Vehicle noise evaluation method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a vehicle noise evaluation method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps that multiple pieces of noise data of a target vehicle in each preset driving scene are acquired, and each piece of noise data is matched with a preset noise evaluation value; respectively converting the plurality of noise data into noise loudness data in a human ear domain; for each piece of noise loudness data, determining a matched sound pressure level matched with the noise loudness data; performing masking correction on each matched sound pressure level to obtain a target sound pressure level corresponding to the preset noise evaluation value; and according to a target sound pressure level corresponding to the preset noise evaluation value, constructing a noise evaluation mapping relation of the target vehicle so as to perform vehicle noise evaluation. By adopting the method, the vehicle noise evaluation accuracy can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a vehicle noise evaluation method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the widespread development and application of vehicles, users have increasingly higher requirements for vehicle use. Vehicles inevitably generate some noise during operation, including but not limited to wind noise, engine noise, road noise, tire noise, and resonance noise. Reducing and masking the noise generated during vehicle operation is a key focus of vehicle design and development, and the foundation for noise reduction is vehicle noise evaluation. Therefore, there is an urgent need for an accurate method for evaluating vehicle noise.

[0003] Currently, due to the numerous sources of noise generated during vehicle operation, and the superposition of noise from multiple sources, directly evaluating vehicle noise based on noise data can easily lead to low accuracy in vehicle noise assessment. Summary of the Invention

[0004] Therefore, it is necessary to provide a vehicle noise assessment method, device, vehicle, computer-readable storage medium, and computer program product that can improve the accuracy of vehicle noise assessment in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a vehicle noise evaluation method, including:

[0006] Acquire multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data is matched with a preset noise evaluation value;

[0007] The multiple noise data are respectively converted into noise loudness data in the human ear domain;

[0008] For each of the noise loudness data, determine the matching sound pressure level that matches the noise loudness data;

[0009] Masking correction is applied to each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value;

[0010] Based on the target sound pressure level corresponding to the preset noise evaluation value, a noise evaluation mapping relationship for the target vehicle is constructed to evaluate vehicle noise.

[0011] In one embodiment, determining the matching sound pressure level that matches the noise loudness data includes:

[0012] Obtain the initial sound pressure level;

[0013] Convert the initial sound pressure level into initial loudness data;

[0014] Based on the initial loudness data and the noise loudness data, the initial sound pressure level is iteratively optimized to obtain a matching sound pressure level that matches the noise loudness data.

[0015] In one embodiment, the step of iteratively optimizing the initial sound pressure level based on the initial loudness data and the noise loudness data to obtain a matching sound pressure level that matches the noise loudness data includes:

[0016] Obtain the loudness difference between the initial loudness data and the noise loudness data;

[0017] If the loudness difference characterization iteration fails to converge, obtain the rate of change of loudness between the initial loudness data and the noise loudness data;

[0018] The initial sound pressure level is updated based on the loudness difference and the loudness change rate;

[0019] Return to the step of converting the initial sound pressure level into initial loudness data, until the loudness difference characterization iteratively converges to obtain a matched sound pressure level that matches the noise loudness data.

[0020] In one embodiment, converting the plurality of noise data into noise loudness data in the human ear domain includes:

[0021] For each of the noise data, the noise data is converted from the time domain to the frequency domain to obtain noise frequency data;

[0022] Determine the Buck scale rate corresponding to the noise frequency data;

[0023] The Buck scale rate is then subjected to loudness conversion to obtain converted loudness data;

[0024] In the case where each of the converted loudness data includes first loudness data with noise masking and second loudness data without noise masking, the first loudness data is subjected to loudness mitigation correction to obtain corrected loudness data;

[0025] Each of the second loudness data and the corrected loudness data is determined as noise loudness data in the human ear domain.

[0026] In one embodiment, the step of masking and correcting each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value includes:

[0027] For each of the matched sound pressure levels, the matched sound pressure level is attenuated by noise masking to obtain the attenuated sound pressure level;

[0028] The attenuated sound pressure level is determined as the target sound pressure level corresponding to the preset noise evaluation value.

[0029] In one embodiment, constructing the noise evaluation mapping relationship of the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value includes:

[0030] Obtain the wheel information of the target vehicle;

[0031] Based on the wheel information, determine the motor operating data corresponding to each preset driving scenario;

[0032] Based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value, a noise evaluation mapping relationship for the target vehicle is constructed.

[0033] In one embodiment, constructing the noise evaluation mapping relationship of the target vehicle based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value includes:

[0034] Based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value, a first noise evaluation mapping relationship of the target vehicle under the preset noise evaluation value is constructed.

[0035] The target sound pressure level corresponding to the preset noise evaluation value is gradually adjusted to obtain the second noise evaluation mapping relationship of the target vehicle under various other noise evaluation values, wherein the other noise evaluation values ​​are noise evaluation values ​​other than the preset noise evaluation value within the preset noise evaluation range;

[0036] The first noise evaluation mapping relationship and each of the second noise evaluation mapping relationships are determined as the noise evaluation mapping relationship of the target vehicle.

[0037] Secondly, this application also provides a vehicle noise evaluation device, comprising:

[0038] The data acquisition module is used to acquire multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data is matched with a preset noise evaluation value;

[0039] The data conversion module is used to convert the multiple noise data into noise loudness data in the human ear domain, respectively.

[0040] A sound pressure matching module is used to determine a matching sound pressure level that matches each noise loudness data.

[0041] The sound pressure correction module is used to mask and correct each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value;

[0042] The relationship construction module is used to construct a noise evaluation mapping relationship for the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value, so as to conduct vehicle noise evaluation.

[0043] Thirdly, this application also provides a vehicle, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0044] Acquire multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data is matched with a preset noise evaluation value;

[0045] The multiple noise data are respectively converted into noise loudness data in the human ear domain;

[0046] For each of the noise loudness data, determine the matching sound pressure level that matches the noise loudness data;

[0047] Masking correction is applied to each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value;

[0048] Based on the target sound pressure level corresponding to the preset noise evaluation value, a noise evaluation mapping relationship for the target vehicle is constructed to evaluate vehicle noise.

[0049] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0050] Acquire multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data is matched with a preset noise evaluation value;

[0051] The multiple noise data are respectively converted into noise loudness data in the human ear domain;

[0052] For each of the noise loudness data, determine the matching sound pressure level that matches the noise loudness data;

[0053] Masking correction is applied to each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value;

[0054] Based on the target sound pressure level corresponding to the preset noise evaluation value, a noise evaluation mapping relationship for the target vehicle is constructed to evaluate vehicle noise.

[0055] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0056] Acquire multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data is matched with a preset noise evaluation value;

[0057] The multiple noise data are respectively converted into noise loudness data in the human ear domain;

[0058] For each of the noise loudness data, determine the matching sound pressure level that matches the noise loudness data;

[0059] Masking correction is applied to each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value;

[0060] Based on the target sound pressure level corresponding to the preset noise evaluation value, a noise evaluation mapping relationship for the target vehicle is constructed to evaluate vehicle noise.

[0061] The aforementioned vehicle noise evaluation method, apparatus, computer equipment, computer-readable storage medium, and computer program product acquire multiple noise data of a target vehicle under various preset driving scenarios, wherein each noise data is matched with a preset noise evaluation value; convert the multiple noise data into noise loudness data in the human ear domain; for each noise loudness data, determine a matching sound pressure level that matches the noise loudness data; perform masking correction on each matching sound pressure level to obtain a target sound pressure level corresponding to the preset noise evaluation value; and construct a noise evaluation mapping relationship for the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value to perform vehicle noise evaluation.

[0062] Therefore, considering that noise data includes the superposition of noise from multiple sources, multiple noise data points that have already undergone noise evaluation are first acquired. This is done by converting noise data from various driving scenarios into noise loudness data within the human ear domain, thus transforming objective noise data into noise loudness data that characterizes the subjective experience of the human ear and its impact. A matching sound pressure level is then determined to match the noise loudness data, achieving a matching conversion between loudness and sound pressure level. Considering the existence of noise masking, the matching sound pressure level is masked to make the corrected target sound pressure level more consistent with the sound pressure level under the influence of the masking effect in psychology. Based on the target sound pressure level, a noise evaluation mapping relationship is constructed, enabling the noise evaluation mapping relationship to characterize the mapping relationship between preset driving scenarios and sound pressure levels under preset noise evaluation values. This allows noise evaluation to be achieved through driving scenarios and noise sound pressure levels, improving the accuracy of noise evaluation. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a diagram illustrating the application environment of a vehicle noise evaluation method in one embodiment.

[0065] Figure 2 This is a flowchart illustrating a vehicle noise evaluation method in one embodiment;

[0066] Figure 3 This is a flowchart illustrating the steps of determining a matching sound pressure level that matches the noise loudness data in one embodiment.

[0067] Figure 4 This is a flowchart illustrating the steps of constructing a noise evaluation mapping relationship for the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value in one embodiment.

[0068] Figure 5 This is a structural block diagram of a vehicle noise evaluation device in one embodiment;

[0069] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0071] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations. The acquisition, storage, use and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0072] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various data, but these data are not limited by these terms. These terms are only used to distinguish the first data from the second data. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0073] Current vehicle noise assessment methods determine the order of the howling noise and slice it into noise segments to evaluate the noise based on bandwidth data. However, this process does not consider the impact of other noises such as wind noise and road noise coupled with vehicle noise on the overall noise generated by the vehicle during operation. This can easily lead to inaccurate noise slicing, resulting in low accuracy in vehicle noise assessment.

[0074] To address this, this application provides a more accurate vehicle noise assessment method. Considering that noise data includes the superposition of noise from multiple sources, multiple noise data points that have already undergone noise assessment are first acquired. Noise data from various driving scenarios are then converted into noise loudness data within the human ear domain, transforming objective noise data into noise loudness data that characterizes the subjective experience of the human ear and its impact. A matching sound pressure level is then determined to match the noise loudness data, achieving a matching conversion between loudness and sound pressure level. Considering the existence of noise masking, the matching sound pressure level is masked to make the corrected target sound pressure level more consistent with the sound pressure level under the influence of the masking effect in psychology. Based on the target sound pressure level, a noise assessment mapping relationship is constructed, enabling the noise assessment mapping relationship to characterize the mapping relationship between preset driving scenarios and sound pressure levels under preset noise assessment values. This allows noise assessment to be achieved through driving scenarios and noise sound pressure levels, improving the accuracy of noise assessment.

[0075] The vehicle noise evaluation method provided in this application embodiment is used in the following application environment. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 acquires multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data belongs to a preset noise evaluation value; converts the multiple noise data into noise loudness data in the human ear domain; for each noise loudness data, determines a matching sound pressure level that matches the noise loudness data; performs masking correction on each matching sound pressure level to obtain the target sound pressure level corresponding to the preset noise evaluation value; and constructs a noise evaluation mapping relationship for the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value to perform vehicle noise evaluation. The server 102 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0076] The vehicle noise evaluation method provided in this application embodiment can also be applied to, for example, Figure 1 In the application environment shown, vehicle 104 communicates with server 102 via a network. Server 102 obtains the noise sound pressure level and driving scenario sent by vehicle 104, and performs a noise evaluation on vehicle 104 based on the noise sound pressure level and driving scenario through a noise evaluation mapping relationship. Vehicle 104 may be, but is not limited to, a new energy vehicle, a fuel vehicle, or a hybrid vehicle.

[0077] In one exemplary embodiment, such as Figure 2 As shown, a vehicle noise evaluation method is provided, which can be applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps 202 to 210. Wherein:

[0078] Step 202: Obtain multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data belongs to a preset noise evaluation value.

[0079] In step 202, the number of target vehicles can be single or multiple, without restriction. Multiple target vehicles can be multiple vehicles belonging to the same vehicle type to ensure the accurate construction of the noise evaluation mapping relationship for that vehicle type; or they can be multiple vehicles belonging to different vehicle types to realize the construction of noise evaluation mapping relationships under different vehicle types.

[0080] Optionally, vehicles of different vehicle types may have different tire widths, electric drive reduction ratios, tire aspect ratios, and wheel sizes.

[0081] In step 202, the driving speed varies under different preset driving scenarios. The driving speed can be, but is not limited to, constant speeds of 10kph, 20kph, 35kph, 50kph, 60kph, 75kph, 90kph and 100kph, etc., and can be set as needed. There are no restrictions here.

[0082] In step 202, the preset noise evaluation value can be obtained by a single user, multiple users, or experts; there are no restrictions on this. The preset noise evaluation value can be single or multiple; there are no restrictions on this. The preset noise evaluation value can be the highest, lowest, or intermediate evaluation value; there are no restrictions on this.

[0083] Optionally, in step 202, the noise data is collected at a frequency greater than 25600Hz, and the interval between two adjacent collection times is greater than 20s.

[0084] This ensures that the amount of noise data obtained from sampling is as large as possible, and that the sampling time distribution of the noise data is as uniform as possible.

[0085] Step 204: Convert the multiple noise data into noise loudness data in the human ear domain.

[0086] As an embodiment, step 204 includes: for each noise data, converting the noise data from the time domain to the frequency domain to obtain noise frequency data; determining the Buck scale rate corresponding to the noise frequency data; performing loudness conversion on the Buck scale rate to obtain converted loudness data; when each converted loudness data includes first loudness data with noise masking and second loudness data without noise masking, performing loudness mitigation correction on the first loudness data to obtain corrected loudness data; and determining each second loudness data and the corrected loudness data as noise loudness data in the human ear domain.

[0087] Optionally, the above method further includes: when each converted loudness data includes only first loudness data with noise masking, performing loudness mitigation correction on the first loudness data to obtain corrected loudness data; determining the corrected loudness data as noise loudness data in the human ear domain; or, when each converted loudness data includes only second loudness data without noise masking, determining the second loudness data as noise loudness data in the human ear domain.

[0088] Furthermore, the noise data is converted from the time domain to the frequency domain to obtain noise frequency data, including: converting the noise data from the time domain to the frequency domain to obtain 1 / 3 octave spectrum data, and determining the 1 / 3 octave spectrum data as noise frequency data.

[0089] Optionally, the Buck scale rate corresponding to the noise frequency data can be determined by a loudness calculation model, which can be the Zwicker ISO532B model (the calculation method proposed by Zwicker according to the international standard ISO532B).

[0090] Alternatively, the loudness calculation model can be expressed by the following formula:

[0091]

[0092] in, For the Buck scale, This is noise frequency data.

[0093] As one embodiment, the loudness conversion of the Buck scale rate to obtain converted loudness data includes: obtaining the sound pressure level correction and hearing threshold of the critical bandwidth of the Buck scale rate, and obtaining a conversion constant; and determining the converted loudness data based on the sound pressure level correction, hearing threshold and conversion constant.

[0094] Optionally, based on the sound pressure level correction, the hearing threshold, and the conversion constant, the converted loudness data can be expressed by the following formula:

[0095]

[0096] in, To convert loudness data, Hearing threshold This is the sound pressure level correction amount. To convert constants.

[0097] Alternatively, the conversion constant can be an empirical value, for example, it can be set to 0.25.

[0098] As an embodiment, the above method further includes: for each converted loudness data, if the converted loudness data is smaller than the converted loudness data under the previous human ear domain unit step size, then the converted loudness data is determined as a first loudness data with noise masking; if the converted loudness data is larger than or equal to the converted loudness data under the previous human ear domain unit step size, then the converted loudness data is determined as a second loudness data without noise masking.

[0099] As one embodiment, the loudness data of the first loudness data is modified by loudness mitigation to obtain modified loudness data, including: modifying the first loudness data by using the ramp loudness value to obtain modified loudness data.

[0100] The ramp loudness value can be set by the user as needed, or it can be an empirical value.

[0101] It is understandable that noise masking refers to the phenomenon that the human ear's sensitivity to another sound is reduced and the hearing threshold shifts due to the presence of noise. Considering that there may be noise masking between loudness data at a unit step size in adjacent human ear domains, resulting in large differences between loudness data at a unit step size in adjacent human ear domains, it is necessary to perform loudness smoothing correction on the first loudness data to ensure a smooth loudness transition between the first loudness data and the converted loudness data at the previous human ear domain unit step size. From the dimension of smooth transition of loudness data between adjacent human ear domain unit step sizes, the resulting loudness data conforms to the human ear's auditory experience.

[0102] Step 206: For each noise loudness data, determine the matching sound pressure level that matches the noise loudness data.

[0103] For example, step 206 includes: performing iterative optimization of sound pressure level based on noise loudness data to obtain a matching sound pressure level that matches the noise loudness data.

[0104] Step 208: Masking correction is performed on each matched sound pressure level to obtain the target sound pressure level corresponding to the preset noise evaluation value.

[0105] For example, step 208 includes: for each matched sound pressure level, performing noise masking attenuation on the matched sound pressure level to obtain an attenuated sound pressure level; and determining the attenuated sound pressure level as the target sound pressure level corresponding to a preset noise evaluation value.

[0106] The attenuation level of the sound pressure level can be an empirical value, for example, 3dB.

[0107] Therefore, considering the existence of noise masking effect, that is, the phenomenon that noise causes the human ear to have reduced auditory sensitivity to another sound and hearing threshold shift, and the matching sound pressure level is matched with the noise loudness data in the human ear domain, the matching sound pressure level is obtained after hearing threshold shift. Therefore, the actual sound pressure level should be lower than the matching sound pressure level. Therefore, the matching sound pressure level is attenuated by noise masking so that the target sound pressure level matches the actual sound pressure level corresponding to the noise data. The peak of the noise sound pressure level is reduced from the overall dimension so that the obtained sound pressure level meets the human ear's auditory experience.

[0108] Step 210: Based on the target sound pressure level corresponding to the preset noise evaluation value, construct the noise evaluation mapping relationship of the target vehicle to conduct vehicle noise evaluation.

[0109] In step 210, the noise evaluation mapping relationship can be a mapping table, a mapping function relationship, or a mapping graph.

[0110] For example, step 210 includes: constructing a mapping relationship under a preset noise evaluation value based on the correspondence between each preset driving scenario and the target sound pressure level, and determining the mapping relationship under the preset noise evaluation value as the noise evaluation mapping relationship of the target vehicle.

[0111] Understandably, noise evaluation mapping relationships for different vehicle types can be constructed in the manner described above.

[0112] Optionally, after constructing the noise evaluation mapping relationship of the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value, the above method further includes: obtaining the vehicle type, driving scenario, and sound pressure level corresponding to the vehicle under test; filtering the target evaluation mapping relationship corresponding to the vehicle under test according to the vehicle type; and mapping the driving scenario under test through the target evaluation mapping relationship to obtain the target noise evaluation value.

[0113] Optionally, when the target evaluation mapping relationship is a mapping table or a mapping image, the target noise evaluation value can be obtained by interpolation.

[0114] In the aforementioned vehicle noise assessment method, considering that the noise data includes the superposition of noise from multiple sources, multiple noise data points that have already undergone noise assessment are first acquired. This is done by converting the noise data under various driving scenarios into noise loudness data in the human ear domain, thus transforming objective noise data into noise loudness data that characterizes the subjective experience of the human ear and its impact. A matching sound pressure level is then determined to match the noise loudness data, achieving a matching conversion between loudness and sound pressure level. Considering the existence of noise masking, the matching sound pressure level is masked to make the corrected target sound pressure level more consistent with the sound pressure level under the influence of the masking effect in psychology. Based on the target sound pressure level, a noise assessment mapping relationship is constructed, enabling the noise assessment mapping relationship to characterize the mapping relationship between various preset driving scenarios and sound pressure levels under preset noise assessment values. This allows noise assessment to be achieved through driving scenarios and noise sound pressure levels, improving the accuracy of noise assessment.

[0115] In one exemplary embodiment, such as Figure 3 As shown, step 206, determining the matching sound pressure level that matches the noise loudness data, includes steps 302 to 306. Wherein:

[0116] Step 302: Obtain the initial sound pressure level.

[0117] As one embodiment, step 302 includes: determining the randomly selected sound pressure level as the initial sound pressure level.

[0118] In another embodiment, step 302 includes: acquiring historical iteration data, wherein the historical iteration data includes the correspondence between multiple loudness data and sound pressure level; selecting the target loudness data that is closest to the noise loudness data from the historical iteration data, and determining the sound pressure level corresponding to the target loudness data as the initial sound pressure level.

[0119] Therefore, considering the correlation between loudness data and sound pressure level, using the sound pressure level from historical iterations as the initial sound pressure level avoids unnecessary iteration time wastage caused by inappropriate selection of the initial sound pressure level, thus improving the efficiency of determining the matching sound pressure level.

[0120] Step 304: Convert the initial sound pressure level into initial loudness data.

[0121] For example, step 304 includes: determining the sound pressure level correction amount and the hearing threshold corresponding to the initial sound pressure level, and determining the initial loudness data based on the sound pressure level correction amount, the hearing threshold and the conversion constant.

[0122] Optionally, the specific implementation method for determining the initial loudness data based on the sound pressure level correction, hearing threshold, and conversion constant can refer to the above formula for determining the converted loudness data based on the sound pressure level correction, hearing threshold, and conversion constant, and will not be repeated here.

[0123] Step 306: Based on the initial loudness data and noise loudness data, iteratively optimize the initial sound pressure level to obtain a matching sound pressure level that matches the noise loudness data.

[0124] For example, step 306 includes: iteratively optimizing the initial sound pressure level based on the loudness difference between the initial loudness data and the noise loudness data to obtain a matched sound pressure level.

[0125] As one embodiment, the initial sound pressure level is iteratively optimized based on the difference between the initial loudness data and the noise loudness data to obtain a matching sound pressure level that matches the noise loudness data. This includes: obtaining the loudness difference between the initial loudness data and the noise loudness data; if the loudness difference characterization iteration has not converged, obtaining the loudness change rate between the initial loudness data and the noise loudness data; updating the initial sound pressure level based on the loudness difference and the loudness change rate; and returning to the step of converting the initial sound pressure level into initial loudness data until the loudness difference characterization iteration converges to obtain a matching sound pressure level that matches the noise loudness data.

[0126] As an embodiment, the above method further includes: when the loudness difference is less than a preset difference threshold, determining that the loudness difference characterizes the iteration convergence; and when the loudness difference is not less than the preset difference threshold, determining that the loudness difference characterizes the iteration non-convergence.

[0127] The rate of change of loudness can be obtained by differentiating the loudness data. For example, in the formula above that determines the converted loudness data based on the sound pressure level correction, the hearing threshold and the conversion constant, the converted loudness data is differentiated.

[0128] As one embodiment, updating the initial sound pressure level based on the loudness difference and the loudness change rate includes: determining the ratio between the loudness difference and the loudness change rate as the sound pressure level update amount; and updating the initial sound pressure level based on the sound pressure level update amount.

[0129] Optionally, the update method for updating the initial sound pressure level based on the sound pressure level update amount can be a difference update, for example, the difference between the initial sound pressure level and the sound pressure level update amount can be determined as the updated initial sound pressure level.

[0130] Optionally, the difference between the initial sound pressure level and the updated sound pressure level can be determined as the updated initial sound pressure level, which can be expressed by the following formula:

[0131]

[0132] in, for The initial sound pressure level at the next iteration. for The initial sound pressure level at the next iteration. For noise loudness data, For initial loudness data, This represents the rate of change in loudness.

[0133] In this way, by using both the loudness difference and the loudness change rate as the basis for updating the initial sound pressure level, the initial loudness data corresponding to the initial sound pressure level can be as close as possible to the noise loudness data, and the update amplitude matches the change amplitude between the initial loudness data and the noise loudness data, thus ensuring the iterative efficiency of matching the sound pressure level.

[0134] Optionally, to avoid excessive iterations, the number of iterations needs to be limited. When the maximum number of iterations is reached, the matching sound pressure level can be determined by a bisection method.

[0135] In this embodiment, an initial sound pressure level is obtained; the initial sound pressure level is converted into initial loudness data; and the initial sound pressure level is iteratively optimized based on the initial loudness data and noise loudness data to obtain a matching sound pressure level. By setting an initial sound pressure level and converting it into initial loudness data, noise loudness data can be used as the optimization target to iteratively optimize the initial sound pressure level, thereby improving the iterative accuracy of the matching sound pressure level.

[0136] It is understandable that the above description uses different driving speeds of the target vehicle as the definition of different preset driving scenarios. However, in real-world scenarios, vehicles of the same type may have different motor speeds corresponding to the same driving speed due to different vehicle models or other reasons, resulting in poor generalization of the noise evaluation mapping relationship.

[0137] In one exemplary embodiment, such as Figure 4 As shown, step 208, which involves constructing the noise evaluation mapping relationship for the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value, includes steps 402 to 406. Wherein:

[0138] Step 402: Obtain the wheel information of the target vehicle.

[0139] The wheel information in step 402 includes at least one of the following: tire width, tire aspect ratio, wheel rim size, transmission ratio, and electric drive reduction ratio.

[0140] Step 404: Determine the motor operating data corresponding to each preset driving scenario based on the wheel information.

[0141] The motor operating data in step 404 includes the motor speed.

[0142] For example, step 404 includes: for each preset driving scenario, converting the driving speed into wheel speed based on tire width, tire aspect ratio and wheel size; fusing the wheel speed and transmission ratio to obtain the half-shaft speed; and converting the half-shaft speed into motor speed based on the electric drive reduction ratio.

[0143] Step 406: Based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value, construct the noise evaluation mapping relationship of the target vehicle.

[0144] For example, step 406 includes: constructing a first noise evaluation mapping relationship for the target vehicle under the preset noise evaluation value based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value; gradually adjusting the target sound pressure level corresponding to the preset noise evaluation value to obtain a second noise evaluation mapping relationship for the target vehicle under various other noise evaluation values, wherein the other noise evaluation values ​​are noise evaluation values ​​other than the preset noise evaluation value within the preset noise evaluation range; and determining the first noise evaluation mapping relationship and each second noise evaluation mapping relationship as the noise evaluation mapping relationship for the target vehicle.

[0145] As one embodiment, the target sound pressure level corresponding to the preset noise evaluation value is gradually adjusted to obtain the second noise evaluation mapping relationship of the target vehicle under various other noise evaluation values. This includes: determining the unit sound pressure level corresponding to the unit evaluation value; gradually increasing the target sound pressure level corresponding to the preset noise evaluation value according to the unit sound pressure level to obtain the second noise evaluation mapping relationship under each first noise evaluation value; and gradually decreasing the target sound pressure level corresponding to the preset noise evaluation value according to the unit sound pressure level to obtain the second noise evaluation mapping relationship of the target vehicle under each second noise evaluation value. In this case, each first noise evaluation value is greater than the preset noise evaluation value, and each second noise evaluation value is less than the preset noise evaluation value.

[0146] The unit sound pressure level can be an empirical value, for example, 3dB.

[0147] For example, if the preset noise evaluation value is 7, and the sound pressure level corresponding to the motor speed at the preset noise evaluation value is 60dB, and the unit evaluation value is 1 with a unit sound pressure level of 3dB, then the sound pressure level corresponding to the motor speed at the noise evaluation value is 63dB at the noise evaluation value 8, 66dB at the noise evaluation value 9, 57dB at the noise evaluation value 6, and 54dB at the noise evaluation value 5, and so on, the sound pressure level corresponding to each motor speed at each noise evaluation value can be obtained.

[0148] In this embodiment, the wheel information of the target vehicle is obtained; the motor operation data corresponding to each preset driving scenario is determined based on the wheel information; and the noise evaluation mapping relationship of the target vehicle is constructed based on the target sound pressure level corresponding to each motor operation data and the preset noise evaluation value. By using the motor operation data as an explicit representation of the preset driving scenario, this noise evaluation mapping relationship is ensured to be applicable to vehicles of the same type, thereby improving the generalizability of the noise evaluation method.

[0149] As a detailed embodiment, multiple noise data of the target vehicle under various preset driving scenarios are acquired, wherein each noise data belongs to a preset noise evaluation value; for each noise data, the noise data is converted from the time domain to the frequency domain to obtain noise frequency data; the Buck scale rate corresponding to the noise frequency data is determined; the Buck scale rate is converted to loudness to obtain converted loudness data; when each converted loudness data includes first loudness data with noise masking and second loudness data without noise masking, the first loudness data is modified to obtain modified loudness data; and each second loudness data and The loudness data is corrected to noise loudness data in the human ear domain; for each noise loudness data, an initial sound pressure level is obtained; the initial sound pressure level is converted into initial loudness data; the loudness difference between the initial loudness data and the noise loudness data is obtained; if the loudness difference representation iteration does not converge, the loudness change rate between the initial loudness data and the noise loudness data is obtained; the initial sound pressure level is updated according to the loudness difference and the loudness change rate; the step of converting the initial sound pressure level into initial loudness data is returned to execute until the loudness difference representation iteration converges, and a matching sound pressure level that matches the noise loudness data is obtained.

[0150] Furthermore, for each matched sound pressure level, noise masking and attenuation are performed to obtain an attenuated sound pressure level; the attenuated sound pressure level is determined as the target sound pressure level corresponding to the preset noise evaluation value; wheel information of the target vehicle is acquired; motor operating data corresponding to each preset driving scenario is determined based on the wheel information; a first noise evaluation mapping relationship for the target vehicle under the preset noise evaluation value is constructed based on each motor operating data and the target sound pressure level corresponding to the preset noise evaluation value; the target sound pressure level corresponding to the preset noise evaluation value is gradually adjusted to obtain a second noise evaluation mapping relationship for the target vehicle under various other noise evaluation values, wherein other noise evaluation values ​​are noise evaluation values ​​other than the preset noise evaluation value within the preset noise evaluation range; the first noise evaluation mapping relationship and each second noise evaluation mapping relationship are determined as the noise evaluation mapping relationship for the target vehicle to perform vehicle noise evaluation.

[0151] Therefore, considering that noise data includes the superposition of noise from multiple sources, multiple noise data points that have already undergone noise evaluation are first acquired. This is done by converting noise data from various driving scenarios into noise loudness data within the human ear domain, thus transforming objective noise data into noise loudness data that characterizes the subjective experience of the human ear and its impact. A matching sound pressure level is then determined to match the noise loudness data, achieving a matching conversion between loudness and sound pressure level. Considering the existence of noise masking, the matching sound pressure level is masked to make the corrected target sound pressure level more consistent with the sound pressure level under the influence of the masking effect in psychology. Based on the target sound pressure level, a noise evaluation mapping relationship is constructed, enabling the noise evaluation mapping relationship to characterize the mapping relationship between preset driving scenarios and sound pressure levels under preset noise evaluation values. This allows noise evaluation to be achieved through driving scenarios and noise sound pressure levels, improving the accuracy of noise evaluation.

[0152] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0153] Based on the same inventive concept, this application also provides a vehicle noise evaluation device for implementing the vehicle noise evaluation method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more vehicle noise evaluation device embodiments provided below can be found in the limitations of the vehicle noise evaluation method described above, and will not be repeated here.

[0154] In one exemplary embodiment, such as Figure 5 As shown, a vehicle noise evaluation device is provided, including: a data acquisition module, a data conversion module, a sound pressure matching module, and a relationship construction module, wherein:

[0155] The data acquisition module is used to acquire multiple noise data of the target vehicle under various preset driving scenarios, where each noise data belongs to a preset noise evaluation value.

[0156] The data conversion module is used to convert multiple noise data into noise loudness data in the human ear domain.

[0157] The sound pressure matching module is used to determine the matching sound pressure level that matches the noise loudness data for each noise loudness data.

[0158] The sound pressure correction module is used to mask and correct each matched sound pressure level to obtain the target sound pressure level corresponding to the preset noise evaluation value.

[0159] The relationship construction module is used to construct a noise evaluation mapping relationship for a target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value, so as to conduct vehicle noise evaluation.

[0160] In one embodiment, the sound pressure matching module is further configured to obtain an initial sound pressure level; convert the initial sound pressure level into initial loudness data; and iteratively optimize the initial sound pressure level based on the initial loudness data and noise loudness data to obtain a matched sound pressure level that matches the noise loudness data.

[0161] In one embodiment, the sound pressure matching module is further configured to obtain the loudness difference between the initial loudness data and the noise loudness data; if the loudness difference characterization iteration fails to converge, obtain the loudness change rate between the initial loudness data and the noise loudness data; update the initial sound pressure level based on the loudness difference and the loudness change rate; and return to the step of converting the initial sound pressure level into initial loudness data until the loudness difference characterization iteration converges, thereby obtaining a matched sound pressure level that matches the noise loudness data.

[0162] In one embodiment, the data conversion module is further configured to, for each noise data, convert the noise data from the time domain to the frequency domain to obtain noise frequency data; determine the Buck scale rate corresponding to the noise frequency data; perform loudness conversion on the Buck scale rate to obtain converted loudness data; when each converted loudness data includes first loudness data with noise masking and second loudness data without noise masking, perform loudness mitigation correction on the first loudness data to obtain corrected loudness data; and determine each second loudness data and the corrected loudness data as noise loudness data in the human ear domain.

[0163] In one embodiment, the sound pressure correction module is further configured to perform noise masking attenuation on each matched sound pressure level to obtain an attenuated sound pressure level; and determine the attenuated sound pressure level as the target sound pressure level corresponding to a preset noise evaluation value.

[0164] In one embodiment, the relationship construction module is further configured to obtain wheel information of the target vehicle; determine motor operation data corresponding to each preset driving scenario based on the wheel information; and construct a noise evaluation mapping relationship for the target vehicle based on the target sound pressure level corresponding to each motor operation data and the preset noise evaluation value.

[0165] In one embodiment, the relationship construction module is further configured to construct a first noise evaluation mapping relationship for the target vehicle under the preset noise evaluation value based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value; gradually adjust the target sound pressure level corresponding to the preset noise evaluation value to obtain a second noise evaluation mapping relationship for the target vehicle under various other noise evaluation values, wherein the other noise evaluation values ​​are noise evaluation values ​​other than the preset noise evaluation value within the preset noise evaluation range; and determine the first noise evaluation mapping relationship and each second noise evaluation mapping relationship as the noise evaluation mapping relationship for the target vehicle.

[0166] Each module in the aforementioned vehicle noise evaluation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the vehicle's processor in hardware form or independent of it, or stored in the vehicle's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0167] In one exemplary embodiment, a vehicle is provided, the internal structure of which can be as follows: Figure 5 As shown, the vehicle includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The vehicle's processor provides computing and control capabilities. The vehicle's memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The vehicle's input / output interface is used for exchanging information between the processor and external devices. The vehicle's communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a vehicle noise evaluation method. The vehicle's display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the vehicle can be a touch layer covering the display screen, or buttons, trackballs or touchpads set on the vehicle body, or external keyboards, touchpads or mice, etc.

[0168] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the vehicle to which the present application is applied. A specific vehicle may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0169] In one embodiment, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0170] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0171] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0172] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0173] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0174] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for evaluating vehicle noise, characterized in that, The method includes: Acquire multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data is matched with a preset noise evaluation value; The multiple noise data are respectively converted into noise loudness data in the human ear domain; For each of the noise loudness data, determine the matching sound pressure level that matches the noise loudness data; Masking correction is applied to each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value; Based on the target sound pressure level corresponding to the preset noise evaluation value, a noise evaluation mapping relationship for the target vehicle is constructed to evaluate vehicle noise.

2. The method according to claim 1, characterized in that, Determining the matching sound pressure level that matches the noise loudness data includes: Obtain the initial sound pressure level; Convert the initial sound pressure level into initial loudness data; Based on the initial loudness data and the noise loudness data, the initial sound pressure level is iteratively optimized to obtain a matching sound pressure level that matches the noise loudness data.

3. The method according to claim 2, characterized in that, The step of iteratively optimizing the initial sound pressure level based on the initial loudness data and the noise loudness data to obtain a matching sound pressure level that matches the noise loudness data includes: Obtain the loudness difference between the initial loudness data and the noise loudness data; If the loudness difference characterization iteration fails to converge, obtain the rate of change of loudness between the initial loudness data and the noise loudness data; The initial sound pressure level is updated based on the loudness difference and the loudness change rate; Return to the step of converting the initial sound pressure level into initial loudness data, until the loudness difference characterization iteratively converges to obtain a matched sound pressure level that matches the noise loudness data.

4. The method according to claim 1, characterized in that, The step of converting the plurality of noise data into noise loudness data in the human ear domain includes: For each of the noise data, the noise data is converted from the time domain to the frequency domain to obtain noise frequency data; Determine the Buck scale rate corresponding to the noise frequency data; The Buck scale rate is then subjected to loudness conversion to obtain converted loudness data; In the case where each of the converted loudness data includes first loudness data with noise masking and second loudness data without noise masking, the first loudness data is subjected to loudness mitigation correction to obtain corrected loudness data; Each of the second loudness data and the corrected loudness data is determined as noise loudness data in the human ear domain.

5. The method according to claim 1, characterized in that, The step of masking and correcting each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value includes: For each of the matched sound pressure levels, the matched sound pressure level is attenuated by noise masking to obtain the attenuated sound pressure level; The attenuated sound pressure level is determined as the target sound pressure level corresponding to the preset noise evaluation value.

6. The method according to claim 1, characterized in that, The step of constructing a noise evaluation mapping relationship for the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value includes: Obtain the wheel information of the target vehicle; Based on the wheel information, determine the motor operating data corresponding to each preset driving scenario; Based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value, a noise evaluation mapping relationship for the target vehicle is constructed.

7. The method according to claim 6, characterized in that, The step of constructing a noise evaluation mapping relationship for the target vehicle based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value includes: Based on the operating data of each motor and the target sound pressure level corresponding to the preset noise evaluation value, a first noise evaluation mapping relationship of the target vehicle under the preset noise evaluation value is constructed. The target sound pressure level corresponding to the preset noise evaluation value is gradually adjusted to obtain the second noise evaluation mapping relationship of the target vehicle under various other noise evaluation values, wherein the other noise evaluation values ​​are noise evaluation values ​​other than the preset noise evaluation value within the preset noise evaluation range; The first noise evaluation mapping relationship and each of the second noise evaluation mapping relationships are determined as the noise evaluation mapping relationship of the target vehicle.

8. A vehicle noise evaluation device, characterized in that, The device includes: The data acquisition module is used to acquire multiple noise data of the target vehicle under various preset driving scenarios, wherein each noise data is matched with a preset noise evaluation value; The data conversion module is used to convert the multiple noise data into noise loudness data in the human ear domain, respectively. A sound pressure matching module is used to determine a matching sound pressure level that matches each noise loudness data. The sound pressure correction module is used to mask and correct each of the matched sound pressure levels to obtain the target sound pressure level corresponding to the preset noise evaluation value; The relationship construction module is used to construct a noise evaluation mapping relationship for the target vehicle based on the target sound pressure level corresponding to the preset noise evaluation value, so as to conduct vehicle noise evaluation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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