Automobile seat safety detection method based on pressure sensing

Abstract

The present invention discloses a car seat safety detection method based on pressure sensing, which relates to the technical field of car seat safety detection. The car seat safety detection method based on pressure sensing includes: data pre-collection accuracy assessment, detection data sampling frequency correction, wing adjustment safety detection data acquisition, and wing adjustment safety detection performance analysis. The present invention uses the pressure data of the seat to be detected in the pressure data pre-collection experiment to perform data pre-collection accuracy assessment to optimize the pressure data, then optimizes the sampling frequency of the wing adjustment safety detection based on the pressure change frequency, and finally quantitatively determines the wing detection performance during the wing adjustment safety detection process. The wing adjustment safety detection is performed based on the detection parameters, thereby improving the accuracy of the car seat wing adjustment safety detection and solving the problem of low accuracy of the car seat wing adjustment safety detection caused by deviation in the pressure sensor data collection in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile seat safety detection, and in particular to an automobile seat safety detection method based on pressure sensing. Background Art

[0002] The angle of a car seat's side wing typically refers to the angle between the seat's side wing surface and the main plane of the seat back. The primary function of a car seat's side wing is to provide lateral support, effectively reducing occupant displacement and injury in the event of a side impact. Car seat side wing safety testing involves multiple aspects, including crash testing, strength testing, airbag compatibility, and material testing. Pressure-sensing car seat side wing safety testing uses pressure sensors installed on the car seat to measure the pressure applied to the seat, then compares historical data to detect any anomalies. Finally, the side wing angle is adjusted according to preset safety standards. Its core purpose is to obtain critical information about occupant safety by monitoring seat pressure changes in real time, which facilitates the evaluation of the car seat's protective effectiveness and its performance under collisions or other external forces. Pressure sensors monitor the pressure distribution in the car seat in real time, assessing whether the side wing provides adequate support in critical areas.

[0003] For example, the automobile seat quality detection method, device and equipment announced in the invention patent with announcement number: CN118913908B include: obtaining a first cushion image; analyzing the first cushion image to obtain first cushion quality detection data; obtaining first backrest information; analyzing the moving speed of the first backrest information to obtain first backrest quality detection data; obtaining pressure information and obtaining a second cushion image; analyzing the second cushion image and pressure information to obtain second cushion quality detection data; obtaining second backrest information based on a second movement signal; analyzing the second backrest information to obtain second backrest quality detection data; obtaining automobile seat quality detection data based on the first cushion quality detection data, the first backrest quality detection data, the second cushion quality detection data and the second backrest quality detection data.

[0004] For example, the patent application with publication number CN113654929A discloses a handheld automobile seat local hardness tester and testing method, which includes: a force measuring system module capable of measuring force value, a pressing depth indicator frame and a pressure sensing pressure plate, wherein a pressure plate connecting rod with a scale is fixed to the bottom of the force measuring system module, the pressing depth indicator frame is sleeved on the outside of the pressure plate connecting rod, the pressure sensing pressure plate is threadedly connected to the bottom end of the pressure plate connecting rod, and the upper and lower edges of the pressing depth indicator frame are respectively provided with pressing depth indicator lines, and contact indicator balls are respectively provided at both ends of the bottom.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] In existing technologies, sensors may experience data deviations due to aging, damage, or environmental factors, affecting detection reliability. During vehicle driving simulations, factors such as uneven road surfaces and engine vibrations can cause fluctuations in the data collected by the pressure sensor, potentially introducing noise in the pressure sensor signal and leading to misjudgments (such as triggering an alert without actually detecting an occupant), impacting detection accuracy. If the accuracy of the data acquired by the sensor is low, it will be unable to capture subtle adjustments, resulting in deviations in the adjustment of the car seat's wing angle. Due to different vehicle operating conditions, the pressure sensor will sense constantly changing seat pressure data. Dynamic factors such as vehicle driving, deceleration, and turning can cause frequent pressure changes. The sensor's continuous data output can lead to insufficient bandwidth and data transmission delays. This creates a problem of low accuracy in safety detection of car seat wing adjustments due to deviations in the data collected by the pressure sensor. Summary of the Invention

[0007] The embodiment of the present application solves the problem of low accuracy of automobile seat side wing adjustment safety detection caused by deviation in pressure sensor data collection in the prior art by providing a car seat safety detection method based on pressure sensing, thereby improving the accuracy of automobile seat side wing adjustment safety detection.

[0008] The embodiment of the present application provides a car seat safety detection method based on pressure sensing, comprising the following steps: S1, obtaining non-load pressure data and load pressure data by pre-collecting pressure data of the seat to be detected, evaluating the accuracy of the data pre-collection, and optimizing the non-load pressure data according to the evaluation results; S2, judging whether to optimize the initial sampling frequency of the wing adjustment safety detection according to the pressure change frequency of the optimized non-load pressure data; S3, if the initial sampling frequency of the wing adjustment safety detection is optimized, then the non-load pressure data obtained at the optimized sampling frequency during the wing adjustment safety detection process and the corresponding pressure sensor data are collected. The wing angle is obtained by mapping the position of the pressure sensor to the wing angle, otherwise the non-no-load pressure data obtained at the initial sampling frequency during the wing adjustment safety detection process and the corresponding pressure sensor position are mapped to obtain the wing angle, and the actual wing adjustment angle is collected synchronously; S4, the wing detection performance parameters of the side adjustment safety detection of the seat to be detected are quantitatively determined based on the side adjustment detection performance parameters obtained from one side wing adjustment safety detection, and whether to perform detection optimization is determined based on the average quantitative determination result of the preset number of detections. If detection optimization is performed, the wing adjustment safety detection is continued according to the optimized detection parameters after the detection optimization, otherwise the wing adjustment safety detection is performed according to the initial detection parameters.

[0009] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0010] 1. The pressure change frequency is used to determine whether the initial sampling frequency of the wing adjustment safety test should be optimized, thereby improving the quality of data acquisition during the wing adjustment safety test. If the pressure change frequency is greater than the first preset pressure change frequency, it means that the current pressure change frequency is higher and a higher sampling frequency may be required to capture rapidly changing pressure data, ensuring that the acquired pressure data is more accurate. If the pressure change frequency is less than the second preset pressure change frequency, it means that the current pressure change frequency is lower and data is collected at a lower sampling frequency, thereby reducing the amount of non-no-load pressure data.

[0011] 2. By establishing a coordinate system and obtaining the center offset coefficient, when the center offset coefficient is not equal to 0, the first wing angle influencing parameter is input into the right wing angle mapping set to obtain the right wing angle, and the first wing angle influencing parameter is input into the left wing angle mapping set to obtain the left wing angle. Otherwise, the second wing angle influencing parameter is input into the wing angle mapping set to obtain the wing angle. This ensures that during the wing adjustment safety detection process, the changes in the wing angle can be captured in a timely manner, thereby improving the accuracy of the wing adjustment safety detection in the car seat safety detection.

[0012] 3. Vibration noise suppression is performed and a low-pass filter is used to remove noise signals from non-no-load pressure data that are above the cutoff frequency. This effectively suppresses the noise interference caused by vibration in the non-no-load pressure data, improves the signal-to-noise ratio of the pressure data, and enables the pressure data to more accurately reflect the actual pressure changes. By comparing the data collected by the two pressure sensors, data deviations caused by factors such as sensor errors are eliminated, further improving the reliability and accuracy of the pressure data, thereby improving the reliability of the safety detection of the car seat side adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of data pre-collection accuracy assessment and detection data sampling frequency correction in the pressure sensing-based automobile seat safety detection method provided in an embodiment of the present application;

[0014] Figure 2 This is a flow chart of obtaining wing adjustment safety detection data and analyzing wing adjustment safety detection performance in the pressure sensing-based automobile seat safety detection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The embodiment of the present application solves the problem of low accuracy of automobile seat wing adjustment safety detection caused by pressure sensor data collection deviation in the prior art by providing an automobile seat safety detection method based on pressure sensing. The accuracy of data pre-collection is evaluated on the non-load pressure data and the load pressure data obtained in the pressure data pre-collection experiment of the seat to be tested, and it is determined whether to optimize the non-load pressure data. Then, the non-load pressure data obtained at the sampling frequency and the corresponding pressure sensor position in the wing adjustment safety detection process are mapped to obtain the wing angle. Then, according to the pressure change frequency, it is determined whether to optimize the initial sampling frequency of the wing adjustment safety detection. Finally, in the wing adjustment safety detection process, the wing detection performance is quantitatively determined. Based on the average quantitative determination result of the preset number of detections, it is determined whether to optimize the detection. The wing adjustment safety detection is continued according to the optimized detection parameters, thereby improving the accuracy of automobile seat wing adjustment safety detection.

[0016] The technical solution in the embodiment of the present application is to solve the problem of low accuracy of safety detection of car seat wing adjustment caused by deviation in data collected by the pressure sensor. The overall idea is as follows:

[0017] The accuracy of data pre-collection is evaluated by using the pressure data of the seat to be tested in the pressure data pre-collection experiment to optimize the pressure data. Then, the sampling frequency of the side wing adjustment safety test is optimized according to the pressure change frequency. Finally, the side wing detection performance is quantitatively judged during the side wing adjustment safety test. The side wing adjustment safety test is continued according to the optimized detection parameters, thereby improving the accuracy of the side wing adjustment safety test of the car seat.

[0018] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0019] like Figure 1FIG. 1 is a flowchart of data pre-acquisition accuracy assessment and test data sampling frequency correction in a pressure sensing-based automobile seat safety testing method according to an embodiment of the present application. The flowchart describes the following steps: first, a data pre-acquisition accuracy assessment is performed on the non-idle pressure data and the no-load pressure data obtained in the pressure data pre-acquisition experiment; then, a determination is made on the acquisition accuracy assessment result to determine whether the acquisition accuracy assessment result is less than a preset acquisition accuracy assessment threshold; if so, the non-idle pressure data obtained in the pressure data pre-acquisition experiment is optimized; otherwise, a determination is made on the pressure change frequency; then, a determination is made on whether the optimized acquisition accuracy assessment result is less than the preset acquisition accuracy assessment threshold; if not, a determination is made on the pressure change frequency; otherwise, feedback is provided; finally, a determination is made on whether the pressure change frequency is greater than a first preset pressure change frequency; if so, a correction sampling frequency is obtained based on the first pressure change frequency deviation and the initial sampling frequency, and non-idle pressure data is obtained based on the correction sampling frequency; otherwise, non-idle pressure data is obtained based on the initial sampling frequency.

[0020] like Figure 2 As shown, it is a flowchart of the side wing adjustment safety detection data acquisition and side wing adjustment safety detection performance analysis in the pressure sensing-based automobile seat safety detection method provided in an embodiment of the present application. It describes the side wing angle and the actual side wing angle obtained by mapping the non-no-load pressure data and the corresponding pressure sensor position, and quantitatively judging the side wing detection performance of the seat to be detected. Then, it is judged whether the side wing detection judgment result is less than the preset side wing detection judgment threshold. If so, the detection optimization is performed, and the side wing adjustment safety detection is continued according to the optimized detection parameters after the detection optimization. Otherwise, the side wing adjustment safety detection is directly performed according to the initial detection parameters.

[0021] The embodiment of the present application provides a pressure-sensing-based automobile seat safety detection method, in which a simulation test of the vehicle driving condition is performed to ensure the accuracy of the automobile seat safety detection, including the following steps: S1, before the wing adjustment safety detection, the data pre-collection accuracy is evaluated by performing a data pre-collection accuracy evaluation on the non-load pressure data and the load pressure data obtained in the pressure data pre-collection experiment of the seat to be detected, to obtain an acquisition accuracy evaluation result; the simulation detection section and the simulation detection vehicle used in the pressure data pre-collection experiment and the wing adjustment safety detection are the same.

[0022] S2. Determine whether to optimize the non-no-load pressure data obtained in the pressure data pre-acquisition experiment through the sampling accuracy evaluation result. If optimized, determine whether to optimize the initial sampling frequency of the wing adjustment safety detection based on the optimized pressure change frequency. Otherwise, directly determine whether to optimize the initial sampling frequency of the wing adjustment safety detection based on the pressure change frequency in the pressure data pre-acquisition experiment. Optimize the non-no-load pressure data obtained in the pressure data pre-acquisition experiment to improve the accuracy of pressure data acquisition in the wing adjustment safety detection. The pressure change frequency refers to the fluctuation frequency of the pressure data obtained within the preset data pre-acquisition period in the pressure data pre-acquisition experiment. The initial sampling frequency is set according to the preset personnel. The preset data pre-acquisition period is set according to the preset personnel, for example, it can be set to 1 hour.

[0023] S3. If the initial sampling frequency of the wing adjustment safety detection is optimized, the non-no-load pressure data obtained at the corrected sampling frequency during the wing adjustment safety detection and the corresponding pressure sensor position are mapped to obtain the wing angle, and the actual wing adjustment angle is collected synchronously. Otherwise, the non-no-load pressure data obtained at the initial sampling frequency during the wing adjustment safety detection and the corresponding pressure sensor position are mapped to obtain the wing angle, and the actual wing adjustment angle is collected synchronously. The corrected sampling frequency refers to the sampling frequency obtained after optimizing the initial sampling frequency of the wing adjustment safety detection.

[0024] S4, based on the side wing adjustment detection performance parameters obtained from a side wing adjustment safety detection, the side wing detection performance of the seat to be detected is quantitatively determined, and based on the average quantitative determination results of the preset number of detections, it is determined whether to perform detection optimization. If detection optimization is performed, the side wing adjustment safety detection is continued according to the optimized detection parameters after the detection optimization, otherwise the side wing adjustment safety detection is directly performed according to the initial detection parameters; the preset number of detections is set according to the preset personnel.

[0025] Before designing a car seat safety detection method based on pressure sensing, a database for storing various setting data is established. The database includes but is not limited to preset non-no-load pressure, preset processing time, acquisition accuracy assessment compensation value, and sampling frequency mapping set. Various numerical values ​​therein are directly set by technical personnel. Among them, the setting basis of the preset processing time can be set according to the preset personnel. For example, the preset processing time is represented by the average value of the historical processing time in the historical time period in the database. In addition, various numerical values ​​in the database can be set and fine-tuned by technical personnel according to actual debugging.

[0026] In this embodiment, in this application, the pressure data pre-collection experiment is used to improve the accuracy of data obtained during the side wing adjustment safety detection process; through the data pre-collection accuracy evaluation, the collected pressure data is ensured to be accurate and reliable, and erroneous detection caused by inaccurate sensors is avoided; through the detection data sampling frequency correction module, the sampling frequency is dynamically adjusted according to the changes in the pressure data to ensure the accuracy and comprehensiveness of the data collected during the seat adjustment process, thereby enhancing the accuracy of automobile seat safety detection; through the side wing adjustment safety detection data acquisition module, the quantitative judgment of the side wing detection performance of the seat to be tested in the subsequent side wing adjustment safety detection performance analysis module is provided, which provides a basis for determining the accuracy of the side wing angle adjustment, thereby effectively evaluating the side wing adjustment safety detection; through the quantitative analysis of the side wing adjustment safety detection performance analysis module, it can be optimized according to the average quantitative judgment result of the preset number of detections to improve the reliability and stability of subsequent automobile seat safety detection.

[0027] Furthermore, based on the optimized pressure change frequency, it is determined whether to optimize the initial sampling frequency of the wing adjustment safety detection. The specific process is as follows:

[0028] Obtain the optimized non-no-load pressure data in S1.

[0029] Determine whether the pressure change frequency is greater than a first preset pressure change frequency obtained from a preset database:

[0030] If the pressure change frequency is greater than the first preset pressure change frequency obtained from the preset database, the first pressure change frequency deviation and the initial sampling frequency are input into the sampling frequency mapping set to obtain a corrected sampling frequency, and non-no-load pressure data is obtained according to the corrected sampling frequency within the preset single safety detection period during the flank adjustment safety detection process. The first pressure change frequency deviation is obtained by deviation processing between the pressure change frequency and the first preset pressure change frequency. The sampling frequency mapping set is a set of mapping relationships between the first pressure change frequency deviation, the initial sampling frequency and the corrected sampling frequency obtained from the preset database; the first preset pressure change frequency and the second preset pressure change frequency are set according to the preset personnel, for example, the first preset pressure change frequency can be represented by 1.5 times the average value of the pressure change frequency in the historical time period, and the second preset pressure change frequency can be represented by 0.5 times the average value of the pressure change frequency in the historical time period; the preset single safety detection period is set according to the preset personnel, for example, it can be set to 1 hour.

[0031] If the pressure change frequency is less than the second preset pressure change frequency obtained from the preset database, the second pressure change frequency deviation and the initial sampling frequency are input into the sampling frequency mapping set to obtain a corrected sampling frequency, and non-no-load pressure data is obtained according to the corrected sampling frequency within a preset single safety detection cycle during the flank adjustment safety detection process. The second pressure change frequency deviation is obtained by performing deviation processing on the second preset pressure change frequency and the pressure change frequency. Otherwise, non-no-load pressure data is directly obtained according to the initial sampling frequency within the preset single safety detection cycle during the flank adjustment safety detection process; deviation processing means performing a difference operation on the two data.

[0032] In this embodiment, determining whether to optimize the initial sampling frequency of the wing adjustment safety test by the pressure change frequency is part of the data pre-acquisition experiment and is used to improve the quality of data acquisition during the wing adjustment safety test. If the pressure change frequency is greater than the first preset pressure change frequency, it means that the higher the current pressure change frequency is, the higher the sampling frequency may be required to capture rapidly changing pressure data, ensuring more frequent sampling and more accurate pressure data. If the pressure change frequency is less than the second preset pressure change frequency, it means that the lower the current pressure change frequency is, the lower the sampling frequency is used for data acquisition, which can avoid the acquisition of redundant data and thus reduce the amount of non-idle pressure data. By dynamically adjusting the sampling frequency under different pressure fluctuation conditions, the efficiency and accuracy of pressure data acquisition during the automobile seat wing adjustment safety test are improved, resource waste is avoided, and resource utilization efficiency is optimized.

[0033] Furthermore, optimizing the test parameters includes optimizing the test pressure range and optimizing the remaining number of tests. The specific acquisition methods are as follows:

[0034] The deviation coefficient of the flank detection result, the maximum value of the non-no-load pressure data and the minimum value of the non-no-load pressure data are input into the test pressure range mapping set to obtain the optimized test pressure range. The test pressure range mapping set is a set obtained from a preset database that represents the mapping relationship between the deviation coefficient of the flank detection result, the maximum value of the non-no-load pressure data, the minimum value of the non-no-load pressure data and the optimized test pressure range; the maximum value of the non-no-load pressure data represents the maximum value of the non-no-load pressure data obtained during the flank adjustment safety detection process; the minimum value of the non-no-load pressure data represents the minimum value of the non-no-load pressure data obtained during the flank adjustment safety detection process.

[0035] The deviation coefficient of the flank detection result is input into the detection remaining times mapping set to obtain the optimized detection remaining times. The detection remaining times mapping set is a set obtained from a preset database that represents the mapping relationship between the deviation coefficient of the flank detection result and the optimized detection remaining times.

[0036] Determine whether the maximum value of the optimized test pressure in the optimized test pressure range is less than the preset test pressure maximum value. If so, adjust the maximum value of the optimized test pressure in the optimized test pressure range to the preset test pressure maximum value. Otherwise, do not adjust the maximum value of the optimized test pressure in the optimized test pressure range. The preset test pressure maximum value is set according to the preset personnel. For example, the preset test pressure maximum value is represented by the maximum value of the non-no-load pressure data in the historical time period.

[0037] Determine whether the remaining number of optimization tests is greater than the preset maximum remaining number of optimization tests. If so, adjust the remaining number of optimization tests to the preset maximum remaining number of optimization tests. Otherwise, do not adjust the remaining number of optimization tests. The preset maximum remaining number of optimization tests is set according to the preset personnel.

[0038] In this embodiment, the optimized detection parameters are parameters obtained by optimizing the initial detection parameters in the side wing adjustment safety test after quantitatively determining the side wing adjustment safety test performance for a preset number of tests; the optimized test pressure range is determined by comprehensively considering the deviation coefficient of the side wing detection determination result, the maximum value of the non-no-load pressure data, and the minimum value of the non-no-load pressure data, which can more accurately simulate the various pressure conditions that the car seat may face during actual use, and improve the accuracy and comprehensiveness of the side wing adjustment safety test during the car seat safety test; the optimized number of safety tests is determined by the deviation coefficient of the side wing detection determination result, thereby avoiding the increase in data volume and data processing delay caused by redundant tests, and realizing the improvement of test efficiency in the car seat side wing adjustment safety test.

[0039] Furthermore, the accuracy of data pre-collection is evaluated by conducting a pressure data pre-collection experiment on the non-load pressure data and the load pressure data obtained from the seat to be tested, and the collection accuracy evaluation result is obtained. The specific process is as follows:

[0040] E1. In the pressure data pre-collection experiment, the initial non-load pressure data and no-load pressure data of the seat to be tested within the preset data pre-collection period are obtained. The no-load pressure data is the pressure of the seat to be tested measured by the pressure sensor in the no-load state obtained through the pressure data pre-collection experiment. The initial non-load pressure data is the pressure of the seat to be tested in the non-load state obtained through the pressure data pre-collection experiment.

[0041] E2, obtains the non-no-load pressure data by performing a difference calculation on the initial non-no-load pressure data and no-load pressure data of the seat to be tested; the no-load pressure data is the measurement value of the pressure sensor when no preset measuring object is placed, the initial non-no-load pressure data is the measurement value of the pressure sensor when the preset measuring object is placed, and the non-no-load pressure data is the measured weight of the preset measuring object.

[0042] E3: A pressure deviation coefficient is obtained by performing relative deviation processing on the acquired average non-load pressure data and the preset non-load pressure obtained from the preset database. The average non-load pressure data is the average pressure of the seat to be tested in the non-load state obtained through the pressure data pre-collection experiment. The specific limiting expression of the pressure deviation coefficient is: ;

[0043] Where YPC represents the pressure deviation coefficient of the seat to be tested in the pressure data pre-collection experiment, PYL represents the average non-no-load pressure data of the seat to be tested in the pressure data pre-collection experiment, and the average non-no-load pressure data represents the average value of the non-no-load pressure data obtained during the preset data pre-collection period. Indicates the preset non-no-load pressure, which is set by the preset personnel.

[0044] E4. After introducing the acquisition accuracy evaluation compensation value to perform assignment coupling processing on the de-unitized non-no-load pressure data standard deviation, pressure deviation coefficient and de-unitized no-load pressure obtained in the pressure data pre-acquisition experiment, the result of the assignment coupling processing is inversely proportionally operated to obtain the acquisition accuracy evaluation result. The acquisition accuracy evaluation compensation value includes the first acquisition accuracy evaluation compensation value, the second acquisition accuracy evaluation compensation value and the third acquisition accuracy evaluation compensation value. The acquisition accuracy evaluation result is used to quantitatively evaluate the accuracy of the pressure data obtained through the pressure data pre-acquisition experiment in the automobile seat safety inspection; the non-no-load pressure data standard deviation represents the value obtained by performing standard deviation operation on the non-no-load pressure data obtained within the preset data pre-acquisition period.

[0045] The specific restriction expression of the acquisition accuracy evaluation result is:

[0046] ;

[0047] Where ZN represents the collection accuracy evaluation result of the seat to be tested in the pressure data pre-collection experiment, YBC represents the standard deviation of the non-no-load pressure data of the seat to be tested in the pressure data pre-collection experiment, and YPC represents the pressure deviation coefficient of the seat to be tested in the pressure data pre-collection experiment. Indicates the no-load pressure of the seat to be tested in the pressure data pre-collection experiment. represents the first acquisition accuracy evaluation compensation value, represents the second acquisition accuracy evaluation compensation value, represents the third acquisition accuracy evaluation compensation value.

[0048] The acquisition accuracy assessment compensation values ​​involved are obtained from a preset database. The first acquisition accuracy assessment compensation value represents the degree of influence of the standard deviation of non-no-load pressure data on the acquisition accuracy assessment result. The second acquisition accuracy assessment compensation value represents the degree of influence of the average non-no-load pressure data on the acquisition accuracy assessment result. The third acquisition accuracy assessment compensation value represents the degree of influence of the no-load pressure on the acquisition accuracy assessment result. The sum of the three is 1. For example, the standard deviation of the non-no-load pressure data and the preset first acquisition accuracy assessment compensation value form a non-no-load pressure data standard deviation mapping set. The real-time non-no-load pressure data standard deviation is input into the non-no-load pressure data standard deviation mapping set to obtain the corresponding first acquisition accuracy assessment compensation value. The average non-no-load pressure data and the preset second acquisition accuracy assessment compensation value form an average non-no-load pressure data mapping set. The real-time average non-no-load pressure data is input into the average non-no-load pressure data mapping set to obtain the corresponding second acquisition accuracy assessment compensation value. The no-load pressure and the preset third acquisition accuracy assessment compensation value form a no-load pressure mapping set. The real-time no-load pressure is input into the no-load pressure mapping set to obtain the corresponding third acquisition accuracy assessment compensation value. The mapping relationship can be one-to-one or many-to-one.

[0049] In this embodiment, a smaller standard deviation of the non-no-load pressure data generally means that the acquired non-no-load pressure data is more stable, which may result in a smaller pressure deviation coefficient, indicating that the non-no-load pressure acquired by the pressure sensor is closer to the actual pressure; a larger no-load pressure means a larger reference of the pressure sensor zero point, which may result in a larger standard deviation of the non-no-load pressure data and a larger pressure deviation coefficient.

[0050] The data pre-collection accuracy assessment is carried out before the side wing adjustment safety test. By comparing the average non-no-load pressure data with the preset non-no-load pressure in the preset database, the performance of the pressure sensor under standard conditions is evaluated. If the measured value of the sensor is less than the preset pressure value, it may indicate that there is a problem with the sensor; the standard deviation of the non-no-load pressure data indicates the degree of fluctuation of the data; the de-unitized non-no-load pressure data standard deviation, the pressure deviation coefficient and the de-unitized no-load pressure are assigned and coupled to comprehensively consider the accuracy, stability and performance deviation of the pressure sensor; through the above steps, the accuracy and reliability of the pressure sensor in obtaining pressure data in the automobile seat side wing adjustment safety test are ensured, and the accuracy of the automobile seat side wing adjustment safety test is further improved.

[0051] Furthermore, the non-no-load pressure data obtained at the corrected sampling frequency during the wing adjustment safety detection process is mapped to the corresponding pressure sensor position to obtain the wing angle. The specific process is as follows:

[0052] A coordinate system is established with the center point of the seat as the origin, the direction pointing to the seat back as the vertical axis, and the direction parallel to the right of the seat edge as the horizontal axis; the average non-no-load pressure data of each pressure sensor are arranged in descending order to obtain the position of the pressure sensor corresponding to the maximum average non-no-load pressure data, and the horizontal coordinate of the position of the corresponding pressure sensor is marked as the center offset coefficient; the position of each pressure sensor on the car seat to be tested is set according to the preset personnel.

[0053] When the center offset coefficient is not equal to 0, the first wing angle influencing parameter is input into the right wing angle mapping set to obtain the right wing angle, and the first wing angle influencing parameter is input into the left wing angle mapping set to obtain the left wing angle; otherwise, the second wing angle influencing parameter is input into the wing angle mapping set to obtain the wing angle, the wing angles including the right wing angle and the left wing angle, the first wing angle influencing parameter including the number of pressure sensors used, the maximum average non-no-load pressure data, the backrest pressure ratio, the seat cushion pressure ratio and the center offset coefficient, and the second wing angle influencing parameter including the number of pressure sensors used, the maximum average non-no-load pressure data, the backrest pressure ratio and the seat cushion pressure ratio; The maximum average non-no-load pressure data represents the maximum value of the average values ​​of the non-no-load pressure data obtained from all pressure sensors; the backrest pressure ratio is obtained by ratioing the sum of the average non-no-load pressure data corresponding to all pressure sensors on the seat back of the seat to be tested with the total pressure data; the cushion pressure ratio is obtained by ratioing the sum of the average non-no-load pressure data corresponding to all pressure sensors on the seat cushion of the seat to be tested with the total pressure data; the total pressure data is obtained by summing the sum of the average non-no-load pressure data corresponding to all pressure sensors on the seat back of the seat to be tested with the sum of the average non-no-load pressure data corresponding to all pressure sensors on the seat cushion.

[0054] The right wing angle mapping set is a set obtained from the preset database, which represents the mapping relationship between the first wing angle influencing parameter and the right wing angle; the left wing angle mapping set is a set obtained from the preset database, which represents the mapping relationship between the first wing angle influencing parameter and the left wing angle; the wing angle mapping set is a set obtained from the preset database, which represents the mapping relationship between the second wing angle influencing parameter and the wing angle.

[0055] The wing adjustment detection performance parameters obtained from a wing adjustment safety test are used to quantitatively determine the wing detection performance of the seat to be tested. The specific process is as follows:

[0056] F1, according to the wing adjustment time and the adjustment command output time, the total wing adjustment processing time is obtained by coupling processing. If the total wing adjustment processing time is not less than the preset processing time, the deviation is compared based on the total wing adjustment processing time and the preset processing time to obtain the wing adjustment time deviation coefficient. Otherwise, the wing adjustment time deviation coefficient is recorded as 0. The specific limiting expression of the wing adjustment processing deviation coefficient is:

[0057] ;

[0058] Where, YCT represents the side wing adjustment time deviation coefficient of the seat to be tested in the side wing adjustment safety test; YTZ represents the side wing adjustment time of the seat to be tested in the side wing adjustment safety test. The side wing adjustment time represents the time it takes for the car seat side wing to receive the adjustment command and complete the adjustment command; TSC represents the adjustment command output time of the seat to be tested in the side wing adjustment safety test. The adjustment command output time represents the time it takes from the start of the side wing adjustment safety test to the successful transmission of the adjustment command to the receipt of the adjustment command by the car seat side wing; Indicates the preset processing time. The preset processing time is set according to the preset personnel. For example, it can be set to 1 second.

[0059] F2, the wing adjustment deviation coefficient is obtained by performing relative deviation processing based on the actual wing adjustment angle and the wing angle, that is, Where CEP represents the actual side wing adjustment angle of the seat to be tested in the side wing adjustment safety test. The actual side wing adjustment angle is the angle value of the side wing finally reached as measured by the angle sensor. Indicates the wing angle of the seat to be tested in the wing adjustment safety test.

[0060] F3, according to the relative deviation processing of the non-no-load pressure data volume and the preset non-no-load pressure data volume, the non-no-load pressure data volume comparison coefficient is obtained, that is, , where YJU represents the amount of non-empty pressure data of the seat to be tested in the side wing adjustment safety test. The amount of non-empty pressure data represents the total number of pressure data collected by multiple pressure sensors on the car seat according to the corrected sampling frequency; It indicates the preset non-no-load pressure data volume, which is obtained from the preset database and set according to the preset personnel, for example, it can be set to 1000.

[0061] F4 introduces the side wing detection and judgment compensation value to assign and couple the side wing adjustment duration deviation coefficient, the side wing adjustment deviation coefficient, the non-no-load pressure data comparison coefficient, and the de-unitized adjustment instruction output duration jitter, and then performs an inverse proportional operation to obtain the side wing detection and judgment result. The side wing detection and judgment compensation value includes the first side wing detection and judgment compensation value, the second side wing detection and judgment compensation value, the third side wing detection and judgment compensation value, and the fourth side wing detection and judgment compensation value. The side wing detection and judgment result is used to quantitatively evaluate the detection response efficiency of the side wing adjustment safety detection in the automobile seat safety detection; the adjustment instruction output duration jitter represents the standard deviation of the adjustment instruction output duration.

[0062] The wing detection and judgment compensation values ​​involved are obtained from a preset database. The first wing detection and judgment compensation value indicates the degree of influence of the wing adjustment duration and the adjustment instruction output duration on the wing detection and judgment result. The second wing detection and judgment compensation value indicates the degree of influence of the actual wing adjustment angle on the wing detection and judgment result. The third wing detection and judgment compensation value indicates the degree of influence of the non-no-load pressure data volume on the wing detection and judgment result. The fourth wing detection and judgment compensation value indicates the degree of influence of the adjustment instruction output duration jitter on the wing detection and judgment result. The sum of the four is 1. For example, the wing adjustment duration, the adjustment instruction output duration and the preset first wing detection and judgment compensation value form a wing adjustment processing deviation coefficient mapping set. The real-time wing adjustment duration and the adjustment instruction output duration are input into the wing adjustment processing deviation coefficient mapping set to obtain the corresponding One wing detection and determination compensation value; the actual wing adjustment angle and the preset second wing detection and determination compensation value form an actual wing adjustment angle mapping set, and the real-time actual wing adjustment angle is input into the actual wing adjustment angle mapping set to obtain the corresponding second wing detection and determination compensation value; the non-no-load pressure data volume and the preset third wing detection and determination compensation value form a non-no-load pressure data volume mapping set, and the real-time non-no-load pressure data volume is input into the non-no-load pressure data volume mapping set to obtain the corresponding third wing detection and determination compensation value; the adjustment instruction output duration jitter and the preset fourth wing detection and determination compensation value form an instruction output duration jitter mapping set, and the real-time adjustment instruction output duration jitter is input into the instruction output duration jitter mapping set to obtain the corresponding fourth wing detection and determination compensation value; the mapping relationship can be one-to-one or many-to-one.

[0063] Among them, the specific restriction expression of the flanking detection judgment result is:

[0064] ;

[0065] Where TY represents the side wing detection result of the seat to be tested in the side wing adjustment safety test, YCT represents the side wing adjustment time deviation coefficient of the seat to be tested in the side wing adjustment safety test, XOU represents the adjustment command output time jitter of the seat to be tested in the side wing adjustment safety test, Indicates the first flank detection judgment compensation value, Indicates the second flank detection judgment compensation value, Indicates the third flank detection judgment compensation value, Indicates the fourth flank detection and judgment compensation value.

[0066] In this embodiment, by establishing a coordinate system and obtaining the center offset coefficient, the wing angle can be obtained more accurately, ensuring that changes in the wing angle can be captured in a timely manner during the wing adjustment safety detection process, thereby improving the accuracy of the wing adjustment safety detection in the car seat safety detection.

[0067] When the wing adjustment deviation coefficient is larger, it may take longer to adjust the angle error, thereby extending the total wing adjustment processing time; the more non-no-load pressure data, the longer it may take to collect more non-no-load pressure data, thereby increasing the total wing adjustment processing time; the greater the jitter of the adjustment instruction output time, that is, the higher the instability of the instruction, the more likely it is that the response delay or instability of the adjustment process may be delayed, thereby increasing the total wing adjustment processing time; the jitter of the instruction output time will affect the stability of the instruction and the accuracy of execution during the adjustment process. If the jitter of the instruction output time is greater, it may cause a greater deviation in the adjustment process, thereby affecting the wing adjustment deviation coefficient; the larger the amount of non-no-load pressure data, the greater the jitter of the adjustment instruction output time.

[0068] Through the side wing adjustment time deviation coefficient, the command transmission time and the actual adjustment time are comprehensively considered, which can more comprehensively reflect the response speed of the side wing adjustment, avoid the inaccurate evaluation problem caused by considering only a single time factor, and quantify the deviation degree of the side wing adjustment time; through the side wing adjustment deviation coefficient, the deviation degree between the actual angle and the target angle of the side wing adjustment is reflected, and the accuracy of the side wing adjustment is evaluated, thereby ensuring the accurate adjustment of the seat side wing angle; through the non-no-load pressure data volume comparison coefficient, the duration of the side wing adjustment processing is quantitatively evaluated; through the above steps, the detection response efficiency of the side wing adjustment safety detection in the automobile seat safety detection is quantitatively evaluated, which provides a basis for subsequent detection optimization and realizes the improvement of the detection efficiency of the side wing adjustment safety detection in the automobile seat safety detection.

[0069] Furthermore, the sampling accuracy evaluation results are used to determine whether to optimize the non-no-load pressure data obtained in the pressure data pre-collection experiment. The specific process is as follows:

[0070] A1 determines whether the collection accuracy assessment result is less than a preset collection accuracy assessment threshold obtained from a preset database. If the collection accuracy assessment result is less than the preset collection accuracy assessment threshold obtained from the preset database, vibration noise suppression is performed. Otherwise, the non-no-load pressure data obtained in the pressure data pre-collection experiment is not optimized. The preset collection accuracy assessment threshold is represented by the average value of the collection accuracy assessment results within a historical time period. The vehicle speed is directly obtained through a speed sensor installed on the designated test vehicle. The acceleration is directly obtained through an acceleration sensor installed on the designated test vehicle.

[0071] The vibration noise suppression involved removes the noise signal of the non-no-load pressure data above the cutoff frequency through a low-pass filter, which is used to suppress the noise generated by vibration in the non-no-load pressure data. The cutoff frequency is obtained by inputting the vehicle speed, acquisition accuracy evaluation result, pressure signal frequency, and noise frequency into the cutoff frequency mapping set. The cutoff frequency mapping set is a set of mapping relationships between the vehicle speed, acquisition accuracy evaluation result, pressure signal frequency, noise frequency and cutoff frequency obtained from a preset database; the pressure signal frequency is obtained by performing spectral analysis on the collected non-no-load pressure data, and the pressure signal is converted from the time domain to the frequency domain through the fast Fourier transform method to determine the main frequency components of the pressure signal; the noise frequency is represented by performing spectral analysis on the collected pressure data to identify the frequency components that are not related to the main frequency components of the pressure signal.

[0072] A2 determines whether the acquisition accuracy evaluation result after vibration noise suppression is less than the preset acquisition accuracy evaluation threshold obtained from the preset database. If the acquisition accuracy evaluation result after vibration noise suppression is less than the preset acquisition accuracy evaluation threshold obtained from the preset database, dual-sensor cross-validation is performed; otherwise, the non-no-load pressure data optimization is terminated. Dual-sensor cross-validation is used to eliminate the deviation of the non-no-load pressure data caused by the pressure sensor.

[0073] A3, determines whether the acquisition accuracy evaluation result after dual-sensor cross-validation is less than the preset acquisition accuracy evaluation threshold obtained from the preset database. If the acquisition accuracy evaluation result after dual-sensor cross-validation is less than the preset acquisition accuracy evaluation threshold obtained from the preset database, feedback is provided; otherwise, the non-no-load pressure data optimization is terminated; the non-no-load pressure data obtained in the pressure data pre-acquisition experiment is optimized, including vibration noise suppression and dual-sensor cross-validation.

[0074] The specific process of dual-sensor cross-validation is as follows:

[0075] A21, the non-no-load pressure data obtained by the two first pressure sensors at the same preset sampling point in the pressure data pre-acquisition experiment are subjected to deviation processing to obtain the center point pressure deviation. The first pressure sensor represents the pressure sensor set at the center point of the seat cushion of the car seat to be tested; the preset sampling point is set according to the preset personnel.

[0076] A22, determines whether the center point pressure deviation obtained at the same preset sampling point in the pressure data pre-collection experiment is greater than the preset pressure deviation obtained from the preset database. If the center point pressure deviation is greater than the preset pressure deviation obtained from the preset database, execute A23, otherwise use any first pressure sensor to obtain non-no-load pressure data; the preset pressure deviation is set according to the preset personnel.

[0077] A23, respectively judge whether the first center point pressure standard deviation and the second center point pressure standard deviation obtained at the same preset sampling point in the pressure data pre-collection experiment are greater than the preset pressure standard deviation obtained from the preset database. If the first judgment condition is met, the first pressure sensor corresponding to the second center point pressure standard deviation is used to obtain non-no-load pressure data. The first judgment condition indicates that the first center point pressure standard deviation is greater than the preset pressure standard deviation obtained from the preset database, and the second center point pressure standard deviation is not greater than the preset pressure standard deviation obtained from the preset database; the preset pressure standard deviation is set according to the preset personnel.

[0078] If the second judgment condition is met, the first pressure sensor corresponding to the first center point pressure standard deviation is used to obtain non-no-load pressure data. The second judgment condition indicates that the first center point pressure standard deviation is not greater than the preset pressure standard deviation obtained from the preset database, and the second center point pressure standard deviation is greater than the preset pressure standard deviation obtained from the preset database.

[0079] If the third judgment condition is met, execute A24, otherwise execute A25. The third judgment condition indicates that the first center point pressure standard deviation is not greater than the preset pressure standard deviation obtained from the preset database, and the second center point pressure standard deviation is not greater than the preset pressure standard deviation obtained from the preset database; the first center point pressure standard deviation indicates the standard deviation of the non-no-load pressure data obtained by any first pressure sensor and other pressure sensors on the seat cushion at the same preset sampling point in the pressure data pre-collection experiment, and the second center point pressure standard deviation indicates the standard deviation of the non-no-load pressure data obtained by another first pressure sensor and other pressure sensors on the seat cushion at the same preset sampling point in the pressure data pre-collection experiment.

[0080] A24: If the pressure standard deviation of the first center point is greater than the pressure standard deviation of the second center point, the pressure sensor corresponding to the pressure standard deviation of the second center point is used to obtain non-no-load pressure data; if the pressure standard deviation of the first center point is less than the pressure standard deviation of the second center point, the pressure sensor corresponding to the pressure standard deviation of the first center point is used to obtain non-no-load pressure data; otherwise, any one of the first pressure sensors is used to obtain non-no-load pressure data.

[0081] A25. In the pressure data pre-collection experiment, a dual-sensor cross-validation is performed for a preset number of verifications within a preset verification time. If the verification pass rate is not less than the preset verification pass rate obtained from the preset database, the first pressure sensor is used to obtain non-no-load pressure data. Otherwise, the first pressure sensor is stopped from being used, and the non-no-load pressure data obtained by the second pressure sensor is used to replace the non-no-load pressure data obtained by the first pressure sensor. The verification pass rate represents the ratio of the number of verifications in which the verification results do not meet the fourth judgment condition in the dual-sensor cross-validation to the preset number of verifications. The second pressure sensor represents the pressure sensor closest to the center point of the seat cushion of the automobile seat. The fourth judgment condition represents that the pressure standard deviation of the first center point is greater than the preset pressure standard deviation obtained from the preset database, and the pressure standard deviation of the second center point is greater than the preset pressure standard deviation obtained from the preset database. The preset verification time, preset verification times and preset verification pass rate are set according to the preset personnel.

[0082] In this embodiment, the non-no-load pressure data obtained in the pressure data pre-acquisition experiment is optimized before the vehicle seat side adjustment safety test. The noise signal in the non-no-load pressure data above the cutoff frequency is removed by a low-pass filter, which effectively suppresses the noise interference caused by vibration, improves the signal-to-noise ratio of the data, and makes the data more accurately reflect the actual pressure changes; by comparing the data collected by the two pressure sensors, the data deviation caused by factors such as the sensor itself error is eliminated, and the reliability and accuracy of the data are further improved, ensuring that the optimized data can better meet actual needs; when the acquisition accuracy evaluation result after the cross-validation of the two sensors still does not meet the requirements, the feedback mechanism can prompt relevant personnel to further check and adjust the data acquisition process, and promptly discover and solve possible problems, such as sensor failure, so as to ensure the improvement of the quality of pressure data obtained in the vehicle seat safety test; the preset pressure change amplitude is obtained by comprehensive mapping of vehicle speed, acceleration, and acquisition accuracy evaluation results, thereby improving the accuracy of the preset value setting of the pressure change amplitude.

[0083] Through dual-sensor cross-validation, the pressure data deviation and standard deviation of the two sensors are compared, and the sensor with higher stability is selected to ensure the accuracy of pressure data collection, thereby improving the reliability of car seat side adjustment safety detection.

[0084] Furthermore, the specific method for determining whether to perform detection optimization based on the average quantitative determination result of the preset number of detections is as follows:

[0085] Determine whether the average side wing detection result of a preset number of detections during the side wing adjustment safety detection process is less than a preset side wing detection determination threshold. If the side wing detection result is less than the preset side wing detection determination threshold, perform side wing adjustment detection optimization; otherwise, do not perform side wing adjustment detection optimization. The average side wing detection determination result represents the average detection response efficiency of the side wing adjustment safety detection in the automobile seat safety detection, and represents the average value of the side wing detection determination results. The preset side wing detection determination threshold is represented by the average value of the average side wing detection determination results in the historical time period.

[0086] The optimization of wing adjustment detection includes distributed parallel processing and data compression processing. Distributed parallel processing distributes data acquisition, data processing and adjustment feedback in the wing adjustment safety detection process to different processing units to obtain data acquisition units, data processing units and adjustment feedback units. Then, a real-time operating system (RTOS) is used to assign priorities and resources to different units to improve the response efficiency of the wing adjustment safety detection process in automobile seat safety detection. Data compression processing is used to reduce the data transmission burden and improve the response efficiency of automobile seat safety detection.

[0087] The specific process of data compression processing is as follows:

[0088] Non-no-load pressure data is transmitted through an incremental compression algorithm; if the average pressure change of all two adjacent preset sampling points in a preset single safety detection cycle during the flank adjustment safety detection process is less than the preset pressure change threshold, the first compression algorithm is used, otherwise the flank detection judgment result and the data transmission speed during the flank adjustment safety detection process are input into the compression depth mapping set to obtain the compression depth, and the compression depth in the second compression algorithm is adjusted according to the compression depth. The compression depth mapping set is a set obtained from a preset database that represents the mapping relationship between the flank detection judgment result, the data transmission speed and the compression depth during the flank adjustment safety detection process. The first compression algorithm is the Huffman coding compression algorithm, and the second compression algorithm is the gzip compression algorithm. The data processing efficiency of the second compression algorithm optimizes the first compression algorithm.

[0089] In this embodiment, distributed parallel processing is used to improve the speed and efficiency of data processing, and resource allocation is optimized to ensure that each task in the automobile seat wing adjustment safety detection can respond quickly and reduce delays. Through data compression processing, the amount of data transmission is reduced, the burden on network bandwidth is alleviated, and the real-time response capability of the automobile seat safety detection is improved, thereby improving the overall detection efficiency of the automobile seat wing adjustment safety detection, providing a foundation for subsequent automobile seat wing adjustment safety detection.

[0090] The incremental compression algorithm avoids the transmission of duplicate data, thereby reducing the amount of data transmitted and reducing data processing delays. By judging the pressure change, different compression algorithms are intelligently selected to avoid data transmission delays caused by complex compression, improve the response speed and quality of data transmission, and thus improve the performance of side wing adjustment safety testing in automobile seat safety testing. The above steps provide a foundation for subsequent automobile seat side wing adjustment safety testing.

[0091] To sum up, the embodiment of the present application evaluates the accuracy of data pre-acquisition through the pressure data obtained in the pressure data pre-acquisition experiment, then optimizes the sampling frequency of the side wing adjustment safety detection according to the pressure change frequency, and finally quantitatively determines the side wing detection performance during the side wing adjustment safety detection process, and performs the side wing adjustment safety detection according to the detection parameters, thereby improving the accuracy of data acquisition in the side wing adjustment safety detection, and further achieving an improvement in the accuracy of the automobile seat side wing adjustment safety detection, effectively solving the problem of low accuracy of the automobile seat side wing adjustment safety detection caused by the deviation of the pressure sensor data collected in the prior art.

[0092] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0096] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0097] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A car seat safety detection method based on pressure sensing, characterized in that: The following steps are involved: S1, pre-collecting pressure data of the seat to be tested, obtaining non-loaded pressure data and no-load pressure data, and evaluating the accuracy of the pre-collected data, and optimizing the non-loaded pressure data based on the evaluation results; S2, judging whether to optimize the initial sampling frequency of the wing adjustment safety detection according to the pressure change frequency of the optimized non-no-load pressure data; S3. If the initial sampling frequency of the wing adjustment safety test is optimized, then the non-no-load pressure data acquired during the wing adjustment safety test at the optimized sampling frequency and the corresponding pressure sensor position are mapped to obtain the wing angle. Otherwise, the non-no-load pressure data acquired during the wing adjustment safety test at the initial sampling frequency and the corresponding pressure sensor position are mapped to obtain the wing angle, and the actual wing adjustment angle is simultaneously collected. S4, quantitatively determining the side wing detection performance of the seat to be tested based on the side wing adjustment detection performance parameters obtained from a single side wing adjustment safety test, and determining whether to perform test optimization based on an average of the quantitative determination results from a preset number of tests. If test optimization is performed, then continuing the side wing adjustment safety test based on the optimized detection parameters after the test optimization; otherwise, performing the side wing adjustment safety test based on the initial detection parameters; The specific process of determining whether to optimize the initial sampling frequency of the wing adjustment safety detection based on the pressure change frequency of the optimized non-no-load pressure data is as follows: Obtain the pressure change frequency of the optimized non-no-load pressure data in S1; If the pressure change frequency is greater than a first preset pressure change frequency obtained from a preset database, the first pressure change frequency deviation and the initial sampling frequency are input into a sampling frequency mapping set to obtain a corrected sampling frequency, and non-no-load pressure data is obtained according to the corrected sampling frequency within a preset single safety detection period during the wing adjustment safety detection process. The first pressure change frequency deviation is obtained by performing deviation processing on the pressure change frequency and the first preset pressure change frequency. The sampling frequency mapping set is a set obtained from the preset database that represents a mapping relationship between the first pressure change frequency deviation, the initial sampling frequency, and the corrected sampling frequency. If the pressure change frequency is less than the second preset pressure change frequency obtained from the preset database, the second pressure change frequency deviation and the initial sampling frequency are input into the sampling frequency mapping set to obtain a corrected sampling frequency, and non-no-load pressure data is obtained according to the corrected sampling frequency within a preset single safety detection cycle during the wing adjustment safety detection process. The second pressure change frequency deviation is obtained by performing deviation processing on the second preset pressure change frequency and the pressure change frequency. Otherwise, non-no-load pressure data is directly obtained according to the initial sampling frequency within the preset single safety detection cycle during the wing adjustment safety detection process.

2. The automobile seat safety detection method based on pressure sensing as claimed in claim 1, characterized in that: The optimized test parameters include the optimized test pressure range and the optimized test remaining times. The specific acquisition method is as follows: Inputting the wing detection result deviation coefficient, the maximum value of the non-no-load pressure data, and the minimum value of the non-no-load pressure data into a test pressure range mapping set to obtain an optimized test pressure range, wherein the test pressure range mapping set is a set obtained from a preset database and represents a mapping relationship between the wing detection result deviation coefficient, the maximum value of the non-no-load pressure data, the minimum value of the non-no-load pressure data, and the optimized test pressure range; Inputting the deviation coefficient of the flanking detection result into a detection remaining number mapping set to obtain an optimized detection remaining number, wherein the detection remaining number mapping set is a set obtained from a preset database and represents a mapping relationship between the deviation coefficient of the flanking detection result and the optimized detection remaining number; Determine whether the maximum value of the optimized test pressure in the optimized test pressure range is less than the preset maximum value of the test pressure; if so, adjust the maximum value of the optimized test pressure in the optimized test pressure range to the preset maximum value of the test pressure; otherwise, do not adjust the maximum value of the optimized test pressure in the optimized test pressure range; Determine whether the remaining number of optimization tests is greater than the preset maximum remaining number of optimization tests. If so, adjust the remaining number of optimization tests to the preset maximum remaining number of optimization tests; otherwise, do not adjust the remaining number of optimization tests.

3. The automobile seat safety detection method based on pressure sensing as claimed in claim 1, characterized in that: The specific process of performing data pre-collection accuracy assessment is as follows: Acquiring initial non-loaded pressure data and no-load pressure data of the seat to be tested within a preset data pre-collection period during the pressure data pre-collection process, wherein the no-load pressure data is the pressure of the seat to be tested measured by the pressure sensor in an unloaded state and obtained through the pressure data pre-collection process, and the initial non-loaded pressure data is the pressure of the seat to be tested in a non-loaded state and obtained through the pressure data pre-collection process; Performing deviation processing on the initial non-load pressure data and the no-load pressure data of the seat to be tested to obtain the non-load pressure data; performing relative deviation processing on the acquired average non-load pressure data and a preset non-load pressure acquired from a preset database to obtain a pressure deviation coefficient, wherein the average non-load pressure data is the average pressure of the seat to be tested in a non-load state acquired through pre-collection of pressure data; An acquisition accuracy evaluation compensation value is introduced to perform assignment coupling processing on the de-unitized non-no-load pressure data standard deviation, pressure deviation coefficient and de-unitized no-load pressure obtained in the pressure data pre-acquisition, and then an inverse proportional operation is performed on the result of the assignment coupling processing to obtain an acquisition accuracy evaluation result. The acquisition accuracy evaluation compensation value includes a first acquisition accuracy evaluation compensation value, a second acquisition accuracy evaluation compensation value and a third acquisition accuracy evaluation compensation value. The acquisition accuracy evaluation result is used to quantitatively evaluate the accuracy of the pressure data obtained through pressure data pre-acquisition in automobile seat safety testing.

4. The automobile seat safety detection method based on pressure sensing as claimed in claim 1, characterized in that: The non-no-load pressure data obtained at the optimized sampling frequency during the wing adjustment safety detection process and the corresponding pressure sensor position are mapped to obtain the wing angle. The specific process is as follows: Establish a coordinate system with the center of the seat as the origin, the direction pointing to the seat back as the vertical axis, and the direction parallel to the right of the seat edge as the horizontal axis; Arrange the average non-no-load pressure data of each pressure sensor in descending order to obtain the position of the pressure sensor corresponding to the maximum average non-no-load pressure data, and mark the horizontal coordinate of the position of the corresponding pressure sensor as the center offset coefficient; When the center offset coefficient is not equal to 0, the first wing angle influencing parameter is input into the right wing angle mapping set to obtain the right wing angle, and the first wing angle influencing parameter is input into the left wing angle mapping set to obtain the left wing angle; otherwise, the second wing angle influencing parameter is input into the wing angle mapping set to obtain the wing angle, the first wing angle influencing parameter including the number of pressure sensors used, the maximum average non-no-load pressure data, the backrest pressure ratio, the seat cushion pressure ratio, and the center offset coefficient; the second wing angle influencing parameter including the number of pressure sensors used, the maximum average non-no-load pressure data, the backrest pressure ratio, and the seat cushion pressure ratio; The right flank angle mapping set is a set obtained from a preset database and represents a mapping relationship between the first flank angle influencing parameter and the right flank angle; The left flank angle mapping set is a set obtained from a preset database and represents a mapping relationship between the first flank angle influencing parameter and the left flank angle; The flank angle mapping set is a set obtained from a preset database and represents a mapping relationship between the second flank angle influencing parameter and the flank angle.

5. The automobile seat safety detection method based on pressure sensing as claimed in claim 4, characterized in that: The side wing adjustment detection performance parameters obtained based on a side wing adjustment safety test are used to quantitatively determine the side wing detection performance of the seat to be tested. The specific process is as follows: A coupling process is performed based on the wing adjustment time and the adjustment command output time to obtain the total wing adjustment processing time. If the total wing adjustment processing time is not less than the preset processing time, a deviation comparison process is performed based on the total wing adjustment processing time and the preset processing time to obtain the wing adjustment time deviation coefficient. Otherwise, the wing adjustment time deviation coefficient is recorded as 0. Perform relative deviation processing on the actual wing adjustment angle and the wing angle to obtain the wing adjustment deviation coefficient; A non-no-load pressure data volume comparison coefficient is obtained by performing relative deviation processing on the non-no-load pressure data volume and the preset non-no-load pressure data volume; A side wing detection and judgment compensation value is introduced to perform assignment coupling processing on the side wing adjustment time deviation coefficient, the side wing adjustment deviation coefficient, the non-no-load pressure data volume comparison coefficient and the de-unitized adjustment instruction output time jitter, and then an inverse proportional operation is performed to obtain the side wing detection and judgment result. The side wing detection and judgment compensation value includes a first side wing detection and judgment compensation value, a second side wing detection and judgment compensation value, a third side wing detection and judgment compensation value and a fourth side wing detection and judgment compensation value. The side wing detection and judgment result is used to quantitatively evaluate the detection response efficiency of the side wing adjustment safety detection in the automobile seat safety detection.

6. The automobile seat safety detection method based on pressure sensing as claimed in claim 3, characterized in that: The specific process of optimizing the non-no-load pressure data according to the evaluation results is as follows: S11, if the evaluation result is less than a preset acquisition accuracy evaluation threshold obtained from a preset database, vibration noise suppression is performed; otherwise, the optimized non-no-load pressure data is the non-no-load pressure data obtained by pre-acquisition of pressure data of the seat to be tested; S12, if the acquisition accuracy evaluation result after vibration noise suppression is less than a preset acquisition accuracy evaluation threshold obtained from a preset database, then performing dual-sensor cross-validation, otherwise terminating the non-no-load pressure data optimization, wherein the dual-sensor cross-validation is used to eliminate the deviation of the non-no-load pressure data caused by the pressure sensor; S13, if the acquisition accuracy evaluation result after the dual-sensor cross-validation is less than the preset acquisition accuracy evaluation threshold obtained from the preset database, feedback is given; otherwise, the non-no-load pressure data optimization is terminated; The optimization of non-no-load pressure data includes vibration noise suppression and dual-sensor cross-validation.

7. The automobile seat safety detection method based on pressure sensing as claimed in claim 5, characterized in that: The specific method for determining whether to perform detection optimization based on the average quantitative determination result of the preset number of detections is as follows: determining whether an average side wing detection determination result of a preset number of detections during the side wing adjustment safety detection process is less than a preset side wing detection determination threshold, and if so, performing side wing adjustment detection optimization; otherwise, not performing side wing adjustment detection optimization, the average side wing detection determination result representing an average detection response efficiency of the side wing adjustment safety detection in the vehicle seat safety detection; The flank adjustment detection optimization includes distributed parallel processing and data compression processing; The distributed parallel processing is used to improve the response efficiency during the wing adjustment safety detection process in the automobile seat safety detection.

8. The automobile seat safety detection method based on pressure sensing as claimed in claim 6, characterized in that: The vibration noise suppression removes noise signals of the non-no-load pressure data that are higher than a cutoff frequency through a low-pass filter, thereby suppressing noise generated by vibration in the non-no-load pressure data. The cutoff frequency is obtained by inputting vehicle speed, acquisition accuracy assessment result, pressure signal frequency, and noise frequency into a cutoff frequency mapping set. The cutoff frequency mapping set is a set of mapping relationships between vehicle speed, acquisition accuracy assessment result, pressure signal frequency, noise frequency, and cutoff frequency, obtained from a preset database. The specific process of the dual-sensor cross-validation is as follows: A21, performing deviation processing on the non-no-load pressure data acquired by two first pressure sensors at the same preset sampling point in the pressure data pre-acquisition to obtain a center point pressure deviation, where the first pressure sensor is a pressure sensor provided at the center point of the seat cushion of the automobile seat to be tested; A22, determining whether the center point pressure deviation obtained at the same preset sampling point in the pressure data pre-collection is greater than the preset pressure deviation obtained from the preset database; if the center point pressure deviation is greater than the preset pressure deviation obtained from the preset database, executing A23; otherwise, using any one of the first pressure sensors to obtain non-no-load pressure data; A23, respectively determining whether a first center point pressure standard deviation and a second center point pressure standard deviation obtained at the same preset sampling point in the pressure data pre-collection are greater than a preset pressure standard deviation obtained from a preset database. If a first judgment condition is met, acquiring non-no-load pressure data using the first pressure sensor corresponding to the second center point pressure standard deviation, the first judgment condition being that the first center point pressure standard deviation is greater than the preset pressure standard deviation obtained from the preset database, and the second center point pressure standard deviation is not greater than the preset pressure standard deviation obtained from the preset database. If a second judgment condition is met, the non-no-load pressure data is obtained using the first pressure sensor corresponding to the first center point pressure standard deviation, wherein the second judgment condition indicates that the first center point pressure standard deviation is not greater than the preset pressure standard deviation obtained from the preset database, and the second center point pressure standard deviation is greater than the preset pressure standard deviation obtained from the preset database; If the third judgment condition is met, execute A24, otherwise execute A25, wherein the third judgment condition indicates that the first center point pressure standard deviation is not greater than the preset pressure standard deviation obtained from the preset database, and the second center point pressure standard deviation is not greater than the preset pressure standard deviation obtained from the preset database; The first center point pressure standard deviation represents the standard deviation of the non-no-load pressure data obtained by any first pressure sensor and other pressure sensors on the seat cushion at the same preset sampling point in the pressure data pre-collection; the second center point pressure standard deviation represents the standard deviation of the non-no-load pressure data obtained by another first pressure sensor and other pressure sensors on the seat cushion at the same preset sampling point in the pressure data pre-collection; A24: If the first center point pressure standard deviation is greater than the second center point pressure standard deviation, use the pressure sensor corresponding to the second center point pressure standard deviation to obtain non-no-load pressure data. If the first center point pressure standard deviation is less than the second center point pressure standard deviation, use the pressure sensor corresponding to the first center point pressure standard deviation to obtain non-no-load pressure data. Otherwise, use any one of the first pressure sensors to obtain non-no-load pressure data. A25. Perform dual-sensor cross-validation for a preset number of verifications within a preset verification time in pressure data pre-collection. If the verification pass rate is not less than the preset verification pass rate obtained from the preset database, use the first pressure sensor to obtain non-no-load pressure data. Otherwise, stop using the first pressure sensor and use the non-no-load pressure data obtained by the second pressure sensor to replace the non-no-load pressure data obtained by the first pressure sensor. The verification pass rate indicates that the number of verifications in which the verification results meet the fourth judgment condition in the dual-sensor cross-validation accounts for the proportion of the preset verification times. The second pressure sensor indicates the pressure sensor closest to the center point of the car seat cushion. The fourth judgment condition indicates that the pressure standard deviation of the first center point is greater than the preset pressure standard deviation obtained from the preset database, and the pressure standard deviation of the second center point is greater than the preset pressure standard deviation obtained from the preset database.

9. The automobile seat safety detection method based on pressure sensing as claimed in claim 7, characterized in that: The specific process of the data compression process is as follows: Transmission of non-idle pressure data through incremental compression algorithm; If the average pressure change of all two adjacent preset sampling points in a preset single safety detection cycle during the flank adjustment safety detection process is less than a preset pressure change threshold, the first compression algorithm is used; otherwise, the flank detection judgment result and the data transmission speed during the flank adjustment safety detection process are input into the compression depth mapping set to obtain the compression depth, and the compression depth in the second compression algorithm is adjusted according to the compression depth. The compression depth mapping set is a set obtained from a preset database that represents the mapping relationship between the flank detection judgment result, the data transmission speed and the compression depth during the flank adjustment safety detection process. The data processing efficiency of the second compression algorithm is better than that of the first compression algorithm.

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