Air conditioning noise prediction method and device of vehicle, electronic equipment and storage medium

Abstract

The application relates to the technical field of vehicle noise control, in particular to an air conditioner noise prediction method and device of a vehicle, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring air conditioner noise data of each gear of a vehicle-mounted air conditioner; generating a relationship curve between each gear noise value and the rotating speed of a blower fan according to the air conditioner noise data of each gear; and predicting the air conditioner noise corresponding to each rotating speed of the blower fan in the whole vehicle state based on the relationship curve. Therefore, the problems that the overall test period is relatively long and the test cost is relatively high due to the fact that the related art depends on a large number of repeated tests to determine the air conditioner noise of the vehicle, so that the rapid evaluation and optimization design of the air conditioner noise characteristics cannot be efficiently completed are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle noise control technology, and in particular to a method, device, electronic device and storage medium for predicting air conditioning noise in a vehicle. Background Technology

[0002] In related technologies, vehicle air conditioning noise testing is usually performed by repeating the test at each speed setting to identify whether there is abnormal noise in the air conditioning and to carry out targeted control optimization accordingly.

[0003] However, the relevant technologies rely on a large number of repetitive tests, resulting in a long overall testing cycle and high testing costs. This makes it difficult to efficiently complete the rapid evaluation and optimization design of vehicle air conditioning characteristics, and is also not conducive to shortening the vehicle development cycle. Summary of the Invention

[0004] This application provides a method, device, electronic device, and storage medium for predicting air conditioning noise in vehicles, in order to solve the problem that in related technologies, due to the reliance on a large number of repeated tests, the overall test cycle is long and the test cost is high, making it difficult to efficiently complete the rapid evaluation and optimization design of air conditioning noise characteristics.

[0005] The first aspect of this application provides a method for predicting air conditioning noise in a vehicle, comprising the following steps: acquiring air conditioning noise data at each speed setting of the vehicle air conditioner; generating a relationship curve between the noise value at each speed setting and the blower speed based on the air conditioning noise data at each speed setting; and predicting the air conditioning noise corresponding to each blower speed in the vehicle under the overall vehicle condition based on the relationship curve.

[0006] Using the above technical means, a relationship curve between the noise value of each air conditioner and the blower speed is generated based on the air conditioner noise data of each gear. Based on the relationship curve, the air conditioner noise corresponding to each blower speed under the whole vehicle condition is predicted, and a quantitative correlation between air conditioner noise and blower speed is established. At the same time, based on this relationship curve, the number of tests can be effectively reduced, the development and verification cycle of air conditioner noise can be significantly shortened, the cost of testing manpower and equipment can be reduced, and the noise performance of the entire speed range can be quickly covered, providing more complete data support for design optimization.

[0007] Optionally, in one embodiment of this application, generating a relationship curve between noise values ​​and blower speed for each air conditioner speed setting based on the noise data for each speed setting includes: generating a noise curve with blower speed on the horizontal axis and air conditioner noise value on the vertical axis based on the noise data for each speed setting; calculating the trend line function and correlation coefficient of the noise curve; and generating a relationship curve based on the trend line function and correlation coefficient.

[0008] By using the above technical means, a relationship curve can be generated based on the trend line function and correlation coefficient of the noise curve. This can accurately describe the variation law of air conditioner noise with blower speed and realize the prediction of air conditioner noise at any blower speed. At the same time, the correlation coefficient can be used to determine the degree of agreement between the measured data and the fitted curve, thereby evaluating the reliability and prediction accuracy of the established relationship curve.

[0009] Optionally, in one embodiment of this application, obtaining air conditioning noise data at each level of the vehicle air conditioner includes: using the air conditioning control buttons in the vehicle to control the vehicle air conditioner at each level of the coldest internal circulation mode, to test each level and continuously for a preset duration, so as to obtain the noise signal perceived by the user and the vibration signal of the blower housing; and generating air conditioning noise data at each level based on the noise signal and the vibration signal.

[0010] By using the above technical means, noise signals and vibration signals of the blower casing are obtained at each setting of the coldest internal circulation mode, thereby generating air conditioning noise data for each setting. This data can more realistically reflect the acoustic characteristics of the air conditioning system itself, and the generated data is typical and representative of engineering, thus providing a reliable data foundation for subsequent noise assessment, anomaly identification, and control optimization.

[0011] Optionally, in one embodiment of this application, generating air conditioner noise data for each setting based on noise signals and vibration signals includes: calculating the A-weighted sound pressure level of the noise based on the noise signals; determining the rotational frequency of the blower motor based on the vibration signals to calculate the motor speed of the blower; and generating air conditioner noise data for each setting based on the A-weighted sound pressure level and the motor speed.

[0012] By using the above technical means, air conditioner noise data for each speed setting can be generated based on A-weighted sound pressure level and motor speed. This can comprehensively characterize air conditioner noise data, that is, it is not limited to acoustic data, but can also be linked to the key operating parameter of motor speed. This helps to distinguish noise sources and provides more accurate data support for targeted optimization.

[0013] Optionally, in one embodiment of this application, the vehicle air conditioning noise prediction method further includes: generating air conditioning calibration data that meets preset air conditioning noise requirements based on the air conditioning noise corresponding to each blower speed in the whole vehicle state; and generating air conditioning comfort test data of the vehicle air conditioner based on the air conditioning calibration data.

[0014] By using the above technical means, air conditioning comfort test data of vehicle air conditioning can be generated based on air conditioning calibration data. This data can be used to develop standard air conditioning comfort evaluation methods, thereby effectively evaluating the thermal comfort performance of the air conditioning system, avoiding multiple tests due to repeated real vehicle testing, significantly shortening the development cycle and reducing testing costs.

[0015] A second aspect of this application provides a vehicle air conditioning noise prediction device, comprising: an acquisition module for acquiring air conditioning noise data at each speed of the vehicle air conditioner; a generation module for generating a relationship curve between the noise value at each speed and the blower speed based on the air conditioning noise data at each speed; and a prediction module for predicting the air conditioning noise corresponding to each blower speed in the vehicle under the overall vehicle condition based on the relationship curve.

[0016] Optionally, in one embodiment of this application, the generation module includes: a first generation unit, used to generate a noise curve with the blower speed on the horizontal axis and the air conditioner noise value on the vertical axis based on the air conditioner noise data of each gear; a calculation unit, used to calculate the trend line function and correlation coefficient of the noise curve; and a second generation unit, used to generate a relationship curve based on the trend line function and correlation coefficient.

[0017] Optionally, in one embodiment of this application, the acquisition module includes: a testing unit, used to control the vehicle air conditioner at various levels of the coldest internal circulation mode using the air conditioning control buttons in the vehicle, to test each level for a preset duration, so as to obtain the noise signal perceived by the user and the vibration signal of the blower housing; and a third generation unit, used to generate air conditioning noise data for each level based on the noise signal and vibration signal.

[0018] Optionally, in one embodiment of this application, the third generation unit includes: a first calculation subunit, used to calculate the A-weighted sound pressure level of noise based on the noise signal; a second calculation subunit, used to determine the rotational frequency of the blower motor based on the vibration signal to calculate the motor speed of the blower; and a generation subunit, used to generate air conditioning noise data for each gear according to the A-weighted sound pressure level and the motor speed.

[0019] Optionally, in one embodiment of this application, the vehicle air conditioning noise prediction device further includes: a calibration module, used to generate air conditioning calibration data that meets preset air conditioning noise requirements based on the air conditioning noise corresponding to each blower speed in the whole vehicle state; and a test module, used to generate air conditioning comfort test data of the vehicle air conditioning based on the air conditioning calibration data.

[0020] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle air conditioning noise prediction method as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting air conditioning noise in a vehicle.

[0022] A fifth aspect of this application provides a vehicle including the above-described vehicle air conditioning noise prediction device, or electronic device, or computer-readable storage medium for implementing the above-described vehicle air conditioning noise prediction method.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a vehicle air conditioning noise prediction method according to an embodiment of this application; Figure 2 This is a block diagram of a vehicle air conditioning noise prediction device according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0025] Figure label: 10-Vehicle air conditioning noise prediction device; 100-Acquisition module, 200-Generation module, 300-Prediction module; 301-Memory, 302-Processor, 303-Communication interface. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0027] The following description, with reference to the accompanying drawings, describes a method, apparatus, electronic device, and storage medium for predicting air conditioning noise in a vehicle according to embodiments of this application.

[0028] First, the technical issues involved in this application will be explained.

[0029] In related technologies, the factors affecting air conditioning noise at various speeds mainly include the noise of the air conditioning housing itself, the sound transmission loss of the air duct, the structure of the air outlet, and the speed of the blower. These factors are interdependent and jointly determine the overall noise performance of the vehicle's air conditioning system. Among these, the structure of the air conditioning housing, the structure of the air duct, and the form of the air outlet are mostly structural parameters that are difficult to adjust and optimize in the later stages of vehicle development. However, the blower speed, as a controllable operating parameter, can be adjusted through control strategies and has a direct impact on airflow excitation and aerodynamic noise, thus becoming an important means of regulating air conditioning noise.

[0030] Related technologies typically involve repeated noise tests at multiple speed settings. By collecting noise signals under different operating conditions, they aim to identify whether abnormal air conditioning noise exists at the current speed setting and optimize noise control accordingly. However, this method relies on numerous repeated tests, resulting in long testing cycles, low efficiency, and difficulty in comprehensively reflecting the noise variation patterns under the coupled effects of multiple factors. This limits the accuracy and efficiency of air conditioning noise optimization.

[0031] To address at least one of the aforementioned technical problems, this application provides a method for predicting air conditioning noise in vehicles, aiming to predict the air conditioning noise at each blower speed, providing support for rapid evaluation and design iteration of air conditioning noise performance, avoiding a large number of redundant and repetitive tests, thereby shortening the development cycle and reducing testing costs.

[0032] Specifically, Figure 1 This is a flowchart illustrating a method for predicting air conditioning noise in a vehicle, as provided in an embodiment of this application.

[0033] like Figure 1 As shown, the method for predicting air conditioning noise in this vehicle includes the following steps: In step S101, the noise data of the vehicle air conditioner at each speed setting is obtained.

[0034] Among them, the air conditioning noise data at each speed refers to the acoustic data collected at different air conditioning speeds to characterize the noise level and characteristics of the air conditioning system, usually expressed as A-weighted sound pressure level; it may also include the correlation information corresponding to operating parameters such as blower speed and airflow state, which can provide a data basis for establishing the mapping relationship between noise characteristics and operating state, thereby being used for the analysis, identification and optimization control of air conditioning noise.

[0035] In the embodiments of this application, the noise data of the vehicle air conditioner at each setting can be obtained by deploying acoustic acquisition equipment in a vehicle or bench test environment to measure the noise of the air conditioning system at different settings and operating conditions, and simultaneously acquiring the corresponding operating parameters to form noise data for each setting. For example, in the embodiments of this application, a microphone can be placed near the driver's ear in a semi-anechoic chamber of the vehicle. During the gradual switching of the air conditioning system from low to high settings, acoustic data at each setting is collected. Simultaneously, parameters such as blower speed, damper opening, and air conditioning operating mode are read via the vehicle's CAN (Controller Area Network) bus, and the acoustic data is synchronized and matched with the corresponding operating parameters for storage to obtain the noise data of the vehicle air conditioner at each setting.

[0036] This application embodiment obtains air conditioner noise data at various speeds, which can provide data support for subsequently constructing a mapping relationship between air conditioner noise and operating parameters, as well as for achieving noise prediction and optimized control.

[0037] As one possible implementation method, in one embodiment of this application, the noise data of the vehicle air conditioner at each level is obtained, including: using the air conditioner control buttons in the vehicle to control the vehicle air conditioner at each level of the coldest internal circulation mode, to test each level and continuously for a preset duration, so as to obtain the noise signal perceived by the user and the vibration signal of the blower housing; and generating the noise data of the air conditioner at each level based on the noise signal and the vibration signal.

[0038] This application embodiment was tested under the coldest internal circulation condition with the air conditioning blowing directly onto the surface. At this time, the air conditioner is typically operating under high load, with high blower speed and high airflow velocity, resulting in the most significant aerodynamic excitation. This makes it easier to trigger potential aerodynamic noise and structural resonance problems, thus facilitating the exposure and identification of abnormal noise. Simultaneously, in internal circulation mode, there is less air exchange between the inside and outside of the vehicle, resulting in relatively less interference from external environmental noise, which helps improve the consistency and accuracy of noise test data. Furthermore, in surface-blowing mode, the airflow path is relatively concentrated, and the propagation path in the air duct is more clearly defined, which helps in analyzing the propagation characteristics of noise in the air duct and locating specific noise sources.

[0039] In the embodiments of this application, the preset duration can be set to 15s or 30s, which can be specifically limited according to the actual situation. It is understood that after a gear shift, the vehicle air conditioner needs a certain amount of time to enter steady-state operation (such as stable airflow and stable engine speed), typically about 2s to 5s. Furthermore, to ensure that the collected noise signal has sufficient statistical stability and spectral resolution, it is necessary to continuously sample for a certain period during the steady-state phase, typically about 15s to 30s, to obtain reliable sound pressure level and spectral characteristics. Therefore, the embodiments of this application test each gear and continuously preset the duration to improve the accuracy and stability of noise feature extraction while maintaining testing efficiency.

[0040] It can be explained that air conditioner noise signals are multi-source superposition signals, including aerodynamic noise, structural transmission noise, and environmental interference. Relying solely on the noise signal perceived by the user (acoustic signal) makes accurate identification difficult. However, the vibration signal of the blower casing has a more direct correlation with the blower's operating state, which helps enhance the ability to identify blower-related noise. Furthermore, by performing correlation or coherence analysis on the noise and vibration signals, their common dominant frequency components can be identified, thereby determining whether noise in a specific frequency band is caused by blower vibration, thus improving the accuracy of noise source localization. Therefore, the embodiments of this application can generate air conditioner noise data for each setting based on the noise signal perceived by the user and the vibration signal of the blower casing, which helps improve the accuracy of air conditioner noise identification and noise source localization.

[0041] As a specific example, obtaining the noise signal perceived by the user and the vibration signal of the blower housing in this application embodiment may include the following steps: A) Test preparation: A microphone was placed in the driver's inner ear; vibration sensors and data acquisition equipment were placed on the blower housing.

[0042] B) Start the test: Use the air conditioning control buttons in the car to control the air conditioning at each of the coldest internal circulation settings, and test each setting for 30 seconds.

[0043] C) Data recording: Record the noise signal measured by the driver's inner ear microphone throughout the process; record the vibration signal of the blower housing, such as acceleration signal, velocity signal or displacement signal collected by vibration sensor.

[0044] D) Data Processing: This application embodiment can perform noise reduction filtering (such as bandpass filtering and noise reduction algorithm processing) on ​​the collected raw noise and vibration signals to eliminate environmental background noise and test interference; then, the filtered signals are framed and windowed, and the spectrum information is obtained through fast Fourier transform to extract characteristic parameters such as sound pressure level, main frequency component and energy distribution corresponding to each level; furthermore, this application embodiment can compare and analyze the noise characteristics between different levels to identify abnormal frequency bands or abrupt changes, and then determine whether the abnormal noise comes from the air conditioner and its corresponding specific components, so as to guide the subsequent vehicle air conditioning noise analysis and optimization processing. For example, during the process of the air conditioner increasing from level 2 to level 3, after processing the collected noise and vibration signals, it was found that the overall sound pressure level increased from approximately 45 dB to 52 dB, and a significant energy surge occurred in the 800 Hz to 1200 Hz frequency band. Since this frequency band matches the frequency of the blower blades or the resonant frequency of the air duct, it can be determined that this frequency band is an abnormal frequency band. Furthermore, combined with the change in blower speed from approximately 2000 rpm to 3000 rpm, it can be determined that the abnormal noise mainly originates from the aerodynamic noise of the air conditioner blower. Therefore, the embodiments of this application can analyze the noise of the vehicle air conditioner based on the spectral characteristics of the noise and vibration signals and their corresponding operating parameters.

[0045] It should be noted that data processing can be flexibly configured according to specific application scenarios and accuracy requirements, and is not limited to the above processing methods.

[0046] Optionally, in one embodiment of this application, generating air conditioner noise data for each setting based on noise signals and vibration signals includes: calculating the A-weighted sound pressure level of the noise based on the noise signals; determining the rotational frequency of the blower motor based on the vibration signals to calculate the motor speed of the blower; and generating air conditioner noise data for each setting based on the A-weighted sound pressure level and the motor speed.

[0047] Data processing can also be performed by A-weighting the noise signal to calculate its sound pressure level, while frequency domain analysis of the vibration signal is performed to identify the dominant frequency component to determine the blower motor rotation frequency, and the blower motor speed is calculated based on the rotation frequency, thereby obtaining the correspondence between air conditioning noise and blower operating status for subsequent air conditioning noise analysis.

[0048] In this embodiment, the noise signal analysis frequency can be set to 25600Hz to calculate the A-weighted sound pressure level of the noise; the vibration signal analysis frequency can be set to 6400Hz to calculate the speed (rpm) of the blower motor from the first peak frequency in the vibration acceleration spectrum, i.e., the rotational frequency of the blower motor.

[0049] Specifically, firstly, in the embodiments of this application, A-weighted filtering can be applied to the discrete sound pressure signal to obtain an A-weighted sound pressure signal. ; Secondly, the root mean square value of the A-weighted sound pressure signal is calculated. : , Subsequently, based on the reference sound pressure Calculate the A-weighted sound pressure level: , in, This indicates the A-weighted sound pressure level.

[0050] Furthermore, in this embodiment, the vibration acceleration signal is discretely acquired at a sampling frequency of 6400Hz, and the acquired signal is subjected to spectral analysis to obtain the vibration acceleration spectrum. The first peak frequency in the spectrum is selected as the blower motor rotational frequency f (unit: Hz). Subsequently, the blower motor speed is calculated based on the conversion relationship between frequency and rotational speed.

[0051] Wherein, motor speed (rpm) = rotational frequency f (Hz) × 60.

[0052] The data processing described above in this application embodiment yields the corresponding relationship between air conditioning speed, blower speed, and A-weighted sound pressure level, as shown in Table 1.

[0053] Table 1

[0054] Using this table, the embodiments of this application can analyze the trend of air conditioning noise changing with blower speed at different settings, with blower speed as the independent variable and A-weighted sound pressure level as the dependent variable. This can then be used for subsequent noise prediction and optimization control, which will be explained in detail below.

[0055] In step S102, a curve showing the relationship between the noise value of each air conditioner setting and the blower speed is generated based on the noise data of each setting.

[0056] As mentioned above, the noise data for each air conditioning setting can include, but is not limited to, the A-weighted sound pressure level calculated based on the noise signal perceived by the user and the blower speed calculated based on the vibration signal of the blower casing. The noise value for each setting can refer to the noise sound pressure level value obtained by collecting data through a microphone and processing it using A-weighting under different air conditioning settings (such as fan speed setting or blower speed control setting), which is used to characterize the noise level of the air conditioning system corresponding to that setting.

[0057] It is understandable that, since the air conditioner noise data for each speed setting is discrete and the data points are scattered, it is not easy to intuitively observe the pattern of air conditioner noise value changing with blower speed. Therefore, the embodiments of this application can further pair the air conditioner noise value with the corresponding blower speed and form a relationship curve between noise value and speed, so as to realize the visualization of the air conditioner noise change trend and the pattern analysis.

[0058] As one possible approach, the relationship curve can be plotted based on the A-weighted sound pressure level and blower motor speed for each gear in Table 1. By matching and sorting the corresponding data, the relationship curve between the noise value and the blower speed can be obtained with the blower motor speed as the horizontal axis and the A-weighted sound pressure level as the vertical axis.

[0059] The embodiments of this application can intuitively characterize the change of air conditioner noise with blower speed through the relationship curve, which makes it easier to identify the sensitive range and abnormal points of noise change with speed, and helps to reduce testing complexity and improve analysis efficiency.

[0060] As a specific example, the relationship curve between noise value and blower speed at each air conditioner speed setting is generated based on the noise data at each speed setting. This includes: generating a noise curve with blower speed on the horizontal axis and air conditioner noise value on the vertical axis based on the noise data at each speed setting; calculating the trend line function and correlation coefficient of the noise curve; and generating the relationship curve based on the trend line function and correlation coefficient.

[0061] Trendline functions are mathematical function expressions used to characterize the relationship between variables, obtained by fitting discrete data (such as least squares fitting, linear regression, polynomial fitting), for example, a linear function representing a linear relationship. Polynomial functions of nonlinear relationships Examples of such data are used to reflect trends in data changes.

[0062] Air conditioning noise does not exhibit a simple linear relationship with engine speed; rather, it is influenced by the coupled effects of multiple factors, including aerodynamic noise, structural vibration, and duct resonance. As the blower speed increases, the airflow velocity and turbulence intensity increase nonlinearly, potentially triggering modal resonance in the duct and casing structure, causing the noise growth rate to accelerate significantly at high speeds. Therefore, noise variation with engine speed often exhibits nonlinear characteristics, with its trend line function frequently being a polynomial function.

[0063] It can be noted that the noise level of an air conditioner and the speed of a blower typically exhibit a discrete distribution, exhibiting fluctuations and test noise interference, which is not conducive to directly reflecting the overall change pattern. The trend line function calculated in this application embodiment can fit discrete data, thereby extracting the overall trend relationship between noise and speed, achieving a continuous expression of discontinuous data. The calculation of the trend line function in this application embodiment may include the following steps: First, the embodiments of this application can construct a discrete data point set based on the blower speed and A-weighted sound pressure level corresponding to each gear position; then, the embodiments of this application can use the least squares method to fit the discrete data point set to obtain a trend line function. Finally, the embodiments of this application can calculate or interpolate the noise value at any speed based on the trend line function, realize the continuous expression of discrete noise data, and predict the air conditioning noise value at any speed, thereby realizing the continuous evaluation and analysis of noise changes under different operating conditions.

[0064] In the embodiments of this application, the correlation coefficient refers to a statistical index used to measure the degree of correlation between air conditioner noise levels and blower speed. The calculation of the correlation coefficient in the embodiments of this application can quantitatively evaluate the degree of correlation between air conditioner noise levels and speed, thereby measuring the reliability and consistency of the established trend line function, determining whether noise changes are mainly affected by blower speed, and providing a basis for subsequent adjustment of blower speed, thus achieving targeted control and optimization of air conditioner noise.

[0065] Among them, the correlation coefficient The calculation formula can be expressed as: , in, Indicates rotational speed. Indicates sound pressure level. This represents the average rotational speed. This represents the average sound pressure level.

[0066] It can be explained that the correlation coefficient The value range is typically from -1 to 1, with the absolute value closer to 1 indicating a stronger correlation. When R ≥ 0.9, the trend line function in this embodiment can be considered... This trend line function can represent the relationship between blower speed and air conditioner noise. Predict the air conditioning noise corresponding to the blower speed.

[0067] This application embodiment can fit the relationship between the blower speed and the air conditioner noise value according to the trend line function to predict the air conditioner noise value at any speed; then, by evaluating the trend line function through the correlation coefficient, the correlation strength between the blower speed and the air conditioner noise can be quantified to verify the effectiveness of the influence of the blower speed on the change of air conditioner noise, thereby providing a reliable basis for subsequent noise optimization control based on blower speed.

[0068] In step S103, the air conditioning noise corresponding to the speed of each blower in the vehicle under the whole vehicle state is predicted based on the relationship curve.

[0069] Among them, air conditioning noise refers to the comprehensive acoustic noise generated by the vehicle air conditioning system under different blower speeds. The change in its noise level will affect the user's riding comfort and sound quality experience. This application embodiment identifies the air conditioning noise to optimize the operation control strategy of the air conditioning system, thereby reducing the noise level and improving the overall vehicle sound quality.

[0070] It is understandable that the relationship curve includes, but is not limited to, trend line functions and correlation coefficients, which can be used to predict air conditioning noise under any blower speed condition, and can also be used to illustrate the correlation strength between blower speed and air conditioning noise.

[0071] This application's embodiments predict the air conditioning noise corresponding to the speed of each blower under the vehicle's overall condition through the relationship curve. This enables advance assessment and quantitative analysis of the air conditioning noise level under different operating conditions, thereby providing a basis for noise optimization control and improvement of the overall vehicle sound quality.

[0072] For example, the expression for the relationship curve in this application embodiment is: , in, Indicates air conditioner noise. This indicates the speed of the blower.

[0073] According to the relationship curve, the air conditioning noise is predicted to be about 80dB(A) when the blower speed is 3000rpm, which is higher than the noise threshold of 75dB(A). Under this condition, the air conditioning system may cause obvious auditory discomfort and noise interference. Therefore, control measures to optimize the impedance matching of the air conditioning duct can be adopted to reduce the air conditioning noise level and improve the overall vehicle sound quality.

[0074] Optionally, in one embodiment of this application, the vehicle air conditioning noise prediction method further includes: generating air conditioning calibration data that meets preset air conditioning noise requirements based on the air conditioning noise corresponding to each blower speed in the whole vehicle state; and generating air conditioning comfort test data of the vehicle air conditioner based on the air conditioning calibration data.

[0075] The preset air conditioning noise requirements refer to the target range of air conditioning noise set in advance to meet the user's riding comfort and sound quality experience. The A-weighted sound pressure level can be used as the evaluation index to limit the maximum allowable noise level of the air conditioning system at different blower speeds. For example, it can be set to no more than 65 dB(A) under low air volume conditions, no more than 71 dB(A) under medium air volume conditions, and no more than 75 dB(A) under high air volume conditions, so as to achieve graded control and optimization of sound quality under different usage scenarios.

[0076] Air conditioning comfort test data may include, but are not limited to, data on air conditioning noise, air conditioning cooling temperature and heating temperature under different blower speeds, which are used to characterize the overall comfort performance of the air conditioning system under different operating conditions.

[0077] This application embodiment generates air conditioning calibration data based on the air conditioning noise corresponding to each blower speed, and further expands it to form air conditioning comfort test data. Since the air conditioning calibration data only includes blower speed and corresponding air conditioning noise, this application embodiment can also obtain air conditioning thermal comfort-related parameters under different blower speed conditions, including but not limited to cooling outlet air temperature, heating outlet air temperature, and air volume parameters, to supplement multi-dimensional comfort indicators and thus obtain complete air conditioning comfort test data.

[0078] For example, in this embodiment of the application, based on the calibration data of blower speed and air conditioner noise: when the speed is 1500 rpm, the noise is 65 dB(A). On this basis, to form air conditioner comfort test data, it is necessary to further obtain thermal comfort-related parameters under the same speed conditions, such as a cooling outlet air temperature of 19°C, a heating outlet air temperature of 30°C, and an air volume of 260 m³ / h at 1500 rpm. 3 / h. Therefore, in this embodiment of the application, multi-dimensional parameters such as "blower speed - air conditioner noise - cooling temperature - heating temperature" are combined to construct air conditioner comfort test data for subsequent comfort evaluation and optimization analysis.

[0079] Based on the air conditioning comfort test data of vehicle air conditioners, the embodiments of this application can form a unified calibration specification and evaluation benchmark, which can be used to quantitatively constrain and evaluate the noise level and thermal comfort index under different blower speed conditions. This provides the air conditioning department with standardized data basis and design reference in the process of vehicle development, system matching and calibration optimization, and realizes the standardized development and optimized control of air conditioning system performance.

[0080] According to the vehicle air conditioning noise prediction method proposed in this application, a relationship curve between the noise value of each gear and the blower speed is generated based on the air conditioning noise data of each gear. Then, the air conditioning noise level of the vehicle at any blower speed under the whole vehicle state can be predicted based on the relationship curve. This can realize the early assessment and quantitative analysis of noise levels under different operating conditions, reduce repeated testing processes, reduce the number of calibration tests and data acquisition costs in the whole vehicle development process, thereby shortening the whole vehicle development cycle, improving the efficiency and consistency of air conditioning noise calibration, and providing data support for the calibration optimization of the air conditioning system, control strategy adjustment and sound quality classification management, which helps to improve the noise performance and ride comfort of the whole vehicle.

[0081] Next, referring to the accompanying drawings, a vehicle air conditioning noise prediction device according to an embodiment of this application is described.

[0082] Figure 2 This is a block diagram of a vehicle air conditioning noise prediction device according to an embodiment of this application.

[0083] like Figure 2 As shown, the air conditioning noise prediction device 10 of the vehicle includes: an acquisition module 100, a generation module 200 and a prediction module 300.

[0084] The acquisition module 100 is used to acquire the noise data of the vehicle air conditioner at each speed setting.

[0085] The generation module 200 is used to generate the relationship curve between the noise value of each level and the speed of the blower based on the noise data of each level of the air conditioner.

[0086] The prediction module 300 is used to predict the air conditioning noise corresponding to the speed of each blower in the vehicle under the whole vehicle condition based on the relationship curve.

[0087] Optionally, in one embodiment of this application, the generation module 200 includes: a first generation unit, a calculation unit, and a second generation unit.

[0088] The first generation unit is used to generate a noise curve with the blower speed on the horizontal axis and the air conditioner noise value on the vertical axis based on the air conditioner noise data of each gear.

[0089] The calculation unit is used to calculate the trend line function and correlation coefficient of the noise curve.

[0090] The second generation unit is used to generate relationship curves based on trend line functions and correlation coefficients.

[0091] Optionally, in one embodiment of this application, the acquisition module 100 includes a testing unit and a third generation unit.

[0092] The testing unit is used to control the vehicle's air conditioning system at various levels of the coldest internal circulation mode using the air conditioning control buttons inside the vehicle. Each level is tested and the test is performed for a preset duration to obtain noise signals perceived by the user and vibration signals from the blower housing.

[0093] The third generation unit is used to generate air conditioner noise data for each speed setting based on noise and vibration signals.

[0094] Optionally, in one embodiment of this application, the third generation unit includes: a first calculation subunit, a second calculation subunit, and a generation subunit.

[0095] The first calculation subunit is used to calculate the A-weighted sound pressure level of the noise based on the noise signal. The second calculation subunit is used to determine the rotational frequency of the blower motor based on the vibration signal in order to calculate the motor speed of the blower.

[0096] The generation subunit is used to generate air conditioner noise data for each speed setting based on A-weighted sound pressure level and motor speed.

[0097] Optionally, in one embodiment of this application, the vehicle air conditioning noise prediction device 10 further includes a calibration module and a test module.

[0098] The calibration module is used to generate air conditioning calibration data that meets preset air conditioning noise requirements based on the air conditioning noise corresponding to the speed of each blower in the whole vehicle state.

[0099] The testing module is used to generate air conditioning comfort test data for vehicle air conditioning based on air conditioning calibration data.

[0100] It should be noted that the explanation of the above-mentioned embodiment of the vehicle air conditioning noise prediction method also applies to the vehicle air conditioning noise prediction device of this embodiment, and will not be repeated here.

[0101] According to the vehicle air conditioning noise prediction device and the vehicle air conditioning noise prediction method proposed in the embodiments of this application, the relationship curve between the noise value of each gear and the blower speed is generated based on the air conditioning noise data of each gear. Then, the air conditioning noise level of the vehicle at any blower speed under the whole vehicle state can be predicted based on the relationship curve. It can realize the early assessment and quantitative analysis of the noise level under different operating conditions, reduce the repeated test process, reduce the number of calibration tests and data acquisition costs in the whole vehicle development process, thereby shortening the whole vehicle development cycle and improving the efficiency and consistency of air conditioning noise calibration results.

[0102] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0103] When the processor 302 executes the program, it implements the vehicle air conditioning noise prediction method provided in the above embodiments.

[0104] Furthermore, electronic devices also include: Communication interface 303 is used for communication between memory 301 and processor 302.

[0105] The memory 301 is used to store computer programs that can run on the processor 302.

[0106] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0107] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0108] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0109] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0110] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for predicting air conditioning noise in a vehicle.

[0111] This application also provides a vehicle, including the above-mentioned vehicle air conditioning noise prediction device, or electronic device, or computer-readable storage medium, to implement the vehicle air conditioning noise prediction method provided in this application.

[0112] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0113] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0114] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0115] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0116] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0117] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0118] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0119] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting air conditioning noise in a vehicle, characterized in that, Includes the following steps: Obtain noise data for each setting of the vehicle's air conditioning system; Based on the air conditioning noise data for each speed setting, generate a curve showing the relationship between the noise value and the blower speed for each speed setting. as well as Based on the relationship curve, predict the air conditioning noise corresponding to the speed of each blower in the vehicle under the overall vehicle condition.

2. The method according to claim 1, characterized in that, The step of generating the relationship curve between the noise value of each air conditioner setting and the blower speed based on the noise data of each setting includes: Based on the air conditioner noise data for each gear, a noise curve is generated with the blower speed on the horizontal axis and the air conditioner noise value on the vertical axis. Calculate the trend line function and correlation coefficient of the noise curve; The relationship curve is generated based on the trend line function and the correlation coefficient.

3. The method according to claim 1, characterized in that, The acquisition of noise data for each setting of the vehicle's air conditioning system includes: The vehicle's air conditioning system is controlled using the air conditioning control buttons inside the vehicle. Each setting is tested and the duration is preset to obtain noise signals perceived by the user and vibration signals of the blower housing. The noise data for each air conditioner setting is generated based on the noise signal and the vibration signal.

4. The method according to claim 3, characterized in that, The step of generating the air conditioner noise data for each speed setting based on the noise signal and the vibration signal includes: Based on the noise signal, calculate the A-weighted sound pressure level of the noise; Based on the vibration signal, the rotational frequency of the blower motor is determined in order to calculate the motor speed of the blower; The air conditioning noise data for each gear is generated based on the A-weighted sound pressure level and the motor speed.

5. The method according to claim 1, characterized in that, Also includes: Based on the air conditioning noise corresponding to the speed of each blower in the whole vehicle state, air conditioning calibration data that meets the preset air conditioning noise requirements is generated. The air conditioning comfort test data of the vehicle air conditioner is generated based on the air conditioning calibration data.

6. A vehicle air conditioning noise prediction device, characterized in that, include: The acquisition module is used to acquire noise data of the vehicle's air conditioning at various speeds. The generation module is used to generate a curve showing the relationship between the noise value of each air conditioner setting and the blower speed based on the noise data of each setting. as well as The prediction module is used to predict the air conditioning noise corresponding to the speed of each blower in the vehicle under the overall vehicle condition based on the relationship curve.

7. The apparatus according to claim 6, characterized in that, The generation module includes: The first generation unit is used to generate a noise curve with the blower speed on the horizontal axis and the air conditioner noise value on the vertical axis based on the air conditioner noise data of each gear. A calculation unit is used to calculate the trend line function and correlation coefficient of the noise curve; The second generation unit is used to generate the relationship curve based on the trend line function and the correlation coefficient.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the air conditioning noise prediction method for a vehicle as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the air conditioning noise prediction method for a vehicle as described in any one of claims 1-5.

10. A vehicle, characterized in that, The vehicle includes an air conditioning noise prediction device as described in claims 6-7, an electronic device as described in claim 8, or a computer-readable storage medium as described in claim 9.

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