Multi-parameter integrated detection system and method for automobile product storage device

Abstract

The invention discloses a multi-parameter integrated detection system and method for an automobile product storage device, and relates to the technical field of automobile product detection, and the method comprises the following steps: collecting deviation data between storage device measurement data and set standard parameter data when the storage device uses various materials and processes and is detected under various detection environment data, detecting abnormal sound data in the process; according to the multi-parameter integrated detection system and method for the storage device of the automobile product, errors between actual measurement parameters and design standard parameters of the storage device manufactured and assembled by each scheme in each environment, the abnormal sound size and frequency caused by the storage device and the scheme cost are calculated, and each scheme is scored; therefore, a producer can conveniently select a proper production scheme according to the score, and the key point of the evaluation score is on the improvement direction of the material and process data of each storage device relative to the material and process data of the target storage device by calculating the improvement effect of increasing the cost per unit quantity on each piece of deviation data.

Description

Technical Field

[0001] This invention relates to the field of automotive product testing technology, specifically to a multi-parameter integrated testing system and method for automotive product storage devices. Background Technology

[0002] With the increasing popularity of private cars and the rising car ownership rate, vehicle safety has become paramount. It is necessary to inspect vehicle parameters before they leave the factory to ensure a high yield rate. For example, prior art (CN114627080A) discloses a computer vision-based method for detecting defects in vehicle stamping parts. This method involves acquiring part images of stamped components; obtaining matching model images similar to the part images based on a constructed 3D model of the part, thereby identifying the location regions of different components; calculating the wrinkle heat of pixels within each component's location region, and dividing pixels with equal wrinkle heat into a judgment region; establishing corresponding grayscale fluctuation curves for each judgment region, and calculating the grayscale fluctuation direction and degree of grayscale fluctuation for each judgment region to obtain the wrinkle rate of that region; when the wrinkle rate is greater than a set threshold, the judgment region is considered a wrinkled region; and calculating the wrinkle degree of the wrinkled region based on the variance corresponding to the grayscale fluctuation degree and direction. This existing technology can assess the degree of wrinkling of each component of a part by measuring the grayscale fluctuations in each judgment area.

[0003] However, as users' demands for cars increase, while ensuring that cars meet basic usage requirements, users are paying more attention to details such as comfort and functionality. Current technology for testing cars mostly focuses on major safety or performance components, lacking testing for components that improve comfort. This is especially true for storage devices that need to be opened and closed frequently. The appearance of the switch cover, the gap at the closure, the force and damping required when opening and closing, and whether there are any abnormal noises all affect the user's experience while driving or using the car. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-parameter integrated testing system and method for automotive product storage devices, in order to overcome the above-mentioned shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-parameter integrated detection method for automotive product storage devices, comprising the following steps:

[0006] S1. Collect the storage device measurement data when using various materials and processes, the deviation data between the storage device and the set standard parameter data when testing under various testing environment data, and the abnormal noise data during the testing process;

[0007] S2. Construct an evaluation function for the storage device with the goal of minimizing deviation data, abnormal noise data, and storage device cost data;

[0008] S3. Calculate and process the deviation data, abnormal noise data, and storage device cost data of each storage device material and process data and a selected storage device material and process data under the same testing environment data, respectively, to generate deviation change data, noise change data, and cost change data. Based on the deviation change data, noise change data, and cost change data, dynamically adjust the weight of the storage device evaluation function corresponding to different testing environment data.

[0009] S4. Input the deviation data, abnormal noise data and storage device cost data into the dynamically adjusted storage device evaluation function with corresponding weights to obtain the storage device material and process evaluation score.

[0010] S5. Based on the ascending order of the material and process evaluation scores of the storage device, sort the material and process data of the storage device to generate a selection sequence of process materials for the storage device.

[0011] Furthermore, S1 includes the following steps:

[0012] S1.1 Adjust the environment of the testing site according to the i-th set testing environment data, where i is initially equal to 1; where the testing environment data includes temperature, humidity, temperature change rate, humidity change rate, etc.

[0013] S1.2. Collect the appearance and dimensional information of the storage device to generate storage device measurement data; collect the material, processing technology, and corresponding cost information of the storage device to generate storage device material and process data and storage device cost data respectively; the storage device measurement data may specifically include the length, width, height, volume, key position dimensions, and assembly gap dimensions of the storage device, and may also include whether the surface of the storage device is flat and whether it is contaminated. The storage device material and process data includes the material and proportion data and processing technology and flow data used to manufacture the storage device. The storage device cost data is the average cost required to manufacture the storage device using the storage device material and process data.

[0014] S1.3 Test the damping of the storage device when it is opened and closed, and collect the sound during the opening and closing to generate storage device opening and closing test data and opening and closing audio data respectively; when conducting the opening and closing test, ensure that the force applied to the storage device is the same each time, and use different forces for testing. During the test, collect the sequence of storage device damping changes over time to generate storage device opening and closing test data, and collect the sound information of the test process to generate opening and closing audio data.

[0015] S1.4. Vibration test of the car is carried out using a vibration table, and the sound information inside the car is collected during the vibration test to generate vibration test in-vehicle audio data; wherein, the vibration table is used to simulate the vibration of the car when it is traveling at various speeds on various road conditions.

[0016] S1.5 Determine if i is greater than or equal to n. If not, let i = i + 1 and return to S1. n is the total number of categories of detected environmental data.

[0017] S1.6 If so, the deviations between the storage device measurement data, the storage device opening and closing test data and the corresponding set standard parameter data under each detection environment data are analyzed and processed to generate corresponding deviation data. The deviation data includes storage device measurement deviation data and storage device test deviation data.

[0018] S1.7 Extract abnormal noise from the opening and closing audio data and the vibration test vehicle audio data under each detection environment data to generate corresponding abnormal noise sound data. The abnormal noise sound data includes abnormal noise sound data of the opening and closing of the storage device and abnormal noise sound data of the vibration of the storage device.

[0019] Furthermore, the detection environment data includes fixed detection environment data and dynamic detection environment data, which are respectively processed through the following steps during detection:

[0020] When conducting tests under the fixed testing environment data, the storage device or a vehicle equipped with a storage device is placed under the fixed testing environment data for a set time before testing.

[0021] The dynamic detection environment data includes initial detection environment data, final detection environment data, and detection environment change rate data. When the dynamic detection environment data is being tested, the storage device or the car equipped with the storage device is tested after the initial detection environment data is placed and set for a certain time. During the test, the detection environment is adjusted in real time according to the detection environment change rate data until the detection environment data reaches the final detection environment data.

[0022] Furthermore, the deviation data is obtained through the following steps:

[0023] Set standard measurement parameter data and standard opening and closing test parameter data for the storage device.

[0024] The deviation between the storage device measurement data and the standard measurement parameter data is calculated to obtain the storage device measurement deviation calculation data; for each measurement parameter, the storage device measurement deviation calculation data = storage device measurement data - standard measurement parameter data, such as the storage device length measurement deviation calculation data = storage device length measurement data - length standard measurement parameter data;

[0025] The opening and closing test data and standard opening and closing test parameter data of the storage device are standardized to generate the opening and closing test vector of the storage device and the standard opening and closing test parameter vector, respectively.

[0026] The vector distance between the test vectors of each generated storage device opening and closing and the standard test parameter vectors is calculated to obtain the test deviation calculation data of the storage device.

[0027] The storage device measurement deviation calculation data and storage device test deviation calculation data are standardized to generate storage device measurement deviation data and storage device test deviation data respectively.

[0028] The measurement deviation data and test deviation data of the storage device are collected and combined to generate deviation data.

[0029] Furthermore, the abnormal noise data is obtained through the following steps:

[0030] Collect background audio data under various testing environment data at the testing site and vibration test background audio data when the car is not equipped with storage devices;

[0031] Based on spectral subtraction, the spectrum of the background audio data is subtracted from the spectrum of the opening and closing audio data, and the spectrum of the vibration test background sound data is subtracted from the spectrum of the vibration test in-vehicle audio data, to obtain the spectrum data of the abnormal opening and closing noise of the storage device and the spectrum data of the abnormal vibration noise of the storage device, respectively.

[0032] Based on the spectrum data of abnormal opening and closing noises and abnormal vibration noises of the storage device, the proportion and average decibel level of abnormal opening and closing noises and abnormal vibration noises of the storage device that exceed the set decibel level during the opening and closing test and vibration test are calculated and processed to generate abnormal opening and closing noise data and abnormal vibration noise data of the storage device.

[0033] The abnormal noise data of the opening and closing of the storage device and the abnormal noise data of the vibration of the storage device are collected and combined to generate abnormal noise data.

[0034] Furthermore, S2 includes the following steps:

[0035] S2.1. Standardize each item of the corresponding deviation data and abnormal noise data, as well as the storage device cost data. When standardizing, normalize the absolute value of the data to between 0 and 1, and retain the original sign of the data. The larger the normalization result, the larger the absolute value of the corresponding deviation data, the larger the proportion and average decibel of abnormal noise data of storage device opening and closing noise and storage device vibration noise exceeding the set decibel level, and the larger the storage device cost data.

[0036] S2.2. After assigning weights to each item of the standardized deviation data and abnormal noise data, as well as the storage device cost data, sum them up to construct a storage device evaluation function, as shown in the following formula:

[0037]

[0038] Where m represents each item of the deviation data and abnormal noise data, and the m-th item in the sequence consisting of the storage device cost data items; M represents the total number of items in the sequence consisting of each item of the deviation data and abnormal noise data, and the storage device cost data items. Let m be the weight of the m-th data item. This represents the standardized data for the m-th item. This is the scaling factor for the exp function.

[0039] Furthermore, S3 includes the following steps:

[0040] S3.1 Select the storage device material and process data with the lowest cost data as the target material and process data, and use the remaining storage device material and process data as the comparison material and process data;

[0041] S3.2. For the i-th test environment data, calculate the absolute value of the deviation data between each comparative material and process data and the target material and process data, the abnormal noise data and the storage device cost data, and normalize the calculation results to generate deviation change data, i is initially equal to 1;

[0042] S3.3, Determine the above Whether it is a weight for biased data;

[0043] S3.4, If so, then ,in The deviation between the selected comparative material and process data and the target material and process data for the storage device cost data. This refers to the m-th data item corresponding to the target material and process data.

[0044] S3.5 If not, then determine the above. Whether it is a weight for abnormal noise data;

[0045] S3.6 If so, then:

[0046]

[0047] Where a and b are the scaling factors of the exp function; through this embodiment, the improvement effect of increasing the cost per unit quantity on various abnormal noise sound data can be calculated, and when there is no suppression effect on abnormal noise (i.e. When the noise level is greater than or equal to 0, the greater the increase in abnormal noise, the greater the weight assigned to the corresponding abnormal noise sound data, so that the storage device evaluation function can focus on the problem of abnormal noise in the storage device.

[0048] S3.7 If not, then:

[0049]

[0050] The Mth item represents the cost data of the storage device. This embodiment allows us to calculate the overall improvement effect of increasing the cost per unit. The higher the overall improvement effect, the higher the weight of the storage device cost data.

[0051] S3.8 Determine if i is greater than or equal to n. If not, let i = i + 1 and return to S3.2. n is the total number of categories of detected environmental data.

[0052] Furthermore, S5 includes the following steps:

[0053] Based on the ascending order of the material and process evaluation scores of the storage device, the material and process data of the storage device are sorted, and the storage device process material selection sequence is generated from the first set number of storage device material and process data.

[0054] A multi-parameter integrated testing system for automotive product storage devices, including a sensor module, an environmental control module, a vibration table, a storage device, a processor, and a display module;

[0055] The sensor module includes various sensors for collecting measurement data and abnormal sound data of the storage device and storing them in the storage device; such as temperature sensors, humidity sensors, laser rangefinders, vibration sensors, microphone arrays, etc.

[0056] The environmental control module is used to control the temperature and humidity of the testing site;

[0057] The vibration table is used to simulate the vibration of a car driving at various speeds under various road conditions.

[0058] The storage device is also used to store computer programs;

[0059] The processor is used to execute the computer program to realize a multi-parameter integrated detection method for automotive product storage devices;

[0060] The display module is used to visualize the data.

[0061] 1. Compared with the prior art, the multi-parameter integrated testing system and method for automotive product storage devices provided by the present invention calculates the error between the actual measured parameters and the design standard parameters of the storage devices manufactured and assembled under various environments, the magnitude and frequency of abnormal noises caused by the storage devices, and the cost of the solutions, and scores each solution so that manufacturers can select a suitable production solution based on the scores.

[0062] 2. Compared with the prior art, the multi-parameter integrated detection system and method for automotive product storage devices provided by the present invention calculates the improvement effect of increasing the cost per unit quantity on various deviation data, and assigns higher weights to deviation data items with higher improvement effects based on the calculated improvement effect. This allows the evaluation score to focus on the improvement direction of each storage device's material and process data relative to the target storage device's material and process data.

[0063] 3. Compared with the prior art, the multi-parameter integrated detection system and method for automotive product storage devices provided by the present invention, by assigning greater weight to abnormal noise data with a greater increase in abnormal noise when there is no suppression effect, and dynamically adjusting the weight of abnormal noise data according to the suppression effect of abnormal noise per unit quantity when there is a suppression effect, can enable the storage device evaluation function to focus on the problem of abnormal noise of the storage device, and prevent abnormal noise of the storage device from affecting the driver's experience during driving. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0065] Figure 1 This is a flowchart illustrating the method steps provided in an embodiment of the present invention;

[0066] Figure 2 This is a system structure block diagram provided for an embodiment of the present invention. Detailed Implementation

[0067] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0068] Exemplary embodiments will be described more fully below with reference to the accompanying drawings; however, these exemplary embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of this disclosure.

[0069] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0070] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0071] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded.

[0072] The embodiments described herein can be described with reference to plan views and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the example illustrations can be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to those shown in the drawings, but include modifications to configurations formed based on manufacturing processes. Therefore, the areas illustrated in the drawings are schematic in nature, and the shapes of the areas shown in the figures illustrate specific shapes of areas of an element, but are not intended to be limiting.

[0073] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the same meaning as they have in the context of the relevant art and this disclosure.

[0074] Please see Figure 1 A multi-parameter integrated testing method for automotive product storage devices includes the following steps:

[0075] S1. Collect the storage device's measurement data when using various materials and processes, the deviation data between the measured data and the set standard parameters when testing under various testing environment data, and the abnormal noise data during the testing process. Specifically, this includes the following steps:

[0076] S1.1 Adjust the environment of the testing site according to the i-th set testing environment data, where i is initially equal to 1; where the testing environment data includes temperature, humidity, temperature change rate, humidity change rate, etc.

[0077] S1.2. Collect the appearance and dimensional information of the storage device to generate storage device measurement data; collect the material, processing technology, and corresponding cost information of the storage device to generate storage device material and process data and storage device cost data respectively; the storage device measurement data may specifically include the length, width, height, volume, key position dimensions, and assembly gap dimensions of the storage device, and may also include whether the surface of the storage device is flat and whether it is contaminated. The storage device material and process data includes the material and proportion data and processing technology and flow data used to manufacture the storage device. The storage device cost data is the average cost required to manufacture the storage device using the storage device material and process data.

[0078] S1.3 Test the damping of the storage device when it is opened and closed, and collect the sound during the opening and closing to generate storage device opening and closing test data and opening and closing audio data respectively; when conducting the opening and closing test, ensure that the force applied to the storage device is the same each time, and use different forces for the test. During the test, collect the sequence of the storage device damping change over time to generate storage device opening and closing test data, and collect the sound information of the test process to generate opening and closing audio data.

[0079] S1.4. Vibration test of the car is carried out using a vibration table, and the sound information inside the car is collected during the vibration test to generate vibration test in-vehicle audio data; wherein, the vibration table is used to simulate the vibration of the car when it is traveling at various speeds on various road conditions. Through this embodiment, the noise situation inside the car when it is traveling on various road conditions can be simulated.

[0080] S1.5 Determine if i is greater than or equal to n. If not, let i = i + 1 and return to S1. n is the total number of categories of detected environmental data.

[0081] S1.6 If so, the deviations between the storage device measurement data, storage device opening and closing test data and the corresponding set standard parameter data under each detection environment data are analyzed and processed to generate corresponding deviation data. The deviation data includes storage device measurement deviation data and storage device test deviation data.

[0082] S1.7 Extract abnormal noise from the opening and closing audio data and the vibration test vehicle audio data under each test environment data, and generate corresponding abnormal noise data. The abnormal noise data includes the abnormal noise data of the opening and closing of the storage device and the abnormal noise data of the vibration of the storage device.

[0083] The testing environment data includes both fixed and dynamic testing environment data, which are tested using the following steps:

[0084] (1) When testing under fixed testing environment data, the storage device or the car equipped with the storage device is placed under fixed testing environment data for a set time before testing; through this embodiment, the appearance, size, opening and closing damping and noise parameters of the storage device under various constant environmental conditions can be detected.

[0085] (2) Dynamic detection environment data, including initial detection environment data, final detection environment data, and detection environment change rate data. During dynamic detection environment data testing, the storage device or vehicle equipped with the storage device is tested after the initial detection environment data is set for a specified time. The detection environment is adjusted in real-time based on the detection environment change rate data until the detection environment data reaches the final detection environment data. This embodiment can simulate situations where a vehicle experiences sudden or significant environmental changes, such as a vehicle being left outdoors in summer under direct sunlight, causing a rapid increase in interior temperature, or a user opening windows and turning on the air conditioning before using the vehicle, causing a rapid decrease in interior temperature. The stability of the storage device is determined by detecting changes in parameters such as appearance, size, opening / closing damping, and noise under these conditions.

[0086] Deviation data are obtained through the following steps:

[0087] (1) Set the standard measurement parameter data and standard opening and closing test parameter data for the storage device.

[0088] (2) Calculate the deviation between the storage device measurement data and the standard measurement parameter data to obtain the storage device measurement deviation calculation data; for each measurement parameter, the storage device measurement deviation calculation data = storage device measurement data - standard measurement parameter data, such as the storage device length measurement deviation calculation data = storage device length measurement data - length standard measurement parameter data;

[0089] (3) Standardize the opening and closing test data of the storage device and the standard opening and closing test parameter data to generate the opening and closing test vector of the storage device and the standard opening and closing test parameter vector, respectively;

[0090] (4) Calculate the vector distance between the test vector of each generated storage device and the standard test parameter vector to obtain the test deviation calculation data of the storage device;

[0091] (5) Standardize the calculation data of the storage device measurement deviation and the calculation data of the storage device test deviation to generate the storage device measurement deviation data and the storage device test deviation data respectively;

[0092] (6) Collect and combine the measurement deviation data and test deviation data of the storage device to generate deviation data.

[0093] The abnormal noise data is obtained through the following steps:

[0094] (1) Collect background audio data under various testing environment data at the testing site and vibration test background audio data when the car is not equipped with storage devices;

[0095] (2) Based on spectral subtraction, the spectrum of the background audio data is subtracted from the spectrum of the opening and closing audio data, and the spectrum of the vibration test background sound data is subtracted from the spectrum of the vibration test audio data inside the vibration test vehicle, so as to obtain the spectrum data of the abnormal opening and closing sound of the storage device and the spectrum data of the abnormal vibration sound of the storage device.

[0096] (3) Based on the spectrum data of abnormal opening and closing noise of storage device and the spectrum data of abnormal vibration noise of storage device, calculate and process the proportion and average decibel of abnormal opening and closing noise of storage device and abnormal vibration noise of storage device exceeding the set decibel level during the opening and closing test and vibration test, and generate abnormal opening and closing noise data of storage device and abnormal vibration noise data of storage device.

[0097] (4) Collect and combine the abnormal noise data of the opening and closing of the storage device and the abnormal noise data of the vibration of the storage device to generate abnormal noise data.

[0098] S2. To minimize deviation data, abnormal noise data, and storage unit cost data, construct a storage unit evaluation function, including the following steps:

[0099] S2.1 Standardize each item of the corresponding deviation data and abnormal noise data, as well as the storage device cost data. When standardizing, normalize the absolute value of the data to between 0 and 1, and retain the original sign of the data. The larger the normalization result, the larger the absolute value of the corresponding deviation data, the larger the proportion and average decibel of abnormal noise data of storage device opening and closing noise and storage device vibration noise exceeding the set decibel level, and the larger the storage device cost data.

[0100] S2.2. After assigning weights to each item of the standardized deviation data and abnormal noise data, as well as the storage device cost data, sum them up to construct the storage device evaluation function, as shown in the following formula:

[0101]

[0102] Where m represents each item of the deviation data and abnormal noise data, and the m-th item in the sequence consisting of the storage device cost data items; M represents the total number of items in the sequence consisting of each item of the deviation data and abnormal noise data, and the storage device cost data items. Let m be the weight of the m-th data item. This represents the standardized data for the m-th item. This is the scaling factor for the exp function.

[0103] In one embodiment, This can provide the average deviation of the storage device's length, width, height, volume, key location dimensions, and assembly gap dimensions; the percentage and average decibel level of abnormal noises exceeding set decibel levels during storage device opening / closing and vibration tests; and storage device cost data. Through a constructed storage device evaluation function, the data's storage device material and process evaluation score is positively correlated with the deviation data, abnormal noise data, and storage device cost data; and the larger these data are, the faster the storage device material and process evaluation score increases.

[0104] S3. Calculate and process the deviation data, abnormal noise data, and storage device cost data of each storage device material and process data compared with the material and process data of a selected storage device under various identical testing environment data. Generate deviation change data, noise change data, and cost change data. Based on the deviation change data, noise change data, and cost change data, dynamically adjust the weights of the storage device evaluation function corresponding to different testing environment data, including the following steps:

[0105] S3.1 Select the storage device material and process data with the lowest cost data as the target material and process data, and use the remaining storage device material and process data as the comparison material and process data;

[0106] S3.2. For the i-th test environment data, calculate the absolute value of the deviation data between each comparative material and process data and the target material and process data, the abnormal noise data and the storage device cost data, and normalize the calculation results to generate deviation change data, i is initially equal to 1;

[0107] S3.3, Judgment Whether it is a weight for biased data;

[0108] S3.4, If so, then ,in The deviation between the selected comparative material and process data and the target material and process data for the storage device cost data. This is the m-th data item corresponding to the target material and process data. Through this embodiment, the improvement effect of increasing the cost per unit quantity on each deviation data item can be calculated. Based on the calculated improvement effect, the deviation data item with the higher improvement effect is assigned a higher weight, so that the focus of the evaluation score can be placed on the improvement direction of each storage device material and process data relative to the target storage device material and process data.

[0109] S3.5 If not, then determine Whether it is a weight for abnormal noise data;

[0110] S3.6 If so, then:

[0111]

[0112] Where a and b are the scaling factors of the exp function; through this embodiment, the improvement effect of increasing the cost per unit quantity on various abnormal noise sound data can be calculated, and when there is no suppression effect on abnormal noise (i.e. When the noise level is greater than or equal to 0, the greater the increase in abnormal noise, the greater the weight assigned to the corresponding abnormal noise sound data, so that the storage device evaluation function can focus on the problem of abnormal noise in the storage device.

[0113] S3.7 If not, then:

[0114]

[0115] The Mth item represents the cost data of the storage device. This embodiment allows us to calculate the overall improvement effect of increasing the cost per unit. The higher the overall improvement effect, the higher the weight of the storage device cost data.

[0116] S3.8 Determine if i is greater than or equal to n. If not, let i = i + 1 and return to S3.2. n is the total number of categories of detected environmental data.

[0117] S4. Input the deviation data, abnormal noise data, and storage device cost data into the dynamically adjusted storage device evaluation function with corresponding weights to obtain the storage device material and process evaluation score.

[0118] S5. Based on the ascending order of the storage device material and process evaluation scores, sort the storage device material and process data. That is, the lower the evaluation score, the higher the corresponding storage device material and process data appears. Furthermore, a set number of storage device material and process data can be used to generate a storage device process material selection sequence. This filters out most of the storage device material and process data with poor evaluation scores, ensuring that the storage device process material selection sequence only contains the optimal few storage device material and process data. This facilitates the selection of suitable storage device material and process data from the storage device process material selection sequence for manufacturing the storage device.

[0119] Please refer to Figure 2 A multi-parameter integrated testing system for automotive product storage devices, including a sensor module, an environmental control module, a vibration table, a storage device, a processor, and a display module;

[0120] The sensor module includes various sensors for collecting measurement data and abnormal sound data of the storage device and storing them in the storage device; such as temperature sensors, humidity sensors, laser rangefinders, vibration sensors, microphone arrays, etc.

[0121] The environmental control module is used to control the temperature and humidity of the testing site;

[0122] Vibration tables are used to simulate the vibrations caused by a car traveling at various speeds under various road conditions.

[0123] Storage is also used to store computer programs;

[0124] The processor is used to execute computer programs to implement the multi-parameter integrated detection method for automotive product storage devices provided by this invention.

[0125] The display module is used to visualize the data.

[0126] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A multi-parameter integrated testing method for automotive product storage devices, characterized in that, Includes the following steps: S1. Collect the storage device measurement data when using various materials and processes, the deviation data between the storage device and the set standard parameter data when testing under various testing environment data, and the abnormal noise data during the testing process; S2. Construct an evaluation function for the storage device with the goal of minimizing deviation data, abnormal noise data, and storage device cost data; S3. Calculate and process the deviation data, abnormal noise data, and storage device cost data of each storage device material and process data and a selected storage device material and process data under the same testing environment data, respectively, to generate deviation change data, noise change data, and cost change data. Based on the deviation change data, noise change data, and cost change data, dynamically adjust the weight of the storage device evaluation function corresponding to different testing environment data. S4. Input the deviation data, abnormal noise data and storage device cost data into the dynamically adjusted storage device evaluation function with corresponding weights to obtain the storage device material and process evaluation score. S5. Based on the ascending order of the material and process evaluation scores of the storage device, sort the material and process data of the storage device to generate a selection sequence of process materials for the storage device.

2. The multi-parameter integrated detection method for automotive product storage devices according to claim 1, characterized in that, S1 includes the following steps: S1.1 Adjust the environment of the testing site according to the i-th set testing environment data, where i is initially equal to 1; S1.2 Collect the appearance and size information of the storage device and generate the measurement data of the storage device; collect the material, processing technology and corresponding cost information of the storage device and generate the material and process data and cost data of the storage device respectively. S1.3 Test the damping of the storage device when it is opened and closed, and collect the sound when it is opened and closed to generate storage device opening and closing test data and opening and closing audio data respectively. S1.

4. Conduct vibration tests on the car using a vibration table, collect the sound information inside the car during the vibration test, and generate in-vehicle audio data for vibration testing. S1.5 Determine if i is greater than or equal to n. If not, let i = i + 1 and return to S1. n is the total number of categories of detected environmental data. S1.6 If so, the deviations between the storage device measurement data, the storage device opening and closing test data and the corresponding set standard parameter data under each detection environment data are analyzed and processed to generate corresponding deviation data. The deviation data includes storage device measurement deviation data and storage device test deviation data. S1.7 Extract abnormal noise from the opening and closing audio data and the vibration test vehicle audio data under each detection environment data to generate corresponding abnormal noise sound data. The abnormal noise sound data includes abnormal noise sound data of the opening and closing of the storage device and abnormal noise sound data of the vibration of the storage device.

3. The multi-parameter integrated detection method for automotive product storage devices according to claim 1, characterized in that, The detection environment data includes fixed detection environment data and dynamic detection environment data, which are respectively processed through the following steps during detection: When conducting tests under the fixed testing environment data, the storage device or a car equipped with a storage device is placed under the fixed testing environment data for a set time before testing. The dynamic detection environment data includes initial detection environment data, final detection environment data, and detection environment change rate data. When the dynamic detection environment data is being tested, the storage device or the car equipped with the storage device is tested after the initial detection environment data is placed and set for a certain time. During the test, the detection environment is adjusted in real time according to the detection environment change rate data until the detection environment data reaches the final detection environment data.

4. The multi-parameter integrated detection method for automotive product storage devices according to claim 1, characterized in that, The deviation data is obtained through the following steps: Set standard measurement parameter data and standard opening and closing test parameter data for the storage device. The deviation between the measurement data of the storage device and the standard measurement parameter data is calculated and processed to obtain the measurement deviation calculation data of the storage device; The opening and closing test data and standard opening and closing test parameter data of the storage device are standardized to generate the opening and closing test vector of the storage device and the standard opening and closing test parameter vector, respectively. The vector distance between the test vectors of each generated storage device opening and closing and the standard test parameter vectors is calculated to obtain the test deviation calculation data of the storage device. The storage device measurement deviation calculation data and storage device test deviation calculation data are standardized to generate storage device measurement deviation data and storage device test deviation data respectively. The measurement deviation data and test deviation data of the storage device are collected and combined to generate deviation data.

5. The multi-parameter integrated detection method for automotive product storage devices according to claim 1, characterized in that, The abnormal sound data is obtained through the following steps: Collect background audio data under various testing environment data at the testing site and vibration test background audio data when the car is not equipped with storage devices; Based on spectral subtraction, the spectrum of the background audio data is subtracted from the spectrum of the opening and closing audio data, and the spectrum of the vibration test background sound data is subtracted from the spectrum of the vibration test in-vehicle audio data, to obtain the spectrum data of the abnormal opening and closing noise of the storage device and the spectrum data of the abnormal vibration noise of the storage device, respectively. Based on the spectrum data of abnormal opening and closing noises and abnormal vibration noises of the storage device, the proportion and average decibel level of abnormal opening and closing noises and abnormal vibration noises of the storage device that exceed the set decibel level during the opening and closing test and vibration test are calculated and processed to generate abnormal opening and closing noise data and abnormal vibration noise data of the storage device. The abnormal noise data of the opening and closing of the storage device and the abnormal noise data of the vibration of the storage device are collected and combined to generate abnormal noise data.

6. The multi-parameter integrated detection method for automotive product storage devices according to claim 1, characterized in that, S2 includes the following steps: S2.1 Standardize each item of the corresponding deviation data and abnormal sound data, as well as the storage device cost data; S2.

2. After assigning weights to each item of the standardized deviation data and abnormal noise data, as well as the storage device cost data, sum them up to construct a storage device evaluation function, as shown in the following formula: Where m represents each item of the deviation data and abnormal noise data, and the m-th item in the sequence consisting of the storage device cost data items; M represents the total number of items in the sequence consisting of each item of the deviation data and abnormal noise data, and the storage device cost data items. Let m be the weight of the m-th data item. This represents the standardized data for the m-th item. This is the scaling factor for the exp function.

7. The multi-parameter integrated detection method for automotive product storage devices according to claim 6, characterized in that, S3 includes the following steps: S3.1 Select the storage device material and process data with the lowest cost data as the target material and process data, and use the remaining storage device material and process data as the comparison material and process data; S3.

2. For the i-th test environment data, calculate the absolute value of the deviation data between each comparative material and process data and the target material and process data, the abnormal noise data and the storage device cost data, and normalize the calculation results to generate deviation change data, i is initially equal to 1; S3.3, Determine the above Whether it is a weight for biased data; S3.4, If so, then ,in The deviation between the selected comparative material and process data and the target material and process data for the storage device cost data. This refers to the m-th data item corresponding to the target material and process data; S3.5 If not, then determine the above. Whether it is a weight for abnormal noise data; S3.6 If so, then: Where a and b are the scaling factors of the exp function; S3.7 If not, then: Among them, the Mth item is the cost data of the storage device; S3.8 Determine if i is greater than or equal to n. If not, let i = i + 1 and return to S3.

2. n is the total number of categories of detected environmental data.

8. The multi-parameter integrated detection method for automotive product storage devices according to claim 1, characterized in that, S5 includes the following steps: Based on the ascending order of the material and process evaluation scores of the storage device, the material and process data of the storage device are sorted, and the storage device process material selection sequence is generated from the first set number of storage device material and process data.

9. A multi-parameter integrated testing system for automotive product storage devices, used to execute the multi-parameter integrated testing method for automotive product storage devices as described in any one of claims 1-8, characterized in that: Includes a sensor module, an environmental control module, a vibration table, a storage device, a processor, and a display module; The sensor module includes multiple sensors for collecting measurement data and abnormal noise data of the storage device, and storing them in the storage device; The environmental control module is used to control the temperature and humidity of the testing site; The vibration table is used to simulate the vibration of a car driving at various speeds under various road conditions. The storage device is also used to store computer programs; The processor is used to execute the computer program to implement the multi-parameter integrated detection method for automotive product storage devices according to any one of claims 1-8; The display module is used to visualize the data.

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