A method and system for monitoring in-vehicle noise

Abstract

The present application relates to the technical field of data analysis, in particular to a kind of in-vehicle noise monitoring method and system, comprising: collecting sound data in the process of vehicle driving and converting into digital form;According to the numerical value of the sound data converted into digital form in the process of automobile driving, obtain noise probability parameter;According to the fluctuation of noise data, obtain noise degree coefficient;According to the noise probability and noise degree coefficient of each sound data, obtain noise influence factor;According to the noise degree coefficient and standard noise influence factor of each sound data, obtain noise data;According to the fluctuation and size of noise data, obtain noise user experience influence index;According to user experience influence index threshold and the user experience influence index threshold of each vehicle noise, judge whether vehicle noise monitoring is qualified or not.The present application improves the accuracy of vehicle noise detection.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a method and system for monitoring in-vehicle noise. Background Technology

[0002] As automobiles become a crucial mode of personal transportation, people's expectations for vehicle comfort are increasing, with the quietness of the in-car environment becoming a key focus. During operation, cars are subject to various noises, such as engine noise, wind noise, tire noise, air conditioning noise, and motor noise. These noises not only affect driver comfort but can also seriously endanger driving safety. Therefore, monitoring and controlling in-car noise is becoming increasingly important. Currently, in-car noise monitoring primarily relies on manual recording or measurement using specialized noise measuring instruments. However, manual recording methods are not accurate or real-time enough and require professional hearing training, making them unsuitable for long-term monitoring. Using noise measuring devices, on the other hand, requires specialized technology and equipment, resulting in high costs and inconvenience in portability and use. Furthermore, in addition to noise, the sounds of in-car multimedia systems often accompany driving, further affecting the analysis results of noise data. Summary of the Invention

[0003] This invention provides a method and system for monitoring in-vehicle noise to solve existing problems: ***.

[0004] The in-vehicle noise monitoring method and system of the present invention adopts the following technical solution:

[0005] One embodiment of the present invention provides a method for monitoring in-vehicle noise, the method comprising the following steps:

[0006] Collect sound data during vehicle operation and convert it into digital form;

[0007] Noise probability parameters are obtained based on the magnitude of sound data converted into digital form during vehicle operation; noise level coefficients are obtained based on the fluctuation of noise data.

[0008] The noise impact factor is obtained based on the noise probability and noise level coefficient of each audio data point; noise data is obtained based on the noise level coefficient and standard noise impact factor of each audio data point.

[0009] The impact of noise on user experience is obtained based on the fluctuation and magnitude of noise data; the pass rate of vehicle noise monitoring is determined based on the user experience impact index threshold and the user experience impact index threshold for each vehicle.

[0010] Furthermore, the specific method for obtaining the noise level coefficient based on the fluctuation of the noise data includes:

[0011] All sliding intervals are obtained based on the preset sliding interval L;

[0012] The degree of fluctuation in each sliding interval is obtained based on the degree of fluctuation in the data within different sliding intervals;

[0013] The noise level coefficient of each audio data point is obtained based on the noise level of each sliding interval.

[0014] Furthermore, the specific method for obtaining the noise impact index on user experience based on the fluctuation and magnitude of noise data includes:

[0015]

[0016] In the formula, Imx b N represents the impact index of the noise of vehicle b on user experience. b U represents the number of noise data points retained for the b-th vehicle. b,j This indicates that the j-th noise data point in the retained noise data of the b-th vehicle is retained. U represents the mean of all bit-level noise data in the retained noise data for vehicle b. b,I This indicates that the i-th noise data point in the retained noise data of vehicle b is used.

[0017] Furthermore, the specific method for obtaining all sliding intervals based on the preset sliding interval length L includes:

[0018] The first data point of the preset sliding interval is the first data point in the collected data. The sliding interval moves towards the end of the collected data each time until the end of the sliding interval coincides with the end data in the collected data. The number of times the sliding interval moves is M, and the data in the interval at different positions is recorded.

[0019] Furthermore, the specific method for obtaining the fluctuation level of each sliding interval based on the fluctuation level of data in different sliding intervals includes:

[0020]

[0021] In the formula, Nol b,y c represents the degree of fluctuation in the data corresponding to the y-th sliding interval for the b-th vehicle. b,y,i This represents the audio data value of the i-th bit in the y-th sliding interval corresponding to the b-th vehicle, and m+1 represents the number of sliding intervals. This represents the average value of all sound data in the y-th sliding interval corresponding to the b-th vehicle. The fluctuation level of the data in each sliding interval corresponding to each vehicle is calculated using the method described above.

[0022] Furthermore, the specific method for obtaining the noise level coefficient of each bit of audio data based on the noise level of each sliding interval includes:

[0023] Count the number of times the x-bit audio data corresponding to vehicle b appears in different sliding intervals, accumulate the fluctuation degree of the x-bit audio data corresponding to vehicle b in different sliding intervals, and divide the accumulated result by the number of times the x-bit audio data corresponding to vehicle b appears in different sliding intervals to obtain the noise level coefficient of the x-bit audio data corresponding to vehicle b. The noise level coefficient of the x-bit audio data corresponding to vehicle b is denoted as Noc. b,x The noise level coefficient for each bit of audio data corresponding to each vehicle is obtained using the same method.

[0024] Furthermore, the specific method for obtaining the noise probability parameter based on the numerical value of the sound data converted into digital form during vehicle operation includes:

[0025]

[0026] In the formula, Par b,x c represents the noise probability parameter of the x-th bit audio data corresponding to the b-th vehicle. b,x This represents the audio data value of the xth bit corresponding to the bth vehicle, c b,max c represents the maximum value of the collected sound data corresponding to the b-th vehicle. b,min This represents the minimum value of the sound data collected for the b-th vehicle.

[0027] Furthermore, the specific method for obtaining the noise impact factor based on the noise probability and noise level coefficient of each audio data point includes:

[0028] Nif b,x =Par b,x ×Noc b,x

[0029] In the formula, Nif b,x Par represents the noise impact factor of the x-th bit of sound data corresponding to the b-th vehicle. b,x Noc represents the noise probability parameter of the x-th bit of audio data corresponding to the b-th vehicle. b,x This represents the noise level coefficient of the x-th bit of the audio data corresponding to the b-th vehicle.

[0030] Furthermore, the specific method for determining whether vehicle noise monitoring is qualified based on the user experience impact index threshold and the user experience impact index threshold for each vehicle's noise is as follows:

[0031] By comparing the noise impact index of each vehicle on user experience with the user experience impact index threshold, the vehicle noise is marked as a qualified vehicle when the noise impact index is less than the user experience impact index, and the vehicle noise is marked as a qualified vehicle when the noise impact index is greater than the user experience impact index.

[0032] Embodiments of the present invention provide an in-vehicle noise monitoring system, which includes a data acquisition module, a data processing module, a data analysis module, and a data judgment module, wherein:

[0033] The data acquisition module is used to collect sound data during vehicle operation and convert it into digital form;

[0034] The data processing module is used to obtain noise probability parameters based on the magnitude of the sound data converted into digital form during vehicle operation; and to obtain noise level coefficients based on the fluctuation of the noise data.

[0035] The data analysis module is used to obtain the noise impact factor based on the noise probability and noise level coefficient of each audio data point; and to obtain noise data based on the noise level coefficient and standard noise impact factor of each audio data point.

[0036] The data judgment module is used to obtain the noise impact index on user experience based on the fluctuation and magnitude of noise data; and to determine whether the vehicle noise monitoring is qualified based on the user experience impact index threshold and the noise impact index threshold for each vehicle.

[0037] The beneficial effects of the technical solution of this invention are as follows: Traditional in-vehicle noise monitoring relies on manual recording or measurement using professional noise measuring instruments. However, manual recording is not accurate or real-time enough, requires professional auditory training, and is unsuitable for long-term monitoring. Using noise measuring devices requires specialized technology and equipment, is costly, and is inconvenient to carry and use. Furthermore, during driving, in addition to noise, there are often sounds from in-vehicle multimedia systems, which also affect the analysis results of noise data. This embodiment collects sound information from an in-vehicle sound sensor during vehicle operation and converts it into digital form. By analyzing the fluctuations of the sound information in different ranges, it determines whether the sound is in-vehicle music or noise. The amplitude changes of in-vehicle music information are relatively gradual, while the amplitude changes of noise information are relatively large. The noise information is retained and analyzed to determine the degree and magnitude of noise fluctuation. The greater the fluctuation and the higher the value of the noise information, the more severe the noise situation during vehicle operation. Finally, a threshold is used to determine whether the noise during vehicle operation meets the vehicle noise monitoring standards. This invention improves the accuracy of vehicle noise monitoring and reduces the influence of sound data other than noise on noise detection. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating the steps of an in-vehicle noise monitoring method according to the present invention.

[0040] Figure 2 This is a structural block diagram of an in-vehicle noise monitoring system according to the present invention. Detailed Implementation

[0041] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an in-vehicle noise monitoring method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0043] The following description, in conjunction with the accompanying drawings, details the specific solution of the in-vehicle noise monitoring method and system provided by the present invention.

[0044] Please see Figure 1 The diagram illustrates a flowchart of a method for monitoring in-vehicle noise according to an embodiment of the present invention, which includes the following steps:

[0045] Step S001: Collect sound data during vehicle operation and convert it into digital form.

[0046] It's important to note that the process of converting audio into data can be simply described as sampling the amplitude values ​​of a sound signal at certain time intervals and converting these samples into digital form. This process involves analog-to-digital conversion (ADC). During ADC, the amplitude values ​​of the sound signal are measured and recorded at certain time intervals. These measurements are converted into digital form, that is, the magnitude of the amplitude is represented by numbers. These numbers are then encoded into binary form, becoming a digital signal.

[0047] The data characteristics of digital signals include sampling rate and bit depth. Sampling rate represents the number of samples collected per second, and bit depth represents the precision of each sample. These data characteristics determine the quality and precision of the digital signal. Commonly used sampling rates include 44.1kHz and 48kHz. Bit depth refers to the amount of data per sampling point; commonly used bit depths include 16 bits and 24 bits. In noise acquisition, common digital signal data sampling rates are 44.1kHz or 48kHz, and bit depths are 16 bits or 24 bits. This embodiment presets a sampling rate F = 44.1kHz, a bit depth D = 24 bits, a data acquisition duration T = 3600 seconds, and a number of vehicles L = 100. This embodiment does not specifically limit the sampling rate, bit depth, data acquisition duration T, or number of vehicles L. Other embodiments may specify specific limits on the sampling rate, bit depth, data acquisition duration, and number of vehicles based on specific implementation conditions.

[0048] Specifically, in order to implement the in-vehicle noise monitoring method proposed in this embodiment, it is first necessary to collect sound data during vehicle operation. The specific process is as follows:

[0049] The sound data generated by L vehicles during their operation is collected using a sound sensor at a preset sampling rate F, bit depth D, and data collection duration T. The collected sound data is then converted into digital form using an analog-to-digital converter.

[0050] Thus, sound data of multiple vehicles during their operation were obtained using the methods described above.

[0051] Step S002: Obtain the noise probability parameter based on the numerical value of the sound data converted into digital form during the car's operation; obtain the noise level coefficient based on the fluctuation of the noise data;

[0052] It should be noted that the digital form records the amplitude of the sound data. Playing music while the car is in motion makes noise detection more difficult because music itself generates noise, which may interfere with the detection of other environmental noises. The noise that this invention needs to detect includes various noises encountered during driving, such as engine noise, wind noise, tire noise, air conditioning noise, and motor noise. The amplitude of these noises is relatively low compared to the amplitude of music, so the smaller the converted digital data value, the greater the probability that the data is noise.

[0053] Specifically, noise probability parameters are obtained based on the magnitude of the sound data converted into digital form during vehicle operation:

[0054]

[0055] In the formula, Par b,x c represents the noise probability parameter of the x-th bit audio data corresponding to the b-th vehicle.b,x This represents the audio data value of the xth bit corresponding to the bth vehicle, c b,max c represents the maximum value of the collected sound data corresponding to the b-th vehicle. b,min This represents the minimum value of the sound data collected for the b-th vehicle.

[0056] It should be noted that the above method can obtain the noise probability parameter corresponding to each audio data point of each vehicle. The audio data value represents the amplitude. The larger the amplitude, the greater the probability that the data is normal audio data. This is because the volume of car music playback is generally greater than the noise volume during vehicle movement. The larger the volume, the larger the amplitude, and the larger the value after conversion to digital form. Therefore, the smaller the audio data during vehicle movement, the greater the probability that the data is noise data.

[0057] It should be further noted that the amplitude of noise data fluctuates significantly, and some noise amplitudes may be greater than those of the in-vehicle music. This portion of the data may be misjudged as data with a low probability of noise, which is not conducive to judging the noise situation of the vehicle. Therefore, this step judges the noise situation of the sound data by judging the fluctuation of different sound data and neighboring data, and obtains the noise level coefficient. This method requires a preset sliding interval length L = 100, that is, an interval contains L sound data. This embodiment does not specifically limit the interval length; other embodiments will depend on the specific implementation.

[0058] Specifically, the method for obtaining all sliding intervals based on the preset sliding interval length L is as follows:

[0059] The first data point of the preset sliding interval is the first data point in the collected data. The sliding interval moves towards the end of the collected data each time until the end of the sliding interval coincides with the end data in the collected data. The number of times the sliding interval moves is M, and the data in the interval at different positions is recorded.

[0060] Furthermore, the specific method for obtaining the fluctuation level of each sliding interval based on the fluctuation level of the data in different sliding intervals is as follows:

[0061]

[0062] In the formula, Nol b,y c represents the degree of fluctuation in the data corresponding to the y-th sliding interval for the b-th vehicle. b,y,i This represents the audio data value of the i-th bit in the y-th sliding interval corresponding to the b-th vehicle, and M+1 represents the number of sliding intervals. This represents the average value of all sound data in the y-th sliding interval corresponding to the b-th vehicle. The fluctuation level of the data in each sliding interval corresponding to each vehicle is calculated using the method described above.

[0063] Furthermore, the specific method for obtaining the noise level coefficient of each bit of audio data based on the noise level of each sliding interval is as follows:

[0064] Count the number of times the x-bit audio data corresponding to vehicle b appears in different sliding intervals, accumulate the fluctuation degree of the x-bit audio data corresponding to vehicle b in different sliding intervals, and divide the accumulated result by the number of times the x-bit audio data corresponding to vehicle b appears in different sliding intervals to obtain the noise level coefficient of the x-bit audio data corresponding to vehicle b. The noise level coefficient of the x-bit audio data corresponding to vehicle b is denoted as Noc. b,x The noise level coefficient for each bit of audio data corresponding to each vehicle is obtained using the same method.

[0065] It should be noted that because music playback and noise generation during vehicle operation are uncertain, it is impossible to determine in which range the sound data is relatively stable or relatively unstable. This step uses a sliding interval method to calculate the degree of fluctuation of sound data in different sliding intervals, which can reflect the stability of sound in different intervals. The higher the stability of sound in an interval, the smaller the noise level coefficient, indicating that the fluctuation of sound data in that interval is relatively stable and the sound data in that interval is less affected by noise. Using the sound data in that interval to judge the noise situation during vehicle operation may not be accurate enough.

[0066] Thus, the noise probability parameters and noise level coefficients for each voice data point corresponding to each vehicle were obtained using the above method.

[0067] Step S003: Obtain the noise impact factor based on the noise probability and noise level coefficient of each audio data point; obtain the noise data based on the noise level coefficient and standard noise impact factor of each audio data point.

[0068] It should be noted that the noise probability parameters and noise level coefficients obtained above can be used to obtain the noise impact factor, which is used to distinguish between in-vehicle music and noise. Sound data that is more affected by noise during vehicle operation is selected, and then this part of the sound data is calculated to determine the degree of impact of noise on user comfort during vehicle operation.

[0069] Specifically, the method for obtaining the noise impact factor based on the noise probability and noise level coefficient of each audio data point is as follows:

[0070] Nif b,x =Par b,x ×Noc b,x

[0071] In the formula, Nif b,x Par represents the noise impact factor of the x-th bit of sound data corresponding to the b-th vehicle.b,x Noc represents the noise probability parameter of the x-th bit of audio data corresponding to the b-th vehicle. b,x This represents the noise level coefficient of the x-th bit of the audio data corresponding to the b-th vehicle.

[0072] It should be noted that, according to the above method, the noise impact factor of each audio data point corresponding to each vehicle can be obtained. The larger the noise probability parameter of the audio data, the greater the probability that the audio data is noise. The larger the noise level coefficient of the audio data, the more chaotic the fluctuation of the audio data in different intervals is. In this case, the audio data is more affected by noise and the noise level is greater.

[0073] It should be further noted that this step obtains the noise impact factor for each bit of audio data for each vehicle, and a standard noise impact factor needs to be selected as the threshold Nif. sta To determine whether sound data belongs to noise data, the standard noise impact factor value selected in this embodiment is 30000. In other embodiments, the value of the noise impact factor depends on the specific implementation.

[0074] Specifically, the method for obtaining noise data based on the noise level coefficient and standard noise impact factor of each audio data point is as follows:

[0075] The noise impact factor of each audio data point is compared with the selected standard noise impact factor. Audio data with a noise impact factor greater than the standard noise impact factor is recorded as noise data and retained. Audio data with a noise impact factor less than the standard noise impact factor is recorded as normal sound and discarded. The number of retained noise data points for vehicle b is denoted as N. b The magnitude of the m-th noise data is denoted as U. b,m .

[0076] Thus, the noise data of each vehicle during its operation was obtained using the above method.

[0077] Step S004: Obtain the noise impact index on user experience based on the fluctuation and magnitude of the noise data; determine whether the vehicle noise monitoring is qualified based on the user experience impact index threshold and the noise impact index threshold for each vehicle.

[0078] It should be noted that the above method retains noise data during vehicle operation. This step can determine the impact of noise on the user based on the fluctuation and magnitude of the noise data. The greater the fluctuation amplitude and the larger the value of the noise data during vehicle operation, the more serious the noise situation during vehicle operation.

[0079] Specifically, the method for obtaining the noise impact index on user experience based on the fluctuation and magnitude of noise data is as follows:

[0080]

[0081] In the formula, Imx b N represents the impact index of the noise of vehicle b on user experience. b U represents the number of noise data points retained for the b-th vehicle. b,j This indicates that the j-th noise data point in the retained noise data of the b-th vehicle is retained. U represents the mean of all bit-level noise data in the retained noise data for vehicle b. b,I This indicates that the i-th noise data point in the retained noise data of vehicle b is used.

[0082] It should be noted that, based on the above method, the user experience impact index of each vehicle's noise can be obtained. The greater the fluctuation range and the higher the noise data value during vehicle operation, the more severe the noise situation. This step requires selecting a threshold for the user experience impact index to judge the impact of each vehicle's noise on the user experience and determine whether the vehicle noise meets the monitoring standards. In this embodiment, the user experience impact index threshold imx is used. sta =425000 is used for discussion. The user experience impact index threshold for other embodiments depends on the specific implementation.

[0083] Specifically, the method for determining whether vehicle noise monitoring is qualified based on the user experience impact index threshold and the user experience impact index threshold for each vehicle's noise is as follows:

[0084] By comparing the noise impact index of each vehicle on user experience with the user experience impact index threshold, the vehicle noise is marked as a qualified vehicle when the noise impact index is less than the user experience impact index, and the vehicle noise is marked as a qualified vehicle when the noise impact index is greater than the user experience impact index.

[0085] The vehicle noise test is completed by following the steps above.

[0086] Please see Figure 2 The diagram illustrates a structural block diagram of an in-vehicle noise monitoring system according to an embodiment of the present invention. The system includes the following modules:

[0087] The system includes a data acquisition module, a data processing module, a data analysis module, and a data judgment module.

[0088] Traditional in-vehicle noise monitoring relies on manual recording or measurement using specialized noise measuring instruments. However, manual recording is inaccurate and lacks real-time performance, requiring specialized auditory training and is unsuitable for long-term monitoring. Using noise measuring devices, on the other hand, requires specialized technology and equipment, is costly, and inconvenient to carry and use. Furthermore, during driving, in addition to ambient noise, there are often sounds from in-vehicle multimedia systems, which can also affect the analysis results of noise data. This embodiment collects sound information from an in-vehicle sound sensor during vehicle operation and converts it into digital form. By analyzing the fluctuations of the sound information in different ranges, it determines whether the sound is in-vehicle music or noise. In-vehicle music information shows relatively smooth amplitude changes, while noise information shows relatively large amplitude changes. The noise information is retained and analyzed to determine the degree and magnitude of noise fluctuations. The greater the fluctuation and the higher the value of the noise information, the more severe the noise situation during vehicle operation. Finally, a threshold is used to determine whether the noise during vehicle operation meets the vehicle noise monitoring standards. This invention improves the accuracy of vehicle noise monitoring and reduces the influence of other sound data on noise detection.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of monitoring in-vehicle noise, characterized by, The method comprises the following steps: Collecting sound data during vehicle driving and converting into digital form; Obtaining noise probability parameters according to the numerical size of the sound data converted into digital form during vehicle driving; obtaining noise degree coefficients according to the fluctuation of the noise data; Obtaining noise influence factors according to the noise probability and noise degree coefficients of each sound data; obtaining noise data according to the noise degree coefficients of each sound data and the standard noise influence factor; Obtaining noise user experience influence indexes according to the fluctuation and size of the noise data; judging whether the vehicle noise monitoring is qualified according to the user experience influence index threshold and the noise user experience influence index threshold of each vehicle.

2. The method of claim 1, wherein The method for obtaining the noise degree coefficients according to the fluctuation of the noise data comprises the following specific method: Obtaining all sliding intervals according to a preset sliding interval L; Obtaining the fluctuation degree of each sliding interval according to the fluctuation degree of the data in different sliding intervals; Obtaining the noise degree coefficients of each sound data according to the noise degree of each sliding interval.

3. The method of claim 1, wherein The method for obtaining the noise user experience influence indexes according to the fluctuation and size of the noise data comprises the following specific method: In the formula, represents the bth vehicle noise user experience impact index, represents the number of noise data corresponding to the bth vehicle reserved noise, represents the jth noise data in the bth vehicle reserved noise, represents the mean of all noise data in the bth vehicle reserved noise data, represents the Ith noise data in the bth vehicle reserved noise.

4. The method of claim 2, wherein The method for obtaining all sliding intervals according to a preset sliding interval length L comprises the following specific method: The initial position interval of the preset sliding interval is the first data in the collected data; the sliding interval moves to the end of the collected data each time until the end of the sliding interval coincides with the end of the collected data; the number of movements of the sliding interval is recorded as M and the data in the sliding interval at different positions are recorded.

5. The method of claim 2, wherein The method for obtaining the fluctuation degree of each sliding interval according to the fluctuation degree of the data in different sliding intervals comprises the following specific method: In the formula, represents the fluctuation degree of the data corresponding to the yth sliding interval of the bth vehicle, represents the value of the ith sound data in the bth vehicle corresponding to the yth sliding interval, represents the number of sliding intervals, represents the mean value of all sound data values in the bth vehicle corresponding to the yth sliding interval, and the fluctuation degree of the data corresponding to each sliding interval of each vehicle is calculated according to the above method.

6. The method of claim 2, wherein The method for obtaining the noise degree coefficients of each sound data according to the noise degree of each sliding interval comprises the following specific method: The number of times that the xth sound data corresponding to the bth vehicle appears in different sliding intervals is counted, the fluctuation degree corresponding to the xth sound data of the bth vehicle in different sliding intervals is accumulated, and the result obtained by the accumulation is divided by the number of times that the xth sound data of the bth vehicle appears in different sliding intervals to obtain the noise degree coefficient of the xth sound data corresponding to the bth vehicle. The noise degree coefficient of the xth sound data corresponding to the bth vehicle is denoted as The noise degree coefficient of each sound data corresponding to each vehicle is obtained in the same way.

7. The method of claim 1, wherein The method for obtaining the noise probability parameters according to the numerical size of the sound data converted into digital form during vehicle driving comprises the following specific method: In the formula, represents the noise probability parameter of the bth vehicle corresponding to the xth sound data, represents the value of the bth vehicle corresponding to the xth sound data, represents the maximum value of the bth vehicle corresponding to the collected sound data, represents the minimum value of the bth vehicle corresponding to the collected sound data.

8. The method of claim 1, wherein The method for obtaining the noise influence factors according to the noise probability and noise degree coefficients of each sound data comprises the following specific method: In the formula, represents the noise influence factor of the xth sound data corresponding to the bth vehicle, represents the noise probability parameter of the xth sound data corresponding to the bth vehicle, represents the noise degree coefficient of the xth sound data corresponding to the bth vehicle.

9. The method of claim 1, wherein The method for judging whether the vehicle noise monitoring is qualified according to the user experience influence index threshold and the noise user experience influence index threshold of each vehicle comprises the following specific method: Comparing the noise user experience influence index of each vehicle with the user experience influence index threshold; when the noise user experience influence index of the vehicle is less than the user experience influence index, the vehicle noise is marked as a qualified vehicle; when the noise user experience influence index of the vehicle is greater than the user experience influence index, the vehicle noise is marked as an unqualified vehicle.

10. An in-vehicle noise monitoring system characterized by comprising: The system comprises the following modules: A data collection module for collecting sound data during vehicle driving and converting into digital form; A data operation module for obtaining noise probability parameters according to the numerical size of the sound data during vehicle driving; Obtaining noise degree coefficients according to the fluctuation of the noise data; A data analysis module for obtaining noise influence factors according to the noise probability and noise degree coefficients of each sound data; Obtaining noise data according to the noise degree coefficients of each sound data and the standard noise influence factor; Obtaining noise user experience influence indexes according to the fluctuation and size of the noise data; The data judging module is used to acquire a noise influence index on user experience according to the fluctuation and size of the noise data; and to judge whether the vehicle noise monitoring is qualified according to the user experience influence index threshold and the user experience influence index threshold of each vehicle noise.

Images