.

[0070] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for testing automotive sunroof sealing strips, characterized in that, include: Build a library of critical failure scenarios for sealing strips; Identify the sunroof sealing strip of the car to be inspected and read the user's driving coverage information. Starting from the critical failure scenario library of the sealing strip, perform failure scenario adaptation adjustment to generate an adaptation critical failure scenario library. Each scenario in the adaptive critical failure scenario library is simulated using a pre-built closed simulation chamber, and a leakage sensor inside the cabin is used for detection to generate a simulation detection dataset. The simulated test dataset is used to perform seal verification, and the seal verification results are used to predict and remind users to replace the sealing strip.

2. The testing method for automotive sunroof sealing strips as described in claim 1, characterized in that, The leakage sensor includes a micro-pressure sensor and a humidity / water droplet sensing sensor.

3. The testing method for automotive sunroof sealing strips as described in claim 1, characterized in that, Construct a library of critical failure scenarios for sealing strips, including: Construct a scene element matrix, including wind vector, rain vector, and vehicle driving posture vector; Collect historical sealing failure scenarios, and use the scenario element matrix as a basis to perform element assignment and construct a scenario feature matrix set; The critical failure scenario library of the sealing strip is generated using the set of scenario feature matrices.

4. The testing method for automotive sunroof sealing strips as described in claim 3, characterized in that, Execution element assignment includes continuous quantitative assignment, continuous variable assignment, and instantaneous fluctuation assignment.

5. The testing method for automotive sunroof sealing strips as described in claim 1, characterized in that, The enclosed simulation chamber includes a multi-axis directional fan array, a spray rain curtain system, a dynamic attitude simulation platform, and a terminal controller.

6. The testing method for automotive sunroof sealing strips as described in claim 1, characterized in that, The system identifies the sunroof sealing strip to be inspected and reads the user's driving coverage information. Starting from the critical failure scenario library for the sealing strip, it performs failure scenario adaptation adjustment to generate an adapted critical failure scenario library, including: Based on the user driving coverage information, the trigger probability analysis is performed on each scenario in the critical failure scenario library of the sealing strip, and the trigger probability of each scenario is generated. Based on the trigger probability of each scenario, the first type of scenario with a probability greater than or equal to the preset probability is added to the adaptation critical failure scenario library. Starting with the second type of scenario where the probability is less than the preset probability, the feature values ​​of elements within the scenario are gradually reduced based on the preset adjustment step size. After each adjustment, the trigger probability analysis is performed until the adjusted trigger probability is greater than or equal to the preset probability. After the scenario at that time passes the conflict verification with the first type of scenario, it is added to the adaptation critical failure scenario library.

7. The testing method for automotive sunroof sealing strips as described in claim 6, characterized in that, Based on the user driving coverage information, the trigger probability analysis is performed on each scenario in the critical failure scenario library of the sealing strip, and the trigger probability of each scenario is generated, including: Based on the user's driving coverage information, a dataset of historical windy and rainy weather records is collected; In the historical wind and rain weather record dataset, the proportion of records whose weather feature similarity to each scenario is greater than or equal to a preset similarity threshold is selected, and the trigger probability of each scenario is generated.

8. The testing method for automotive sunroof sealing strips as described in claim 1, characterized in that, The simulated detection dataset is used for seal verification, and the seal verification results are used to predict and remind users to replace the sealing strip, including: Based on the simulated detection dataset, determine whether there are any sealing leaks, and generate the sealing verification result; If the sealing test results show a leak, a reminder will be issued to replace the sealing strip. If the sealing test results show no leakage points, the sealing performance of the sunroof sealing strip under inspection is predicted, and a reminder is given to replace the sealing strip based on the performance maintenance cycle.

9. The testing method for automotive sunroof sealing strips as described in claim 8, characterized in that, Predicting the retention of sealing performance for the sunroof sealing strip of the vehicle under inspection, including: The image of the sunroof sealing strip to be inspected is compared with a sequence of standard sealing strip performance degradation images to generate a first prediction result; Real-time material hardness data is collected on the sunroof sealing strip of the vehicle under inspection. Elastic recovery analysis is performed based on the real-time material hardness to generate the elastic recovery level. The first prediction result is optimized based on the elastic recovery level to generate a predicted sealing performance retention time.

10. A testing device for automotive sunroof sealing strips, characterized in that, The apparatus is used to implement the detection method for automotive sunroof sealing strips according to any one of claims 1 to 9, the apparatus comprising: The scenario library construction module is used to build a critical failure scenario library for sealing strips; The adaptation and adjustment module is used to determine the sunroof sealing strip of the car to be inspected and read the user's driving coverage information. Starting from the critical failure scenario library of the sealing strip, it performs failure scenario adaptation and adjustment to generate an adaptation critical failure scenario library. The simulation detection module is used to simulate each scenario in the adaptive critical failure scenario library through a pre-built closed simulation chamber, and to detect the leakage using the leakage sensors in the cabin, thereby generating a simulation detection dataset. The sealing verification module is used to perform sealing verification using the simulated detection dataset, and to predict and remind users to replace the sealing strip based on the sealing verification results.

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