Method and device for detecting dynamic abnormal sound of whole vehicle

Abstract

The invention discloses a vehicle dynamic abnormal sound detection method and device, and relates to the technical field of abnormal sound detection, and the method comprises the following steps: constructing a data structure of an abnormal sound problem database; obtaining road surface amplitude spectrums and background noise amplitude spectrums corresponding to different preset road surfaces; abnormal sound problem numbers, abnormal sound problem descriptions and abnormal sound problem amplitude spectrums corresponding to different preset pavements and different single known abnormal sound problems are obtained; the to-be-detected vehicle is controlled to run on the target road surface according to the vehicle speed set by the driver in real time, and an abnormal sound detection amplitude spectrum is obtained; and road surface type identification is carried out on the target road surface, and abnormal sound analysis is carried out based on the abnormal sound detection amplitude spectrum and the abnormal sound problem database. The abnormal sound problem data structure is constructed based on the whole vehicle dynamic abnormal sound detection correlation factors, the detection principle is simple, operation is convenient, and the abnormal sound detection efficiency and reliability are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormal noise detection, and particularly to a method and device for detecting abnormal noise in a whole vehicle dynamically. Background Art

[0002] With the development of the times, the abnormal noise performance of vehicles has been increasingly valued by customers. To improve the abnormal noise performance of vehicles, the abnormal noise performance of vehicles and their components is detected in all links of mass production vehicle manufacturing. Among them, the dynamic abnormal noise detection of the whole vehicle is the last and most important detection link.

[0003] For mass production vehicles, the abnormal noise performance of the vehicles has stabilized. Due to the production consistency problems of various vehicle components, it is inevitable that individual vehicles have abnormal noise problems. In many cases, the abnormal noise problems that occur are known and there are mature solutions. For mass production vehicles, there may be several known abnormal noise problems.

[0004] The dynamic abnormal noise detection of the whole vehicle is to let the test driver drive the mass production vehicle to a dedicated test lane to detect the abnormal noise performance; the test lane includes various road surfaces, such as twist roads, stone roads, pebble roads, etc.; after the vehicle runs through these road surfaces, the test driver determines which known abnormal noise problems have occurred based on his subjective feelings. If abnormal noise occurs, the vehicle will be repaired.

[0005] The above-mentioned abnormal noise detection method is highly subjective; for the same vehicle, different test drivers may get different detection results when evaluating. In addition, after working fatigue, the subjective perception ability of people will decline, which may lead to missed detection of abnormal noise problems; when abnormal noise occurs in the rear row of the vehicle, the test driver sitting in the front row may not notice the abnormal noise, which will also lead to missed detection of problems. Moreover, it is easy to identify when there is one abnormal noise in a certain direction of the vehicle. When there are two or more abnormal noises, it is difficult to identify which abnormal noises have occurred because these abnormal noises are mixed together. In summary, this traditional abnormal noise detection method is highly subjective, the detection results are unreliable and inaccurate, and major quality accidents such as missed detection of problems will occur.

[0006] Therefore, to meet the actual needs, a whole vehicle dynamic abnormal noise detection technology is provided now. Summary of the Invention

[0007] Aiming at the defects existing in the prior art, the purpose of the present invention is to provide a method and device for detecting abnormal noise in a whole vehicle dynamically. Based on the associated factors of the whole vehicle dynamic abnormal noise detection, an abnormal noise problem data structure is constructed, and abnormal noise detection is carried out in combination with the road surface amplitude spectrum, the background noise amplitude spectrum, and the abnormal noise problem amplitude spectrum. The principle is simple, the operation is convenient, and the efficiency and reliability of abnormal noise detection are effectively improved.

[0008] To achieve the above purpose, the technical solution adopted by the present invention is:

[0009] In a first aspect, the present application provides a method for detecting dynamic abnormal noises of a whole vehicle, and the method includes the following steps:

[0010] Construct the data structure of the abnormal noise problem database;

[0011] Based on a reference vehicle in an initial state and of the same model as the vehicle to be tested, drive on a preset road surface of different road surface types at a speed set by the driver in real time, obtain the road surface amplitude spectrum and the background noise amplitude spectrum corresponding to different preset road surfaces, and write them into the abnormal noise problem database;

[0012] Based on multiple reference vehicles of the same model as the vehicle to be tested, each having a different single known abnormal noise problem with a known abnormal noise direction, drive on different preset road surfaces at a speed set by the driver in real time, obtain the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different preset road surfaces and different single known abnormal noise problems, and write them into the abnormal noise problem database;

[0013] Control the vehicle to be tested to drive on the target road surface at a speed set by the driver in real time, and obtain the abnormal noise detection amplitude spectrum;

[0014] Identify the road surface type of the target road surface, and perform abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database; wherein,

[0015] The data structure of the abnormal noise problem database is a three-layer structure. The first layer is the road surface data layer, including the road surface numbers, road surface descriptions, and road surface amplitude spectra of different road surface types. The second layer is the abnormal noise direction layer, including different abnormal noise directions of different abnormal noise problems. The third layer is the abnormal noise data layer, including the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different abnormal noise directions of different abnormal noise problems.

[0016] Based on the above technical solution, the performing abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database includes the following steps:

[0017] Based on the abnormal noise detection amplitude spectrum, identify the corresponding abnormal noise problem number and abnormal noise problem description in the abnormal noise problem database.

[0018] Based on the above technical solution, the method is based on the vehicle ECU, vehicle accelerometers, and microphones configured at the left front, right front, left rear, and right rear in the vehicle;

[0019] Each of the microphones corresponds to a different abnormal noise direction.

[0020] Based on the above technical solution, the identifying the road surface type of the target road surface includes the following steps:

[0021] Based on the road surface amplitude spectrum obtained when controlling the vehicle under test to travel on the target road surface at the real-time set speed of the driver, match it with the road surface amplitude spectra corresponding to different preset road surfaces to perform road surface recognition.

[0022] Based on the above technical solution, the method includes a road surface amplitude spectrum test process, and the road surface amplitude spectrum test process includes the following steps:

[0023] Perform a test based on the vehicle accelerometer to obtain the road surface amplitude spectrum.

[0024] In a second aspect, the present application also provides a vehicle dynamic abnormal noise detection device, and the device includes:

[0025] A database construction module, which is used to construct the data structure of the abnormal noise problem database;

[0026] A reference road surface monitoring module, which is used to drive a reference vehicle based on the initial state and of the same model as the vehicle under test on preset road surfaces of different road surface types at the real-time set speed of the driver, obtain the road surface amplitude spectra and background noise amplitude spectra corresponding to different preset road surfaces, and write them into the abnormal noise problem database;

[0027] A reference abnormal noise monitoring module, which is used to drive a reference vehicle based on multiple known abnormal noise problems that each have a different single and known abnormal noise orientation and of the same model as the vehicle under test on different preset road surfaces at the real-time set speed of the driver, obtain the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise amplitude spectra corresponding to different preset road surfaces and different single known abnormal noise problems, and write them into the abnormal noise problem database;

[0028] A to-be-tested abnormal noise detection module, which is used to control the vehicle under test to travel on the target road surface at the real-time set speed of the driver to obtain an abnormal noise detection amplitude spectrum;

[0029] A to-be-tested abnormal noise analysis module, which is used to identify the road surface type of the target road surface and perform abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database; wherein,

[0030] The data structure of the abnormal noise problem database is a three-layer structure. The first layer is the road surface data layer, including the road surface numbers, road surface descriptions, and road surface amplitude spectra of different road surface types. The second layer is the abnormal noise orientation layer, including different abnormal noise orientations of different abnormal noise problems. The third layer is the abnormal noise data layer, including the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise amplitude spectra corresponding to different abnormal noise orientations of different abnormal noise problems.

[0031] On the basis of the above technical solution, the abnormal noise analysis module to be measured is further configured to identify and obtain the corresponding abnormal noise problem number and abnormal noise problem description in the abnormal noise problem database based on the abnormal noise detection amplitude spectrum.

[0032] On the basis of the above technical solution, the device is signal-connected to the vehicle ECU, the vehicle accelerometer, and the microphones configured at the front left, front right, rear left, and rear right in the vehicle;

[0033] Each of the microphones corresponds to a different abnormal noise direction.

[0034] On the basis of the above technical solution, the abnormal noise analysis module to be measured is further configured to match the road surface amplitude spectrum obtained when controlling the vehicle to be measured to travel on the target road surface at the real-time set vehicle speed by the driver with the road surface amplitude spectra corresponding to different preset road surfaces, and perform road surface identification.

[0035] On the basis of the above technical solution, the device further includes:

[0036] A road surface amplitude spectrum test module, which is configured to control the vehicle accelerometer to perform detection and obtain the road surface amplitude spectrum.

[0037] Compared with the prior art, the advantages of the present invention are as follows:

[0038] Based on the correlation factors of the vehicle's dynamic abnormal noise detection, the present invention constructs an abnormal noise problem data structure, and combines the road surface amplitude spectrum, the background noise amplitude spectrum, and the abnormal noise problem amplitude spectrum to perform abnormal noise detection. The principle is simple, the operation is convenient, and the efficiency and reliability of abnormal noise detection are effectively improved. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 It is the hardware basic principle diagram of the vehicle's dynamic abnormal noise detection method according to the embodiment of the present invention;

[0041] Figure 2 It is the data structure schematic diagram of the abnormal noise problem database of the vehicle's dynamic abnormal noise detection method according to the embodiment of the present invention;

[0042] Figure 3 It is the step flow chart of the vehicle's dynamic abnormal noise detection method according to the embodiment of the present invention;

[0043] Figure 4It is a structural block diagram of a vehicle dynamic abnormal noise detection device according to an embodiment of the present invention. Detailed implementation manners

[0044] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0045] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0046] The embodiments of the present application provide a vehicle dynamic abnormal noise detection method and device. Based on the associated factors of vehicle dynamic abnormal noise detection, an abnormal noise problem data structure is constructed, and abnormal noise detection is performed in combination with the road surface amplitude spectrum, background noise amplitude spectrum, and abnormal noise problem amplitude spectrum. The principle is simple, the operation is convenient, and the efficiency and reliability of abnormal noise detection are effectively improved.

[0047] To achieve the above technical effects, the general idea of the present application is as follows:

[0048] A vehicle dynamic abnormal noise detection method, the abnormal noise detection method includes the following steps:

[0049] S1. Construct the data structure of the abnormal noise problem database;

[0050] S2. Based on a reference vehicle in the initial state and of the same model as the vehicle to be tested, drive on preset road surfaces of different road surface types at the vehicle speed set by the driver in real time, obtain the road surface amplitude spectrum and background noise amplitude spectrum corresponding to different preset road surfaces, and write them into the abnormal noise problem database;

[0051] S3. Based on multiple reference vehicles of the same model as the vehicle to be tested, each having a different single known abnormal noise problem with a known abnormal noise orientation, drive on different preset road surfaces at the vehicle speed set by the driver in real time, obtain the abnormal noise problem number, abnormal noise problem description, and abnormal noise problem amplitude spectrum corresponding to different preset road surfaces and different single known abnormal noise problems, and write them into the abnormal noise problem database;

[0052] S4. Control the vehicle to be tested to drive on the target road surface at the vehicle speed set by the driver in real time to obtain the abnormal noise detection amplitude spectrum;

[0053] S5. Identify the road surface type of the target road surface, and perform abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database; wherein,

[0054] The data structure of the abnormal noise problem database is a three - layer structure. The first layer is the road surface data layer, including the road surface numbers, road surface descriptions, and road surface amplitude spectra of different road surface types. The second layer is the abnormal noise direction layer, including different abnormal noise directions of different abnormal noise problems. The third layer is the abnormal noise data layer, including the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different abnormal noise directions of different abnormal noise problems.

[0055] The following further elaborates on the embodiments of the present application in conjunction with the accompanying drawings.

[0056] In the first aspect, as shown in Figures 1 to 3 the embodiments of the present application provide a method for detecting abnormal noise in a whole vehicle during dynamic operation. The abnormal noise detection method includes the following steps:

[0057] S1. Construct the data structure of the abnormal noise problem database;

[0058] S2. Based on a reference vehicle in the initial state and of the same model as the vehicle to be tested, drive on preset road surfaces of different road surface types at the speed set by the driver in real - time, obtain the road surface amplitude spectra and background noise amplitude spectra corresponding to different preset road surfaces, and write them into the abnormal noise problem database;

[0059] S3. Based on multiple reference vehicles of the same model as the vehicle to be tested, each having a different single known abnormal noise problem with a known abnormal noise direction, drive on different preset road surfaces at the speed set by the driver in real - time, obtain the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different preset road surfaces and different single known abnormal noise problems, and write them into the abnormal noise problem database;

[0060] S4. Control the vehicle to be tested to drive on the target road surface at the speed set by the driver in real - time, and obtain the abnormal noise detection amplitude spectrum;

[0061] S5. Identify the road surface type of the target road surface, and based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database, conduct abnormal noise analysis; where

[0062] the data structure of the abnormal noise problem database is a three - layer structure. The first layer is the road surface data layer, including the road surface numbers, road surface descriptions, and road surface amplitude spectra of different road surface types. The second layer is the abnormal noise direction layer, including different abnormal noise directions of different abnormal noise problems. The third layer is the abnormal noise data layer, including the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different abnormal noise directions of different abnormal noise problems.

[0063] It should be noted that for the technical solution of the embodiments of the present application, the structural schematic diagram of the device involved is as shown in the Figure 1As shown in the figure, it is built into the whole vehicle and includes an in-vehicle microphone, a vehicle accelerometer, a vehicle ECU, and a car head unit (including a whole vehicle dynamic abnormal noise detection APP). The above-mentioned hardware is all powered by the vehicle battery.

[0064] The function of the in-vehicle microphone is to collect in-vehicle noise signals and transmit the signals to the vehicle ECU. For various vehicles, the number of in-vehicle microphones varies. As the vehicle intelligence level gets higher and higher, generally speaking, a vehicle has at least four microphones, namely the driver's microphone, the co-driver's microphone, and two microphones on the left and right sides of the rear row, such as the microphones M1 - M4 in the attached drawings of the specification. Figure 1 in the figure.

[0065] The function of the vehicle accelerometer is to measure the vertical acceleration received by the wheels and send the measured data to the vehicle ECU. The accelerometer is a unidirectional accelerometer, and the measurement direction is vertical. It is installed on the left rear wheel hub bearing support. The reason for installing it on the rear wheel is that compared with the front wheels that undertake the steering function, the force on the rear wheels is relatively simple.

[0066] The function of the vehicle ECU is to act as a bridge for transmitting data between the car head unit, the in-vehicle microphone, and the vehicle accelerometer - the noise and acceleration data are transmitted to the car head unit through the vehicle ECU.

[0067] The function of the car head unit is to receive the noise and acceleration data transmitted by the vehicle ECU.

[0068] The function of the whole vehicle dynamic abnormal noise detection APP in the car head unit is to perform spectral analysis on the noise and acceleration data to obtain the amplitude spectrum; add road surface amplitude spectrum data and background noise amplitude spectrum data; add abnormal noise problem amplitude spectrum data; perform road surface recognition; perform abnormal noise detection, display the detection results on the car head unit screen; perform data change of the abnormal noise problem database. The APP can be configured with multiple working modes according to actual needs.

[0069] Furthermore, the reference vehicle in the initial state refers to a normal vehicle without any abnormal noise problems.

[0070] In the embodiment of the present application, based on the associated factors of the whole vehicle dynamic abnormal noise detection, an abnormal noise problem data structure is constructed, and abnormal noise detection is carried out in combination with the road surface amplitude spectrum, the background noise amplitude spectrum, and the abnormal noise problem amplitude spectrum. The principle is simple, the operation is convenient, and the efficiency and reliability of abnormal noise detection are effectively improved.

[0071] Further, the abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database includes the following steps:

[0072] Based on the abnormal noise detection amplitude spectrum, identify and obtain the corresponding abnormal noise problem number and abnormal noise problem description in the abnormal noise problem database.

[0073] Further, the method is based on a vehicle ECU, a vehicle accelerometer, and microphones configured at the front left, front right, rear left, and rear right in the vehicle;

[0074] Each of the microphones corresponds to a different abnormal noise direction.

[0075] Further, the road surface type identification for the target road surface includes the following steps:

[0076] Based on the road surface amplitude spectrum obtained when controlling the vehicle under test to travel on the target road surface at the real-time set speed of the driver, match it with the road surface amplitude spectra corresponding to different preset road surfaces to perform road surface identification.

[0077] Further, the method includes a road surface amplitude spectrum test process, and the road surface amplitude spectrum test process includes the following steps:

[0078] Based on the vehicle accelerometer for testing, obtain the road surface amplitude spectrum.

[0079] Based on the technical solution of the embodiment of the present application, in specific implementation, the specific process is as follows:

[0080] Step 1: Construction of the abnormal noise problem data structure.

[0081] In the embodiment of the present application, the abnormal noise detection is performed by using the abnormal noise problem database in the vehicle dynamic abnormal noise detection APP in the vehicle. Since there are many associated factors in vehicle dynamic abnormal noise detection, such as the road surface on which the vehicle travels, the direction of the abnormal noise source, and abnormal noise detection, etc.;

[0082] Among them, the direction of the abnormal noise source is determined by the installation position of the in-vehicle microphones used. In the technical solution of the embodiment of the present application, 4 microphones including the driver's seat microphone, the co-driver's seat microphone, and the left and right rear row microphones are used, which determines that the abnormal noise source directions are the front left, front right, rear left, and rear right of the vehicle;

[0083] And the abnormal noise detection specifically corresponds to which abnormal noise problem in the abnormal noise problem database;

[0084] Therefore, the abnormal noise problem database structure is relatively complex and needs to be carefully constructed to facilitate the operation, maintenance, and abnormal noise problem detection of the abnormal noise problem database.

[0085] The Figure 2 The abnormal noise problem data structure constructed for the embodiment of the present application, and its corresponding database is built into the vehicle dynamic abnormal noise detection APP and has a three-layer structure.

[0086] The first layer is the road surface data layer, which includes road surface number, road surface description, and road surface amplitude spectrum.

[0087] The second layer is the abnormal noise orientation layer, which represents the orientation where the vehicle abnormal noise source is located.

[0088] In the embodiments of the present application, four orientations - front left, front right, rear left, and rear right - are set according to the positions of the in-vehicle microphones, and are represented by the numbers 1 - 4 respectively, corresponding to the Figure 1 microphones M1 - M4 in the attached drawings of the specification.

[0089] The third layer is the abnormal noise data layer, which includes the abnormal noise problem number, the abnormal noise problem description, and the abnormal noise problem amplitude spectrum.

[0090] The above three hierarchical levels are different and progressive; the first level is the highest, the second level is the second, and the third level is the lowest.

[0091] For example, each type of road surface includes all four abnormal noise orientations; and in each abnormal noise orientation, it includes all the abnormal noise problems where the abnormal noise source is in this orientation on this road surface.

[0092] It should be noted that for the set of abnormal noise problems under each abnormal noise orientation, the first problem is not a real abnormal noise problem, and its corresponding amplitude spectrum is the amplitude spectrum when the vehicle is driving on the corresponding road surface without any abnormal noise problems, which can be called the background noise amplitude spectrum. Since it is not counted in the number of real abnormal noise problems, its number is set to 0, as shown in the Figure 2 attached drawings of the specification.

[0093] Step 2: Add the road surface amplitude spectrum and the background noise amplitude spectrum.

[0094] After the abnormal noise problem data structure is determined, abnormal noise problem data can be supplemented into it, and the road surface amplitude spectrum and the background noise amplitude spectrum data are supplemented first.

[0095] It should be noted that the road surface amplitude spectrum mentioned in the embodiments of the present application does not describe the undulation degree of the road surface, but describes the acceleration amplitude spectrum of the vehicle at the accelerometer installation location when the vehicle is driving on the road surface;

[0096] Simply put, the measured quantity of the road surface amplitude spectrum is not the undulation data of the road surface, but the acceleration response data of the vehicle when driving on the road surface.

[0097] It should be emphasized that when adding these data, it must be ensured that the vehicle used for adding data is in a normal state and the vehicle has no any abnormal noise problems.

[0098] Set the vehicle's in-vehicle dynamic abnormal noise detection APP in the vehicle head unit to the "Add road surface and background noise amplitude spectra" mode. When driving the vehicle into the starting point of the specified road surface under the specified working conditions (such as specified vehicle speed, etc.), click the "Start" button in the APP, and the ECU starts to receive data sent from 4 microphones and 1 accelerometer. When the vehicle exits the ending point of the specified road surface, click the "End" button in the APP, and the ECU stops receiving data. The data collected by the microphones and accelerometer during the period of clicking the "Start" and "End" buttons will be transmitted to the vehicle's in-vehicle dynamic abnormal noise detection APP in the vehicle head unit through the vehicle ECU. The APP automatically performs spectral analysis on these data to obtain the amplitude spectra. The calculation of the amplitude spectra is a known technology and will not be elaborated here.

[0099] The amplitude spectra obtained from the data measured by the accelerometer are added to the road surface amplitude spectra, such as the road surface numbered 1 in the Figure 2 of the specification drawings, and its amplitude spectra are (aa1…aa p …aa v ), 1 ≤ p ≤ v, where v is the length of the road surface amplitude spectra;

[0100] The amplitude spectra obtained from the data measured by the 4 microphones are respectively added to the corresponding background noise amplitude spectra, such as the road surface numbered 1 in Figure 2 with abnormal noise directions 1, 2, 3, 4, and among the 4 background noise amplitude spectra with abnormal noise number 0, their values are respectively (ab1…ab q …ab w ), (ae1…ae q …aew), (ag1…ag q …ag w ), (aj1…aj q …aj w ), 1 ≤ q ≤ w, where w is the length of the background noise amplitude spectra.

[0101] As described above, in the "Add road surface and background noise amplitude spectra" mode, the APP can add one road surface amplitude spectrum and four azimuth background noise amplitude spectra for each road surface passed.

[0102] It should be noted that for the amplitude spectra of background noise, linear weighting (i.e., unweighted) is adopted; the amplitude of the amplitude spectra of background noise is represented by the actual physical quantity (i.e., sound pressure), with the unit of Pa, rather than being represented by dB.

[0103] Step 3: Add the amplitude spectra of abnormal noise problems.

[0104] In the embodiments of the present application, the realization of abnormal noise detection requires the support of an abnormal noise problem database.

[0105] The so-called abnormal noise problem database is to adopt Figure 2The data structure shown, a database formed by adding road surface amplitude spectrum data, background noise amplitude spectrum data, and abnormal noise problem amplitude spectrum data to it.

[0106] Among them, the addition of road surface amplitude spectrum data and background noise amplitude spectrum data has been introduced in Step 2. In this step, abnormal noise problem amplitude spectrum data will be added to the abnormal noise problem database.

[0107] Naturally, when adding abnormal noise problem amplitude spectrum data, it is necessary to ensure that the vehicle used for adding data has only the corresponding one abnormal noise problem and no other abnormal noise problems.

[0108] Set the vehicle's in-vehicle dynamic abnormal noise detection APP to the "Add abnormal noise problem amplitude spectrum" mode, select the road surface number, such as Figure 2 the road number 1 in

[0109] Select the abnormal noise direction (the direction where the abnormal noise source is located, that is, the direction where the abnormal noise is most obvious), such as Figure 2 the abnormal noise direction 1 under the road surface numbered 1 in

[0110] When driving the vehicle into the starting point of the road surface under a specified working condition (such as a specified vehicle speed, etc.), click the "Start" button in the APP, and the ECU starts to receive data sent from the microphone corresponding to the abnormal noise direction;

[0111] When the vehicle exits the end point of the road surface, click the "End" button in the APP, and the ECU stops receiving data.

[0112] The data collected by the microphone during the click of the "Start" and "End" buttons will be transmitted to the vehicle's in-vehicle dynamic abnormal noise detection APP through the vehicle ECU. The APP automatically performs spectral analysis on these data to obtain the abnormal noise problem amplitude spectrum. The abnormal noise problem amplitude spectrum is added to the corresponding position in the abnormal noise problem database, such as Figure 2 under the road surface numbered 1 in, the abnormal noise direction is 1, and the abnormal noise problem amplitude spectrum with the abnormal noise number 1, whose value is (ac1……ac q ……ac [[ID=3o]] w ), 1≤q≤w, where w is the length of the abnormal noise problem amplitude spectrum. It should be noted that the added abnormal noise problems are all known problems and have mature solutions.

[0113] Similar to Step 2, the abnormal noise problem amplitude spectrum uses linear weighting (i.e., unweighted); the amplitude of the abnormal noise problem amplitude spectrum is represented by the actual physical quantity (i.e., sound pressure), with the unit of Pa, rather than being represented by dB.

[0114] Step Four: Road surface identification.

[0115] Execute Step 3. After adding all the amplitude spectra of abnormal noise problems, abnormal noise detection can be carried out. To improve the efficiency of abnormal noise detection, it is necessary to identify in real time which road the vehicle is driving on to narrow down the matching range (quantity) of abnormal noise problems. In this step, road identification is to be achieved.

[0116] Set the vehicle dynamic abnormal noise detection APP in the vehicle head unit to the "abnormal noise problem detection" mode, and drive the vehicle on each road under the specified working conditions (such as specified vehicle speed, etc.). The accelerometer on the vehicle collects acceleration data in real time and sends it to the APP in the vehicle head unit through the vehicle ECU. The APP performs real-time spectrum analysis to obtain the road amplitude spectrum, denoted as real_time_roadspec. The so-called real-time collection and real-time spectrum analysis means that the data collected within a short period of time (such as 0.25 s) is sent to the APP for analysis; because this period of time is very short, it seems like real-time processing.

[0117] Calculate the correlation coefficients between real_time_roadspec and each road amplitude spectrum in the abnormal noise problem database respectively. Take the maximum value of these correlation coefficients, denoted as roadcorr_max. Define a constant, called the road correlation coefficient limit, denoted as roadcorr_limit, and its typical value is 0.75. If roadcorr_max ≥ roadcorr_limit, take the road corresponding to roadcorr_max, which is the road on which the vehicle is currently driving. In this way, road identification is achieved.

[0118] According to the above method, when the vehicle enters the starting point of Road A, the APP determines that the vehicle starts to drive on Road A. At this time, the APP starts to receive the data transmitted by the four microphones through the vehicle ECU;

[0119] When the vehicle exits the end point of Road A, the APP determines that the vehicle starts to drive on another road, Road B. At this time, the APP stops receiving the data transmitted by the four microphones through the vehicle ECU;

[0120] The APP will use the data of the four microphones received during this period to detect the abnormal noise problems occurring on Road A.

[0121] Naturally, after the APP stops receiving the data generated on Road A, it quickly starts to receive the data generated on Road B again because the APP detects that the vehicle starts to drive on another road, Road B.

[0122] As for the above-mentioned road surface recognition process, for the last road surface on the test lane (the detected road surfaces on the test lane are usually connected together, that is, one road surface is adjacent to another), when the vehicle exits its end point, the APP will not be able to find a matching road surface because the amplitude spectrum corresponding to the road surface after the last road surface (usually a flat road surface) is not in the abnormal noise problem database. In other words, the correlation coefficients between the amplitude spectra of all road surfaces in the abnormal noise problem database and the amplitude spectrum of the current road surface (the road surface after the last road surface on the test lane) are not high, that is, roadcorr_max < roadcorr_limit.

[0123] Therefore, for the last road surface on the test lane, the above-mentioned road surface recognition process is defective and cannot determine the moment when the vehicle exits the road surface. A patch must be applied and the following remedial measures should be taken: when roadcorr_max < roadcorr_limit, it is considered that the vehicle has exited the last road surface on the test lane. At this time, the data transmitted from the microphone can be stopped from being received.

[0124] Step Five: Abnormal Noise Detection

[0125] As described in Step Four, the APP can identify the moments when the vehicle enters and exits a certain road surface. Using the in-vehicle noise signals collected by the microphone during the period of entering and exiting, the APP can detect abnormal noise problems. Taking the Figure 2 abnormal noise problem with the road surface numbered 1 and the abnormal noise direction of 1 as an example for illustration.

[0126] Take the amplitude spectrum of the background noise collected by the left front microphone M1 (corresponding to the abnormal noise direction 1) as a row vector, denoted as A0, that is, A0 = (ab1…ab q …ab w ). Suppose there are n known abnormal noise problems in total. Take the amplitude spectrum of the i-th (1 ≤ i ≤ n) known abnormal noise problem as a row vector, denoted as origin_A i .

[0127] Set the vehicle dynamic abnormal noise detection APP in the vehicle to the "abnormal noise problem detection" mode. Calculate the amplitude spectrum of the noise data measured by the microphone corresponding to the abnormal noise direction 1 during the period when the vehicle to be detected enters and exits the road surface numbered 1 (naturally, the amplitude spectrum uses linear weighting, that is, unweighted;

[0128] the amplitude of the amplitude spectrum is represented by the actual physical quantity, that is, sound pressure, with the unit of Pa, rather than being represented by dB), which is called the amplitude spectrum of the abnormal noise problem to be detected, and take it as a row vector, denoted as origin_B.

[0129] Square each element in the row vector A0 to form a row vector, denoted as A0 2, which can be called the background noise power spectrum. Subtract A0 from origin_A i Perform the same operation to form the power spectra of each known abnormal noise problem, which is a row vector, denoted as origin_A i 2 (1 ≤ i ≤ n); perform the same operation on origin_B to form the power spectrum of the abnormal noise problem to be detected, which is a row vector, denoted as origin_B 2 . Obviously, origin_A i 2 and origin_B 2 are mixed with the background noise power spectrum and need to be removed.

[0130] Subtract A0 from origin_A i 2 , that is, subtract each element in origin_A 2 by the corresponding element in A0 i 2 to form a row vector, called the pure power spectrum of each known abnormal noise problem, denoted as A 2 (1 ≤ i ≤ n). i 2 (1 ≤ i ≤ n).

[0131] Similarly, subtract A0 from origin_B 2 , that is, subtract each element in origin_B 2 by the corresponding element in A0 2 to form a row vector, called the pure power spectrum of the abnormal noise problem to be detected, denoted as B 2 (1 ≤ i ≤ n). 2 .

[0132] Based on the principle of linear superposition of power, this application example constructs an abnormal noise detection model on the basis of A i 2 (1 ≤ i ≤ n) and B 2 .

[0133] Assume that in the abnormal noise problem to be detected, the occurrence state of the i-th (1 ≤ i ≤ n) known abnormal noise problem is a i , then the following abnormal noise detection model can be constructed.

[0134]

[0135] Among them, 1 ≤ i ≤ n; A i 2 is the pure power spectrum of the i-th known abnormal noise problem; B 2 is the pure power spectrum of the abnormal noise problem to be detected.

[0136] In Equation 1, A i(1 ≤ i ≤ n) and the value of B have been determined, only a i is an unknown quantity.

[0137] Furthermore, calculate a i to make Equation 1 hold.

[0138] where a i has only two values, 0 and 1;

[0139] 0 indicates that the corresponding known abnormal noise problem has not occurred, and 1 indicates that the corresponding known abnormal noise problem has occurred.

[0140] Construct a row vector sth = [a1,... a i ... a n , which is called the occurrence state vector.

[0141] Thus, Equation 1 can be rewritten in the following form.

[0142] sth × E_matrix = B 2 Equation 2

[0143] where can be called the power spectrum matrix.

[0144] As mentioned before, a i (1 ≤ i ≤ n) has 2 values, so sth has a total of 2 n kinds of values. Arrange these values in order to form a matrix, which is called the occurrence state matrix, denoted as sth_value, and each row in it is a value of the occurrence state vector sth. Let the elements of the first row of sth_value be all 0, that is, the first value of the occurrence intensity vector sth, and all its elements are 0. The k-th row data of the occurrence state matrix (the k-th value of sth, 1 ≤ k ≤ 2 n ), is denoted as sth_value(k). Substitute it into the left side of Equation 2, and the result can be called the combined power spectrum, denoted as E_syn(k), that is

[0145] E_syn(k) = sth_value(k) × E_matrix Equation 3

[0146] Calculate the correlation coefficient between E_syn(k) and B using the following formula.

[0147]

[0148] where γ(k) is the correlation coefficient, 2 ≤ k ≤ 2 n , which is used to describe the similarity relationship between E_syn(k) and B. The larger its value, the more similar the two are;

[0149] $E_{syn}(k,j)$ is the $j$-th element of the row vector $E_{syn}(k)$, where $2\leq k\leq2$ n , and $1\leq j\leq w$.

[0150] For Equation (4), traverse $k$ ($2\leq k\leq2$ n ), and obtain all the correlation coefficients $\gamma$ (which can be regarded as a vector);

[0151] Extract the maximum value from $\gamma$, and denote its serial number as $max\_val\_seq$, that is, the maximum correlation coefficient is $\gamma(max\_val\_seq)$, and its value is denoted as $\gamma\_max$;

[0152] The corresponding occurrence status vector is $sth\_value(max\_val\_seq)$, and its value is denoted as $sth\_value\_result$, which is the value to be finally obtained. Obviously, $\gamma\_max$ is a numerical value, and $sth\_value\_result$ is a row vector.

[0153] Define a constant, called the correlation coefficient limit value, denoted as $corr\_limit$, and its typical value is $0.75$.

[0154] If $\gamma\_max < corr\_limit$, it indicates that the similarity between $E_{syn}(max\_val\_seq)$ and $B$ is low. The reason may be that unknown abnormal sound problems are mixed in the abnormal sound problem to be detected, and the detection of the abnormal sound problem to be detected fails. At this time, it is necessary to first analyze the mixed unknown abnormal sound problem, then execute Step 3, and finally re - conduct the detection.

[0155] If $\gamma\_max\geq corr\_limit$, it indicates that the similarity between $E_{syn}(max\_val\_seq)$ and $B$ is high, and the detection of the abnormal sound problem to be detected is successful.

[0156] At this time, the values of each element in $sth\_value\_result$ describe the occurrence status of each known abnormal sound problem in the abnormal sound problem to be detected; when the element value is $0$, the corresponding abnormal sound problem does not occur; when the element value is $1$, the corresponding abnormal sound problem occurs. If the values of multiple elements are $1$, it means that multiple abnormal sound problems occur.

[0157] When traversing $k$ for Equation (4), the value range of $k$ is $2\leq k\leq2$ n .

[0158] It should be noted that since when $k = 1$, all elements of $sth\_value(1)$ are $0$, substituting $sth\_value(1)$ into Equation (3), all elements of $E_{syn}(1)$ are $0$, and substituting $E_{syn}(1)$ into Equation (4), an overflow ($0 / 0$ calculation error) will occur on the right - hand side of Equation (4). This situation should be avoided, so $k$ does not start from $1$ for value - taking.

[0159] The sth_value(1) indicates that the occurrence intensities of n known abnormal noise problems are all 0, that is, no known abnormal noise problems occur. That is, for Equation 1, sth_value(1) is the solution corresponding to the situation where no known abnormal noise problems occur. For the dynamic abnormal noise detection of the whole vehicle, this solution is the most common one, which means that the vast majority of mass-produced vehicles do not have any abnormal noise problems. Obviously, this solution cannot be obtained through Equations 1, 2, 3, and 4, and must be obtained through other means.

[0160] For sth_value(1), all its elements are 0. Substituting the values of these elements into Equation 1, the left side of Equation 1 is 0; if no unknown abnormal noise problems occur in the vehicle being detected, the right side of Equation 1 should also be equal to 0. It should be noted that both 0s here are zero vectors, not the scalar 0.

[0161] Therefore, a constant can be defined, called the minimum power limit, denoted as min_energy_limit, which is a positive number close to 0. When the modulus of B on the right side of Equation 1 2 |B 2 | < min_energy_limit, it is considered that the vehicle does not have any abnormal noise problems, corresponding to the solution sth_value(1).

[0162] Therefore, in this step, after obtaining B 2 , first directly calculate its modulus |B 2 |. If |B 2 | < min_energy_limit, it is determined that the vehicle does not have any abnormal noise problems; otherwise, execute the processes corresponding to Equations 1 - 4. Detect the abnormal noise problems under the road surface number 1, abnormal noise directions 2, 3, and 4 in the attached drawings of the specification according to the above process. Figure 2 Detect the abnormal noise problems on other road surfaces according to the above process.

[0163] According to the detection results, display information such as road description, abnormal noise direction, and abnormal noise description corresponding to the detection results on the in-vehicle screen for the inspectors to view.

[0164]

[0165] Step Six: Modify the Abnormal Noise Problem Database

[0166] The so-called modification of the abnormal noise problem database refers to performing various operations on the data in the abnormal noise problem database, such as adding road surface amplitude spectra and background noise amplitude spectra, adding abnormal noise problem amplitude spectra, deleting road surface amplitude spectra and background noise amplitude spectra, and deleting abnormal noise problem amplitude spectra. The addition of road surface amplitude spectra and background noise amplitude spectra, and the addition of abnormal noise problem amplitude spectra have been elaborated in Step Two and Step Three respectively. ​

[0167] When performing deletion operations, set the vehicle dynamic abnormal noise detection APP in the vehicle head unit to the "Delete Data" mode, and click the corresponding delete button to complete the deletion of the corresponding amplitude spectrum data. It should be noted that after deleting the amplitude spectrum of a certain abnormal noise problem (such as deleting the amplitude spectrum of the abnormal noise problem with road surface number 2, abnormal noise direction 3, and abnormal noise number 1 in Figure 2 ), the subsequent abnormal noise problem numbers must be updated (that is, change the subsequent problem number 2 to 1, and the subsequent problem number 3 to 2); when deleting the road surface amplitude spectrum data, the same processing should be done. Obviously, the amplitude spectrum of the abnormal noise problem numbered 0, that is, the background noise amplitude spectrum, cannot be deleted alone. It should be emphasized that when deleting the road surface amplitude spectrum data, all the abnormal noise problems to which it belongs will be deleted. For example, when deleting the road surface amplitude spectrum numbered 2 in Figure 2 , all the abnormal noise problems under the abnormal noise directions 1, 2, 3, and 4 below it will be deleted.

[0168] As for modifying the amplitude spectrum data in the abnormal noise problem database, since these amplitude spectrum data are generated by the APP's self-analysis of the data collected by the in-vehicle microphone and accelerometer, the impact of manual operation is very small, and there is basically no need to modify these data. To put it another way, even if modification is needed, deletion operations can be taken first, and then new addition operations can be taken to achieve it.

[0169] In the second aspect, as shown in Figures 1 to 2 and 4, an embodiment of the present application provides a vehicle dynamic abnormal noise detection device, which includes:

[0170] A database construction module, which is used to construct the data structure of the abnormal noise problem database;

[0171] A reference road surface monitoring module, which is used to drive a reference vehicle based on the initial state and of the same model as the vehicle to be tested on preset road surfaces of different road surface types at the real-time set speed of the driver, obtain the road surface amplitude spectrum and background noise amplitude spectrum corresponding to different preset road surfaces, and write them into the abnormal noise problem database;

[0172] A reference abnormal noise monitoring module, which is used to drive a reference vehicle based on multiple known abnormal noise problems that each have different single and known abnormal noise directions and of the same model as the vehicle to be tested on different preset road surfaces at the real-time set speed of the driver, obtain the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different preset road surfaces and different single known abnormal noise problems, and write them into the abnormal noise problem database;

[0173] A to-be-tested abnormal noise detection module, which is used to control the vehicle to be tested to drive on the target road surface at the real-time set speed of the driver to obtain the abnormal noise detection amplitude spectrum;

[0174] The abnormal noise analysis module to be measured is used to identify the road surface type of the target road surface, and perform abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database; wherein,

[0175] The data structure of the abnormal noise problem database is a three-layer structure. The first layer is the road surface data layer, including the road surface numbers, road surface descriptions, and road surface amplitude spectra of different road surface types. The second layer is the abnormal noise direction layer, including different abnormal noise directions of different abnormal noise problems. The third layer is the abnormal noise data layer, including the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different abnormal noise directions of different abnormal noise problems.

[0176] It should be noted that for the technical solution of the embodiment of the present application, the structural schematic diagram of the device involved is as shown in the Figure 1 description of the drawings of the specification. It is built into the whole vehicle and includes an in-vehicle microphone, a vehicle accelerometer, a vehicle ECU, and a car machine (including the whole vehicle dynamic abnormal noise detection APP). The above hardware is all powered by the vehicle battery.

[0177] The function of the in-vehicle microphone is to collect the in-vehicle noise signal and transmit the signal to the vehicle ECU. For various vehicles, the number of in-vehicle microphones is different. As the vehicle intelligence level gets higher and higher, generally speaking, the vehicle has at least four microphones, namely the driver's seat microphone, the passenger seat microphone, and two microphones on the left and right sides of the rear row, such as the Figure 1 microphones M1 - M4 in the description of the drawings of the specification.

[0178] The function of the vehicle accelerometer is to measure the vertical acceleration received by the wheels and send the measured data to the vehicle ECU. The accelerometer is a unidirectional accelerometer, and the measurement direction is vertical. It is installed on the left rear wheel hub bearing support. The reason for installing it on the rear wheel is that compared with the front wheel that undertakes the steering function, the force on the rear wheel is relatively simple.

[0179] The function of the vehicle ECU is to act as a bridge for transmitting data between the car machine, the in-vehicle microphone, and the vehicle accelerometer - the noise and acceleration data are transmitted to the car machine through the vehicle ECU.

[0180] The function of the car machine is to receive the noise and acceleration data transmitted by the vehicle ECU.

[0181] The function of the whole vehicle dynamic abnormal noise detection APP in the car machine is to perform spectral analysis on the noise and acceleration data to obtain the amplitude spectrum; add the road surface amplitude spectrum data and the background noise amplitude spectrum data; add the abnormal noise problem amplitude spectrum data; perform road surface identification; perform abnormal noise detection, and display the detection result on the car machine screen; perform data change of the abnormal noise problem database. The APP can be configured with multiple working modes according to actual needs.

[0182] Furthermore, the reference vehicle in the initial state refers to a normal vehicle without any abnormal noise problems.

[0183] In the embodiments of the present application, based on the relevant factors associated with the dynamic abnormal noise detection of the whole vehicle, an abnormal noise problem data structure is constructed, and abnormal noise detection is carried out by combining the road surface amplitude spectrum, the background noise amplitude spectrum, and the abnormal noise problem amplitude spectrum. The principle is simple, the operation is convenient, and the efficiency and reliability of abnormal noise detection are effectively improved.

[0184] Further, the abnormal noise analysis module to be measured is further configured to identify and obtain the corresponding abnormal noise problem number and abnormal noise problem description in the abnormal noise problem database based on the abnormal noise detection amplitude spectrum.

[0185] Further, the device is signal-connected to the vehicle ECU, the vehicle accelerometer, and the microphones configured at the left front, right front, left rear, and right rear in the vehicle;

[0186] Each of the microphones corresponds to a different abnormal noise direction.

[0187] Further, the abnormal noise analysis module to be measured is further configured to match the road surface amplitude spectrum obtained when controlling the vehicle to be measured to travel on the target road surface at the speed set by the driver in real time with the road surface amplitude spectra corresponding to different preset road surfaces, so as to perform road surface identification.

[0188] Further, the device further includes:

[0189] A road surface amplitude spectrum test module, which is configured to control the vehicle accelerometer to perform detection and obtain the road surface amplitude spectrum.

[0190] It should be noted that the whole vehicle dynamic abnormal noise detection device mentioned in the second aspect is similar to the technical principle of the whole vehicle dynamic abnormal noise detection method mentioned in the first aspect in terms of technical problems, technical means, and technical effects, and will not be elaborated here.

[0191] In the description of the present application, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application. Unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0192] It should be noted that in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

[0193] The above are only specific embodiments of the present application, which enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for detecting dynamic abnormal noises of a whole vehicle, characterized in that, The method includes the following steps: Construct the data structure of the abnormal noise problem database; Based on a reference vehicle in the initial state and of the same model as the vehicle to be tested, drive on preset road surfaces of different road surface types at the vehicle speed set by the driver in real time, obtain the road surface amplitude spectra and background noise amplitude spectra corresponding to different preset road surfaces, and write them into the abnormal noise problem database; Based on multiple reference vehicles of the same model as the vehicle to be tested, each having a different single known abnormal noise problem with a known abnormal noise orientation, drive on different preset road surfaces at the vehicle speed set by the driver in real time, obtain the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise amplitude spectra corresponding to different preset road surfaces and different single known abnormal noise problems, and write them into the abnormal noise problem database; Control the vehicle to be tested to drive on the target road surface at the vehicle speed set by the driver in real time, and obtain the abnormal noise detection amplitude spectrum; Identify the road surface type of the target road surface, and perform abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database; wherein, The data structure of the abnormal noise problem database is a three-layer structure. The first layer is the road surface data layer, including the road surface numbers, road surface descriptions, and road surface amplitude spectra of different road surface types. The second layer is the abnormal noise orientation layer, including different abnormal noise orientations of different abnormal noise problems. The third layer is the abnormal noise data layer, including the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise amplitude spectra corresponding to different abnormal noise orientations of different abnormal noise problems.

2. The vehicle dynamic abnormal noise detection method according to claim 1, characterized in that The performing abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database includes the following steps: Based on the abnormal noise detection amplitude spectrum, identify the corresponding abnormal noise problem number and abnormal noise problem description in the abnormal noise problem database.

3. The vehicle dynamic abnormal noise detection method according to claim 1, wherein: The method is based on the vehicle ECU, vehicle accelerometers, and microphones configured at the left front, right front, left rear, and right rear inside the vehicle; Each of the microphones corresponds to a different abnormal noise orientation.

4. The vehicle dynamic abnormal noise detection method according to claim 1, wherein, The identifying the road surface type of the target road surface includes the following steps: Based on the road surface amplitude spectrum obtained when controlling the vehicle to be tested to drive on the target road surface at the vehicle speed set by the driver in real time, match it with the road surface amplitude spectra corresponding to different preset road surfaces to perform road surface identification.

5. The vehicle dynamic abnormal noise detection method according to claim 3, wherein, The method includes a road surface amplitude spectrum test process, and the road surface amplitude spectrum test process includes the following steps: Perform a test based on the vehicle accelerometer to obtain the road surface amplitude spectrum.

6. A vehicle dynamic abnormal noise detection device, characterized in that, The device includes: A database construction module for constructing the data structure of the abnormal noise problem database; A reference road surface monitoring module for, based on a reference vehicle in the initial state and of the same model as the vehicle to be tested, driving on preset road surfaces of different road surface types at the vehicle speed set by the driver in real time, obtaining the road surface amplitude spectra and background noise amplitude spectra corresponding to different preset road surfaces, and writing them into the abnormal noise problem database; Referring to the abnormal noise monitoring module, which is used to obtain the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different preset road surfaces and different single known abnormal noise problems based on multiple reference vehicles that each have a different single known abnormal noise problem with a known abnormal noise orientation and are of the same vehicle model as the vehicle to be tested. The reference vehicles are driven on different preset road surfaces at the real-time speed set by the driver and the obtained data is written into the abnormal noise problem database; The abnormal noise detection module to be tested, which is used to control the vehicle to be tested to drive on the target road surface at the real-time speed set by the driver to obtain the abnormal noise detection amplitude spectrum; The abnormal noise analysis module to be tested, which is used to identify the road surface type of the target road surface and perform abnormal noise analysis based on the abnormal noise detection amplitude spectrum and the abnormal noise problem database; wherein, The data structure of the abnormal noise problem database is a three-layer structure. The first layer is the road surface data layer, including the road surface numbers, road surface descriptions, and road surface amplitude spectra of different road surface types. The second layer is the abnormal noise orientation layer, including different abnormal noise orientations of different abnormal noise problems. The third layer is the abnormal noise data layer, including the abnormal noise problem numbers, abnormal noise problem descriptions, and abnormal noise problem amplitude spectra corresponding to different abnormal noise orientations of different abnormal noise problems.

7. The vehicle dynamic abnormal noise detection device according to claim 6, characterized in that: The abnormal noise analysis module to be tested is further used to identify the corresponding abnormal noise problem number and abnormal noise problem description in the abnormal noise problem database based on the abnormal noise detection amplitude spectrum.

8. The vehicle dynamic abnormal noise detection device according to claim 6, characterized in that: The device is signal-connected to the vehicle ECU, the vehicle accelerometer, and the microphones configured in the left front, right front, left rear, and right rear of the vehicle; Each of the microphones corresponds to a different abnormal noise orientation.

9. The vehicle dynamic abnormal noise detection device according to claim 6, characterized in that: The abnormal noise analysis module to be tested is further used to match the road surface amplitude spectrum obtained when controlling the vehicle to be tested to drive on the target road surface at the real-time speed set by the driver with the road surface amplitude spectra corresponding to different preset road surfaces to perform road surface identification.

10. The vehicle dynamic abnormal noise detection device according to claim 8, characterized in that The device further includes: The road surface amplitude spectrum test module, which is used to control the vehicle accelerometer to perform detection to obtain the road surface amplitude spectrum.

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