Collaborative test and comprehensive evaluation method for automobile cross-working-condition static and dynamic noise

Abstract

The invention belongs to the technical field of automobile noise test and evaluation, and provides an automobile cross-working-condition static and dynamic noise collaborative test and comprehensive evaluation method, which comprises the following steps: acquiring vehicle operation parameters in real time, and identifying different working condition states through a working condition identification model; according to the working condition states, carrying out data acquisition on noise in different working condition states to obtain noise data; preprocessing the noise data, and calculating a noise index to obtain a noise reduction amount result; based on the noise reduction amount result, comprehensively scoring the noise performance of the automobile under the static and dynamic working conditions through a comprehensive evaluation model in combination with a fuzzy comprehensive evaluation method to obtain a comprehensive scoring result; according to different working conditions, multiple indexes in the automobile noise are tested, a more comprehensive view angle is provided for automobile noise testing, automobile manufacturers are helped to determine the advantages and defects of the automobile noise, targeted improvement and optimization are achieved, and therefore the automobile noise is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile noise testing and evaluation, and in particular to a method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile across operating conditions. Background Art

[0002] As consumers' requirements for driving experience continue to increase, accurately evaluating automobile noise performance becomes increasingly important, and noise performance is one of the key indicators for measuring vehicle quality and comfort.

[0003] However, existing technologies often focus on single-condition testing. For example, static testing, conducted only with a stationary vehicle, simulates the noise environment of a parked vehicle and fails to capture the noise variations caused by dynamic factors such as engine operation, tire-road friction, and air flow during driving. Dynamic testing, while focused on driving noise, is often limited to specific speeds or road conditions, making it difficult to fully capture the noise profile of a vehicle under various real-world driving conditions. This hinders automakers from fully understanding the noise characteristics of a vehicle in different usage scenarios, making it difficult to develop targeted noise reduction strategies.

[0004] Furthermore, some existing technologies rely on a single metric to evaluate vehicle noise, such as measuring sound pressure level alone. While sound pressure level can reflect noise intensity to a certain extent, it cannot capture the frequency distribution characteristics of noise, making it difficult to determine the specific source and generation mechanism of the noise. For example, noise of the same sound pressure level can have vastly different effects on the driver's and passenger's auditory perception and comfort depending on its frequency components. A single metric assessment cannot comprehensively and accurately describe vehicle noise performance, resulting in a lack of in-depth analysis of vehicle noise issues and difficulty in effectively guiding vehicle noise reduction improvements.

[0005] Furthermore, during data collection, test conditions were not strictly controlled, resulting in unstable ambient sound and a high degree of arbitrariness in the collection location and frequency. This unstable ambient sound interfered with test results, preventing the collected noise data from truly reflecting the vehicle's own noise levels. Furthermore, non-standard collection locations and frequencies could miss crucial noise information, significantly compromising the accuracy and reliability of the data.

[0006] To this end, technicians in this field have proposed a method for collaborative testing and comprehensive evaluation of static and dynamic noise of automobiles across different working conditions. The method aims to conduct collaborative testing and comprehensive evaluation of multiple indicators of automobile noise according to different working conditions, providing a more comprehensive perspective for vehicle noise testing, helping automobile manufacturers to identify the advantages and disadvantages of vehicle noise, and make targeted improvements and optimizations, thereby reducing vehicle noise and improving vehicle comfort and quality. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention provides a method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile across working conditions, so as to solve the problems raised in the background technology.

[0008] A method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile across operating conditions includes the following steps:

[0009] S1. Real-time collection of vehicle operating parameters and identification of different operating conditions through the operating condition identification model;

[0010] S2. According to the working state, collecting noise data under different working conditions to obtain noise data;

[0011] S3. Preprocess the noise data, calculate the noise index, and obtain the noise reduction result;

[0012] S4. Based on the noise reduction result, a comprehensive evaluation model is used in combination with a fuzzy comprehensive evaluation method to comprehensively score the noise performance of the vehicle under static and dynamic conditions to obtain a comprehensive scoring result.

[0013] Preferably, the real-time collection of vehicle operating parameters and identification of different operating conditions through an operating condition identification model include:

[0014] The vehicle operating parameters and environmental data are collected in real time by a variety of sensors. The vehicle operating parameters and environmental data form a feature vector x = [x1, x2, ..., x n ], where x1 represents velocity, x2 represents acceleration, and n is the number of features;

[0015] The characteristic vector is used as input, and the operating condition category label is identified through the operating condition identification model to determine the current operating condition state; the operating condition category label is y = [y1, y2, y3, y4], where y1 represents the static operating condition, and y2, y3, y4 represent the dynamic operating conditions of 60km / h, 80km / h, and 100km / h respectively; the operating condition state is divided into static operating condition and dynamic operating condition.

[0016] Preferably, according to the operating state, when it is determined that the vehicle is in a static operating state, high-precision microphones arranged at the driver's seat or the co-driver's seat in the vehicle and the side of the vehicle are activated, and at the same time, acoustic equipment is turned on to produce a constant ambient sound of 100 decibels; the height of the microphones is set at a position flush with the human ear;

[0017] A multi-channel data acquisition instrument is used to synchronously collect noise signals from the vehicle's interior and exterior pickups at a sampling frequency of no less than 48kHz for 3 to 5 minutes to obtain noise data under static conditions.

[0018] When the vehicle is judged to be in a dynamic working condition, the vehicle is driven to the starting point of the selected test section, and the acoustic equipment on both sides of the road is started to play a constant ambient sound of 100 decibels; the multi-channel data acquisition instrument, vehicle driving status monitoring equipment and microphone are started, and the vehicle is driven at a constant speed of 60km / h, 80km / h or 100km / h according to the currently identified speed. The driving distance in each speed section is not less than 1 kilometer. The noise data inside and outside the vehicle and the vehicle driving status data are collected during the process. The ambient sound intensity is monitored in real time during driving. If the fluctuation exceeds ±1 decibel, the test is stopped and the ambient sound is readjusted and collected again to obtain the noise data under dynamic working conditions.

[0019] Preferably, the preprocessing of the noise data, calculating the noise index, and obtaining the noise reduction result includes:

[0020] The preprocessing includes denoising, environmental factor correction and time synchronization;

[0021] The noise index is calculated for the noise data under the static working condition and the dynamic working condition, and the sound pressure level is calculated by the root mean square method. rms :

[0022]

[0023] According to the reference sound pressure x0, take x0=2×10 -5 PaCalculated sound pressure level L P :

[0024]

[0025] By calculating the sound pressure level, the average, maximum and minimum values ​​of the sound pressure collected by the microphones inside and outside the vehicle are obtained respectively;

[0026] Fast Fourier transform is used to convert the noise data under the static and dynamic working conditions from time domain to frequency domain, and the noise spectrum is analyzed using the following formula:

[0027]

[0028] Where x(n) is the original noise sequence, X(k) is its N-point fast Fourier transform result, and k = 0, 1, ..., N-1. Based on the spectrum distribution after fast Fourier transform, analyze the noise components in different frequency bands to determine the main sources and characteristics of the noise;

[0029] The noise reduction Q for the static and dynamic conditions is obtained by comparing the sound pressure levels outside and inside the vehicle using the following formula:

[0030]

[0031] in, is the sound pressure level outside the vehicle, is the sound pressure level inside the vehicle, and the noise reduction Q index is used to evaluate the sound insulation performance of the vehicle.

[0032] Preferably, the indicator weights are determined based on the noise reduction result through a comprehensive evaluation model, including:

[0033] The noise reduction Q, sound pressure level L are calculated by comprehensive evaluation model. P and noise spectrum features X(k) as input parameters, and the evaluation index set is U={u1,u2,u3,...,u n}, where u1 is the noise reduction amount, u2 is the sound pressure level, u3 is the noise spectrum characteristics, and n is the total number of indicators, which is used to cover the noise data processing results under static and dynamic conditions;

[0034] The weight of each indicator is determined by the hierarchical analysis method. Through the hierarchical structure model, the target layer is the comprehensive evaluation of automobile noise performance, the criterion layer is the noise standard U, and the indicator layer is the specific indicator data under static and dynamic conditions. Through the judgment matrix A = (a ij ) n×n , where a ij Indicates the index u i Relative to u j The importance of a ij >0,a ii =1,

[0035] By solving the equation AW = λ max W, calculate the maximum eigenvalue λ of the judgment matrix max and its corresponding eigenvector W; normalize the eigenvector so that The obtained w i That is the indicator u i The weight set is expressed as W = {w1,w2,...,w n}, and use the following formula to calculate the maximum eigenvalue λ max :

[0036]

[0037] Among them, (AW) i Represents the i-th element of vector AW.

[0038] Preferably, the fuzzy comprehensive evaluation method comprehensively scores the noise performance of the automobile under static and dynamic conditions to obtain a comprehensive scoring result, including:

[0039] Determine the review set V = {v1, v2, ..., vm}, where m is the number of review levels;

[0040] Through the membership function, each evaluation index u is obtained i The membership vector R relative to the review set i =(μ i1 ,μ i2 ,...,μ im ), the velocity vectors of all indicators form a membership matrix R;

[0041] Use the following formula to calculate the comprehensive evaluation vector B:

[0042] B=W·R=(b1,b2,...,b m )

[0043] in, j=1,2,...,m,b j Indicates that the car's noise performance belongs to the rating level v j The comprehensive membership degree;

[0044] By m Each level in the} is assigned a corresponding score, forming a score vector C = (c1, c2, ...c m ) T , use the following formula to calculate the comprehensive score:

[0045]

[0046] Among them, S is the comprehensive score result.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. This invention covers multiple operating conditions, both static and dynamic, and comprehensively considers multiple indicators such as noise reduction, sound pressure level, and noise spectrum characteristics. It uses scientific evaluation models and methods to conduct a comprehensive and systematic evaluation of vehicle noise performance. Compared with testing of a single operating condition or indicator, it can more accurately grasp the vehicle's noise characteristics.

[0049] 2. The present invention effectively reduces errors and ensures the accuracy and reliability of test data during the data collection process by strictly controlling test conditions, such as constant ambient sound, specific collection locations and frequencies, thus providing a solid data foundation for the evaluation of vehicle noise performance. Furthermore, accurate noise performance evaluation results can help automakers identify the strengths and weaknesses of vehicle noise, and specifically improve the vehicle's sound insulation design and optimize the power system, thereby reducing vehicle noise and improving vehicle comfort and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the method for collaborative testing and comprehensive evaluation of static and dynamic noise across operating conditions of an automobile according to the present invention. DETAILED DESCRIPTION

[0051] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0052] As attached Figure 1 As shown:

[0053] Embodiment: The present invention provides a method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile across operating conditions, comprising the following steps:

[0054] S1. Real-time collection of vehicle operating parameters and identification of different operating conditions through the operating condition identification model;

[0055] The vehicle operation parameters and environmental data are collected in real time by a variety of sensors. The vehicle operation parameters and environmental data form a feature vector x = [x1, x2, ..., x n ], where x1 represents velocity, x2 represents acceleration, and n is the number of features;

[0056] The feature vector is used as input, and the working condition category label is identified through the working condition identification model to determine the current working condition state; the working condition category label is y = [y1, y2, y3, y4], where y1 represents the static working condition, and y2, y3, y4 represent the dynamic working conditions of 60km / h, 80km / h, and 100km / h respectively; the working condition state is divided into static working condition and dynamic working condition.

[0057] Vehicle operating parameters and environmental data are collected through a variety of sensors to form a feature vector input working condition recognition model, so as to accurately determine whether the vehicle is in a static working condition or a dynamic working condition of 60km / h, 80km / h, or 100km / h, ensuring the match between the test data and the actual operating status of the vehicle, making the test more targeted and accurate.

[0058] S2. According to the working conditions, noise data under different working conditions are collected to obtain noise data;

[0059] Depending on the operating status, when the vehicle is judged to be in a static state, high-precision microphones located in the driver's seat or co-pilot seat and on the side of the vehicle are activated, and acoustic equipment is simultaneously turned on to produce a constant 100-decibel ambient sound; the height of the microphones is set at the same level as the human ear;

[0060] A multi-channel data acquisition instrument is used to synchronously collect noise signals from the vehicle's interior and exterior pickups at a sampling frequency of no less than 48kHz for 3 to 5 minutes to obtain noise data under static conditions.

[0061] When the vehicle is judged to be in a dynamic working condition, the vehicle is driven to the starting point of the selected test section, and the acoustic equipment on both sides of the road is started to play a constant ambient sound of 100 decibels; the multi-channel data acquisition instrument, vehicle driving status monitoring equipment and microphone are started, and the vehicle is driven at a constant speed of 60km / h, 80km / h or 100km / h according to the currently identified speed. The driving distance in each speed section is not less than 1 kilometer. The noise data inside and outside the vehicle and the vehicle driving status data are collected during the process. The ambient sound intensity is monitored in real time during driving. If the fluctuation exceeds ±1 decibel, the test is stopped and the ambient sound is readjusted and collected again to obtain the noise data under dynamic working conditions.

[0062] Under static conditions, specifically positioned microphones, combined with a constant 100dB ambient sound level and a multi-channel data acquisition system, can stably capture noise levels inside and outside the vehicle while it's stationary. Under dynamic conditions, data is collected at varying speeds along selected road sections, while ambient sound intensity is monitored in real time. This ensures the collected noise data truly reflects the vehicle's noise levels at varying speeds, providing comprehensive information on the vehicle's noise levels under various operating conditions.

[0063] S3. Preprocess the noise data, calculate the noise index, and obtain the noise reduction result;

[0064] Among them, preprocessing includes denoising, environmental factor correction and time synchronization;

[0065] The noise index is calculated for the noise data under static and dynamic conditions, and the sound pressure level is calculated using the root mean square method. rms :

[0066]

[0067] According to the reference sound pressure x0, take x0=2×10 -5 PaCalculated sound pressure level L P :

[0068]

[0069] By calculating the sound pressure level, the average, maximum and minimum values ​​of the sound pressure collected by the microphones inside and outside the vehicle are obtained respectively;

[0070] Fast Fourier transform is used to convert the noise data under static and dynamic conditions from time domain to frequency domain, and the noise spectrum is analyzed using the following formula:

[0071]

[0072] Where x(n) is the original noise sequence, X(k) is its N-point fast Fourier transform result, and k = 0, 1, ..., N-1. Based on the spectrum distribution after fast Fourier transform, analyze the noise components in different frequency bands to determine the main sources and characteristics of the noise;

[0073] The noise reduction Q for both static and dynamic conditions is calculated by comparing the sound pressure levels outside and inside the vehicle using the following formula:

[0074]

[0075] in, is the sound pressure level outside the vehicle, The noise reduction index Q is used to evaluate the sound insulation performance of the vehicle.

[0076] Denoising in preprocessing removes interfering signals from noise data, environmental correction eliminates the impact of environmental changes on noise measurements, and time synchronization ensures temporal consistency. Calculating sound pressure levels, performing spectrum analysis, and determining noise reduction values ​​quantify vehicle noise characteristics from multiple dimensions, providing data support for evaluating vehicle sound insulation performance and noise sources.

[0077] S4. Based on the noise reduction results, the noise performance of the vehicle under static and dynamic conditions is comprehensively scored through a comprehensive evaluation model combined with the fuzzy comprehensive evaluation method to obtain a comprehensive scoring result.

[0078] Through the comprehensive evaluation model, the noise reduction Q, sound pressure level L P and noise spectrum features X(k) as input parameters, and the evaluation index set is U={u1,u2,u3,...,u n}, where u1 is the noise reduction amount, u2 is the sound pressure level, u3 is the noise spectrum characteristics, and n is the total number of indicators, which is used to cover the noise data processing results under static and dynamic conditions;

[0079] The weight of each indicator is determined by the hierarchical analysis method. Through the hierarchical structure model, the target layer is the comprehensive evaluation of automobile noise performance, the criterion layer is the noise standard U, and the indicator layer is the specific indicator data under static and dynamic conditions. Through the judgment matrix A = (a ij ) n×n , where a ij Indicates the index u i Relative to u j The importance of a ij >0,a ii =1,

[0080] By solving the equation AW = λ max W, calculate the maximum eigenvalue λ of the judgment matrix max and its corresponding eigenvector W; normalize the eigenvector so that The obtained w i That is the indicator u i The weight set is expressed as W = {w1,w2,...,w n}, and use the following formula to calculate the maximum eigenvalue λ max :

[0081]

[0082] Among them, (AW) i Represents the i-th element of vector AW.

[0083] By determining the review set V = {v1, v2, ..., v m}, where m is the number of review levels;

[0084] Through the membership function, each evaluation index u is obtained i Membership vector R relative to the review set i =(μ i1 ,μ i2 ,...,μ im ), the velocity vectors of all indicators form a membership matrix R;

[0085] Use the following formula to calculate the comprehensive evaluation vector B:

[0086] B=W·R=(b1,b2,...,b m )

[0087] in, j=1,2,...,m,b j Indicates that the car's noise performance belongs to the rating level v j The comprehensive membership degree;

[0088] By evaluating the review set V = {v1, v2, ..., v m Each level in the} is assigned a corresponding score, forming a score vector C = (c1, c2, ...c m ) T , use the following formula to calculate the comprehensive score:

[0089]

[0090] Among them, S is the comprehensive score result.

[0091] By using noise reduction, sound pressure level, and noise spectrum characteristics as input parameters, the analytic hierarchy process (AHP) determines the indicator weights, and then uses the fuzzy comprehensive evaluation method to generate a comprehensive score. This method comprehensively and scientifically evaluates a vehicle's noise performance under both static and dynamic conditions, presenting a quantitative comprehensive score that intuitively reflects the vehicle's overall noise level and facilitates noise performance comparisons between different vehicles.

[0092] As can be seen from the above, this invention overcomes the limitations of traditional single-operation test methods by enabling coordinated noise testing under both static and dynamic conditions. This comprehensively reflects the vehicle's noise performance in different usage scenarios, more closely matching actual usage and providing a more comprehensive perspective for vehicle noise testing. Furthermore, this method uses multiple indicators, including noise reduction, sound pressure level, and noise spectrum characteristics, for a comprehensive evaluation, rather than relying solely on a single metric, to more comprehensively describe vehicle noise characteristics. The combination of the Analytic Hierarchy Process (AHP) and the Fuzzy Comprehensive Evaluation Method (Fuzzy Comprehensive Evaluation Method) makes the evaluation process more scientific and rational, and the results more convincing.

[0093] It is important to note that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are merely illustrative. Although only a few embodiments are described in detail in this disclosure, it will be readily understood by those who consult this disclosure that many modifications are possible without departing substantially from the novel teachings and advantages of the subject matter described in this application. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to specific embodiments, but extends to a variety of modifications that still fall within the scope of the appended claims.

[0094] It will be understood that in the development of any actual embodiment, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but for those of ordinary skill having the benefit of this disclosure, the development effort will be a routine task of design, fabrication, and production without undue experimentation.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile under different operating conditions, characterized in that: The following steps are involved: S1. Real-time collection of vehicle operating parameters and identification of different operating conditions through the operating condition identification model; S2. According to the working state, collecting noise data under different working conditions to obtain noise data; S3. Preprocess the noise data, calculate the noise index, and obtain the noise reduction result; S4. Based on the noise reduction result, a comprehensive evaluation model is used in combination with a fuzzy comprehensive evaluation method to comprehensively score the noise performance of the vehicle under static and dynamic conditions to obtain a comprehensive scoring result.

2. The method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile across operating conditions as claimed in claim 1, characterized in that: The real-time collection of vehicle operating parameters and identification of different operating conditions through the operating condition identification model include: The vehicle operating parameters and environmental data are collected in real time by a variety of sensors. The vehicle operating parameters and environmental data form a feature vector x = [x1, x2, ..., x n ], where x1 represents velocity, x2 represents acceleration, and n is the number of features; The characteristic vector is used as input, and the operating condition category label is identified through the operating condition identification model to determine the current operating condition state; the operating condition category label is y = [y1, y2, y3, y4], where y1 represents the static operating condition, and y2, y3, y4 represent the dynamic operating conditions of 60km / h, 80km / h, and 100km / h respectively; the operating condition state is divided into static operating condition and dynamic operating condition.

3. The method for collaborative testing and comprehensive evaluation of static and dynamic noise across various operating conditions of an automobile as claimed in claim 1, characterized in that: According to the operating state, when it is determined that the vehicle is in a static operating state, high-precision microphones arranged in the driver's seat or the co-pilot seat and the side of the vehicle are activated, and the acoustic equipment is turned on to produce a constant 100 decibel ambient sound; the height of the microphones is set at a position flush with the human ear; A multi-channel data acquisition instrument is used to synchronously collect noise signals from the vehicle's interior and exterior pickups at a sampling frequency of no less than 48kHz for 3 to 5 minutes to obtain noise data under static conditions. When the vehicle is judged to be in a dynamic working condition, the vehicle is driven to the starting point of the selected test section, and the acoustic equipment on both sides of the road is started to play a constant ambient sound of 100 decibels; the multi-channel data acquisition instrument, vehicle driving status monitoring equipment and microphone are started, and the vehicle is driven at a constant speed of 60km / h, 80km / h or 100km / h according to the currently identified speed. The driving distance in each speed section is not less than 1 kilometer. The noise data inside and outside the vehicle and the vehicle driving status data are collected during the process. The ambient sound intensity is monitored in real time during driving. If the fluctuation exceeds ±1 decibel, the test is stopped and the ambient sound is readjusted and collected again to obtain the noise data under dynamic working conditions.

4. The method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile across operating conditions as claimed in claim 1, characterized in that: The preprocessing of the noise data, calculation of the noise index, and obtaining the noise reduction result includes: The preprocessing includes denoising, environmental factor correction and time synchronization; The noise index is calculated for the noise data under the static working condition and the dynamic working condition, and the sound pressure level is calculated by the root mean square method. rms : According to the reference sound pressure x0, take x0=2×10 -5 PaCalculated sound pressure level L P : By calculating the sound pressure level, the average, maximum and minimum sound pressure values ​​of the sound pickup data inside and outside the vehicle are obtained respectively; Fast Fourier transform is used to convert the noise data under the static and dynamic working conditions from time domain to frequency domain, and the noise spectrum is analyzed using the following formula: Where x(n) is the original noise sequence, X(k) is its N-point fast Fourier transform result, and k = 0, 1, ..., N-1. Based on the spectrum distribution after fast Fourier transform, analyze the noise components in different frequency bands to determine the main sources and characteristics of the noise; The noise reduction Q for the static and dynamic conditions is obtained by comparing the sound pressure levels outside and inside the vehicle using the following formula: in, is the sound pressure level outside the vehicle, is the sound pressure level inside the vehicle, and the noise reduction Q index is used to evaluate the sound insulation performance of the vehicle.

5. The method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile across operating conditions as claimed in claim 1, characterized in that: The indicator weights are determined based on the noise reduction results through a comprehensive evaluation model, including: The noise reduction Q, sound pressure level L are calculated by comprehensive evaluation model. P and noise spectrum features X(k) as input parameters, and the evaluation index set is U={u1,u2,u3,...,u n }, where u1 is the noise reduction amount, u2 is the sound pressure level, u3 is the noise spectrum characteristics, and n is the total number of indicators, which is used to cover the noise data processing results under static and dynamic conditions; The weight of each indicator is determined by the hierarchical analysis method. Through the hierarchical structure model, the target layer is the comprehensive evaluation of automobile noise performance, the criterion layer is the noise standard U, and the indicator layer is the specific indicator data under static and dynamic conditions. Through the judgment matrix A = (a ij ) n×n , where a ij Indicates the index u i Relative to u j The importance of ij >0,a ii =1, By solving the equation AW = λ max W, calculate the maximum eigenvalue λ of the judgment matrix max and its corresponding eigenvector W; Normalize the feature vector so that The obtained w i That is the indicator u i The weight set is expressed as W = {w1,w2,...,w n }, and use the following formula to calculate the maximum eigenvalue λ max : Among them, (AW) i Represents the i-th element of vector AW.

6. The method for collaborative testing and comprehensive evaluation of static and dynamic noise of an automobile across operating conditions as claimed in claim 5, characterized in that: The fuzzy comprehensive evaluation method comprehensively scores the noise performance of the vehicle under static and dynamic conditions, and obtains a comprehensive scoring result, including: Determine the review set V = {v1, v2, ..., v m }, where m is the number of review levels; Through the membership function, each evaluation index u is obtained i The membership vector R relative to the review set i =(μ i1 ,μ i2 ,...,μ im ), the velocity vectors of all indicators form a membership matrix R; Use the following formula to calculate the comprehensive evaluation vector B: B=W·R=(b1,b2,...,b m ) in, b j Indicates that the car's noise performance belongs to the rating level v j The comprehensive membership degree; By m Each level in the} is assigned a corresponding score, forming a score vector C = (c1, c2, ...c m ) T , use the following formula to calculate the comprehensive score: Among them, S is the comprehensive score result.

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