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

Abstract

The application discloses a multi-parameter integrated detection system and method for a storage device of an automobile product, and relates to the technical field of automobile product detection. The method comprises the following steps: collecting deviation data of measurement data of the storage device under various detection environment data when various materials and processes are used by the storage device, and setting standard parameter data, and collecting abnormal sound data of a detection process; the multi-parameter integrated detection system and method for the storage device of the automobile product calculates the error between actual measurement parameters of the storage device manufactured and assembled under various schemes and design standard parameters, the size and frequency of abnormal sound caused by the storage device, and the cost of the scheme, scores each scheme, so that producers can select a suitable production scheme according to the score, and the improvement effect of each unit cost on each deviation data is calculated, so that the evaluation score focuses on the improvement direction of the material and process data of each storage device relative to the target material and process data of the storage device.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile product detection, in particular to a multi-parameter integrated detection system and method for a storage device of an automobile product. BACKGROUND

[0002] With the increasing popularity of household cars, the ownership rate of cars is also increasing, so the safety of cars is particularly important. It is necessary to detect the parameters of the car before it is shipped to ensure the yield of the shipped car. The prior art disclosed in the publication number CN114627080A discloses a vehicle stamping part defect detection method based on computer vision. The part image of the stamping part image is obtained. According to the constructed three-dimensional model of the part, a matching model image similar to the part image is obtained, and then the position area of different parts is obtained. The wrinkle heat of the pixel points in each part position area is calculated, and the pixel points with equal wrinkle heat are divided into a judgment area. For each judgment area obtained, a corresponding gray level fluctuation curve is established, and the gray level fluctuation direction and the gray level fluctuation degree of the corresponding judgment area are calculated, and the wrinkle rate of the judgment area is obtained. When the wrinkle rate is greater than the set threshold, the judgment area is a wrinkle area. According to the variance corresponding to the gray level fluctuation degree and the gray level fluctuation direction of the wrinkle area, the wrinkle degree of the wrinkle area is calculated. The prior art can evaluate the wrinkle degree of each part of the part by measuring the gray level fluctuation of each judgment area.

[0003] However, as users have higher and higher requirements for cars, under the premise that the car can meet the basic use requirements, users pay more and more attention to the comfort and functionality of the car. The existing technology mostly detects the main safety or performance parts of the car, and lacks detection of comfort improvement parts, especially for the storage device that needs to be frequently opened and closed. The appearance of the switch cover plate, the gap at the closed part, the force required when opening and closing, and the abnormal sound will all affect the user's experience during driving or use. SUMMARY

[0004] The purpose of the present application is to provide a multi-parameter integrated detection system and method for a storage device of an automobile product to solve the above problems in the prior art.

[0005] In order to achieve the above purpose, the present application provides the following technical scheme: a multi-parameter integrated detection method for a storage device of an automobile product, comprising the following steps:

[0006] S1, collecting the deviation data of the storage device measurement data when each material and process of the storage device is used from the set standard parameter data, and the abnormal sound data of the detection process under each detection environment data;

[0007] S2, construct a storage device evaluation function with the objective of minimizing the deviation data, abnormal sound data and storage device cost data;

[0008] S3, calculate and process the deviation of the deviation data, abnormal sound data and storage device cost data of each storage device material and process data and a selected storage device material and process data under each same detection environment data, generate deviation change data, noise change data and cost change data, and dynamically adjust the weight of the detection environment data corresponding to the storage device evaluation function based on the deviation change data, noise change data and cost change data;

[0009] S4, input the deviation data, abnormal sound data and storage device cost data into the storage device evaluation function with dynamically adjusted weight, and obtain a storage device material and process evaluation score;

[0010] S5, sort the storage device material and process data in ascending order of the storage device material and process evaluation score, and generate a storage device process material selection sequence.

[0011] Further, the S1 includes the following steps:

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

[0013] S1.2, collect the appearance and size 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 can specifically include the length, width, height, volume, key position size and assembly gap size of the storage device, etc., and can also include whether the surface of the storage device is flat and whether it is contaminated, etc. The storage device material and process data are the material and ratio data and processing technology and process 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 opening and closing, and collect the sound when opening and closing to generate storage device opening and closing test data and opening and closing audio data respectively; when performing the opening and closing test, ensure that the same force is applied to the storage device each time, and different forces are used for testing. The sequence of the damping of the storage device changing with time is collected during the test to generate the storage device opening and closing test data, and the sound information during the test is collected to generate the opening and closing audio data.

[0015] S1.4, the vehicle is tested by a vibration table, and sound information in the vehicle during vibration testing is collected to generate audio data in the vehicle during vibration testing; wherein the vibration table is used to simulate vibration of the vehicle when driving on various road conditions at various speeds.

[0016] S1.5, determining whether i is greater than or equal to n, if not, i = i + 1, returning to S1, n is the total number of detection environment data categories;

[0017] S1.6, if yes, the deviation of the storage device measurement data, the storage device opening and closing test data and the corresponding setting standard parameter data is analyzed and processed respectively under each detection environment data to generate corresponding deviation data, the deviation data includes storage device measurement deviation data and storage device test deviation data;

[0018] S1.7, the abnormal sound noise of the opening and closing audio data and the vibration test audio data under each detection environment data is extracted respectively to generate corresponding abnormal sound data, the abnormal sound data includes storage device opening and closing abnormal sound data and storage device vibration abnormal sound data.

[0019] Further, the detection environment data includes fixed detection environment data and dynamic detection environment data, and the detection is performed by the following steps respectively:

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

[0021] The dynamic detection environment data includes initial detection environment data, final detection environment data and detection environment change rate data, when detecting under the dynamic detection environment data, the storage device or the vehicle equipped with the storage device is placed under the initial detection environment data for a set time before detection, and 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] Further, the deviation data is obtained by the following steps:

[0023] Setting the standard measurement parameter data and the standard opening and closing test parameter data of the storage device

[0024] The deviation of the storage device measurement data and the standard measurement parameter data is calculated and processed 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 storage device length measurement deviation calculation data = storage device length measurement data - length standard measurement parameter data.

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

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

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

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

[0029] Further, the abnormal sound data is obtained by the following steps:

[0030] The background audio data under each detection environment data of the detection site and the vibration test background audio data when the vibration test is performed on the car without installing the storage device are collected;

[0031] Based on the spectrum subtraction method, 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 audio data is subtracted from the spectrum of the vibration test in-car audio data, to obtain storage device opening and closing abnormal sound spectrum data and storage device vibration abnormal sound spectrum data respectively;

[0032] Based on the storage device opening and closing abnormal sound spectrum data and the storage device vibration abnormal sound spectrum data, the proportion and average decibel number of the storage device opening and closing abnormal sound and the storage device vibration abnormal sound exceeding the set decibel number during the storage device opening and closing test and the vibration test are calculated and processed to generate storage device opening and closing abnormal sound data and storage device vibration abnormal sound data;

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

[0034] Further, the S2 includes the following steps:

[0035] S2.1, each item of the corresponding deviation data and abnormal sound data, and the storage device cost data are standardized respectively; when the standardization is performed, the absolute value of the data is normalized to 0 to 1, and the original sign of the data is retained, and the larger the normalized result is, the larger the absolute value of the corresponding deviation data is, the larger the proportion and average decibel number of the storage device opening and closing abnormal sound and the storage device vibration abnormal sound exceeding the set decibel number in the abnormal sound data are, and the larger the storage device cost data is.

[0036] S2.2, each of the corresponding deviation data and abnormal sound data after standardization processing, and the storage device cost data is respectively weighted and summed, and the storage device evaluation function is constructed, and the formula is as follows:

[0037]

[0038] Wherein, m represents the mth item data in the sequence composed of each item of deviation data and abnormal sound data, and the storage device cost data, M represents the total number of items in the sequence composed of each item of deviation data and abnormal sound data, and the storage device cost data, The weight of the mth item data, The mth item of data after standardization processing, The scaling coefficient of exp function.

[0039] Further, the S3 includes the following steps:

[0040] S3.1, selecting a storage device cost data minimum storage device material and process data as target material and process data, and the rest of the storage device material and process data as comparative material and process data;

[0041] S3.2, the absolute value of the deviation data of each comparative material and process data and the target material and process data under the i th detection environment data, the deviation amount of each item of abnormal sound data and storage device cost data is calculated, and the calculation result is normalized to generate deviation change data, i is initially equal to 1;

[0042] S3.3, judging whether the Is the weight of the deviation data;

[0043] S3.4, if yes, Wherein The deviation amount of the storage device cost data of the selected comparative material and process data and the target material and process data, The mth item of data corresponding to the target material and process data.

[0044] S3.5, if not, judging whether the Is the weight of the abnormal sound data;

[0045] S3.6, if yes,

[0046]

[0047] Wherein, a and b are the scaling coefficients of exp function; through this embodiment, the promotion effect of increasing each unit amount of cost on each item of abnormal sound data can be calculated, and when the abnormal sound suppression effect is not (i.e. The greater the degree of abnormal sound increase, the greater the weight assigned to the corresponding abnormal sound data, so that the storage device evaluation function can focus on the problem of the abnormal sound of the storage device.

[0048] S3.7, if no, then:

[0049]

[0050] The Mth term is the storage device cost data. Through this embodiment, the promotion effect of increasing the cost per unit on the overall can be calculated, and the higher the promotion effect on the overall, the higher the weight of the storage device cost data.

[0051] S3.8, determining whether i is greater than or equal to n, if no, then i = i + 1, returning to S3.2, and n is the total number of detection environment data categories.

[0052] Further, the S5 includes the following steps:

[0053] According to the ascending order of the storage device material process evaluation, the storage device material and process data are sorted, and the first set of output quantity of storage device material and process data is generated to generate a storage device process material selection sequence output.

[0054] The automobile product storage device multi-parameter integrated detection system includes a sensor module, an environment control module, a vibration table, a storage device, a processor, and a display module.

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

[0056] The environment control module is used to control the temperature and humidity of the detection site.

[0057] The vibration table is used to simulate the vibration of the automobile driven by the automobile running at various speeds on 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 the automobile product storage device multi-parameter integrated detection method.

[0060] The display module is used for visual display of data.

[0061] 1. Compared with the prior art, the automobile product storage device multi-parameter integrated detection system and method provided by the application calculates the error of the actual measurement parameter and the design standard parameter of the storage device manufactured and assembled under each environment, the abnormal sound size and frequency caused by the storage device and the scheme cost, and scores each scheme, so as to facilitate the producer to select a suitable production scheme according to the score.

[0062] 2. Compared with the prior art, the automobile product storage device multi-parameter integrated detection system and method provided by the application calculates the promotion effect of the cost per unit amount on each deviation data item, and based on the calculated promotion effect, gives a higher weight to the deviation data item with a higher promotion effect, so that the evaluation score can focus on the improvement direction of the material and process data of each storage device relative to the target storage device material and process data.

[0063] 3. Compared with the prior art, the automobile product storage device multi-parameter integrated detection system and method provided by the application assigns a larger weight to the abnormal sound data with a larger abnormal sound increase when there is no suppression effect on the abnormal sound, and when there is a suppression effect on the abnormal sound, the weight of the abnormal sound data is dynamically adjusted according to the suppression effect of the abnormal sound per unit amount, so that the storage device evaluation function can focus on the abnormal sound problem of the storage device and prevent the abnormal sound of the storage device from affecting the experience of the driver during driving. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only represent some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0065] Figure 1 The method step diagram provided for the embodiments of the present application;

[0066] Figure 2 The system structure block diagram provided for the embodiments of the present application. DETAILED DESCRIPTION

[0067] In order to make those skilled in the art better understand the technical solutions of the present application, the present application will be further described in detail with reference to the drawings.

[0068] In the following, the example embodiments will be more fully described with reference to the drawings, but the example embodiments can be embodied in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete, and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0069] In the case of no conflict, various embodiments of the present disclosure and various features in the embodiments can be combined with each other.

[0070] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0071] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0072] The embodiments described herein can be described with reference to plan views and / or cross-sectional views by virtue of the present disclosure's idealized schematic representations. Thus, the example illustrations are modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the illustrated embodiments in the drawings, but include modifications based on manufacturing processes. Thus, the zones illustrated in the drawings have schematic properties, and the shapes of the zones shown in the drawings illustrate specific shapes of zones of elements, but are not intended to be limiting.

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

[0074] Please refer to Figure 1 , the automobile product storage device multi-parameter integrated detection method comprises the following steps:

[0075] S1, collecting the deviation data of the storage device measurement data of the storage device using each material and process from the set standard parameter data when detected under each detection environment data, and the abnormal sound data of the detection process, specifically comprising the following steps:

[0076] S1.1, adjust the environment of the detection site according to the i th set detection environment data, i is initially equal to 1; wherein the detection environment data includes temperature, humidity, temperature change rate, humidity change rate, etc.

[0077] S1.2, collect the appearance and size 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 can specifically include the length, width, height, volume, key position size and assembly gap size of the storage device, and can also include whether the surface of the storage device is flat and whether there is pollution, etc. The storage device material and process data are material and ratio data and processing technology and process data used for manufacturing the storage device. The storage device cost data is the average cost required for manufacturing the storage device using the storage device material and process data.

[0078] S1.3, test the damping of the storage device when opening and closing, and collect the sound when opening and closing to generate storage device opening and closing test data and opening and closing audio data respectively; when performing the opening and closing test, ensure that the same force is applied to the storage device each time, and different forces are used for testing respectively; the sequence of the damping of the storage device changing with time is collected during the test to generate the storage device opening and closing test data, and the sound information during the test is collected to generate the opening and closing audio data.

[0079] S1.4, perform vibration test on the automobile through the vibration table, collect the sound information in the vehicle during the vibration test to generate vibration test vehicle audio data; wherein the vibration table is used to simulate the vibration of the automobile when driving on various road conditions at various speeds, and through the embodiment, the noise in the vehicle when the automobile drives on various road conditions can be simulated.

[0080] S1.5, determine whether i is greater than or equal to n, if not, then i = i + 1, return to S1, and n is the total number of detection environment data categories;

[0081] S1.6, if yes, analyze and process the deviation of the storage device measurement data, the storage device opening and closing test data and the corresponding setting standard parameter data under each detection environment data respectively to generate corresponding deviation data, and the deviation data includes storage device measurement deviation data and storage device test deviation data;

[0082] S1.7, extract abnormal sound noise from the opening and closing audio data and the vibration test vehicle audio data under each detection environment data respectively to generate corresponding abnormal sound data, and the abnormal sound data includes storage device opening and closing abnormal sound data and storage device vibration abnormal sound data.

[0083] Wherein, the detection environment data includes fixed detection environment data and dynamic detection environment data, and the detection is performed through the following steps respectively:

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

[0085] (2) Dynamic detection of environmental data, including initial detection of environmental data, final detection of environmental data, and detection of environmental change rate data. When detecting dynamic detection of environmental data, the storage device or the car equipped with the storage device is placed for a set time under the initial detection of environmental data, and the detection environment is adjusted in real time according to the detection environmental change rate data until the detection environmental data reaches the final detection environmental data. Through this embodiment, the appearance, size, opening and closing damping, and noise of the storage device under various constant environmental conditions can be detected.

[0086] The deviation data is obtained by the following steps:

[0087] (1) Setting standard measurement parameter data and standard opening and closing test parameter data of the storage device

[0088] (2) Calculating and processing the deviation of the measurement data of the storage device from 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 storage device length measurement deviation calculation data = storage device length measurement data - length standard measurement parameter data.

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

[0090] (4) Calculating and processing the vector distance of each generated storage device opening and closing test vector and standard opening and closing test parameter vector to obtain the storage device test deviation calculation data.

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

[0092] (6) Collecting and combining the storage device measurement deviation data and the storage device test deviation data to generate the deviation data.

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

[0094] (1) Collect the background audio data under each detection environment data of the detection site and the vibration test background audio data when the vibration test is performed without installing the storage device in the car;

[0095] (2) Based on the 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 audio data is subtracted from the spectrum of the vibration test audio data in the car, to obtain the storage device opening and closing abnormal sound spectrum data and the storage device vibration abnormal sound spectrum data, respectively;

[0096] (3) Based on the storage device opening and closing abnormal sound spectrum data and the storage device vibration abnormal sound spectrum data, the proportion and average decibel number of the storage device opening and closing abnormal sound and the storage device vibration abnormal sound exceeding the set decibel number during the storage device opening and closing test and the vibration test are calculated and processed, to generate the storage device opening and closing abnormal sound data and the storage device vibration abnormal sound data;

[0097] (4) The storage device opening and closing abnormal sound data and the storage device vibration abnormal sound data are collected and combined to generate the abnormal sound data.

[0098] S2, the deviation data, the abnormal sound data and the storage device cost data are standardized, and the storage device evaluation function is constructed, including the following steps:

[0099] S2.1, each item of the corresponding deviation data and abnormal sound data, and the storage device cost data are standardized, respectively; when the standardization is performed, the absolute value of the data is normalized to 0 to 1, and the original sign of the data is retained, and the larger the normalized result is, the larger the absolute value of the corresponding deviation data is, the larger the proportion and average decibel number of the storage device opening and closing abnormal sound and the storage device vibration abnormal sound exceeding the set decibel number in the abnormal sound data is, and the larger the storage device cost data is.

[0100] S2.2, the standardized deviation data and abnormal sound data, and the storage device cost data are weighted and summed, respectively, to construct the storage device evaluation function, and the formula is as follows:

[0101]

[0102] Wherein, m represents the mth item of data in the sequence composed of each item of the deviation data and the abnormal sound data, and the storage device cost data, M represents the total number of items in the sequence composed of each item of the deviation data and the abnormal sound data, and the storage device cost data, is the weight of the mth item of data, represents the mth item of data after standardization, is a scaling factor for the exp function.

[0103] In one embodiment, The average deviation of the length, width, height, volume, key position size, assembly gap size of the storage device, the proportion and average decibel of the opening and closing abnormal sound of the storage device exceeding the set decibel during the opening and closing test and vibration test of the storage device, and the cost data of the storage device. By constructing the storage device evaluation function, the storage device material process evaluation score of its data is positively correlated with the deviation data, abnormal sound data and storage device cost data, and the larger these data, the faster the storage device material process evaluation score increases.

[0104] S3, respectively, the deviation data, abnormal sound data and storage device cost data of each storage device material and process data and a selected storage device material and process data under each same detection environment data are calculated and processed to generate deviation change data, noise change data and cost change data, and the weight of the storage device evaluation function corresponding to different detection environment data is dynamically adjusted based on the deviation change data, noise change data and cost change data, including the following steps:

[0105] S3.1, select a storage device material and process data with the minimum storage device cost data as the target material and process data, and the remaining storage device material and process data as the comparative material and process data;

[0106] S3.2, calculate the deviation amount of each data of the deviation data absolute value, abnormal sound data and storage device cost data of each comparative material and process data and the target material and process data under the i th detection environment data, and normalize the calculation result to generate deviation change data, i initial equal to 1;

[0107] S3.3, judge whether it is the weight of the deviation data;

[0108] S3.4, if yes, wherein is the deviation amount of the storage device cost data of the selected comparative material and process data and the target material and process data, is the m th data corresponding to the target material and process data; through this embodiment, the improvement effect of each unit amount of cost on each deviation data can be calculated, and based on the calculated improvement effect, the higher the weight of the deviation data item with higher improvement effect is given, so that the focus of the evaluation score can be 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, judge weight of the abnormal sound data;

[0110] S3.6, if yes, then:

[0111]

[0112] wherein a and b are scaling coefficients of the exp function; through the embodiment, the promotion effect of increasing the cost per unit amount on each item of abnormal sound data can be calculated, and when there is no suppression effect on the abnormal sound (i.e. when greater than or equal to 0, the greater the abnormal sound increase, the greater the weight allocated to the corresponding abnormal sound data, so that the storage device evaluation function can focus on the problem of abnormal sound of the storage device.

[0113] S3.7, if no, then:

[0114]

[0115] wherein the Mth item is the storage device cost data. Through the embodiment, the promotion effect of increasing the cost per unit amount on the overall can be calculated, and the higher the promotion effect on the overall, the higher the weight of the storage device cost data.

[0116] S3.8, determining whether i is greater than or equal to n, if no, then i = i + 1, returning to S3.2, n is the total number of categories of detection environment data.

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

[0118] S5, sorting the storage device material and process data in ascending order of the storage device material and process evaluation score, i.e. the lower the storage device material and process evaluation score, the more front the corresponding storage device material and process data, and further, the top n storage device material and process data can be outputted to generate a storage device process material selection sequence. Thus, most of the storage device material and process data with poor evaluation scores can be filtered out, so that the storage device process material selection sequence only contains the best several storage device material and process data, facilitating the selection of appropriate storage device material and process data from the storage device process material selection sequence to manufacture the storage device.

[0119] Please refer to Figure 2 , the automobile product storage device multi-parameter integrated detection system comprises a sensor module, an environment control module, a vibration table, a storage device, a processor, and a display module.

[0120] The sensor module comprises various sensors for collecting the measuring data and the abnormal sound data of the storage device and storing them into the storage, such as a temperature sensor, a humidity sensor, a laser range finder, a vibration sensor, a microphone array, etc.

[0121] The environmental control module is used for controlling the temperature and humidity of the detection site.

[0122] The vibration table is used for simulating the vibration of the automobile driven by the automobile running at various speeds on various road conditions.

[0123] The storage is also used for storing the computer program.

[0124] The processor is used for executing the computer program, and realizing the automobile product storage device multi-parameter integrated detection method provided by the application.

[0125] The display module is used for visually displaying the data.

[0126] The above only describes certain exemplary embodiments of the application by way of illustration, and it is needless to say that the described embodiments can be modified in various ways without departing from the spirit and scope of the application for those skilled in the art. Therefore, the above drawings and descriptions are illustrative in nature, and should not be understood as limiting the scope of protection of the claims of the application.

Claims

1. A multi-parameter integrated detection method for automobile product storage devices, characterized in that, The method comprises the following steps: S1, collecting the deviation data of the storage device measurement data of each material and process used by the storage device from the set standard parameter data when detected in each detection environment data, and the abnormal sound data of the detection process, wherein the material and process refer to the material and ratio data and processing technology and process data used to manufacture the storage device, and the detection environment data includes temperature, humidity, temperature change rate, and humidity change rate; S2, constructing a storage device evaluation function with the minimum deviation data, abnormal sound data, and storage device cost data as the target, wherein the storage device cost data is the average cost required to manufacture the storage device using the storage device material and process data; S3, calculating and processing the deviation of each storage device material and process data from a selected storage device material and process data in each same detection environment data, generating deviation change data, noise change data, and cost change data, and dynamically adjusting the weight of the storage device evaluation function corresponding to different detection environment data based on the deviation change data, noise change data, and cost change data; S4, inputting the deviation data, abnormal sound data, and storage device cost data into the storage device evaluation function with dynamically adjusted weight, obtaining the storage device material and process evaluation score; S5, sorting the storage device material and process data in ascending order of the storage device material and process evaluation score, and generating a storage device process material selection sequence; The S2 comprises the following steps: S2.1, standardizing each of the corresponding deviation data and abnormal sound data, and the storage device cost data respectively; S2.2, weighting and summing each of the corresponding standardized deviation data and abnormal sound data, and the storage device cost data respectively, and constructing a storage device evaluation function, the formula is as follows: ； wherein m represents the mth item of data in a sequence of deviation data and each item of abnormal sound data and the item of storage device cost data, M represents the total number of items in the sequence of deviation data and each item of abnormal sound data and the item of storage device cost data, is a weight for the mth item of data, represents the mth item of data after normalization processing, is a scaling coefficient of the exp function.

2. The multi-parameter integrated detection method for automobile product storage device according to claim 1, characterized in that, The S1 comprises the following steps: S1.1, adjusting the environment of the detection site according to the i-th set detection environment data, i initially equals 1; S1.2, collecting the appearance and size information of the storage device to generate storage device measurement data, and collecting 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; S1.3, testing the damping of the storage device when opening and closing, and collecting the sound when opening and closing to generate storage device opening and closing test data and opening and closing audio data respectively; S1.4, performing vibration test on the car through the vibration table, collecting the sound information in the car during the vibration test to generate vibration test in-car audio data; S1.5, determining whether i is greater than or equal to n, if not, then i = i + 1, and returning to S1, n is the total number of detection environment data categories; S1.6, if yes, then analyzing and processing the deviation of the storage device measurement data and the storage device opening and closing test data from the corresponding set standard parameter data under each detection environment data to generate the corresponding deviation data, the deviation data includes storage device measurement deviation data and storage device test deviation data; S1.7, respectively, on each detection environment data under the open and close audio data and vibration test vehicle audio data for abnormal noise extraction, generate corresponding abnormal sound data, the abnormal sound data includes storage device opening and closing abnormal sound data and storage device vibration abnormal sound data.

3. The multi-parameter integrated detection method for automobile product storage device according to claim 1, characterized in that, The detection environment data includes fixed detection environment data and dynamic detection environment data, and the detection is carried out by the following steps: When detecting under the fixed detection environment data, the storage device or the vehicle equipped with the storage device is placed under the fixed detection environment data for a setting time and then detected; The dynamic detection environment data includes initial detection environment data, final detection environment data and detection environment change rate data. When detecting under the dynamic detection environment data, the storage device or the vehicle equipped with the storage device is placed under the initial detection environment data for a setting time and then detected, and 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 automobile product storage device according to claim 1, characterized in that, The deviation data is obtained by the following steps: Set the standard measurement parameter data and the standard opening and closing test parameter data of the storage device Calculate the deviation of the storage device measurement data from the standard measurement parameter data to obtain the storage device measurement deviation calculation data; Standardize the storage device opening and closing test data and the standard opening and closing test parameter data to generate the storage device opening and closing test vector and the standard opening and closing test parameter vector, respectively; Calculate the vector distance between each generated storage device opening and closing test vector and the standard opening and closing test parameter vector to obtain the storage device test deviation calculation data; Standardize the storage device measurement deviation calculation data and the storage device test deviation calculation data to generate the storage device measurement deviation data and the storage device test deviation data, respectively; Collect and combine the storage device measurement deviation data and the storage device test deviation data to generate the deviation data.

5. The multi-parameter integrated detection method for automobile product storage device according to claim 2, characterized in that, The abnormal sound data is obtained by the following steps: Collect the background audio data under each detection environment data and the vibration test background audio data when the vehicle is not equipped with the storage device during the vibration test; Based on the spectrum subtraction, subtract the frequency spectrum of the background audio data from the frequency spectrum of the opening and closing audio data, and subtract the frequency spectrum of the vibration test background audio data from the frequency spectrum of the vibration test vehicle audio data, to obtain the storage device opening and closing abnormal sound spectrum data and the storage device vibration abnormal sound spectrum data, respectively; Based on the storage device opening and closing abnormal sound spectrum data and the storage device vibration abnormal sound spectrum data, calculate and process the proportion and average decibel number of the storage device opening and closing abnormal sound and the storage device vibration abnormal sound that exceed the set decibel number during the storage device opening and closing test and the vibration test, to generate the storage device opening and closing abnormal sound data and the storage device vibration abnormal sound data; Collect and combine the storage device opening and closing abnormal sound data and the storage device vibration abnormal sound data to generate the abnormal sound data.

6. The multi-parameter integrated detection method for automobile product storage device according to claim 1, characterized in that, The S3 includes the following steps: S3.1, select one storage device material and process data with minimum storage device cost data as target material and process data, and the rest of the storage device material and process data as comparative material and process data; S3.2, calculate the deviation amount of each data of the absolute value of the deviation data, the abnormal sound data and the storage device cost data of each comparative material and process data and the target material and process data under the i th detection environment data, and generate the deviation change data by normalizing the calculation results, i is initially equal to 1; S3.3, determining the weight of the data whether it is bias data; S3.4, if yes, then wherein is the amount of deviation of the cost data of the selected contrast material and process data from the target material and process data, is the mth data corresponding to the target material and process data; S3.5, if not, determining whether the weight of the abnormal sound data; S3.6, if yes, then: ； Wherein, a is the scaling factor of exp function; S3.7, if no, then: ； Wherein, the M th is the storage device cost data; S3.8, judge whether i is greater than or equal to n, if no, then let i=i+1, return to S3.2, n is the total number of detection environment data.

7. The multi-parameter integrated detection method for automobile product storage device according to claim 1, characterized in that, The S5 comprises the following steps: According to the ascending order of the storage device material process evaluation score, the storage device material and process data are sorted.

8. The multi-parameter integrated detection system for automobile product storage device, used for executing the multi-parameter integrated detection method for automobile product storage device according to any one of claims 1-7, characterized in that: It comprises a sensor module, an environment control module, a vibration table, a storage, a processor, a display module; The sensor module comprises a plurality of sensors for collecting storage device measurement data and abnormal sound data and storing them in the storage; The environment control module is used to control the temperature and humidity of the detection site; The vibration table is used to simulate the vibration of the car driven by the car driving at various speeds on various road conditions; The storage is also used to store computer programs; The processor is used to execute the computer program to realize the automobile product storage device multi-parameter integrated detection method of any one of claims 1-7; The display module is used for visual display of data.

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