Automobile seat safety detection method based on pressure sensing

Abstract

The invention discloses an automobile seat safety detection method based on pressure sensing, and relates to the technical field of automobile seat safety detection. The automobile seat safety detection method based on pressure sensing comprises the steps of data pre-acquisition accuracy evaluation, detection data sampling frequency correction, side wing adjustment safety detection data acquisition and side wing adjustment safety detection performance analysis. According to the method, data pre-acquisition accuracy evaluation is carried out through the pressure data of the to-be-detected seat in the pressure data pre-acquisition experiment so as to optimize the pressure data, then the sampling frequency of side wing adjustment safety detection is optimized according to the pressure change frequency, and finally, the side wing detection performance is quantitatively judged in the side wing adjustment safety detection process. The side wing adjustment safety detection is carried out according to the detection parameters, the accuracy of the side wing adjustment safety detection of the automobile seat is improved, and the problem that the accuracy of the side wing adjustment safety detection of the automobile seat is low due to deviation of data collected by a pressure sensor in the prior art is solved.

Description

Technical Field

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

[0002] The angle of the automotive seat side wing usually refers to the angle between the surface of the seat side wing and the plane of the seat back body; the main function of the automotive seat side wing is to provide lateral support and effectively reduce the displacement and injury of the occupant during a side collision. The safety detection of the automotive seat side wing involves multiple aspects, including collision testing, strength testing, airbag compatibility, material testing, etc.; the safety detection of the automotive seat side wing based on pressure sensing is to obtain the pressure applied to the seat through the pressure sensors installed on the automotive seat, then compare historical data to detect whether there is an abnormal state, and finally adjust the side wing angle according to the preset safety standards. Its core is to obtain key information about the occupant's safety by real-time monitoring of the pressure change of the seat, which is beneficial to evaluating the protection effect of the automotive seat on the occupant and its performance under collision or other external forces. The pressure sensors monitor the pressure distribution of the automotive seat in real time to evaluate whether the side wing provides sufficient supporting force in the key areas.

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

[0004] For example, a handheld automotive seat local hardness tester and testing method disclosed in the patent application with the publication number: CN113654929A include: a force measuring system module capable of measuring force values, a press-in depth indicating frame and a pressure sensing press plate. The bottom of the force measuring system module is fixed with a scale disk connecting rod, the press-in depth indicating frame is sleeved outside the scale disk connecting rod, the pressure sensing press plate is threadedly connected inside the bottom end of the scale disk connecting rod, and the upper and lower edges of the press-in depth indicating frame are respectively provided with press-in depth indicating lines, and the bottom ends are respectively provided with contact indicating balls.

[0005] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: In the prior art, sensors may have data deviation due to aging, damage or environmental factors, affecting the reliability of detection. During the simulation of vehicle driving, factors such as uneven road surface and engine vibration will cause fluctuations in the data collected by the pressure sensor, which may cause signal noise of the pressure sensor and lead to misjudgment (such as triggering a warning without actually detecting an occupant), affecting the accuracy of detection. If the accuracy of the sensor in obtaining data is low and it cannot capture the subtle adjustment requirements, it will lead to deviation in the adjustment of the flank angle of the car seat; since the vehicle is in different working conditions, the pressure sensor will sense continuously changing seat pressure data. Dynamic factors such as vehicle driving, deceleration, and turning will cause frequent pressure changes, and the sensor continuously outputs data, which may lead to insufficient bandwidth, resulting in data transmission delay, and there is a problem of low accuracy in the safety detection of the flank adjustment of the car seat due to the deviation of the data collected by the pressure sensor. Summary of the Invention

[0006] The embodiments of the present application solve the problem of low accuracy in the safety detection of the flank adjustment of the car seat caused by the deviation of the data collected by the pressure sensor in the prior art by providing a safety detection method for a car seat based on pressure sensing, and improve the accuracy of the safety detection of the flank adjustment of the car seat.

[0007] The embodiments of the present application provide a safety detection method for a car seat based on pressure sensing, including the following steps: S1, pre-collect pressure data of the seat to be detected, obtain non-empty load pressure data and empty load pressure data, and evaluate the accuracy of the pre-collected data, and optimize the non-empty load pressure data according to the evaluation result; S2, judge whether to optimize the initial sampling frequency of the safety detection of the flank adjustment according to the pressure change frequency of the optimized non-empty load pressure data; S3, if the initial sampling frequency of the safety detection of the flank adjustment is optimized, map the non-empty load pressure data obtained at the optimized sampling frequency and the corresponding pressure sensor positions during the safety detection of the flank adjustment to obtain the flank angle, otherwise map the non-empty load pressure data obtained at the initial sampling frequency and the corresponding pressure sensor positions during the safety detection of the flank adjustment to obtain the flank angle, and synchronously collect the actual flank adjustment angle; S4, quantitatively determine the flank detection performance of the seat to be detected based on the flank adjustment detection performance parameters obtained from one safety detection of the flank adjustment, and judge whether to perform detection optimization based on the average quantitative determination result of the preset number of detections. If detection optimization is performed, continue the safety detection of the flank adjustment according to the optimized detection parameters after the detection optimization, otherwise perform the safety detection of the flank adjustment according to the initial detection parameters.

[0008] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Determine whether to optimize the initial sampling frequency for flanking adjustment safety detection by the pressure change frequency, which improves the quality of data acquisition during flanking adjustment safety detection. If the pressure change frequency is greater than the first preset pressure change frequency, it means that the higher the current pressure change frequency, the higher the sampling frequency may be required to capture the rapidly changing pressure data and ensure the accuracy of the acquired 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, the lower the sampling frequency for data acquisition, thereby reducing the amount of non-no-load pressure data.

[0009] 2. By establishing a coordinate system and obtaining the center offset coefficient, finally, when the center offset coefficient is not equal to 0, input the first flanking angle influence parameter into the right flanking angle mapping set to obtain the right flanking angle, and input the first flanking angle influence parameter into the left flanking angle mapping set to obtain the left flanking angle. Otherwise, input the second flanking angle influence parameter into the flanking angle mapping set to obtain the flanking angle, ensuring that during the flanking adjustment safety detection process, the change of the flanking angle can be captured in a timely manner, thereby improving the accuracy of the flanking adjustment safety detection in automotive seat safety detection.

[0010] 3. By suppressing vibration noise, use a low-pass filter to remove the noise signal of the non-no-load pressure data above the cut-off frequency, effectively suppressing the noise interference generated by vibration in the non-no-load pressure data, improving the signal-to-noise ratio of the pressure data, and making the pressure data more accurately reflect the real pressure change situation. By comparing the data collected by two pressure sensors, the data deviation caused by factors such as the error of the sensor itself is eliminated, further improving the reliability and accuracy of the pressure data, and then improving the reliability of the flanking adjustment safety detection of automotive seats. Brief Description of the Drawings

[0011] Figure 1 It is a flowchart of the accuracy evaluation of data pre-acquisition and the correction of the sampling frequency of detection data in the automotive seat safety detection method based on pressure sensing provided by the embodiment of the present application; Figure 2 It is a flowchart of the acquisition of flanking adjustment safety detection data and the analysis of the performance of flanking adjustment safety detection in the automotive seat safety detection method based on pressure sensing provided by the embodiment of the present application. Detailed Embodiment

[0012] Embodiments of the present application provide a method for safety detection of automotive seats based on pressure sensing, which solves the problem of low accuracy in safety detection of adjustment of automotive seat side wings caused by data deviation in pressure sensor data acquisition in the prior art. By performing a data pre-acquisition accuracy evaluation on the non-empty load pressure data and empty load pressure data obtained in the pressure data pre-acquisition experiment of the seat to be detected, it is determined whether to optimize the non-empty load pressure data. Then, the non-empty load pressure data obtained at the sampling frequency during the safety detection of side wing adjustment and the corresponding pressure sensor positions are mapped to obtain the side wing angle. Then, according to the pressure change frequency, it is determined whether to optimize the initial sampling frequency of the safety detection of side wing adjustment. Finally, during the safety detection of side wing adjustment, the side wing detection performance is quantitatively determined, and based on the average quantitative determination result of the preset number of detections, it is determined whether to perform detection optimization. According to the optimized detection parameters, the safety detection of side wing adjustment is continued, achieving an improvement in the accuracy of safety detection of side wing adjustment of automotive seats.

[0013] The technical solution in the embodiments of the present application is to solve the problem of low accuracy in safety detection of adjustment of automotive seat side wings caused by data deviation in pressure sensor data acquisition, and the general idea is as follows: By performing a data pre-acquisition accuracy evaluation on the pressure data of the seat to be detected in the pressure data pre-acquisition experiment to optimize the pressure data, then optimizing the sampling frequency of the safety detection of side wing adjustment according to the pressure change frequency, and finally quantitatively determining the side wing detection performance during the safety detection of side wing adjustment, and continuing the safety detection of side wing adjustment according to the optimized detection parameters, the accuracy of safety detection of side wing adjustment of automotive seats is improved.

[0014] To better understand the above technical solution, the above technical solution will be described in detail below in combination with the accompanying drawings of the specification and specific embodiments.

[0015] As Figure 1As shown, it is a flowchart of data pre - acquisition accuracy evaluation and detection data sampling frequency correction in the vehicle seat safety detection method based on pressure sensing provided by the embodiment of the present application. It describes that first, non - no - load pressure data and no - load pressure data obtained in the pressure data pre - acquisition experiment are used for data pre - acquisition accuracy evaluation, and then the acquisition accuracy evaluation result is judged. It is judged whether the acquisition accuracy evaluation result is less than the preset acquisition accuracy evaluation threshold. If so, the non - no - load pressure data obtained in the pressure data pre - acquisition experiment is optimized. Otherwise, the pressure change frequency is directly judged. Then, it is judged whether the optimized acquisition accuracy evaluation result is less than the preset acquisition accuracy evaluation threshold. If not, the pressure change frequency is judged. Otherwise, feedback is performed. Finally, it is judged whether the pressure change frequency is greater than the first preset pressure change frequency. If so, the corrected sampling frequency is obtained by mapping according to the first pressure change frequency deviation and the initial sampling frequency, and the non - no - load pressure data is obtained according to the corrected sampling frequency. Otherwise, it is judged whether the pressure change frequency is greater than the second preset pressure change frequency. If so, the corrected sampling frequency is obtained by mapping according to the second pressure change frequency deviation and the initial sampling frequency, and the non - no - load pressure data is obtained according to the corrected sampling frequency. Otherwise, the non - no - load pressure data is directly obtained according to the initial sampling frequency.

[0016] As Figure 2 As shown, it is a flowchart of flank adjustment safety detection data acquisition and flank adjustment safety detection performance analysis in the vehicle seat safety detection method based on pressure sensing provided by the embodiment of the present application. It describes that the flank detection performance of the seat to be detected is quantitatively determined according to the flank angle obtained by mapping the non - no - load pressure data and the corresponding pressure sensor positions and the actual flank angle. Then, it is judged whether the flank detection determination result is less than the preset flank detection determination threshold. If so, detection optimization is performed, and the flank adjustment safety detection is continued according to the optimized detection parameters after detection optimization. Otherwise, the flank adjustment safety detection is directly performed according to the initial detection parameters.

[0017] The vehicle seat safety detection method based on pressure sensing provided by the embodiment of the present application simulates the vehicle driving conditions in the vehicle seat safety detection method to ensure the accuracy of vehicle seat safety detection, including the following steps: S1, before the flank adjustment safety detection, the non - no - load pressure data and no - load pressure data obtained in the pressure data pre - acquisition experiment on the seat to be detected are used for data pre - acquisition accuracy evaluation to obtain the acquisition accuracy evaluation result; the simulated detection section and the simulated detection vehicle used in the pressure data pre - acquisition experiment and the flank adjustment safety detection are the same.

[0018] S2. Determine whether to optimize the non - no - load pressure data obtained in the pressure data pre - acquisition experiment based on the sampling accuracy evaluation result. If optimization is performed, then determine whether to optimize the initial sampling frequency of the flank adjustment safety detection according to the optimized pressure change frequency. Otherwise, directly determine whether to optimize the initial sampling frequency of the flank adjustment safety detection according to the pressure change frequency in the pressure data pre - acquisition experiment. Optimizing the non - no - load pressure data obtained in the pressure data pre - acquisition experiment is used to improve the acquisition accuracy of the pressure data in the flank 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 and can be set to 1 hour, for example.

[0019] S3. If the initial sampling frequency of the flank adjustment safety detection is optimized, then map the non - no - load pressure data obtained at the corrected sampling frequency during the flank adjustment safety detection process and the corresponding pressure sensor positions to obtain the flank angle, and synchronously collect the actual flank adjustment angle. Otherwise, map the non - no - load pressure data obtained at the initial sampling frequency during the flank adjustment safety detection process and the corresponding pressure sensor positions to obtain the flank angle, and synchronously collect the actual flank adjustment angle. The corrected sampling frequency represents the sampling frequency obtained after optimizing the initial sampling frequency of the flank adjustment safety detection.

[0020] S4. Quantitatively determine the flank detection performance of the seat to be detected based on the flank adjustment detection performance parameters obtained from one flank adjustment safety detection, and determine whether to perform detection optimization based on the average quantitative determination results of the preset number of detections. If detection optimization is performed, then continue the flank adjustment safety detection according to the optimized detection parameters after detection optimization. Otherwise, directly perform the flank adjustment safety detection according to the initial detection parameters. The preset number of detections is set according to the preset personnel.

[0021] Before designing the automotive seat safety detection method based on pressure sensing, a database for storing various types of set data is established. The database includes, but is not limited to, preset non - no - load pressure, preset processing duration, acquisition accuracy evaluation compensation value, and sampling frequency mapping set. Various values therein are directly set by technicians. Among them, the setting basis of the preset processing duration can be set according to the preset personnel. For example, the preset processing duration is represented by the average of the historical processing durations within the historical time period in the database. In addition, various values in the database can be set and fine - tuned by technicians according to actual debugging.

[0022] In this embodiment, in the present application, the pressure data pre-acquisition experiment is used to improve the accuracy of data acquisition in the process of flank adjustment safety detection; through the evaluation of the accuracy of data pre-acquisition, it is ensured that the collected pressure data is accurate and reliable, avoiding false detection caused by inaccurate sensors; through the detection data sampling frequency correction module, the sampling frequency is dynamically adjusted according to the change of pressure data to ensure the accuracy and comprehensiveness of the data collected during the seat adjustment process, enhancing the accuracy of automotive seat safety detection; through the flank adjustment safety detection data acquisition module, it provides a judgment basis for the quantitative determination of the flank detection performance of the seat to be detected in the subsequent flank adjustment safety detection performance analysis module, thereby effectively evaluating the flank adjustment safety detection; through the quantitative analysis of the flank adjustment safety detection performance analysis module, it can be optimized according to the average quantitative determination result of the preset number of detections to improve the reliability and stability of subsequent automotive seat safety detection.

[0023] Furthermore, it is determined whether to optimize the initial sampling frequency of the flank adjustment safety detection according to the optimized pressure change frequency. The specific process is as follows: Obtain the optimized non-empty load pressure data in S1.

[0024] Judge whether the pressure change frequency is greater than the first preset pressure change frequency obtained from the preset database: 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 the corrected sampling frequency. During the flank adjustment safety detection process, the non-empty load pressure data is obtained according to the corrected sampling frequency within the preset single safety detection period. The first pressure change frequency deviation is obtained through deviation processing of the pressure change frequency and the first preset pressure change frequency. The sampling frequency mapping set is a set representing the mapping relationship 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 within 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 within 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.

[0025] 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 the corrected sampling frequency. During the flank adjustment safety detection process, the no-load pressure data is obtained according to the corrected sampling frequency within the preset single safety detection period. 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, during the flank adjustment safety detection process, the no-load pressure data is directly obtained according to the initial sampling frequency within the preset single safety detection period; deviation processing means performing a subtraction operation on two data.

[0026] In this embodiment, determining whether to optimize the initial sampling frequency of the flank adjustment safety detection through the pressure change frequency is part of the data pre-acquisition experiment, and is used to improve the quality of data acquisition during the flank adjustment safety detection process. If the pressure change frequency is greater than the first preset pressure change frequency, it means that the higher the current pressure change frequency, the higher the sampling frequency may be required to capture the rapidly changing pressure data, ensuring more frequent sampling and more accurate pressure data obtained. If the pressure change frequency is less than the second preset pressure change frequency, it means that the lower the current pressure change frequency, the lower the sampling frequency for data acquisition can avoid the acquisition of redundant data, thereby reducing the amount of no-load pressure data. By dynamically adjusting the sampling frequency under different pressure fluctuation conditions, the efficiency and accuracy of pressure data acquisition during the automotive seat flank adjustment safety detection process are improved, resource waste is avoided, and the resource utilization efficiency is optimized.

[0027] Furthermore, the optimized detection parameters include optimizing the test pressure range and the remaining number of detections. The specific acquisition methods are as follows: The flank detection determination result deviation coefficient, the maximum value of the no-load pressure data, and the minimum value of the 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 the preset database that represents the mapping relationship between the flank detection determination result deviation coefficient, the maximum value of the no-load pressure data, the minimum value of the no-load pressure data, and the optimized test pressure range; the maximum value of the no-load pressure data represents the maximum value of the no-load pressure data obtained during the flank adjustment safety detection process; the minimum value of the no-load pressure data represents the minimum value of the no-load pressure data obtained during the flank adjustment safety detection process.

[0028] The flank detection determination result deviation coefficient is input into the remaining number of detections mapping set to obtain the optimized remaining number of detections. The remaining number of detections mapping set is a set obtained from the preset database that represents the mapping relationship between the flank detection determination result deviation coefficient and the optimized remaining number of detections.

[0029] Determine whether the maximum value of the optimized test pressure in the optimized test pressure range is less than the preset maximum test pressure. If it is less, adjust the maximum value of the optimized test pressure in the optimized test pressure range to the preset maximum test pressure; otherwise, do not adjust the maximum value of the optimized test pressure in the optimized test pressure range. The preset maximum test pressure is set according to the preset personnel. For example, the preset maximum test pressure is represented by the maximum value of the non-no-load pressure data within the historical time period.

[0030] Determine whether the remaining number of optimized detections is greater than the preset maximum remaining number of optimized detections. If it is greater, adjust the remaining number of optimized detections to the preset maximum remaining number of optimized detections; otherwise, do not adjust the remaining number of optimized detections. The preset maximum remaining number of optimized detections is set according to the preset personnel.

[0031] In this embodiment, the optimized detection parameters are the parameters obtained by optimizing the initial detection parameters in the flank adjustment safety detection after the quantitative determination of the safety detection performance by adjusting the flank at the preset number of detections. By comprehensively considering the deviation coefficient of the flank detection determination result, the maximum value and the minimum value of the non-no-load pressure data to determine the optimized test pressure range, it is possible to more accurately simulate various pressure situations that the vehicle seat may face during actual use, and improve the accuracy and comprehensiveness of the flank adjustment safety detection in the vehicle seat safety detection process. By determining the optimized number of safety detections through the deviation coefficient of the flank detection determination result, it is possible to avoid the increase in data volume and data processing delay caused by redundant tests, and achieve an improvement in the test efficiency in the flank adjustment safety test of the vehicle seat.

[0032] Furthermore, by performing a data pre-acquisition accuracy assessment on the non-no-load pressure data and the no-load pressure data obtained in the pressure data pre-acquisition experiment of the seat to be detected, a collection accuracy assessment result is obtained. The specific process is as follows: E1. In the pressure data pre-acquisition experiment, obtain the initial non-no-load pressure data and the no-load pressure data of the seat to be detected within the preset data pre-acquisition period. The no-load pressure data is the pressure measured by the pressure sensor when the seat to be detected is in the no-load state through the pressure data pre-acquisition experiment, and the initial non-no-load pressure data is the pressure of the seat to be detected in the non-no-load state obtained through the pressure data pre-acquisition experiment.

[0033] E2. Perform a subtraction operation on the initial non-no-load pressure data and the no-load pressure data of the seat to be detected to obtain the non-no-load pressure data. The no-load pressure data is the measurement value of the pressure sensor when no preset measurement object is placed, the initial non-no-load pressure data is the measurement value of the pressure sensor when the preset measurement object is placed, and the non-no-load pressure data is the measured weight of the preset measurement object.

[0034] E3. Perform relative deviation processing on the obtained average non-no-load pressure data and the preset non-no-load pressure obtained from the preset database to obtain a pressure deviation coefficient. The average non-no-load pressure data is the average pressure of the seat to be tested in the non-no-load state obtained through the pre-collection experiment of pressure data. The specific limiting expression of the pressure deviation coefficient is: ; In the formula, YPC represents the pressure deviation coefficient of the seat to be tested in the pre-collection experiment of pressure data, PYL represents the average non-no-load pressure data of the seat to be tested in the pre-collection experiment of pressure data, and the average non-no-load pressure data represents the average value of the non-no-load pressure data obtained within the preset data pre-collection period; represents the preset non-no-load pressure, and the preset non-no-load pressure is set according to the preset personnel.

[0035] 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 pre-collection experiment of pressure data, perform inverse proportional operation on the result of the assignment coupling processing 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 pre-collection experiment of pressure data in the safety detection of automotive seats; 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-collection period.

[0036] Among them, the specific limiting expression of the acquisition accuracy evaluation result is: ; In the formula, ZN represents the acquisition accuracy evaluation result of the seat to be tested in the pre-collection experiment of pressure data, YBC represents the non-no-load pressure data standard deviation of the seat to be tested in the pre-collection experiment of pressure data, YPC represents the pressure deviation coefficient of the seat to be tested in the pre-collection experiment of pressure data, represents the no-load pressure of the seat to be tested in the pre-collection experiment of pressure data, represents the first acquisition accuracy evaluation compensation value, represents the second acquisition accuracy evaluation compensation value, represents the third acquisition accuracy evaluation compensation value.

[0037] The acquisition accuracy evaluation compensation values involved are obtained from a preset database. The first acquisition accuracy evaluation compensation value represents the influence degree of the non-no-load pressure data standard deviation on the acquisition accuracy evaluation result. The second acquisition accuracy evaluation compensation value represents the influence degree of the average non-no-load pressure data on the acquisition accuracy evaluation result. The third acquisition accuracy evaluation compensation value represents the influence degree of the no-load pressure on the acquisition accuracy evaluation result. The sum of the three is 1. For example, the non-no-load pressure data standard deviation and the preset first acquisition accuracy evaluation compensation value form a non-no-load pressure data standard deviation mapping set. Inputting the real-time non-no-load pressure data standard deviation into the non-no-load pressure data standard deviation mapping set obtains the corresponding first acquisition accuracy evaluation compensation value. The average non-no-load pressure data and the preset second acquisition accuracy evaluation compensation value form an average non-no-load pressure data mapping set. Inputting the real-time average non-no-load pressure data into the average non-no-load pressure data mapping set obtains the corresponding second acquisition accuracy evaluation compensation value. The no-load pressure and the preset third acquisition accuracy evaluation compensation value form a no-load pressure mapping set. Inputting the real-time no-load pressure into the no-load pressure mapping set obtains the corresponding third acquisition accuracy evaluation compensation value. The mapping relationships therein can be one-to-one or many-to-one relationships.

[0038] In this embodiment, the smaller the non-no-load pressure data standard deviation is, generally it means that the obtained non-no-load pressure data is more stable, which may lead to a smaller pressure deviation coefficient, indicating that the non-no-load pressure obtained by the pressure sensor is closer to the actual pressure. The larger the no-load pressure is, it means that the reference of the zero point of the pressure sensor is larger, which may lead to a larger non-no-load pressure data standard deviation and pressure deviation coefficient.

[0039] The data pre-acquisition accuracy evaluation is carried out before the flank adjustment safety detection. 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 non-no-load pressure data standard deviation represents the degree of data fluctuation. Through the assignment and coupling processing of the de-unified non-no-load pressure data standard deviation, pressure deviation coefficient and de-unified no-load pressure, the accuracy, stability and performance deviation of the pressure sensor are comprehensively considered. Through the above steps, the accuracy and reliability of the pressure data obtained by the pressure sensor in the automotive seat flank adjustment safety detection are ensured, and the accuracy of the automotive seat flank adjustment safety detection is further improved.

[0040] Furthermore, the non-no-load pressure data obtained at the corrected sampling frequency during the flank adjustment safety detection process and the corresponding pressure sensor positions are mapped to obtain the flank angle. The specific process is as follows: Taking the center point of the seat as the origin, the direction pointing to the seat back as the longitudinal axis, and the direction parallel to the seat edge and horizontally to the right as the transverse axis, a coordinate system is established; the average non-empty 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-empty load pressure data, and the abscissa of the position of the corresponding pressure sensor is recorded as the center offset coefficient; the positions of the pressure sensors on the vehicle seat of the seat to be detected are set according to a preset person.

[0041] When the center offset coefficient is not equal to 0, the first flank angle influence parameter is input into the right flank angle mapping set to obtain the right flank angle, and the first flank angle influence parameter is input into the left flank angle mapping set to obtain the left flank angle; otherwise, the second flank angle influence parameter is input into the flank angle mapping set to obtain the flank angle, and the flank angle includes the right flank angle and the left flank angle. The first flank angle influence parameter includes the number of pressure sensors used, the maximum average non-empty load pressure data, the backrest pressure ratio, the seat cushion pressure ratio, and the center offset coefficient. The second flank angle influence parameter includes the number of pressure sensors used, the maximum average non-empty load pressure data, the backrest pressure ratio, and the seat cushion pressure ratio; the maximum average non-empty load pressure data represents the maximum value of the average of the non-empty load pressure data obtained from all pressure sensors; the backrest pressure ratio is obtained by performing a ratio operation on the sum value of the average non-empty load pressure data corresponding to all pressure sensors on the seat back of the seat to be detected and the total pressure data; the seat cushion pressure ratio is obtained by performing a ratio operation on the sum value of the average non-empty load pressure data corresponding to all pressure sensors on the seat cushion of the seat to be detected and the total pressure data; the total pressure data is obtained by performing a summation operation on the sum value of the average non-empty load pressure data corresponding to all pressure sensors on the seat back of the seat to be detected and the sum value of the average non-empty load pressure data corresponding to all pressure sensors on the seat cushion of the seat to be detected.

[0042] The right flank angle mapping set is a set obtained from a preset database representing the mapping relationship between the first flank angle influence parameter and the right flank angle; the left flank angle mapping set is a set obtained from a preset database representing the mapping relationship between the first flank angle influence parameter and the left flank angle; the flank angle mapping set is a set obtained from a preset database representing the mapping relationship between the second flank angle influence parameter and the flank angle.

[0043] Regarding the quantification determination of the flank detection performance of the seat to be detected based on the flank adjustment detection performance parameters obtained from a single flank adjustment safety detection, the specific process is as follows: F1. The total duration of the flank adjustment process is obtained by coupling the flank adjustment duration and the adjustment command output duration. If the total duration of the flank adjustment process is not less than the preset processing duration, the deviation comparison process is performed based on the total duration of the flank adjustment process and the preset processing duration to obtain the flank adjustment duration deviation coefficient; otherwise, the flank adjustment duration deviation coefficient is recorded as 0. The specific limiting expression of the flank adjustment process deviation coefficient is: ; In the formula, YCT represents the flank adjustment duration deviation coefficient of the seat to be detected in the flank adjustment safety detection, YTZ represents the flank adjustment duration of the seat to be detected in the flank adjustment safety detection, and the flank adjustment duration represents the time taken for the flank of the vehicle seat to receive the adjustment command and complete the adjustment command; TSC represents the adjustment command output duration of the seat to be detected in the flank adjustment safety detection, and the adjustment command output duration represents the time taken from the start of the flank adjustment safety test to the adjustment command being successfully transmitted to the flank of the vehicle seat and received; represents the preset processing duration, which is set according to the preset personnel. For example, it can be set to 1 second.

[0044] F2. The relative deviation process is performed based on the actual flank adjustment angle and the flank angle to obtain the flank adjustment deviation coefficient, that is , where CEP represents the actual flank adjustment angle of the seat to be detected in the flank adjustment safety detection, and the actual flank adjustment angle is the angle value finally reached by the flank measured by the angle sensor; represents the flank angle of the seat to be detected in the flank adjustment safety detection.

[0045] F3. The relative deviation process is performed based on the non-empty load pressure data volume and the preset non-empty load pressure data volume to obtain the non-empty load pressure data volume comparison coefficient, that is , where YJU represents the non-empty load pressure data volume of the seat to be detected in the flank adjustment safety detection, and the non-empty load pressure data volume represents the total number of pressure data collected by multiple pressure sensors on the vehicle seat according to the corrected sampling frequency; represents the preset non-empty 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.

[0046] F4, after introducing the flank detection determination compensation value to perform assignment coupling processing on the flank adjustment duration deviation coefficient, flank adjustment deviation coefficient, non-no-load pressure data volume comparison coefficient, and the jitter of the adjusted instruction output duration after de-unifying, perform inverse proportional operation to obtain the flank detection determination result. The flank detection determination compensation value includes the first flank detection determination compensation value, the second flank detection determination compensation value, the third flank detection determination compensation value, and the fourth flank detection determination compensation value. The flank detection determination result is used to quantitatively evaluate the detection response efficiency of the flank adjustment safety detection in the automotive seat safety detection; the jitter of the adjusted instruction output duration represents the standard deviation of the adjusted instruction output duration.

[0047] The involved flank detection determination compensation value is obtained from a preset database. The first flank detection determination compensation value represents the influence degree of the flank adjustment duration and the adjusted instruction output duration on the flank detection determination result. The second flank detection determination compensation value represents the influence degree of the actual flank adjustment angle on the flank detection determination result. The third flank detection determination compensation value represents the influence degree of the non-no-load pressure data volume on the flank detection determination result. The fourth flank detection determination compensation value represents the influence degree of the jitter of the adjusted instruction output duration on the flank detection determination result; the sum of the four is 1. For example, the flank adjustment duration, the adjusted instruction output duration, and the preset first flank detection determination compensation value form a flank adjustment processing deviation coefficient mapping set. Input the real-time flank adjustment duration and the adjusted instruction output duration into the flank adjustment processing deviation coefficient mapping set to obtain the corresponding first flank detection determination compensation value; the actual flank adjustment angle and the preset second flank detection determination compensation value form an actual flank adjustment angle mapping set. Input the real-time actual flank adjustment angle into the actual flank adjustment angle mapping set to obtain the corresponding second flank detection determination compensation value; the non-no-load pressure data volume and the preset third flank detection determination compensation value form a non-no-load pressure data volume mapping set. Input the real-time non-no-load pressure data volume into the non-no-load pressure data volume mapping set to obtain the corresponding third flank detection determination compensation value; the jitter of the adjusted instruction output duration and the preset fourth flank detection determination compensation value form an instruction output duration jitter mapping set. Input the real-time jitter of the adjusted instruction output duration into the instruction output duration jitter mapping set to obtain the corresponding fourth flank detection determination compensation value; the mapping relationship therein can be one-to-one or many-to-one.

[0048] Among them, the specific limit expression of the flank detection determination result is: ; In the formula, TY represents the flank detection determination result of the seat to be detected in the flank adjustment safety detection, YCT represents the flank adjustment duration deviation coefficient of the seat to be detected in the flank adjustment safety detection, XOU represents the jitter of the adjusted instruction output duration of the seat to be detected in the flank adjustment safety detection, represents the first flank detection determination compensation value, Indicates the second flank detection judgment compensation value, Indicates the third flank detection judgment compensation value, Indicates the fourth flank detection judgment compensation value.

[0049] In this embodiment, by establishing a coordinate system and obtaining the center offset coefficient, the flank angle can be obtained more accurately, ensuring that during the flank adjustment safety detection process, the change in the flank angle can be captured in a timely manner, thereby improving the accuracy of the flank adjustment safety detection in the automotive seat safety detection.

[0050] When the flank adjustment deviation coefficient is larger, it may require a longer adjustment time to correct the angle error, thereby extending the total flank adjustment processing time; the larger the non-empty load pressure data volume, it means that collecting more non-empty load pressure data may take a longer time, thereby increasing the total flank adjustment processing time; the greater the jitter in the adjustment command output duration, that is, the higher the instability of the command, it may lead to response delays or instability in the adjustment process, thereby increasing the total flank adjustment processing time; the jitter in the command output duration will affect the stability of the command and the execution accuracy during the adjustment process. If the jitter in the command output duration is larger, it may cause a larger deviation during the adjustment process, thereby affecting the flank adjustment deviation coefficient; the larger the non-empty load pressure data volume, it may cause a larger jitter in the adjustment command output duration.

[0051] Through the flank adjustment duration deviation coefficient, considering both the command transmission time and the actual adjustment time comprehensively, it can more comprehensively reflect the response speed of the flank adjustment, avoiding the problem of inaccurate evaluation caused by only considering a single time factor and quantifying the deviation degree of the flank adjustment duration; through the flank adjustment deviation coefficient, it reflects the deviation degree between the actual angle and the target angle of the flank adjustment, evaluating the accuracy of the flank adjustment, thereby ensuring the accurate adjustment of the seat flank angle; through the non-empty load pressure data volume comparison coefficient, it quantitatively evaluates the duration of the flank adjustment process; through the above steps, it quantitatively evaluates the detection response efficiency of the flank adjustment safety detection in the automotive seat safety detection, providing a basis for subsequent detection optimization and achieving an improvement in the detection efficiency of the flank adjustment safety detection in the automotive seat safety detection.

[0052] Furthermore, it is judged whether to optimize the non-empty load pressure data obtained in the pressure data pre-acquisition experiment based on the sampling accuracy evaluation result. The specific process is as follows: A1. Determine whether the acquisition accuracy evaluation result is less than the preset acquisition accuracy evaluation threshold obtained from the preset database. If the acquisition accuracy evaluation result is less than the preset acquisition accuracy evaluation threshold obtained from the preset database, vibration noise suppression is performed; otherwise, the non-no-load pressure data obtained in the pressure data pre-acquisition experiment is not optimized. The preset acquisition accuracy evaluation threshold is represented by the average value of the acquisition accuracy evaluation results within the historical time period. The vehicle speed is directly obtained through a vehicle speed sensor installed on the specified test vehicle. The acceleration is directly obtained through an acceleration sensor installed on the specified test vehicle.

[0053] Regarding vibration noise suppression, the noise signal of the non-no-load pressure data above the cut-off frequency is removed through a low-pass filter to suppress the noise generated by vibration in the non-no-load pressure data. The cut-off frequency is obtained by inputting the vehicle speed, acquisition accuracy evaluation result, pressure signal frequency, and noise frequency into the cut-off frequency mapping set. The cut-off frequency mapping set is a set representing the mapping relationship between the vehicle speed, acquisition accuracy evaluation result, pressure signal frequency, noise frequency, and cut-off frequency obtained from the preset database. The pressure signal frequency is obtained by performing spectrum analysis on the collected non-no-load pressure data. Through the fast Fourier transform method, the pressure signal is converted from the time domain to the frequency domain to determine the main frequency components of the pressure signal. The noise frequency is represented by identifying the frequency components irrelevant to the main frequency components of the pressure signal through spectrum analysis of the collected pressure data.

[0054] A2. Determine 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 optimization of the non-no-load pressure data ends. Dual-sensor cross-validation is used to eliminate the deviation of the non-no-load pressure data caused by the pressure sensor.

[0055] A3. Determine 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 performed; otherwise, the optimization of the non-no-load pressure data ends. Optimizing the non-no-load pressure data obtained in the pressure data pre-acquisition experiment includes vibration noise suppression and dual-sensor cross-validation.

[0056] Regarding this, the specific process of dual-sensor cross-validation is as follows: A21. Deviate the no-load pressure data obtained by two first pressure sensors at the same preset sampling point in the pressure data pre-acquisition experiment to obtain the center point pressure deviation. The first pressure sensor refers to the pressure sensor set at the center point of the seat cushion of the vehicle seat to be detected; the preset sampling point is set according to the preset personnel.

[0057] A22. Determine whether the center point pressure deviation obtained at the same preset sampling point in the pressure data pre-acquisition 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, then execute A23; otherwise, use any one of the first pressure sensors to obtain the no-load pressure data. The preset pressure deviation is set according to the preset personnel.

[0058] A23. Respectively determine 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-acquisition experiment are greater than the preset pressure standard deviation obtained from the preset database. If the first judgment condition is satisfied, then use the first pressure sensor corresponding to the second center point pressure standard deviation to obtain the no-load pressure data. The first judgment condition means 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.

[0059] If the second judgment condition is satisfied, then use the first pressure sensor corresponding to the first center point pressure standard deviation to obtain the no-load pressure data. The second judgment condition means 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.

[0060] If the third judgment condition is satisfied, then execute A24; otherwise, execute A25. The third judgment condition means 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 refers to the standard deviation of the no-load pressure data obtained by any one of the first pressure sensors and other pressure sensors on the seat cushion at the same preset sampling point in the pressure data pre-acquisition experiment. The second center point pressure standard deviation refers to the standard deviation of the no-load pressure data obtained by the other first pressure sensor and other pressure sensors on the seat cushion at the same preset sampling point in the pressure data pre-acquisition experiment.

[0061] A24. If the standard deviation of the first center point pressure is greater than that of the second center point pressure, use the pressure sensor corresponding to the second center point pressure standard deviation to obtain no-load pressure data. If the standard deviation of the first center point pressure is less than that of the second center point pressure, use the pressure sensor corresponding to the first center point pressure standard deviation to obtain no-load pressure data. Otherwise, use any one of the first pressure sensors to obtain no-load pressure data.

[0062] A25. Conduct double-sensor cross-validation for a preset number of times within a preset validation time in the pressure data pre-acquisition experiment. If the validation pass rate is not less than the preset validation pass rate obtained from the preset database, use the first pressure sensor to obtain no-load pressure data. Otherwise, stop using the first pressure sensor and use the no-load pressure data obtained by the second pressure sensor to replace the no-load pressure data obtained by the first pressure sensor. The validation pass rate is obtained by performing a ratio operation on the number of validation times that do not meet the fourth judgment condition and the preset number of validation times in the double-sensor cross-validation. The second pressure sensor refers to the pressure sensor closest to the center point of the seat cushion of the vehicle seat. The fourth judgment condition means that the standard deviation of the first center point pressure is greater than the preset pressure standard deviation obtained from the preset database, and the standard deviation of the second center point pressure is greater than the preset pressure standard deviation obtained from the preset database. The preset validation time, the preset number of validation times, and the preset validation pass rate are set according to the preset personnel.

[0063] In this embodiment, the optimization of the no-load pressure data obtained in the pressure data pre-acquisition experiment is carried out before the safety detection of the adjustment of the vehicle seat flank. The noise signal in the no-load pressure data higher than the cut-off frequency is removed by a low-pass filter, effectively suppressing the noise interference generated by vibration, improving the signal-to-noise ratio of the data, and making the data more accurately reflect the real pressure change situation. By comparing the data collected by the two pressure sensors, the data deviation caused by factors such as the error of the sensor itself is eliminated, further improving the reliability and accuracy of the data, and ensuring that the optimized data can better meet the actual requirements. When the acquisition accuracy evaluation result after the double-sensor cross-validation still does not meet the requirements, the feedback mechanism can prompt relevant personnel to further check and adjust the data acquisition process, timely discover and solve possible problems, such as sensor failures, etc., so as to ensure the improvement of the quality of the pressure data obtained in the vehicle seat safety detection. The preset pressure change range is obtained by comprehensively mapping the vehicle speed, acceleration, and the acquisition accuracy evaluation result, improving the accuracy of setting the preset value of the pressure change range.

[0064] Through double-sensor cross-validation, compare the pressure data deviation and standard deviation of the two sensors, and select the sensor with higher stability, thereby ensuring the accuracy of the pressure data acquisition, and further improving the reliability of the safety detection of the adjustment of the vehicle seat flank.

[0065] Furthermore, the specific method for determining whether to perform detection optimization based on the average quantization determination result of the preset number of detections is as follows: Determine whether the average flank detection determination result of the preset number of detections during the flank adjustment safety detection is less than the preset flank detection determination threshold. If the flank detection determination result is less than the preset flank detection determination threshold, perform flank adjustment detection optimization; otherwise, do not perform flank adjustment detection optimization. The average flank detection determination result represents the average detection response efficiency of the flank adjustment safety detection in the vehicle seat safety detection and represents the average value of the flank detection determination results; the preset flank detection determination threshold is represented by the average value of the average flank detection determination results within the historical time period.

[0066] The flank adjustment detection optimization includes distributed parallel processing and data compression processing; the distributed parallel processing obtains a data acquisition unit, a data processing unit, and an adjustment feedback unit by distributing data acquisition, data processing, and adjustment feedback in the flank adjustment safety detection process to different processing units, and then assigns priorities and resources to different units through a Real-Time Operating System (RTOS) to improve the response efficiency during the flank adjustment safety detection process in the vehicle seat safety detection; the data compression processing is used to reduce the data transmission burden and improve the response efficiency of the vehicle seat safety detection.

[0067] Specifically, the data compression processing process is as follows: Transmit the non-empty load pressure data through an incremental compression algorithm; if the average pressure change amount between all adjacent two preset sampling points within the preset single safety detection cycle during the flank adjustment safety detection is less than the preset pressure change amount threshold, use the first compression algorithm; otherwise, obtain the compression depth by inputting the flank detection determination result and data transmission speed in the flank adjustment safety detection process into the compression depth mapping set. The compression depth mapping set is a set obtained from the preset database representing the mapping relationship between the flank detection determination result, data transmission speed, and compression depth in the flank adjustment safety detection process. The first compression algorithm is the Huffman coding compression algorithm, the second compression algorithm is the gzip compression algorithm, and the data processing efficiency of the second compression algorithm optimizes the first compression algorithm.

[0068] In this embodiment, through distributed parallel processing, the speed and efficiency of data processing are improved. Through optimizing resource allocation, it is ensured that each task in the safety detection of the adjustment of the car seat side wings can respond quickly and reduce latency. Through data compression processing, the amount of data transmission is reduced, the burden on the network bandwidth is alleviated, and the real-time response ability of the car seat safety detection is improved. Furthermore, the overall detection efficiency of the safety detection of the adjustment of the car seat side wings is enhanced, providing a basis for the subsequent safety detection of the adjustment of the car seat side wings.

[0069] Through the incremental compression algorithm, the transmission of duplicate data is avoided, thereby reducing the amount of data transmission, and further reducing the data processing latency. By judging the pressure change amount, different compression algorithms are intelligently selected to avoid the data transmission latency caused by complex compression, improve the response speed and quality of data transmission, and thus improve the performance of the safety detection of the adjustment of the car seat side wings in the car seat safety detection. Through the above steps, a basis is provided for the subsequent safety detection of the adjustment of the car seat side wings.

[0070] In summary, in the embodiment of the present application, the accuracy of data pre-acquisition is evaluated through the pressure data obtained in the pressure data pre-acquisition experiment, then the sampling frequency of the safety detection of the adjustment of the car seat side wings is optimized according to the pressure change frequency, and finally the performance of the side wing detection is quantitatively determined during the safety detection of the adjustment of the car seat side wings, and the safety detection of the adjustment of the car seat side wings is carried out according to the detection parameters, thereby improving the accuracy of data acquisition in the safety detection of the adjustment of the car seat side wings, and further improving the accuracy of the safety detection of the adjustment of the car seat side wings, effectively solving the problem of low accuracy of the safety detection of the adjustment of the car seat side wings caused by the deviation of the data collected by the pressure sensor in the prior art.

[0071] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] The present invention is described with reference to the 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 flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for realizing the processFigure 1 one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks

[0073] These computer program instructions can 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 a manufactured article including an instruction means that implements the functions in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention

[0076] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations

Claims

1. An automotive seat safety detection method based on pressure sensing, characterized in that, It includes the following steps: S1. Through pre-collecting pressure data of the seat to be detected, non-no-load pressure data and no-load pressure data are obtained, and the accuracy of the pre-collected data is evaluated. According to the evaluation results, the non-no-load pressure data is optimized; S2. According to the pressure change frequency of the optimized non-no-load pressure data, it is judged whether to optimize the initial sampling frequency of the side wing adjustment safety detection; S3. If the initial sampling frequency of the side wing adjustment safety detection is optimized, the non-no-load pressure data obtained at the optimized sampling frequency and the corresponding pressure sensor positions during the side wing adjustment safety detection process are mapped to obtain the side wing angle. Otherwise, the non-no-load pressure data obtained at the initial sampling frequency and the corresponding pressure sensor positions during the side wing adjustment safety detection process are mapped to obtain the side wing angle, and the actual side wing adjustment angle is synchronously collected; S4. Based on the side wing adjustment detection performance parameters obtained from one-time 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 judged 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 performed according to the initial detection parameters.

2. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 1, wherein, The specific process of judging whether to optimize the initial sampling frequency of the side wing adjustment safety detection according to 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 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 the corrected sampling frequency. During the side wing adjustment safety detection process, the non-no-load pressure data is obtained according to the corrected sampling frequency within the preset single safety detection cycle. The first pressure change frequency deviation is obtained by processing the deviation between the pressure change frequency and the first preset pressure change frequency. The sampling frequency mapping set is a set representing the mapping relationship between the first pressure change frequency deviation, the initial sampling frequency, and the corrected sampling frequency obtained from the preset database; 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 the corrected sampling frequency. During the side wing adjustment safety detection process, the non-no-load pressure data is obtained according to the corrected sampling frequency within the preset single safety detection cycle. The second pressure change frequency deviation is obtained by processing the deviation between the second preset pressure change frequency and the pressure change frequency. Otherwise, during the side wing adjustment safety detection process, the non-no-load pressure data is directly obtained according to the initial sampling frequency within the preset single safety detection cycle.

3. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 1, characterized in that, The optimized detection parameters include an optimized test pressure range and an optimized remaining number of detections. The specific obtaining methods are as follows: Input the deviation coefficient of the flank detection determination result, the maximum value of the non - no - load pressure data, and the minimum value of the non - no - load pressure data into the test pressure range mapping set to obtain the optimized test pressure range. The test pressure range mapping set is a set representing the mapping relationship between the deviation coefficient of the flank detection determination 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, which is obtained from a preset database; Input the deviation coefficient of the flank detection determination result into the detection remaining times mapping set to obtain the optimized detection remaining times. The detection remaining times mapping set is a set representing the mapping relationship between the deviation coefficient of the flank detection determination result and the optimized detection remaining times, which is obtained from a preset database; Judge whether the maximum value of the optimized test pressure in the optimized test pressure range is less than the preset maximum test pressure. If it is less than, adjust the maximum value of the optimized test pressure in the optimized test pressure range to the preset maximum test pressure, otherwise do not adjust the maximum value of the optimized test pressure in the optimized test pressure range; Judge whether the optimized detection remaining times is greater than the preset maximum optimized detection remaining times. If it is greater than, adjust the optimized detection remaining times to the preset maximum optimized detection remaining times, otherwise do not adjust the optimized detection remaining times; 4. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 1, wherein, The specific process of evaluating the accuracy of data pre - acquisition is as follows: In the pressure data pre - acquisition, obtain the initial non - no - load pressure data and no - load pressure data of the seat to be detected within the preset data pre - acquisition period. The no - load pressure data is the pressure measured by the pressure sensor when the seat to be detected is in the no - load state through pressure data pre - acquisition, and the initial non - no - load pressure data is the pressure of the seat to be detected in the non - no - load state obtained through pressure data pre - acquisition; Perform deviation processing on the initial non - no - load pressure data and no - load pressure data of the seat to be detected to obtain the non - no - load pressure data; Perform relative deviation processing on the obtained average non - no - load pressure data and the preset non - no - load pressure obtained from the preset database to obtain the pressure deviation coefficient. The average non - no - load pressure data is the average pressure of the seat to be detected in the non - no - load state obtained through pressure data pre - acquisition; After introducing the acquisition accuracy evaluation compensation value to perform assignment coupling processing on the de - unitized standard deviation of the non - no - load pressure data, the pressure deviation coefficient, and the de - unitized no - load pressure obtained in the pressure data pre - acquisition, perform inverse proportional operation on the result of the assignment coupling processing 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 pressure data pre - acquisition in the safety detection of automotive seats; 5. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 1, wherein The specific process of mapping the non - no - load pressure data obtained at the optimized sampling frequency and the corresponding pressure sensor position in the flank adjustment safety detection process to obtain the flank angle is as follows: Taking the center point of the seat as the origin, the direction pointing to the seat back as the longitudinal axis, and the direction parallel to the seat edge and horizontally to the right as the horizontal axis, establish a coordinate system; 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 record the abscissa of the position of the corresponding pressure sensor as the center offset coefficient; When the center offset coefficient is not equal to 0, input the first flank angle influence parameter into the right flank angle mapping set to obtain the right flank angle, and input the first flank angle influence parameter into the left flank angle mapping set to obtain the left flank angle. Otherwise, input the second flank angle influence parameter into the flank angle mapping set to obtain the flank angle. The first flank angle influence parameter includes 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 flank angle influence parameter includes 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 representing the mapping relationship between the first flank angle influence parameter and the right flank angle obtained from a preset database; The left flank angle mapping set is a set representing the mapping relationship between the first flank angle influence parameter and the left flank angle obtained from a preset database; The flank angle mapping set is a set representing the mapping relationship between the second flank angle influence parameter and the flank angle obtained from a preset database.

6. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 5, wherein, Quantitatively determine the flank detection performance of the seat to be detected based on the flank adjustment detection performance parameter obtained from the primary flank adjustment safety detection. The specific process is as follows: Perform coupling processing on the flank adjustment duration and the adjustment command output duration to obtain the total flank adjustment processing duration. If the total flank adjustment processing duration is not less than the preset processing duration, perform deviation comparison processing on the total flank adjustment processing duration and the preset processing duration to obtain the flank adjustment duration deviation coefficient. Otherwise, record the flank adjustment duration deviation coefficient as 0; Perform relative deviation processing on the actual flank adjustment angle and the flank angle to obtain the flank adjustment deviation coefficient; Perform relative deviation processing on the non - no - load pressure data volume and the preset non - no - load pressure data volume to obtain the non - no - load pressure data volume comparison coefficient; After introducing the flank detection determination compensation value and performing assignment coupling processing on the flank adjustment duration deviation coefficient, the flank adjustment deviation coefficient, the non - no - load pressure data volume comparison coefficient, and the jitter of the adjustment command output duration after de - unitization, perform inverse proportion operation to obtain the flank detection determination result. The flank detection determination compensation value includes the first flank detection determination compensation value, the second flank detection determination compensation value, the third flank detection determination compensation value, and the fourth flank detection determination compensation value. The flank detection determination result is used to quantitatively evaluate the detection response efficiency of the flank adjustment safety detection in the safety detection of automotive seats.

7. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 4, wherein Optimize the non - no - load pressure data according to the evaluation result. The specific process is as follows: S11, if the evaluation result is less than the preset acquisition accuracy evaluation threshold obtained from the preset database, perform vibration and noise suppression. Otherwise, the optimized non - no - load pressure data is the non - no - load pressure data obtained by pre - collecting the pressure data of the seat to be detected; S12. If the acquisition accuracy evaluation result after vibration and noise suppression is less than the preset acquisition accuracy evaluation threshold obtained from the preset database, then perform dual-sensor cross-validation, otherwise end the non-empty load pressure data optimization. The dual-sensor cross-validation is used to eliminate the deviation of the non-empty load pressure data caused by the pressure sensor. S13. 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, then give feedback, otherwise end the non-empty load pressure data optimization. The optimization of the non-empty load pressure data includes vibration and noise suppression and dual-sensor cross-validation.

8. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 6, wherein, The specific method for judging whether to perform detection optimization based on the average quantization determination result of the preset detection times is as follows: Judge whether the average flank detection determination result of the preset detection times in the flank adjustment safety detection process is less than the preset flank detection determination threshold. If the flank detection determination result is less than the preset flank detection determination threshold, then perform flank adjustment detection optimization, otherwise do not perform flank adjustment detection optimization. The average flank detection determination result represents the average detection response efficiency of the flank 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 in the flank adjustment safety detection process of the vehicle seat safety detection.

9. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 7, wherein, The vibration and noise suppression removes the noise signal of the non-empty load pressure data higher than the cut-off frequency through a low-pass filter, which is used to suppress the noise generated by vibration in the non-empty load pressure data. The cut-off frequency is obtained by inputting the vehicle speed, acquisition accuracy evaluation result, pressure signal frequency, and noise frequency into the cut-off frequency mapping set. The cut-off frequency mapping set is a set representing the mapping relationship between the vehicle speed, acquisition accuracy evaluation result, pressure signal frequency, noise frequency, and cut-off frequency obtained from the preset database. The specific process of the dual-sensor cross-validation is as follows: A21. Perform deviation processing on the non-empty load pressure data obtained by two first pressure sensors at the same preset sampling point in the pressure data pre-acquisition 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 vehicle seat to be detected. A22. Judge whether the center point pressure deviation obtained at the same preset sampling point in the pressure data pre-acquisition 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, then execute A23, otherwise use any one of the first pressure sensors to obtain the non-empty load pressure data. A23. Determine separately whether the standard deviation of the first center point pressure and the standard deviation of the second center point pressure obtained at the same preset sampling point during the pre-sampling of pressure data are greater than the preset pressure standard deviation obtained from the preset database. If the first judgment condition is met, use the first pressure sensor corresponding to the standard deviation of the second center point pressure to obtain non-empty load pressure data. The first judgment condition means that the standard deviation of the first center point pressure is greater than the preset pressure standard deviation obtained from the preset database, and the standard deviation of the second center point pressure is not greater than the preset pressure standard deviation obtained from the preset database. If the second judgment condition is met, use the first pressure sensor corresponding to the standard deviation of the first center point pressure to obtain non-empty load pressure data. The second judgment condition means that the standard deviation of the first center point pressure is not greater than the preset pressure standard deviation obtained from the preset database, and the standard deviation of the second center point pressure 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. The third judgment condition means that the standard deviation of the first center point pressure is not greater than the preset pressure standard deviation obtained from the preset database, and the standard deviation of the second center point pressure is not greater than the preset pressure standard deviation obtained from the preset database. The standard deviation of the first center point pressure represents the standard deviation of the non-empty load pressure data obtained by any one of the first pressure sensors and other pressure sensors on the seat cushion at the same preset sampling point during the pre-sampling of pressure data. The standard deviation of the second center point pressure represents the standard deviation of the non-empty load pressure data obtained by another first pressure sensor and other pressure sensors on the seat cushion at the same preset sampling point during the pre-sampling of pressure data. A24. If the standard deviation of the first center point pressure is greater than the standard deviation of the second center point pressure, use the pressure sensor corresponding to the standard deviation of the second center point pressure to obtain non-empty load pressure data. If the standard deviation of the first center point pressure is less than the standard deviation of the second center point pressure, use the pressure sensor corresponding to the standard deviation of the first center point pressure to obtain non-empty load pressure data. Otherwise, use any one of the first pressure sensors to obtain non-empty load pressure data. A25. Conduct double-sensor cross-validation for a preset number of times within the preset verification time during the pre-sampling of pressure data. 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-empty load pressure data. Otherwise, stop using the first pressure sensor and use the non-empty load pressure data obtained by the second pressure sensor to replace the non-empty load pressure data obtained by the first pressure sensor. The verification pass rate represents the proportion of the number of verification times whose verification results meet the fourth judgment condition in the preset number of verification times during the double-sensor cross-validation. The second pressure sensor represents the pressure sensor closest to the center point of the seat cushion of the vehicle seat. The fourth judgment condition means that the standard deviation of the first center point pressure is greater than the preset pressure standard deviation obtained from the preset database, and the standard deviation of the second center point pressure is greater than the preset pressure standard deviation obtained from the preset database.

10. The method for detecting the safety of an automotive seat based on pressure sensing according to claim 8, wherein, The specific process of the data compression process is as follows: Transmit the non-empty load pressure data through an incremental compression algorithm. If the average pressure change amount between all adjacent two preset sampling points within the preset single safety detection period during the flank adjustment safety detection is less than the preset pressure change amount threshold, the first compression algorithm is used; otherwise, the flank detection determination result and data transmission speed during the flank adjustment safety detection are input into the compression depth mapping set to obtain the compression depth, and the compression depth in the second compression algorithm is adjusted through the compression depth. The compression depth mapping set is a set representing the mapping relationship between the flank detection determination result, data transmission speed, and compression depth during the flank adjustment safety detection obtained from the preset database, and the data processing efficiency of the second compression algorithm is better than that of the first compression algorithm.

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