Vehicle noise detection method and device, computer equipment and storage medium
By calculating the in-vehicle noise index in the vehicle using the on-vehicle microphone and on-vehicle bus data, the existing vehicle noise detection problems are solved, and efficient and low-cost noise detection for each vehicle is achieved.
Patent Information
- Application Number
- CN202510296206.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-13
- Publication Date
- 2025-06-13
AI Technical Summary
The noise detection efficiency of existing vehicles is low and costly, making it difficult to detect noise on all vehicles before delivery.
By using the on-board microphone to obtain the sound data in the car while the vehicle is in a set state, and combining the on-board bus data, the in-car noise index is calculated. The method includes correction of sound data and identification of noise source, enabling detection of each vehicle after the vehicle is assembled or delivered.
It realizes that the vehicle noise detection efficiency is improved without increasing the cost of additional equipment, and can detect each vehicle after the vehicle is assembled or before delivery, avoid missed inspection and improve product quality.
Smart Images
Figure CN120148548A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of noise detection, and specifically provides a vehicle noise detection method, device, computer device, and storage medium. Background Art
[0002] With the development of intelligent vehicles, the interior of vehicles is becoming more and more like the user's second living room. However, noise is inevitably generated during the use of vehicles, and the magnitude of the noise directly affects the user experience.
[0003] Currently, for the noise detection at the vehicle level in the industry, it mainly relies on manual judgment or uses professional testing equipment for noise detection. The method of manual judgment is highly subjective, making it difficult to accurately quantify the noise index and relying heavily on the experience of the staff. Moreover, using professional testing equipment for noise detection is affected by factors such as detection efficiency and operation cost, and can only achieve sampling tests on batch vehicles, making it difficult to conduct noise detection on all vehicles before delivery.
[0004] Correspondingly, a new technical solution is needed in this field to solve the above problems. Summary of the Invention
[0005] This application aims to solve or improve the above technical problems, that is, to solve or improve the problems of low efficiency and high cost in existing vehicle noise detection.
[0006] In a first aspect, this application provides a vehicle noise detection method, which includes:
[0007] When the vehicle is in a set state, control the on-vehicle microphone to obtain the in-vehicle sound data;
[0008] Obtain the on-vehicle bus data;
[0009] Calculate the in-vehicle noise index based on the sound data and the on-vehicle bus data.
[0010] In a technical solution of the above detection method, the noise includes on-vehicle radar noise, and the set state includes the set operating state of the on-vehicle radar.
[0011] In a technical solution of the above detection method, the noise includes on-vehicle audio device noise, and the set state includes the set operating state of the on-vehicle audio device.
[0012] In a technical solution of the above detection method, the noise includes vehicle driving noise, and the set state includes the set driving conditions of the vehicle.
[0013] In one technical solution of the above detection method, the noise includes the operating noise of in-vehicle functional components, and the set state includes the set operating state of in-vehicle functional components.
[0014] In one technical solution of the above detection method, the detection method further includes:
[0015] Obtain the propagation direction of the sound data, and determine the sound source position based on the propagation direction;
[0016] Correct the sound data according to the spatial difference between the sound source position and the sound pickup area of the in-vehicle microphone to obtain sound correction data;
[0017] The step of "calculating the in-vehicle noise index according to the sound data and the in-vehicle bus data" includes:
[0018] Calculate the in-vehicle noise index according to the sound correction data and the in-vehicle bus data.
[0019] In one technical solution of the above detection method, the detection method is applied to the off-line detection after vehicle assembly is completed; and / or
[0020] The detection method is applied to the maintenance process after vehicle delivery.
[0021] In a second aspect, the present application provides a vehicle noise detection device, which includes:
[0022] A sound data acquisition module, which is used to control an in-vehicle microphone to acquire in-vehicle sound data when the vehicle is in a set state;
[0023] An in-vehicle bus data acquisition module, which is used to acquire in-vehicle bus data;
[0024] A calculation module, which is used to calculate the in-vehicle noise index according to the sound data and the in-vehicle bus data.
[0025] In a third aspect, the present application provides a computer device, including a processor and a storage device, the storage device is adapted to store multiple program codes, and the program codes are adapted to be loaded and run by the processor to execute the detection method described in any one of the first aspects.
[0026] In a fourth aspect, the present application provides a computer-readable storage medium, in which multiple program codes are stored, and the program codes are adapted to be loaded and run by a processor to execute the detection method described in any one of the first aspects.
[0027] In the case of adopting the above technical solutions, the present application uses an in-vehicle microphone for vehicle noise detection, and compared with the traditional noise detection method, has the following advantages:
[0028] First, compared with the traditional method of using proprietary noise testing equipment, the present application uses in-vehicle microphones for noise detection without the need to increase additional equipment costs, which is conducive to cost reduction.
[0029] Second, using in-vehicle microphones for vehicle noise detection can perform off-line inspection on each vehicle on a specific inspection line after the vehicle is assembled and before it is delivered. This can avoid the phenomenon of missed vehicle inspections, which is conducive to improving product quality. Moreover, the present application realizes its noise detection before the vehicle is delivered, eliminating the need for separate spot checks. Thus, it can achieve semi-automated vehicle detection without affecting the vehicle production rhythm and without additional man-hour and labor costs, thereby improving the vehicle noise detection efficiency.
[0030] Third, using in-vehicle microphones for vehicle noise detection can perform noise detection on the vehicle according to user needs throughout its entire life cycle after delivery to determine whether there are any abnormalities in the vehicle, which is conducive to vehicle maintenance and repair, and thus can improve the user experience.
[0031] Fourth, it is possible to build a database based on the cloud big data and artificial intelligence detection platform for vehicles of the same model, combined with statistical analysis based on the vehicle's maintenance history records, to achieve early warning analysis of vehicle abnormalities, send notifications to users when the vehicle may have abnormalities, and achieve real-time feedback and dynamic tracking, thereby further enhancing the user experience and the vehicle's safety performance. Description of the Drawings
[0032] The following describes the preferred embodiments of the present application with reference to the drawings. In the drawings:
[0033] Figure 1 is the main step flow chart of the vehicle noise detection method according to an embodiment of the present application. Detailed Embodiments
[0034] The following describes the preferred embodiments of the present application with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not used to limit the protection scope of the present application. Those skilled in the art can make adjustments according to needs to adapt to specific application scenarios.
[0035] It should be noted that in the description of the present application, terms indicating directions or positional relationships such as "up", "down", "left", "right", "inside", "outside", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the relevant devices or components must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present application. In addition, ordinal numbers such as "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.
[0036] In addition, it should be noted that in the description of this application, unless otherwise clearly specified and limited, the terms "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.
[0037] Currently, with the rapid development of intelligent vehicles, especially electric vehicles, which are increasingly tending to be the second living room of users, reducing the interior noise of vehicles is beneficial to enhancing the user experience. It should be noted that vehicle noise can specifically include on-vehicle radar noise, audio device noise, noise from necessary functional components such as doors, windows and seats, as well as wind noise and road noise generated during vehicle driving according to different sound sources.
[0038] In some related technologies, for the noise detection at the vehicle level, in addition to relying on manual judgment, proprietary noise testing equipment is generally used to conduct targeted detection on specific vehicles. Although the test accuracy is guaranteed by this method compared with manual judgment, the test process is complex. If each vehicle is to be tested separately after vehicle assembly or delivery, it will not only increase the labor cost, but also greatly increase the production cycle and affect the production rhythm. Therefore, this method is usually only applicable to the sampling inspection of batch vehicles and cannot achieve the noise detection of all vehicles, making it difficult to ensure product quality.
[0039] Refer to Figure 1 , which is a main step flowchart of a vehicle noise detection method according to an embodiment of this application, and it includes:
[0040] S101: When the vehicle is in a set state, control the on-vehicle microphone to obtain the interior sound data.
[0041] S102: Obtain the on-vehicle bus data.
[0042] During the operation of the vehicle BGW (Automotive Gateway, as the core control device of the automotive network system, is responsible for coordinating protocol conversion, data exchange, fault diagnosis, etc. between CAN bus networks and other data networks with different structures and characteristics), it is usually necessary to perform data interaction with the vehicle's on-vehicle bus system (such as CAN bus, LIN bus, etc.) to obtain various parameter information of the vehicle system, that is, the on-vehicle bus data. For example, vehicle operation state data (including data such as vehicle speed, engine speed and temperature measured by sensors), vehicle environment data (vehicle position, relative distance from surrounding obstacles), control unit data (such as status information of systems such as ABS, ESP), diagnostic data (such as fault codes, system status), etc.
[0043] Among them, in step S101, the set state refers to a specific state when performing different types of noise detection on the vehicle. For example, when detecting the noise of the vehicle-mounted radar, the above set state is the set operating state of the vehicle-mounted radar. Specifically, the set operating state of the vehicle-mounted radar may include its set rotation speed, set scanning method, etc. Under the condition that the vehicle meets the above set state, control the vehicle-mounted microphone to record sound and upload the sound data to the cloud server.
[0044] In step S012, taking CAN data acquisition as an example, after obtaining the vehicle operating state data and the control unit data related to the vehicle-mounted radar noise through the CAN bus, upload the CAN data to the cloud server.
[0045] For example, in an application scenario of the present application, after the vehicle is assembled in the factory and before delivery, the vehicle-mounted radar noise is detected on the factory inspection line (i.e., off-line inspection). After the vehicle is transported to the vehicle-mounted radar noise detection station, the staff connects the vehicle multi-functional diagnostic instrument to the vehicle, sends a diagnostic command, and starts the vehicle-mounted radar noise detection. In an embodiment of the present application, in order to avoid the interference caused by the external environment and the sounds of other devices outside the vehicle-mounted radar to the detection, the vehicle can be placed in a relatively quiet environment and other sound sources inside the vehicle can be cut off. Then, based on step S101, make the vehicle-mounted radar in the set operating state, call the vehicle-mounted microphone to record the sound inside the vehicle, and upload the obtained sound recording to the cloud server. At the same time, obtain CAN data through the CAN bus. For example, when performing vehicle-mounted radar noise detection, the control unit data related to the vehicle-mounted radar noise in the CAN data can be screened, such as the window state and the sunroof state, to determine whether the windows and the sunroof are in the closed state to reduce the interference caused by external environmental noise, or use the window state and the sunroof state as influencing factors for vehicle-mounted radar noise analysis.
[0046] S103: Calculate the in-vehicle noise index according to the sound data and the vehicle bus data.
[0047] In one embodiment, step S103 may include the following steps:
[0048] S1031: Data synchronization and preprocessing.
[0049] Align the timestamps of the sound data and the CAN data to ensure that the two are analyzed in the same time dimension; perform filtering processing on the sound data to remove the noise irrelevant to the vehicle-mounted radar noise; extract the features related to the vehicle-mounted radar noise from the CAN data (such as the relationship between the window state and the vehicle-mounted radar noise).
[0050] S1032: Noise source identification.
[0051] Noise source localization based on CAN data: Determine the noise source according to the vehicle status information in the CAN data. For example, if the engine speed increases and the noise increases, it may be engine noise. If the vehicle-mounted radar speed increases and the noise increases, the vehicle-mounted radar noise may be the main influencing factor.
[0052] Noise source localization based on sound data:
[0053] In one way, signal processing techniques (such as Fourier transform, wavelet transform) can be used to perform spectral analysis on the sound data to identify noise sources at different frequencies.
[0054] In another way, sound source localization techniques can be used to determine the spatial source of the noise.
[0055] S1033: Sound data correction.
[0056] It should be noted that compared with the method of sampling and detecting using a proprietary noise test device in the prior art, in this application, the vehicle's internal microphone is used for sampling and the obtained sound data is uploaded to the cloud server for analysis. However, for the in-vehicle microphone, its function is for human-machine interaction. Therefore, the sound pickup area of the in-vehicle microphone is facing the driver's seat, while the noise in the vehicle comes from the position of the vehicle parts. For example, when the vehicle-mounted radar is installed at the front end of the vehicle roof, the source of the vehicle-mounted radar noise deviates from the sound pickup area of the in-vehicle microphone. At this time, the sound data obtained by the in-vehicle microphone is distorted, or inaccurate. Therefore, in order to improve the detection accuracy, it is necessary to correct the sound data obtained by the in-vehicle microphone based on the identification and localization of the noise source.
[0057] In an embodiment of the present application, sound source localization techniques are used to determine the spatial source of the noise. That is, in step S101, after the in-vehicle microphone obtains the in-vehicle sound data, first obtain the propagation direction of the sound data, and determine the sound source position based on the propagation direction. Then, correct the sound data according to the spatial difference between the sound source position and the sound pickup area of the in-vehicle microphone to obtain sound correction data. Based on this, in step S103, the in-vehicle noise index can be calculated according to the sound correction data and the vehicle bus data.
[0058] In one implementation, during the vehicle development process, a proprietary noise testing device can be used to analyze the sound field inside the vehicle and the recording ability of on-vehicle microphones. Multiple samples are collected for analysis, and an exclusive calibration curve A = F(f) applicable to different vehicle models in the spatial frequency domain is obtained. Then, this calibration curve A = F(f) is placed into the cloud server for algorithm calculation by the cloud server. That is, this calibration curve A = F(f) is obtained through pre-tests during the vehicle development process. Different vehicle models have different structures and different internal sound fields, so different calibration curves are available for different vehicle models. During the on-vehicle radar noise detection process, the cloud server directly calls this calibration curve to correct the initially obtained sound data to obtain the above-mentioned sound-corrected data.
[0059] S1034: Correlation analysis of noise and vehicle status, calculating the noise index.
[0060] Establish a noise model: Using CAN data and sound data, establish a mathematical model of the noise and vehicle status. For example, on-vehicle radar noise = f(on-vehicle radar rotation speed, on-vehicle radar scanning method).
[0061] Correlation analysis: Use statistical methods (such as correlation coefficient analysis) to determine the correlation between on-vehicle radar noise and vehicle status.
[0062] Machine learning method: Use machine learning algorithms (such as regression analysis, neural network) to predict the on-vehicle radar noise index under specific conditions.
[0063] As described above, this application provides an exemplary illustration of "calculating the noise index by combining CAN data and sound data", but it does not limit this application. Those skilled in the art can make adaptive adjustments to each step according to actual needs.
[0064] In another application scenario of this application, in addition to performing off-line inspection on the vehicle after vehicle assembly and before delivery, when the user needs to perform maintenance on the vehicle after delivery, the on-vehicle microphone can be used to detect the noise. That is, for example, after the vehicle is delivered to the user, during daily use, if the user complains about the on-vehicle radar noise problem, the vehicle can be subjected to noise detection by contacting the after-sales service to determine whether there is a fault in the on-vehicle radar or whether optimization is required. Thus, noise detection can be performed on the vehicle according to the user's needs throughout the vehicle's life cycle, which is beneficial to improving the user experience.
[0065] It should be noted that for the vehicle noise detection method, in the related art, special noise testing equipment is usually used to conduct spot checks on batches of vehicles. This is because in the past, due to the limitation of the test accuracy of in-vehicle microphones, they were usually only applicable to human-machine interaction. Now, with the improvement of the sound pickup ability and sound data processing ability of in-vehicle microphones, their accuracy has approached that of professional recording equipment. Therefore, this application uses in-vehicle microphones for vehicle noise detection. Compared with the traditional noise detection method, it has the following advantages:
[0066] First, compared with the traditional method of using special noise testing equipment, this application uses in-vehicle microphones for noise detection, without the need to increase additional equipment costs, which is conducive to cost reduction.
[0067] Second, using in-vehicle microphones for vehicle noise detection can conduct off-line detection on each vehicle on a specific detection line after the vehicle is assembled and before it is delivered. This can avoid the phenomenon of missed vehicle inspections, which is conducive to improving product quality. Moreover, this application realizes its noise detection before the vehicle is delivered, without the need for separate spot checks, so that semi-automatic detection of the vehicle can be achieved without affecting the vehicle production rhythm and without additional labor and man-hour costs, thereby improving the vehicle noise detection efficiency.
[0068] Third, using in-vehicle microphones for vehicle noise detection can conduct noise detection on the vehicle according to user needs throughout its life cycle after the vehicle is delivered, and judge whether there are abnormalities in the vehicle, which is conducive to vehicle maintenance and repair, thereby improving the user experience.
[0069] Fourth, based on the cloud big data and artificial intelligence detection platform of the same model of vehicle, a database can be built based on the vehicle's maintenance history records, and combined with statistical analysis, early warning analysis of vehicle abnormalities can be realized. When the vehicle may have abnormalities, a notice is sent to the user to achieve real-time feedback and dynamic tracking, thereby further enhancing the user experience and the safety performance of the vehicle.
[0070] As above, although this application takes in-vehicle radar noise detection as an example for illustrative purposes, it does not limit the detection method of this application. The detection method of this application is also applicable to other types of noise detection. The following briefly describes the detection of several common vehicle noises.
[0071] As users' requirements for the audio quality played in the vehicle are getting higher and higher, the abnormal noises generated during the operation of in-vehicle audio devices are becoming increasingly unacceptable. Therefore, during the off-line inspection of the vehicle or in the daily use process of users, the noise of in-vehicle audio devices can be detected. When the noise to be detected is in-vehicle audio noise, the set state in step S101 is the set operating state of the in-vehicle audio device. For example, the set operating state of the in-vehicle audio device may include the volume of the in-vehicle audio device, the type of audio being played, and so on. Specifically, during the off-line inspection of the vehicle, after transporting the assembled vehicle to the inspection station, control the in-vehicle audio device to play music in the set operating state, and then start the noise detection. Similarly, during the use process of the vehicle by users after delivery, the user can contact the after-sales service according to their own needs, drive the vehicle to the designated inspection point for noise detection, and this application will not elaborate too much here.
[0072] During the driving process of the vehicle, driving noises will be generated, and the driving noises include wind noise, road noise, EDS noise (noise related to the electric drive system), etc. generated during the vehicle driving process. When it is necessary to detect the driving noise, the set state in step S101 includes the set driving conditions of the vehicle, and the set driving conditions of the vehicle may include vehicle speed, vehicle driving mode, driving road conditions, and so on. For the off-line inspection of vehicle driving noise, the assembled vehicle can be driven on specific sites such as the factory off-line runway, and the vehicle driving noise can be detected under different driving conditions. During the normal use process of the vehicle after delivery, when the driving noise is too large, the user can also drive the vehicle to the designated inspection point for after-sales service according to their own needs, and the relevant staff can detect the vehicle driving noise.
[0073] Vehicle noise also includes the operating noise of in-vehicle functional components. It should be noted that the in-vehicle functional components include but are not limited to essential functional components of the vehicle such as the vehicle's seats, steering wheel, windshield wipers, doors and windows, compressor, blower, ASU (air supply unit) system, etc. At this time, the set state in step S101 includes the set operating state of the in-vehicle functional components. For example, when detecting the operating noise of the windshield wipers, the set operating state of the in-vehicle functional components includes the operating mode, swing frequency, etc. of the windshield wipers. Similarly, for the off-line inspection of the operating noise of in-vehicle functional components, the assembled vehicle can be transported to a specific inspection station, and the in-vehicle functional components can be adjusted to the set operating state at the inspection station for noise detection. During the normal use process of the vehicle, the user can also drive the vehicle with abnormalities to the designated inspection point for after-sales service according to their own needs.
[0074] The present application also discloses a vehicle noise detection device, which includes a sound data acquisition module, an in-vehicle bus data acquisition module, and a calculation module. Among them, the sound data acquisition module is used to control an in-vehicle microphone to acquire in-vehicle sound data when the vehicle is in a set state, the in-vehicle bus data acquisition module is used to acquire in-vehicle bus data, and the calculation module is used to calculate an in-vehicle noise index based on the sound data and the in-vehicle bus data. In some embodiments, the sound data acquisition module, the in-vehicle bus data acquisition module, and the calculation module can be combined into one module.
[0075] The above vehicle noise detection device is used to execute the embodiments of the above vehicle noise detection method. The technical principles, the technical problems solved, and the technical effects produced by both are similar. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and related descriptions of the vehicle noise detection device can refer to the content described in the embodiments of the vehicle noise detection method, which will not be elaborated here.
[0076] Those skilled in the art can understand that all or part of the processes in implementing the method of an embodiment of the present application can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium that can carry the computer program code. It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.
[0077] Furthermore, the present application also discloses a computer device, which includes a processor and a storage device. The storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the detection method described in any one of the above method embodiments. For the convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, please refer to the method embodiment part of the present application. The computer device can be a computer device formed by various electronic devices.
[0078] Furthermore, the present application also discloses a computer-readable storage medium. In an embodiment of the computer-readable storage medium according to the present application, the computer-readable storage medium may be configured to store a program for executing the vehicle noise detection method in the above method embodiment, and this program may be loaded and run by a processor to implement the above detection method. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. For those specific technical details not disclosed, please refer to the method embodiment part of the present application. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiments of the present application is a non-transitory computer-readable storage medium.
[0079] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principle of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present application.
Claims
1. A vehicle noise detection method, characterized in that: include: When the vehicle is in a set state, the vehicle microphone is controlled to obtain the sound data inside the vehicle; Get vehicle bus data; The in-vehicle noise index is calculated according to the sound data and the in-vehicle bus data.
2. The detection method according to claim 1, characterized in that: The noise includes vehicle-mounted radar noise, and the set state includes a set operating state of the vehicle-mounted radar.
3. The noise detection method according to claim 1, characterized in that: The noise includes noise of an in-vehicle audio device, and the set state includes a set operating state of the in-vehicle audio device.
4. The detection method according to claim 1, characterized in that: The noise includes vehicle running noise, and the set state includes a set driving condition of the vehicle.
5. The detection method according to claim 1, characterized in that: The noise includes the operating noise of the vehicle-mounted functional components, and the set state includes the set operating state of the vehicle-mounted functional components.
6. The noise detection method according to any one of claims 1 to 5, characterized in that: The detection method further comprises: Acquire the propagation direction of the sound data, and determine the sound source position based on the propagation direction; Correcting the sound data according to the spatial difference between the sound source position and the sound pickup area of the vehicle-mounted microphone to obtain sound correction data; The step of "calculating the in-vehicle noise index according to the sound data and the vehicle bus data" includes: The in-vehicle noise index is calculated according to the sound correction data and the in-vehicle bus data.
7. The detection method according to claim 1, characterized in that: The detection method is applied to off-line detection after vehicle assembly; and / or The detection method is applied in the maintenance process of the vehicle after delivery.
8. A vehicle noise detection device, characterized in that: include: A sound data acquisition module, which is used to control the vehicle microphone to acquire the sound data inside the vehicle when the vehicle is in a set state; A vehicle bus data acquisition module, which is used to acquire vehicle bus data; A calculation module is used to calculate the in-vehicle noise index according to the sound data and the vehicle bus data.
9. A computer device comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by the processor to execute the detection method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the detection method according to any one of claims 1 to 7.