Method, device and equipment for predicting abnormal sound in vehicle and storage medium

Abstract

The invention discloses a vehicle interior abnormal sound prediction method, device and equipment and a storage medium, and the method comprises the steps: testing a part based on a preset environment cabin, and obtaining the temperature change information of the part; on the basis of the temperature change information, relative deformation quantity of the part is calculated, and deformation speed information of the part is obtained through the relative deformation quantity; and inputting the obtained temperature, pressure, humidity and deformation speed information of the parts into a neural network model to output a vehicle interior abnormal sound risk prediction result. Friction abnormal sound generated by thermal expansion and cold contraction of vehicle parts can be avoided, and the abnormal sound risk of the vehicle can be predicted.

Description

Technical Field

[0001] The present invention relates to the field of vehicle monitoring technology, and in particular to a method, device, equipment and storage medium for predicting abnormal noise inside a vehicle. Background Art

[0002] Depending on the area and environment of use, the temperature inside the vehicle can be lower than -30℃ and higher than 60℃. To ensure the comfort of the driver and passengers, the cooling, heating and ventilation functions of the air conditioner are widely used. The automobile air conditioning system consists of a cooling system, a heating system, a ventilation system, a control system, and an air purification system. The ventilation system consists of an intake mode damper, a blower, a mixed gas mode damper, an air flow mode damper, an air duct, etc., and is generally installed inside the main instrument.

[0003] When the vehicle air conditioner is in use, the temperature and humidity in the vehicle change rapidly, and the temperature difference can reach 30°C or even higher in a short period of time. The air conditioning and ventilation system is often made of materials such as ABS, HIPS, and PP. When the temperature changes rapidly, the thermal expansion and contraction changes are very obvious, and the parts will deform greatly. In particular, the air ducts and air outlets directly transport hot and cold air, and their deformation will be significantly faster than the parts far away from the hot and cold air. The air ducts and air outlets are assembled inside or on the surface of the instrument, forming direct contact. When they are deformed due to thermal expansion and contraction, they will be relatively deformed with the surrounding parts, resulting in friction noise. When the temperature in the car stabilizes, the temperature difference between the air ducts, air outlets, and parts such as instruments is reduced, and friction noise will be generated again due to thermal expansion and contraction, until the temperatures of the two are close and there is no relative deformation, and the noise disappears.

[0004] Therefore, how to avoid abnormal friction and noise caused by thermal expansion and contraction of vehicle parts is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The main purpose of the present invention is to provide a method, device, equipment and storage medium for predicting abnormal noise inside a vehicle, which can avoid abnormal noise caused by friction of vehicle parts due to thermal expansion and contraction, and can predict the risk of abnormal noise in the vehicle.

[0006] In a first aspect, the present application provides a method for predicting abnormal noise inside a vehicle, wherein the method comprises the steps of:

[0007] Test components based on the preset environmental chamber to obtain temperature change information of components;

[0008] Based on the temperature change information, the relative deformation amount of the component is calculated, and the deformation speed information of the component is obtained through the relative deformation amount;

[0009] The acquired temperature, pressure, humidity and deformation speed information of the components are input into the neural network model to output the prediction result of the abnormal noise risk inside the vehicle.

[0010] In combination with the first aspect above, as an optional implementation method, the material of the component is calibrated at different temperatures, pressures, humidity and speeds to generate a database;

[0011] Construct a neural network model with temperature, pressure, humidity and speed as input and abnormal noise risk as output;

[0012] The temperature, pressure, humidity and deformation speed information of the components are obtained and input into the neural network model, and the temperature, pressure, humidity and deformation speed information are compared with the calibration information in the database using the neural network model to predict the risk of abnormal noise inside the vehicle.

[0013] In combination with the first aspect above, as an optional implementation method, the temperature change condition inside the vehicle is determined, wherein the temperature change condition is the temperature difference between the highest temperature and the lowest temperature inside the vehicle.

[0014] In combination with the first aspect above, as an optional implementation method, component measurement points are set, and thermocouples are arranged in the area of ​​the component to be measured;

[0015] The temperature of the environmental chamber is set based on the temperature change in the vehicle, and the components are tested using the environmental chamber to record the temperature change data of each measuring point of the components over time.

[0016] In combination with the first aspect above, as an optional implementation method, the relative deformation of the component is calculated according to the formula: Δl=l0ΔTα, where l0 is the initial length of the component, ΔT is the temperature difference, and α is the thermal expansion coefficient;

[0017] By calculating the derivative of the relative deformation of the component, the deformation speed information of the component is obtained.

[0018] In combination with the above-mentioned first aspect, as an optional implementation method, different abnormal sound risk levels are set according to needs, wherein the abnormal sound risk levels include a first level, a second level and a third level, and the first risk level is lower than the second risk level and lower than the third risk level.

[0019] In a second aspect, the present application provides a vehicle interior abnormal noise prediction device, the device comprising:

[0020] A test module is used to test components based on a preset environmental chamber to obtain temperature change information of the components;

[0021] A calculation module, which is used to calculate the relative deformation amount of the component based on the temperature change information, and obtain the deformation speed information of the component through the relative deformation amount;

[0022] The processing module is used to input the acquired temperature, pressure, humidity and deformation speed information of the components into the neural network model to output the prediction result of the abnormal noise risk inside the vehicle.

[0023] In combination with the above second aspect, as an optional implementation method, the processing module is further used to calibrate the material of the component under different temperatures, pressures, humidity and speeds to generate a database;

[0024] Construct a neural network model with temperature, pressure, humidity and speed as input and abnormal noise risk as output;

[0025] The temperature, pressure, humidity and deformation speed information of the components are obtained and input into the neural network model, and the temperature, pressure, humidity and deformation speed information are compared with the calibration information in the database using the neural network model to predict the risk of abnormal noise inside the vehicle.

[0026] In a third aspect, the present application further provides an electronic device, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method described in any one of the first aspects is implemented.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium storing computer program instructions, which, when executed by a computer, enables the computer to execute any of the methods described in the first aspect.

[0028] The present application provides a method, device, equipment and storage medium for predicting abnormal noise inside a vehicle, wherein the method comprises the steps of: testing components based on a preset environmental chamber to obtain temperature change information of the components; calculating the relative deformation of the components based on the temperature change information, and obtaining the deformation speed information of the components through the relative deformation; inputting the obtained temperature, pressure, humidity and deformation speed information of the components into a neural network model to output the prediction result of the abnormal noise risk inside the vehicle. The present application can avoid the friction abnormal noise caused by thermal expansion and contraction of vehicle components, and can predict the risk of abnormal noise in the vehicle.

[0029] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0031] Figure 1 A flow chart of a method for predicting abnormal noise inside a vehicle provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of a vehicle interior abnormal noise prediction device provided in an embodiment of the present application;

[0033] Figure 3 A schematic diagram of an electronic device provided in an embodiment of the present application;

[0034] Figure 4 A schematic diagram of a computer-readable program medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0036] Furthermore, the drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the blocks shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities.

[0037] The embodiments of the present application provide a method, device, equipment and storage medium for predicting abnormal noise inside a vehicle, which can avoid abnormal noise caused by friction of vehicle parts due to thermal expansion and contraction, and can predict the risk of abnormal noise in the vehicle.

[0038] In order to achieve the above technical effects, the general idea of ​​this application is as follows:

[0039] A method for predicting abnormal noise inside a vehicle, the method comprising the steps of:

[0040] S101: Testing components based on a preset environmental chamber to obtain temperature change information of the components.

[0041] S102: Based on the temperature change information, the relative deformation amount of the component is calculated, and deformation speed information of the component is obtained through the relative deformation amount.

[0042] S103: Input the acquired temperature, pressure, humidity and deformation speed information of the components into a neural network model to output a prediction result of the abnormal noise risk inside the vehicle.

[0043] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0044] Reference Figure 1 , Figure 1 FIG. 1 is a flow chart of a method for predicting abnormal noise inside a vehicle provided by the present invention. Figure 1 As shown, the method comprises the steps of:

[0045] Step S101: Testing components based on a preset environmental chamber to obtain temperature change information of the components.

[0046] Before testing components based on a preset environmental chamber, including:

[0047] The temperature variation condition in the vehicle is determined, wherein the temperature variation condition is the temperature difference between the highest temperature and the lowest temperature in the vehicle.

[0048] Set up component measurement points and arrange thermocouples in the areas where the components are to be measured;

[0049] The temperature of the environmental chamber is set based on the temperature change in the vehicle, and the components are tested using the environmental chamber to record the temperature change data of each measuring point of the components over time.

[0050] For easy understanding, let's take an example. When a vehicle is in use, the temperature inside the car can vary from -30°C to 70°C depending on the environment, and the parts inside the car are basically similar to this problem. When the air conditioner is in use, the temperature inside the car will be adjusted to about 20°C in a short period of time, and the parts inside the car will quickly heat up or cool down to this temperature. At this time, the maximum temperature difference can reach ΔT = |70-30| = 50°C. It should be noted that ΔT can be increased or decreased according to demand. The subsequent explanation is based on cooling from an ambient temperature of 70°C to 20°C. It should be explained that determining the temperature change inside the car is used to set up an environmental chamber to test components in the same temperature scenario.

[0051] It should be explained that the abnormal noise caused by the temperature change in the car is mainly caused by the air conditioning heating in low temperature environment and the air conditioning cooling in high temperature environment. Due to the effect of the air conditioning, the parts in the car, especially the air outlet area of ​​the air conditioning, will produce obvious relative deformation when the temperature changes rapidly, thus producing abnormal noise.

[0052] Component environmental chamber test: ① Make the components to be tested, such as the air outlet of the main instrument, whose material is PC-ABS (Acrylonitrile Butadiene Styrene). The air outlet parts can be made into samples that meet the design status through rapid printing and other technologies for subsequent testing.

[0053] ②Put the air outlet parts into the environmental chamber, set the temperature to 70℃, and let it stand for a while to ensure that the temperature of the air outlet parts is the same as the environment.

[0054] ③ Arrange thermocouples at the outlet parts to be tested and record the temperature changes.

[0055] ④ According to the maximum cold air flow rate, cold air temperature and flow rate of the vehicle's air outlet, pass the cold air through the air outlet components in ①.

[0056] ⑤Record the temperature change data of each measuring point over time t.

[0057] Step S102: Based on the temperature change information, the relative deformation amount of the component is calculated, and the deformation speed information of the component is obtained through the relative deformation amount.

[0058] Specifically, the relative deformation of the component is calculated according to the formula: Δl=l0ΔTα, where l0 is the initial length of the component, ΔT is the temperature difference, and α is the thermal expansion coefficient; by taking the derivative of the relative deformation of the calculated component, the deformation speed information of the component is obtained.

[0059] For easy understanding, examples are given below: (1) Calculation of relative deformation of parts and expansion length of ABS material due to temperature difference

[0060] Δl=l0ΔTα

[0061] In the formula, l0 is the initial length of the component, which can be selected as the distance between the two fixed points of the air outlet component, ΔT is the temperature difference, and α is the thermal expansion coefficient, which can be taken as 7.38e-5 for PC-ABS material.

[0062] Calculate the relative deformation of components at each time point within time t according to the above formula.

[0063] (2) The (time variable) velocity curve is obtained by differentiating the deformation.

[0064] It can be understood that after obtaining the temperature change information of the parts, the relative deformation of the parts is calculated by using the temperature change information of the parts, and the deformation speed information of the parts is obtained by taking the derivative of the relative deformation. That is, first determine the temperature change condition in the vehicle, then test the temperature change curve of the parts material under the condition through the environmental chamber, and then calculate the relative deformation caused by thermal expansion and contraction between the parts, and calculate the relative sliding speed between the parts.

[0065] Step S103: Input the acquired temperature, pressure, humidity and deformation speed information of the components into the neural network model to output the prediction result of the abnormal noise risk inside the vehicle.

[0066] Specifically, the material of the parts is calibrated at different temperatures, pressures, humidity and speeds to generate a database; a neural network model is constructed with temperature, pressure, humidity and speed as input and abnormal noise risk as output; the temperature, pressure, humidity and deformation speed information of the parts are obtained and input into the neural network model, and the temperature, pressure, humidity and deformation speed information are compared with the calibration information in the database using the neural network model to predict the risk of abnormal noise inside the vehicle.

[0067] For ease of understanding, an example is given to illustrate the establishment of a database: the risk of abnormal noise of PC-ABS materials under different conditions of temperature, pressure, speed and humidity is measured using test equipment to form a database, and a neural network model is constructed with temperature, pressure, speed and humidity as input and abnormal noise risk as output.

[0068] The temperature, pressure, and humidity of components are obtained through sensors and combined with the deformation speed information of the components as input and input into the neural network model. The neural network model is used to compare the temperature, pressure, humidity and deformation speed information with the calibration information in the database to predict the risk of abnormal noise inside the vehicle.

[0069] Among them, the number of input layer nodes of the neural network model is 4, the number of output layer nodes is 1, and the number of intermediate layer nodes can be calculated according to the formula: Among them, is the number of input layer nodes; l is the number of output layer nodes; a is a constant of 1-10, and m can be 8.

[0070] In one embodiment, different abnormal noise risk levels are set according to requirements, wherein the abnormal noise risk levels include a first level, a second level, and a third level, and the first risk level is lower than the second risk level and lower than the third risk level.

[0071] It can be understood that the number of input layers can be selected as needed; the risk of abnormal sound can be set to three levels: no risk, low risk and high risk.

[0072] In summary, the present application tests the components based on a preset environmental chamber to obtain the temperature change information of the components; based on the temperature change information, the relative deformation of the components is calculated, and the deformation speed information of the components is obtained through the relative deformation; the obtained temperature, pressure, humidity and deformation speed information of the components are input into the neural network model to output the prediction result of the abnormal noise risk inside the vehicle. The present application can avoid the friction noise caused by thermal expansion and contraction of vehicle components, and can predict the risk of abnormal noise in the vehicle.

[0073] It is understandable that the temperature change condition in the car is first determined, and then the temperature change curve of the component material under the condition is tested in the environmental chamber, and then the relative deformation caused by thermal expansion and contraction between the components is calculated, and the relative sliding speed between the components is calculated, and finally the calculated data is compared with the database to predict the risk of abnormal noise. Based on this method, engineering and technical personnel can predict the risk of abnormal noise caused by temperature changes in the car during the project development stage, avoiding additional cost investment caused by later design changes.

[0074] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a vehicle interior abnormal noise prediction device provided by the present invention, such as Figure 2 As shown, the device comprises:

[0075] Testing module 201: It is used to test components based on a preset environmental chamber to obtain temperature change information of the components.

[0076] Calculation module 202: used to calculate the relative deformation amount of the component based on the temperature change information, and obtain the deformation speed information of the component through the relative deformation amount.

[0077] Processing module 203: It is used to input the acquired temperature, pressure, humidity and deformation speed information of the components into the neural network model to output the prediction result of the abnormal noise risk inside the vehicle.

[0078] Furthermore, in a possible implementation manner, the processing module is also used to calibrate the material of the component under different temperatures, pressures, humidity and speeds to generate a database;

[0079] Construct a neural network model with temperature, pressure, humidity and speed as input and abnormal noise risk as output;

[0080] The temperature, pressure, humidity and deformation speed information of the components are obtained and input into the neural network model, and the temperature, pressure, humidity and deformation speed information are compared with the calibration information in the database using the neural network model to predict the risk of abnormal noise inside the vehicle.

[0081] Furthermore, in a possible implementation manner, the processing module is also used to determine a temperature change condition inside the vehicle, wherein the temperature change condition is a temperature difference between a maximum temperature and a minimum temperature inside the vehicle.

[0082] Furthermore, in a possible implementation manner, the test module is also used to set component measurement points and arrange thermocouples in the area of ​​the component to be measured;

[0083] The temperature of the environmental chamber is set based on the temperature change in the vehicle, and the components are tested using the environmental chamber to record the temperature change data of each measuring point of the components over time.

[0084] Further, in a possible implementation manner, the calculation module is further used to calculate the relative deformation of the component according to the formula: Δl=l0ΔTα, where l0 is the initial length of the component, ΔT is the temperature difference, and α is the thermal expansion coefficient;

[0085] By calculating the derivative of the relative deformation of the component, the deformation speed information of the component is obtained.

[0086] Furthermore, in a possible implementation, the processing module is also used to set different abnormal sound risk levels according to requirements, wherein the abnormal sound risk levels include a first level, a second level and a third level, and the first risk level is lower than the second risk level and lower than the third risk level.

[0087] Furthermore, in a possible implementation mode,

[0088] Refer to the following Figure 3 The electronic device 300 according to this embodiment of the present invention will be described. Figure 3 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0089] like Figure 3 As shown, the electronic device 300 is in the form of a general computing device. The components of the electronic device 300 may include but are not limited to: at least one processing unit 310, at least one storage unit 320, and a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310).

[0090] The storage unit stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

[0091] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 321 and / or a cache memory unit 322 , and may further include a read-only memory unit (ROM) 323 .

[0092] The storage unit 320 may also include a program / utility 324 having a set (at least one) of program modules 325, such program modules 325 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0093] Bus 330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0094] The electronic device 300 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 300, and / or communicate with any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 350. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 360. As shown, the network adapter 360 communicates with other modules of the electronic device 300 via a bus 330. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0095] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0096] According to the solution of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present invention described in the above "Exemplary Method" section of the present specification.

[0097] refer to Figure 4 As shown, a program product 400 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0098] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0099] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0100] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0101] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0102] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0103] In summary, the present application provides a method, device, equipment and storage medium for predicting abnormal noise inside a vehicle, wherein the method includes the steps of: testing components based on a preset environmental chamber to obtain temperature change information of the components; calculating the relative deformation of the components based on the temperature change information, and obtaining the deformation speed information of the components through the relative deformation; inputting the obtained temperature, pressure, humidity and deformation speed information of the components into a neural network model to output the prediction result of the abnormal noise risk inside the vehicle. The present application can avoid the friction noise caused by thermal expansion and contraction of vehicle components, and can predict the risk of abnormal noise in the vehicle.

[0104] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent 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 conform to the widest range consistent with the principles and novel features applied for herein.

[0105] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

Claims

1. A method for predicting abnormal noise inside a vehicle, characterized in that: include: Test components based on the preset environmental chamber to obtain temperature change information of components; Based on the temperature change information, the relative deformation amount of the component is calculated, and the deformation speed information of the component is obtained through the relative deformation amount; The acquired temperature, pressure, humidity and deformation speed information of the components are input into the neural network model to output the prediction result of the abnormal noise risk inside the vehicle.

2. The method according to claim 1, characterized in that The obtained temperature, humidity, pressure and deformation speed information of the components are input into the neural network model to output the risk prediction result of abnormal noise inside the vehicle, including: Calibrate the material of the parts at different temperatures, pressures, humidity and speeds to generate a database; Construct a neural network model with temperature, pressure, humidity and speed as input and abnormal noise risk as output; The temperature, pressure, humidity and deformation speed information of the components are obtained and input into the neural network model, and the temperature, pressure, humidity and deformation speed information are compared with the calibration information in the database using the neural network model to predict the risk of abnormal noise inside the vehicle.

3. The method according to claim 1, characterized in that Before testing the components based on the preset environmental chamber, the following steps are included: The temperature variation condition in the vehicle is determined, wherein the temperature variation condition is the temperature difference between the highest temperature and the lowest temperature in the vehicle.

4. The method according to claim 1, characterized in that The testing of the components based on the preset environmental chamber to obtain the temperature change information of the components includes: Set up component measurement points and arrange thermocouples in the areas where the components are to be measured; The temperature of the environmental chamber is set based on the temperature change in the vehicle, and the components are tested using the environmental chamber to record the temperature change data of each measuring point of the components over time.

5. The method according to claim 1, characterized in that: The step of calculating the relative deformation amount of the component according to the temperature change information, and obtaining the deformation speed information of the component through the relative deformation amount includes: According to the formula: Δl = l0ΔTα, calculate the relative deformation of the component, where l0 is the initial length of the component, ΔT is the temperature difference, and α is the thermal expansion coefficient; By calculating the derivative of the relative deformation of the component, the deformation speed information of the component is obtained.

6. The method according to claim 1, characterized in that Also includes: Different abnormal noise risk levels are set according to requirements, wherein the abnormal noise risk levels include a first level, a second level and a third level, and the first risk level is lower than the second risk level and lower than the third risk level.

7. A vehicle interior abnormal noise prediction device, characterized in that: include: A test module is used to test components based on a preset environmental chamber to obtain temperature change information of the components; A calculation module, which is used to calculate the relative deformation amount of the component based on the temperature change information, and obtain the deformation speed information of the component through the relative deformation amount; The processing module is used to input the acquired temperature, pressure, humidity and deformation speed information of the components into the neural network model to output the prediction result of the abnormal noise risk inside the vehicle.

8. The device according to claim 7, characterized in that include: The processing module is also used to calibrate the material of the parts under different temperatures, pressures, humidity and speeds to generate a database; Construct a neural network model with temperature, pressure, humidity and speed as input and abnormal noise risk as output; The temperature, pressure, humidity and deformation speed information of the components are obtained and input into the neural network model, and the temperature, pressure, humidity and deformation speed information are compared with the calibration information in the database using the neural network model to predict the risk of abnormal noise inside the vehicle.

9. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: The computer program instructions are stored therein, and when the computer program instructions are executed by a computer, the computer is caused to execute the method according to any one of claims 1 to 6.

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