Evaluation method and device for low wind noise grade of vehicle

Abstract

The invention belongs to the technical field of vehicle wind noise test, and discloses a vehicle low wind noise grade evaluation method and device, and the method comprises the steps: processing the wind noise test data of a plurality of evaluation indexes to obtain a corresponding first score, and preliminarily converting each evaluation index into a score for representation, the reliability of subsequent evaluation results can be guaranteed by combining various types of evaluation indexes; and then, according to the wind noise evaluation data and the wind noise test data of each evaluation index, calculating a corresponding weighting coefficient, and carrying out weighted summation processing in combination with the score corresponding to each evaluation index, so that a test result and a subjective evaluation result of the evaluation indexes can be considered at the same time; and the finally obtained evaluation result of the low-wind-noise grade of the vehicle is more fair and authoritative, so that the product quality optimization in the automobile industry is facilitated.

Description

Technical Field

[0001] This application belongs to the technical field of vehicle wind noise testing, and particularly relates to a method and device for evaluating the low wind noise level of a vehicle. Background Technique

[0002] With the development of new energy vehicle technology and the improvement of road conditions, the contribution of the powertrain noise and tire-road noise of new energy vehicles to the total vehicle noise value is gradually decreasing, while the wind noise problem of vehicles has become particularly important. Moreover, with the increase in vehicle driving speed, the importance of vehicle wind noise control is further highlighted.

[0003] Due to the characteristics of wind tunnel tests such as controllable environment and test conditions, not easily being interfered by other noise sources, high measurement accuracy and repeatability, the wind noise test results of general vehicles can be obtained through wind tunnel tests. In vehicle noise standards and regulations, the A-weighted sound pressure level is often used as the main noise evaluation index. However, this A-weighted sound pressure level does not deeply consider the acoustic quality characteristics of in-vehicle noise, cannot comprehensively reflect the passengers' perception of noise, and easily leads to discomfort of the driver and passengers when the A-weighted sound pressure level meets the standard requirements, thereby affecting the vehicle product quality. Summary of the Invention

[0004] To solve the above-mentioned technical defects that in vehicle noise standards and regulations, the A-weighted sound pressure level is often used as the main noise evaluation index, but this A-weighted sound pressure level does not deeply consider the acoustic quality characteristics of in-vehicle noise, cannot comprehensively reflect the passengers' perception of noise, and easily leads to discomfort of the driver and passengers when the A-weighted sound pressure level meets the standard requirements, thereby affecting the vehicle product quality, etc., this application proposes a method and device for evaluating the low wind noise level of a vehicle, and its technical solutions are as follows:

[0005] In a first aspect, an embodiment of this application provides a method for evaluating the low wind noise level of a vehicle, including:

[0006] Obtain the wind noise test data of at least two evaluation indicators, and process the wind noise test data of each evaluation indicator based on a preset wind noise test database to obtain the corresponding first score;

[0007] Obtain the wind noise evaluation data of each evaluation indicator, and calculate the corresponding weighting coefficient according to the wind noise test data and the wind noise evaluation data of each evaluation indicator;

[0008] Obtain the evaluation result of the low wind noise level of the vehicle according to the first scores of all evaluation indicators and the corresponding weighting coefficients.

[0009] In an alternative solution of the first aspect, the preset wind noise test database includes all evaluation indicators and at least two groups of wind noise historical data of each evaluation indicator;

[0010] Process the wind noise test data of each evaluation index based on a preset wind noise test database to obtain corresponding first scores, including:

[0011] Sort all the historical wind noise data of each evaluation index, and perform a screening process on all the sorted historical wind noise data based on a preset interval coefficient to obtain corresponding data intervals;

[0012] Calculate the mean value of the wind noise test data of each evaluation index to obtain a corresponding mean result, and obtain a corresponding first score according to the mean result and the data interval of each evaluation index.

[0013] In another alternative solution of the first aspect, obtaining a corresponding first score according to the mean result and the data interval of each evaluation index includes:

[0014] Determine an upper threshold and a lower threshold from within the data interval of each evaluation index, and substitute the upper threshold, lower threshold, and mean result of each evaluation index into a preset score calculation formula to obtain a corresponding first score.

[0015] In another alternative solution of the first aspect, all evaluation indexes include a sound pressure level index, a speech intelligibility index, and a sharpness index, and the preset score calculation formula includes a preset first calculation formula and a preset second calculation formula;

[0016] Substituting the upper threshold, lower threshold, and mean result of each evaluation index into the preset score calculation formula to obtain a corresponding first score includes:

[0017] Substitute the upper threshold, lower threshold, and mean result of the sound pressure level index into the preset first calculation formula to obtain a corresponding first score;

[0018] Substitute the upper threshold, lower threshold, and mean result of the speech intelligibility index into the preset second calculation formula to obtain a corresponding first score;

[0019] Substitute the upper threshold, lower threshold, and mean result of the sharpness index into the preset first calculation formula to obtain a corresponding first score.

[0020] In another alternative solution of the first aspect, calculate a corresponding weighting coefficient according to the wind noise test data and the wind noise evaluation data of each evaluation index, including:

[0021] Count the number of values from the wind noise evaluation data of each evaluation index, and perform a screening process on the corresponding wind noise test data based on the number of values of each evaluation index;

[0022] Calculate the correlation between the wind noise evaluation data for each evaluation index and the filtered wind noise test data to obtain the corresponding first correlation coefficient;

[0023] When the absolute value of each first correlation coefficient exceeds a preset coefficient threshold, sum the absolute values of all first correlation coefficients to obtain a summation result;

[0024] Calculate the corresponding weighting coefficient according to the summation result and the first correlation coefficient of each evaluation index.

[0025] In another alternative of the first aspect, according to the first scores of all evaluation indexes and the corresponding weighting coefficients, obtain the evaluation result of the vehicle's low wind noise level, including:

[0026] Perform weighted summation calculation on the first scores of all evaluation indexes and the corresponding weighting coefficients to obtain a second score;

[0027] When the second score is within a preset first score interval, determine that the evaluation result of the vehicle's low wind noise level is level one;

[0028] When the second score is within a preset second score interval, determine that the evaluation result of the vehicle's low wind noise level is level two; where the preset first score interval is higher than the preset second score interval;

[0029] When the second score is within a preset third score interval, determine that the evaluation result of the vehicle's low wind noise level is level three; where the preset second score interval is higher than the preset third score interval.

[0030] In another alternative of the first aspect, the method further includes:

[0031] Based on the evaluation result and the wind noise evaluation data of all evaluation indexes, obtain a second correlation coefficient;

[0032] When the absolute value of the second correlation coefficient does not exceed the absolute value of any one of the first correlation coefficients, perform replacement processing on at least one evaluation index.

[0033] In a second aspect, an embodiment of the present application provides an evaluation device for a vehicle's low wind noise level, including:

[0034] A first processing module, configured to obtain wind noise test data of at least two evaluation indexes, and process the wind noise test data of each evaluation index based on a preset wind noise test database to obtain the corresponding first score;

[0035] A second processing module, configured to obtain wind noise evaluation data of each evaluation index, and calculate the corresponding weighting coefficient according to the wind noise test data and the wind noise evaluation data of each evaluation index;

[0036] An evaluation generation module, configured to obtain an evaluation result of the vehicle's low wind noise level according to the first scores of all evaluation indicators and the corresponding weighting coefficients.

[0037] In a third aspect, an embodiment of the present application further provides an evaluation device for the vehicle's low wind noise level, including a processor and a memory;

[0038] The processor is connected to the memory;

[0039] The memory is used to store executable program codes;

[0040] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the evaluation method for the vehicle's low wind noise level provided in the first aspect or any implementation manner of the first aspect of the embodiment of the present application.

[0041] In a fourth aspect, an embodiment of the present application provides a computer storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the evaluation method for the vehicle's low wind noise level provided in the first aspect or any implementation manner of the first aspect of the embodiment of the present application can be implemented.

[0042] Advantages of the present application:

[0043] When evaluating the low wind noise level of a test vehicle, it is possible to obtain the wind noise test data of at least two evaluation indicators, and process the wind noise test data of each evaluation indicator based on a preset wind noise test database to obtain the corresponding first scores; obtain the wind noise evaluation data of each evaluation indicator, and calculate the corresponding weighting coefficients according to the wind noise test data and the wind noise evaluation data of each evaluation indicator; obtain the evaluation result of the vehicle's low wind noise level according to the first scores of all evaluation indicators and the corresponding weighting coefficients. By processing the wind noise test data of multiple evaluation indicators to obtain the corresponding first scores, each evaluation indicator is initially converted into a score representation, and combining various types of evaluation indicators can ensure the reliability of the subsequent evaluation results; then, calculate the corresponding weighting coefficients according to the wind noise evaluation data and the wind noise test data of each evaluation indicator, and perform weighted summation processing in combination with the scores corresponding to each evaluation indicator above, which can not only consider the test results and subjective evaluation results of the evaluation indicators at the same time, but also make the finally obtained evaluation result of the vehicle's low wind noise level more fair and authoritative, thus contributing to the optimization of product quality in the automotive industry. Description of the Drawings

[0044] To more clearly illustrate the technical solutions in the embodiments of the present application, the accompanying drawings required for the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0045] Figure 1 It is the overall flowchart of an evaluation method for the low wind noise level of a vehicle provided by an embodiment of the present application;

[0046] Figure 2 It is a schematic diagram of the upper and lower limit thresholds of an evaluation index provided by an embodiment of the present application;

[0047] Figure 3 It is a schematic structural diagram of an evaluation device for the low wind noise level of a vehicle provided by an embodiment of the present application;

[0048] Figure 4 It is a schematic structural diagram of another evaluation device for the low wind noise level of a vehicle provided by an embodiment of the present application. Detailed implementation manners

[0049] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application.

[0050] In the following description, the terms "first" and "second" are only for the purpose of description and cannot be construed as indicating or implying relative importance. The following description provides multiple embodiments of the present application. Different embodiments can be replaced or combined. Therefore, the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing all other possible combinations of A, B, C, and D, although such an embodiment may not be explicitly described in the following content.

[0051] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes can be made to the functions and arrangements of the described elements without departing from the scope of the content of the present application. Each example can appropriately omit, substitute, or add various processes or components. For example, the described method can be executed in a different order from the described order, and various steps can be added, omitted, or combined. In addition, the features described in some examples can be combined into other examples.

[0052] Please refer to Figure 1 , Figure 1 which shows the overall flowchart of an evaluation method for the low wind noise level of a vehicle provided by an embodiment of the present application.

[0053] As Figure 1 shown, the evaluation method for the low wind noise level of a vehicle may at least include the following steps:

[0054] Step 102: Obtain the wind noise test data of at least two evaluation indicators, and process the wind noise test data of each evaluation indicator based on a preset wind noise test database to obtain corresponding first scores.

[0055] In the embodiments of the present application, the evaluation method for the low wind noise level of a vehicle can be but is not limited to being applied to a control terminal. The control terminal can obtain the wind noise test data of a test vehicle in a full-vehicle aerodynamic-acoustic wind tunnel wind noise test, as well as the wind noise evaluation data of the driver and passengers when using the test vehicle, and obtain the evaluation result of the low wind noise level of the test vehicle according to the wind noise test data and the wind noise evaluation coefficient. Among them, the full-vehicle aerodynamic-acoustic wind tunnel wind noise test can be understood as a well-known test method in the art for evaluating and improving the vehicle's aerodynamic performance and in-vehicle noise level, aiming to simulate real vehicle driving conditions, such as but not limited to precisely controlling environmental parameters such as the wind speed, temperature, and humidity in the wind tunnel, and obtaining the sensor data of the vehicle repeatedly tested in a specified driving state through various types of sensors installed on the vehicle, so as to analyze and process the data of multiple sensors to obtain the wind noise test data. Here, the wind noise test data can include the test data corresponding to multiple conventional evaluation indicators (i.e., wind noise evaluation indicators), such as but not limited to multiple decibel values corresponding to the sound pressure level indicator (which can also be understood as the A-weighted sound pressure level indicator), multiple clear audio percentages corresponding to the speech intelligibility indicator, and multiple high-frequency percentages corresponding to the sharpness indicator. Of course, it can also include multiple test values corresponding to the spatial distribution indicator, time history, background noise, and vibration data, not limited to this.

[0056] It should be noted that the full-vehicle aerodynamic-acoustic wind tunnel wind noise test and the generation method of the wind noise test data mentioned in the embodiments of the present application can refer to the well-known technical means in the art, and will not be elaborated here.

[0057] The wind noise evaluation data of the vehicle occupants when using the test vehicle can be understood as the wind noise evaluation data recorded by the vehicle occupants according to their own experiences and the preset subjective evaluation criteria (such as SAE J1441-201609) when using the test vehicle for the full-vehicle aerodynamic-acoustic wind tunnel wind noise test. The wind noise evaluation data can include the subjective evaluation scores corresponding to multiple conventional evaluation indicators (i.e., wind noise evaluation indicators) (such as any score within 0-100), for example, but not limited to, the evaluation scores corresponding to the sound pressure level indicator (which can also be understood as the A-weighted sound pressure level indicator), the evaluation scores corresponding to the speech intelligibility indicator, and the evaluation scores corresponding to the sharpness indicator. Of course, it can also include the evaluation scores corresponding to the spatial distribution indicator, the time history, the background noise, and the vibration data, not limited to this.

[0058] Here, the wind noise evaluation data can also, but not limited to, be predicted by a preset deep learning model based on various sensor data collected during the full-vehicle aerodynamic-acoustic wind tunnel wind noise test of the test vehicle. The preset deep learning model can be trained by multiple groups of sensor data samples and the score labels corresponding to each group of sensor data samples, and is not limited to this.

[0059] It can be understood that the control terminal can process the wind noise test data of multiple evaluation indicators to obtain the corresponding first score, so as to initially convert each evaluation indicator into a score representation. Combining various types of evaluation indicators can ensure the reliability of the subsequent evaluation results. Then, according to the wind noise evaluation data and the wind noise test data of each evaluation indicator, the corresponding weighting coefficients are calculated, and weighted summation processing is performed in combination with the scores corresponding to each evaluation indicator above. This can not only consider the test results and subjective evaluation results of the evaluation indicators at the same time, but also make the final evaluation result of the vehicle's low wind noise level more fair and authoritative, thus helping the automotive industry to optimize product quality.

[0060] Specifically, when evaluating the low wind noise level of a test vehicle, the control terminal can, but is not limited to, obtaining the wind noise test data of the test vehicle in the vehicle aerodynamic-acoustic wind tunnel test. The wind noise test data can be understood as the wind noise test data corresponding to at least two of the above-mentioned evaluation indicators. For example, it can include multiple decibel values corresponding to the sound pressure level indicator (which can also be understood as the A-weighted sound pressure level indicator), multiple clear audio percentages corresponding to the speech intelligibility indicator, and multiple high-frequency percentages corresponding to the sharpness indicator. And based on the preset wind noise test database, the wind noise test data of each evaluation indicator is processed to obtain the corresponding first score. Here, the preset wind noise test database can be composed of multiple groups of wind noise historical data of multiple test vehicles in the vehicle aerodynamic-acoustic wind tunnel test (each group of wind noise historical data can include multiple historical test data corresponding to any one of the evaluation indicators), which includes at least two of the above-mentioned evaluation indicators, and all the wind noise historical data corresponding to each evaluation indicator.

[0061] It can be understood that when processing the wind noise test data of each evaluation indicator based on the preset wind noise test database to obtain the corresponding first score, the control terminal can, but is not limited to, performing interval division processing on all the wind noise historical data of each evaluation indicator in the preset wind noise test database, and performing score assignment processing on each interval divided for each evaluation indicator, so that when it is recognized that the wind noise test data of each evaluation indicator is within any one of the grouped intervals of the corresponding evaluation indicator, the score assignment result corresponding to the corresponding grouped interval is used as the first score, and it is not limited to this.

[0062] As an option in the embodiment of the present application, the preset wind noise test database includes all evaluation indicators, and at least two groups of wind noise historical data for each evaluation indicator;

[0063] Processing the wind noise test data of each evaluation indicator based on the preset wind noise test database to obtain the corresponding first score includes:

[0064] Performing sorting processing on all the wind noise historical data of each evaluation indicator, and performing screening processing on all the sorted wind noise historical data based on the preset interval coefficient to obtain the corresponding data interval;

[0065] Performing mean value calculation on the wind noise test data of each evaluation indicator to obtain the corresponding mean value result, and obtaining the corresponding first score according to the mean value result and the data interval of each evaluation indicator.

[0066] Specifically, when processing the wind noise test data of each evaluation index, the control terminal can also, but is not limited to, sort all the wind noise historical data of each evaluation index in the preset wind noise test database in ascending order, and can screen all the wind noise historical data after the sorting process based on the preset interval coefficient to ensure the reliability and effectiveness of the data. For example, but not limited to, when the preset interval coefficient is 10% and 90%, all the wind noise historical data between 10% and 90% can be screened out from all the wind noise historical data after the sorting process, and all the wind noise historical data between 10% and 90% can be used as the data interval of the corresponding evaluation index.

[0067] Next, the control terminal can also calculate the mean value of the wind noise test data of each evaluation index to obtain the corresponding mean result. For example, when an evaluation index is the sound pressure level index, the mean value of multiple decibel values of the sound pressure level index can be calculated; or when an evaluation index is the speech intelligibility index, the mean value of multiple clear audio percentages of the speech intelligibility index can be calculated; or when an evaluation index is the sharpness index, the mean value of multiple high-frequency percentages of the sharpness index can be calculated, and the corresponding first score can be obtained based on the mean result and the data interval of each evaluation index.

[0068] As another option of the embodiment of the present application, obtaining the corresponding first score according to the mean result and the data interval of each evaluation index includes:

[0069] Determine the upper threshold and the lower threshold from within the data interval of each evaluation index, and substitute the upper threshold, the lower threshold, and the mean result of each evaluation index into the preset score calculation formula to obtain the corresponding first score.

[0070] Specifically, when obtaining the corresponding first score according to the mean result and the data interval of each evaluation index, the control terminal can, but is not limited to, determine the upper threshold and the lower threshold from within the data interval of each evaluation index. The upper threshold can be the last data in the data interval, and the lower threshold can be the first data in the data interval, and substitute the upper threshold, the lower threshold, and the mean result of each evaluation index into the preset score calculation formula, and then obtain the corresponding first score.

[0071] Reference can be made here to Figure 2 the schematic diagram of the upper and lower threshold values of an evaluation index provided by the embodiment of the present application shown in Figure 2As shown, taking all evaluation indicators including the sound pressure level indicator (i.e., the A-weighted sound pressure level indicator), the speech intelligibility indicator, and the sharpness indicator as examples, the upper threshold value (i.e., the maximum value) of the sound pressure level indicator can be 69.0 dBA, and the lower threshold value (i.e., the minimum value) of the sound pressure level indicator can be 63.0 dBA; the upper threshold value (i.e., the maximum value) of the speech intelligibility indicator can be 85.0%, and the lower threshold value (i.e., the minimum value) of the speech intelligibility indicator can be 55.0%; the upper threshold value (i.e., the maximum value) of the sharpness indicator can be 0.86%, and the lower threshold value (i.e., the maximum value) of the sharpness indicator can be 0.68%.

[0072] Here, taking all evaluation indicators including the sound pressure level indicator, the speech intelligibility indicator, and the sharpness indicator, and taking the preset score calculation formulas including the preset first calculation formula and the preset second calculation formula as examples, the control terminal can substitute the upper threshold value, the lower threshold value, and the average result of the sound pressure level indicator into the preset first calculation formula to obtain the corresponding first score. The preset first calculation formula can be but is not limited to referring to the following:

[0073] Q SPL = 100*(Ma - SPL) / (Max - Min)

[0074] In the above formula, Q SPL can be the first score of the sound pressure level indicator, Max can be the upper threshold value of the sound pressure level indicator, Min can be the lower threshold value of the sound pressure level indicator, and SPL can be the average result of the sound pressure level indicator.

[0075] The control terminal can also substitute the upper threshold value, the lower threshold value, and the average result of the speech intelligibility indicator into the preset second calculation formula to obtain the corresponding first score. The preset second calculation formula can be but is not limited to referring to the following:

[0076] Q AI = 100*(AI - Min) / (Max - Min)

[0077] In the above formula, Q AI can be the first score of the speech intelligibility indicator, Max can be the upper threshold value of the speech intelligibility indicator, Min can be the lower threshold value of the speech intelligibility indicator, and AI can be the average result of the speech intelligibility indicator.

[0078] The control terminal can also substitute the upper threshold value, the lower threshold value, and the average result of the sharpness indicator into the preset first calculation formula to obtain the corresponding first score. The preset first calculation formula can be but is not limited to referring to the following:

[0079] Q Sharpness = 100*(Max - Sharpness) / (Max - Min)

[0080] In the above formula, Q Sharpness may be the first score of the sharpness index, Max may be the upper threshold of the sharpness index, Min may be the lower threshold of the sharpness index, and Sharpness may be the average result of the sharpness index.

[0081] Step 104: Obtain the wind noise evaluation data for each evaluation index, and calculate the corresponding weighting coefficient according to the wind noise test data and the wind noise evaluation data for each evaluation index.

[0082] Specifically, after obtaining the wind noise test data for each evaluation index, the control terminal can, but is not limited to, obtain the wind noise evaluation data recorded by the driver and passengers based on their own experiences and the preset subjective evaluation criteria (such as SAE J1441-201609) during the vehicle aerodynamic-acoustic wind tunnel wind noise test using the test vehicle. The wind noise evaluation data may include the subjective evaluation scores (such as any score within 0-100) corresponding to multiple conventional evaluation indexes (i.e., wind noise evaluation indexes), such as, but not limited to, the multiple evaluation scores corresponding to the sound pressure level index (which can also be understood as the A-weighted sound pressure level index), the multiple evaluation scores corresponding to the speech intelligibility index, and the multiple evaluation scores corresponding to the sharpness index. Then, count the number of values from the wind noise evaluation data for each evaluation index, and based on the number of values for each evaluation index, screen the wind noise test data for the corresponding evaluation index to make the screened wind noise test data consistent with the number of values.

[0083] It can be understood that before screening the wind noise test data for each evaluation index, preprocessing such as outlier removal, standardization, or normalization can also be performed, but is not limited to these, to ensure the reliability of the wind noise test data.

[0084] Then, after screening the wind noise test data for each evaluation index, the control terminal can, but is not limited to, use the Pearson correlation coefficient algorithm or the Spearman rank correlation coefficient algorithm to calculate the correlation between the wind noise evaluation data and the screened wind noise test data for each evaluation index, obtaining the corresponding first correlation coefficient. Here, the Pearson correlation coefficient algorithm or the Spearman rank correlation coefficient algorithm may be well-known correlation calculation algorithms in the art, and of course, other correlation calculation algorithms can also be used, which will not be elaborated here.

[0085] Next, after calculating the first correlation coefficient of each evaluation index, the control terminal can also determine whether there is a correlation that meets the requirements between the wind noise test data and the wind noise evaluation data of each evaluation index through a preset coefficient threshold. When the absolute value of each first correlation coefficient exceeds the preset coefficient threshold, it indicates that there is a correlation that meets the requirements between the wind noise test data and the wind noise evaluation data of each evaluation index. Furthermore, the sum of the absolute values of all first correlation coefficients can be calculated to obtain a summation result.

[0086] It can be understood that when the absolute value of any one first correlation coefficient does not exceed the preset coefficient threshold, it indicates that there is no correlation that meets the requirements between the wind noise test data and the wind noise evaluation data of this evaluation index. Furthermore, substitution processing can be performed on this evaluation index, but not limited to this, and refer to one or more of the above-mentioned embodiments to calculate the corresponding first correlation coefficient to determine whether there is a correlation that meets the requirements between the wind noise test data and the wind noise evaluation data of the evaluation index after substitution processing.

[0087] Next, after obtaining the summation result, the control terminal can also calculate the ratio of the absolute value of the first correlation coefficient of each evaluation index to the summation result to obtain the corresponding weighting coefficient. Here, taking all evaluation indexes including the sound pressure level index, the speech intelligibility index, and the sharpness index as examples, the first correlation coefficient of the sound pressure level index can be, but not limited to, -0.868, the first correlation coefficient of the speech intelligibility index can be, but not limited to, 0.890, and the first correlation coefficient of the sharpness index can be, but not limited to, -0.749. Then, the weighting coefficient of the sound pressure level index can be calculated to be approximately 0.3, the weighting coefficient of the speech intelligibility index can be calculated to be approximately 0.5, and the weighting coefficient of the sharpness index can be calculated to be approximately 0.2.

[0088] Step 106: Obtain the evaluation result of the vehicle's low wind noise level according to the first scores of all evaluation indexes and the corresponding weighting coefficients.

[0089] Specifically, after obtaining the weighting coefficient of each evaluation index, the control terminal can, but not limited to, perform a weighted summation calculation on the first scores of all evaluation indexes and the corresponding weighting coefficients to obtain a second score, and obtain the evaluation result of the vehicle's low wind noise level according to the score interval where the second score is located. Here, taking all evaluation indexes including the sound pressure level index, the speech intelligibility index, and the sharpness index as examples, the calculation method of the second score is, for example but not limited to, referring to the following:

[0090] S = 0.3 * Q SPL + 0.5 * Q AI + 0.2 * Q Sharpness

[0091] In the above formula, S can be the second score, 0.3 can be the weighting coefficient of the sound pressure level index, and Q SPL can be the first score of the sound pressure level index, 0.5 can be the weighting coefficient of the speech intelligibility index, and Q AI can be the first score of the speech intelligibility index, 0.2 can be the weighting coefficient of the sharpness index, and Q Sharpness can be the first score of the sharpness index.

[0092] It can be understood that when the second score is within the preset first score range, the evaluation result of the vehicle's low wind noise level can be determined as level one. The preset first score range can be, but is not limited to, [90, 100]. This level one can be understood as low wind noise level A, and is not limited thereto; when the second score is within the preset second score range, the evaluation result of the vehicle's low wind noise level can be determined as level two. The preset second score range can be, but is not limited to, [70, 90). This level two can be understood as low wind noise level B, and is not limited thereto; when the second score is within the preset third score range, the evaluation result of the vehicle's low wind noise level can be determined as level three. The preset third score range can be, but is not limited to, [50, 70). This level three can be understood as low wind noise level C, and is not limited thereto.

[0093] As another alternative of the embodiment of the present application, the method further includes:

[0094] Based on the evaluation result and the wind noise evaluation data of all evaluation indicators, obtain a second correlation coefficient;

[0095] When the absolute value of the second correlation coefficient does not exceed the absolute value of any one of the first correlation coefficients, perform replacement processing on at least one evaluation indicator.

[0096] Specifically, after obtaining the evaluation result of the vehicle's low wind noise level, in order to further judge the rationality of the selection of all evaluation indicators in step 102, the control terminal can also, but is not limited to, after obtaining multiple evaluation results, calculate the correlation between the multiple evaluation results and the wind noise evaluation data of all evaluation indicators in step 102. The wind noise evaluation data here can be understood as the wind noise evaluation data of any one evaluation indicator, or the wind noise evaluation data of each evaluation indicator weighted and summed with the above-mentioned weighting coefficients, so as to obtain a second correlation coefficient.

[0097] It can be understood that when the absolute value of the second correlation coefficient does not exceed the absolute value of any one of the first correlation coefficients, it indicates that the selection rationality of all evaluation indicators in step 102 is relatively poor. Furthermore, the evaluation indicator with the smallest absolute value of the first correlation coefficient can be replaced, and the above one or more embodiments can be referred to continue to determine whether the selection of all evaluation indicators is reasonable; when the absolute value of the second correlation coefficient exceeds the absolute value of each first correlation coefficient, it indicates that the selection of all evaluation indicators in step 102 is reasonable. For example, taking all evaluation indicators including the sound pressure level indicator, the speech intelligibility indicator, and the sharpness indicator as an example, the first correlation coefficient of the sound pressure level indicator is -0.868, the first correlation coefficient of the speech intelligibility indicator is 0.890, the first correlation coefficient of the sharpness indicator is -0.749, and the calculated second correlation coefficient is 0.917. Then it can be shown that the selection of the sound pressure level indicator, the speech intelligibility indicator, and the sharpness indicator is reasonable and more in line with the subjective feeling results of the driver and passengers.

[0098] Next, please refer to Figure 3 , Figure 3 which shows a schematic structural diagram of an evaluation device for vehicle low wind noise level provided by an embodiment of the present application.

[0099] As Figure 3 shown, the evaluation device for vehicle low wind noise level may at least include a first processing module 301, a second processing module 302, and an evaluation generation module 303, where:

[0100] The first processing module 301 is configured to obtain wind noise test data of at least two evaluation indicators, and process the wind noise test data of each evaluation indicator based on a preset wind noise test database to obtain corresponding first scores;

[0101] The second processing module 302 is configured to obtain wind noise evaluation data of each evaluation indicator, and calculate corresponding weighting coefficients according to the wind noise test data and the wind noise evaluation data of each evaluation indicator;

[0102] The evaluation generation module 303 is configured to obtain an evaluation result of the vehicle low wind noise level according to the first scores of all evaluation indicators and the corresponding weighting coefficients.

[0103] In some possible embodiments, the preset wind noise test database includes all evaluation indicators and at least two groups of wind noise historical data of each evaluation indicator;

[0104] Processing the wind noise test data of each evaluation indicator based on the preset wind noise test database to obtain corresponding first scores includes:

[0105] Sort all the historical wind noise data for each evaluation index, and perform a screening process on all the sorted historical wind noise data based on a preset interval coefficient to obtain the corresponding data interval.

[0106] Calculate the mean value of the wind noise test data for each evaluation index to obtain the corresponding mean result, and obtain the corresponding first score based on the mean result and the data interval of each evaluation index.

[0107] In some possible embodiments, obtaining the corresponding first score based on the mean result and the data interval of each evaluation index includes:

[0108] Determine the upper threshold and the lower threshold from within the data interval of each evaluation index, and substitute the upper threshold, the lower threshold, and the mean result of each evaluation index into a preset score calculation formula to obtain the corresponding first score.

[0109] In some possible embodiments, all the evaluation indexes include a sound pressure level index, a speech intelligibility index, and a sharpness index, and the preset score calculation formula includes a preset first calculation formula and a preset second calculation formula;

[0110] Substituting the upper threshold, the lower threshold, and the mean result of each evaluation index into the preset score calculation formula to obtain the corresponding first score includes:

[0111] Substitute the upper threshold, the lower threshold, and the mean result of the sound pressure level index into the preset first calculation formula to obtain the corresponding first score;

[0112] Substitute the upper threshold, the lower threshold, and the mean result of the speech intelligibility index into the preset second calculation formula to obtain the corresponding first score;

[0113] Substitute the upper threshold, the lower threshold, and the mean result of the sharpness index into the preset first calculation formula to obtain the corresponding first score.

[0114] In some possible embodiments, calculating the corresponding weighting coefficient based on the wind noise test data and the wind noise evaluation data of each evaluation index includes:

[0115] Count the number of values from the wind noise evaluation data of each evaluation index, and perform a screening process on the corresponding wind noise test data based on the number of values of each evaluation index;

[0116] Calculate the correlation between the wind noise evaluation data and the screened wind noise test data of each evaluation index to obtain the corresponding first correlation coefficient;

[0117] When the absolute value of each first correlation coefficient exceeds a preset coefficient threshold, the absolute values of all the first correlation coefficients are summed to obtain a summation result;

[0118] According to the summation result and the first correlation coefficient of each evaluation index, the corresponding weighting coefficient is calculated.

[0119] In some possible embodiments, according to the first scores of all evaluation indexes and the corresponding weighting coefficients, the evaluation result of the vehicle low wind noise level is obtained, including:

[0120] The first scores of all evaluation indexes and the corresponding weighting coefficients are weighted and summed to obtain a second score;

[0121] When the second score is within a preset first score interval, it is determined that the evaluation result of the vehicle low wind noise level is grade one;

[0122] When the second score is within a preset second score interval, it is determined that the evaluation result of the vehicle low wind noise level is grade two; wherein, the preset first score interval is higher than the preset second score interval;

[0123] When the second score is within a preset third score interval, it is determined that the evaluation result of the vehicle low wind noise level is grade three; wherein, the preset second score interval is higher than the preset third score interval.

[0124] In some possible embodiments, the device further includes:

[0125] Based on the evaluation result and the wind noise evaluation data of all evaluation indexes, a second correlation coefficient is obtained;

[0126] When the absolute value of the second correlation coefficient does not exceed the absolute value of any one of the first correlation coefficients, at least one evaluation index is replaced.

[0127] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented by means of software and / or hardware. The "units" and "modules" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

[0128] Next, please refer to Figure 4 , Figure 4 which shows a schematic structural diagram of another evaluation device for vehicle low wind noise level provided by the embodiments of the present application.

[0129] As Figure 4As shown, the evaluation device 400 for the low wind noise level of a vehicle may include at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.

[0130] Among them, the communication bus 402 can be used to implement the connection and communication of each of the above components.

[0131] Among them, the user interface 403 may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.

[0132] Among them, the network interface 404 can but is not limited to include a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0133] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect each part within the evaluation device 400 for the low wind noise level of the vehicle, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, as well as calling data stored in the memory 405, it executes various functions of the evaluation device 400 for the low wind noise level of the vehicle and processes data. Optionally, the processor 401 can be implemented in at least one of the hardware forms of DSP, FPGA, and PLA. The processor 401 can integrate one or several combinations of a CPU, a GPU, and a modem, etc. Among them, the CPU mainly processes the operating system, the user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 401 and can be implemented separately by a single chip.

[0134] Among them, the memory 405 may include RAM and may also include ROM. Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing each of the above method embodiments, etc.; the data storage area can store the data involved in each of the above method embodiments. Optionally, the memory 405 may also be at least one storage device located far from the aforementioned processor 401. As Figure 4 As shown, the memory 405, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an evaluation application program for the low wind noise level of the vehicle.

[0135] Specifically, the processor 401 can be used to call the evaluation application program for the vehicle low wind noise level stored in the memory 405, and specifically perform the following operations:

[0136] Obtain the wind noise test data of at least two evaluation indicators, and process the wind noise test data of each evaluation indicator based on the preset wind noise test database to obtain the corresponding first score;

[0137] Obtain the wind noise evaluation data of each evaluation indicator, and calculate the corresponding weighting coefficient according to the wind noise test data and the wind noise evaluation data of each evaluation indicator;

[0138] Obtain the evaluation result of the vehicle low wind noise level according to the first scores and the corresponding weighting coefficients of all evaluation indicators.

[0139] In some possible embodiments, the preset wind noise test database includes all evaluation indicators and at least two groups of wind noise historical data of each evaluation indicator;

[0140] Processing the wind noise test data of each evaluation indicator based on the preset wind noise test database to obtain the corresponding first score includes:

[0141] Sort all the wind noise historical data of each evaluation indicator, and perform screening processing on all the sorted wind noise historical data based on the preset interval coefficient to obtain the corresponding data interval;

[0142] Calculate the mean value of the wind noise test data of each evaluation indicator to obtain the corresponding mean result, and obtain the corresponding first score according to the mean result and the data interval of each evaluation indicator.

[0143] In some possible embodiments, obtaining the corresponding first score according to the mean result and the data interval of each evaluation indicator includes:

[0144] Determine the upper threshold and the lower threshold from within the data interval of each evaluation indicator, and substitute the upper threshold, the lower threshold, and the mean result of each evaluation indicator into the preset score calculation formula to obtain the corresponding first score.

[0145] In some possible embodiments, all evaluation indicators include sound pressure level indicators, speech intelligibility indicators, and sharpness indicators, and the preset score calculation formula includes a preset first calculation formula and a preset second calculation formula;

[0146] Substituting the upper threshold, the lower threshold, and the mean result of each evaluation indicator into the preset score calculation formula to obtain the corresponding first score includes:

[0147] Substitute the upper threshold, lower threshold, and mean result of the sound pressure level index into a preset first calculation formula to obtain the corresponding first score;

[0148] Substitute the upper threshold, lower threshold, and mean result of the speech intelligibility index into a preset second calculation formula to obtain the corresponding first score;

[0149] Substitute the upper threshold, lower threshold, and mean result of the sharpness index into the preset first calculation formula to obtain the corresponding first score.

[0150] In some possible embodiments, according to the wind noise test data and wind noise evaluation data of each evaluation index, calculate the corresponding weighting coefficients, including:

[0151] Count the number of values from the wind noise evaluation data of each evaluation index, and based on the number of values of each evaluation index, perform screening processing on the corresponding wind noise test data;

[0152] Calculate the correlation between the wind noise evaluation data of each evaluation index and the screened wind noise test data to obtain the corresponding first correlation coefficient;

[0153] When the absolute value of each first correlation coefficient exceeds the preset coefficient threshold, sum the absolute values of all first correlation coefficients to obtain a summation result;

[0154] Calculate the corresponding weighting coefficients according to the summation result and the first correlation coefficient of each evaluation index.

[0155] In some possible embodiments, according to the first scores of all evaluation indexes and the corresponding weighting coefficients, obtain the evaluation result of the vehicle's low wind noise level, including:

[0156] Perform a weighted sum calculation on the first scores of all evaluation indexes and the corresponding weighting coefficients to obtain a second score;

[0157] When the second score is within the preset first score interval, determine that the evaluation result of the vehicle's low wind noise level is first level;

[0158] When the second score is within the preset second score interval, determine that the evaluation result of the vehicle's low wind noise level is second level; where the preset first score interval is higher than the preset second score interval;

[0159] When the second score is within the preset third score interval, determine that the evaluation result of the vehicle's low wind noise level is third level; where the preset second score interval is higher than the preset third score interval.

[0160] In some possible embodiments, it is also used to execute:

[0161] Based on the evaluation results and the wind noise evaluation data of all evaluation indicators, a second correlation coefficient is obtained;

[0162] When the absolute value of the second correlation coefficient does not exceed the absolute value of any one of the first correlation coefficients, replacement processing is performed on at least one evaluation indicator.

[0163] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0164] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0165] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, the functional units in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0167] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. And the aforementioned memory includes: USB flash drive, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk, or optical disc and other various media that can store program codes.

Claims

1. An evaluation method for the low wind noise level of a vehicle, characterized in that, Including: Obtain the wind noise test data of at least two evaluation indicators, and process the wind noise test data of each evaluation indicator based on a preset wind noise test database to obtain corresponding first scores; Obtain the wind noise evaluation data of each evaluation indicator, and calculate corresponding weighting coefficients according to the wind noise test data and the wind noise evaluation data of each evaluation indicator; Obtain the evaluation result of the vehicle's low wind noise level according to the first scores and the corresponding weighting coefficients of all the evaluation indicators.

2. The method according to claim 1, wherein The preset wind noise test database includes all the evaluation indicators and at least two groups of wind noise historical data for each evaluation indicator; The processing the wind noise test data of each evaluation indicator based on a preset wind noise test database to obtain corresponding first scores includes: Sort all the wind noise historical data of each evaluation indicator, and perform screening processing on all the sorted wind noise historical data based on a preset interval coefficient to obtain corresponding data intervals; Calculate the mean value of the wind noise test data of each evaluation indicator to obtain a corresponding mean result, and obtain a corresponding first score according to the mean result and the data interval of each evaluation indicator.

3. The method according to claim 2, wherein The obtaining a corresponding first score according to the mean result and the data interval of each evaluation indicator includes: Determine the upper threshold and the lower threshold from the data interval of each evaluation indicator, and substitute the upper threshold, the lower threshold, and the mean result of each evaluation indicator into a preset score calculation formula to obtain a corresponding first score.

4. The method according to claim 3, wherein All the evaluation indicators include sound pressure level indicators, speech intelligibility indicators, and sharpness indicators, and the preset score calculation formula includes a preset first calculation formula and a preset second calculation formula; The substituting the upper threshold, the lower threshold, and the mean result of each evaluation indicator into a preset score calculation formula to obtain a corresponding first score includes: Substitute the upper threshold, the lower threshold, and the mean result of the sound pressure level indicator into the preset first calculation formula to obtain a corresponding first score; Substitute the upper threshold, the lower threshold, and the mean result of the speech intelligibility indicator into the preset second calculation formula to obtain a corresponding first score; Substitute the upper threshold, the lower threshold, and the mean result of the sharpness indicator into the preset first calculation formula to obtain a corresponding first score.

5. The method according to any one of claims 1-4, characterized in that, The calculating corresponding weighting coefficients according to the wind noise test data and the wind noise evaluation data of each evaluation indicator includes: Count the number of values from the wind noise evaluation data of each evaluation indicator, and perform screening processing on the corresponding wind noise test data based on the number of values of each evaluation indicator; Calculate the correlation between the wind noise evaluation data and the screened wind noise test data of each evaluation indicator to obtain a corresponding first correlation coefficient; When the absolute value of each of the first correlation coefficients exceeds a preset coefficient threshold, calculate the sum of the absolute values of all the first correlation coefficients to obtain a sum result; Calculate the corresponding weighting coefficients according to the sum result and the first correlation coefficient of each evaluation index.

6. The method according to claim 5, characterized in that, The obtaining of the evaluation result of the vehicle low wind noise level according to the first scores of all the evaluation indexes and the corresponding weighting coefficients includes: Perform a weighted sum calculation on the first scores of all the evaluation indexes and the corresponding weighting coefficients to obtain a second score; When the second score is within a preset first score interval, determine that the evaluation result of the vehicle low wind noise level is level one; When the second score is within a preset second score interval, determine that the evaluation result of the vehicle low wind noise level is level two; wherein, the preset first score interval is higher than the preset second score interval; When the second score is within a preset third score interval, determine that the evaluation result of the vehicle low wind noise level is level three; wherein, the preset second score interval is higher than the preset third score interval.

7. The method according to claim 5, characterized in that, The method further includes: Obtain a second correlation coefficient based on the evaluation result and the wind noise evaluation data of all the evaluation indexes; When the absolute value of the second correlation coefficient does not exceed the absolute value of any of the first correlation coefficients, perform replacement processing on at least one of the evaluation indexes.

8. An evaluation device for the low wind noise level of a vehicle, characterized in that, including: A first processing module, configured to obtain wind noise test data of at least two evaluation indexes, and process the wind noise test data of each evaluation index based on a preset wind noise test database to obtain corresponding first scores; A second processing module, configured to obtain wind noise evaluation data of each evaluation index, and calculate corresponding weighting coefficients according to the wind noise test data and the wind noise evaluation data of each evaluation index; An evaluation generation module, configured to obtain an evaluation result of the vehicle low wind noise level according to the first scores of all the evaluation indexes and the corresponding weighting coefficients.

9. An evaluation device for the low wind noise level of a vehicle, characterized in that including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, Instructions are stored in the computer-readable storage medium, and when the instructions run on a computer or a processor, the computer or the processor is caused to execute the steps of the method according to any one of claims 1-7.

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