Electrically-driven howling noise analysis method and device, storage medium and equipment

Abstract

The invention provides an electric-driven squeal noise analysis method and device, a storage medium and equipment, and the method comprises the steps: obtaining subjective evaluation data and objective data influence parameters according to the in-vehicle electric-driven squeal noise data under a target working condition, carrying out the fitting correlation of the subjective evaluation data and objective data influence parameters, and carrying out the analysis of the subjective evaluation data and objective data influence parameters. A subjective and objective correlation model used for representing an approximate function relationship between subjective evaluation data and objective data influence parameters is obtained, and then based on the subjective and objective correlation model, whether target electric drive system design data reach the standard in the aspect of electric drive howling noise is judged, so that scheme design of the electric drive system is guided. Therefore, the subjective sound quality evaluation and the objective sound quality analysis of the in-vehicle electric drive howling noise are associated, the accuracy of the evaluation result of the electric drive howling noise is effectively improved, and guidance is provided for the forward design of an electric drive system product, so that the development efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of electric drive technology, and more specifically, to an electric drive howling noise analysis method, device, storage medium and equipment. Background Art

[0002] Electric drive whine refers to the high-frequency, sharp noise emitted by the drive system of electric vehicles during operation. This noise is usually caused by mechanical vibration and electromagnetic interference inside the electric drive system. Electric drive whine seriously affects the NVH performance quality of the entire vehicle. Therefore, solving the problem of electric drive whine is a key research topic for automobile companies.

[0003] The current electric drive howling noise evaluation method usually uses subjective evaluation to qualitatively determine the severity of the electric drive howling noise. However, everyone's perception of sound is different, and different people give different subjective scores for the electric drive howling noise of the same vehicle. Therefore, the evaluation results of the electric drive howling noise are not accurate enough, which can easily affect development efficiency. Summary of the invention

[0004] The purpose of this application is to provide an electric drive howling noise analysis method, device, storage medium and equipment, aiming to solve the problem that the evaluation results of the electric drive howling noise evaluation method in the related art are not accurate enough and easily affect the development efficiency.

[0005] In a first aspect, the present application provides an electric drive howling noise analysis method, comprising: obtaining subjective evaluation data and objective data influencing parameters based on the in-vehicle electric drive howling noise data under target working conditions; the objective data influencing parameters are parameters used to analyze the level of the in-vehicle electric drive howling noise; fitting and correlating the subjective evaluation data and the objective data influencing parameters to obtain a subjective-objective correlation model; the subjective-objective correlation model represents an approximate functional relationship between the subjective evaluation data and the objective data influencing parameters; based on the subjective-objective correlation model, determining the noise compliance status of the target electric drive system design data.

[0006] In the above implementation process, subjective evaluation data and objective data influencing parameters are obtained based on the in-vehicle electric drive whistling noise data under the target working conditions, and then the subjective evaluation data and objective data influencing parameters are fitted and associated to obtain a subjective and objective correlation model for representing the approximate functional relationship between the subjective evaluation data and the objective data influencing parameters. Based on the subjective and objective correlation model, it is judged whether the target electric drive system design data meets the standards in terms of electric drive whistling noise, thereby guiding the design of the electric drive system. In this way, by correlating the subjective sound quality evaluation and objective sound quality analysis of the in-vehicle electric drive whistling noise, the accuracy of the electric drive whistling noise evaluation results is effectively improved, and guidance is provided for the forward design of the electric drive system product, thereby improving development efficiency.

[0007] Furthermore, in some examples, the subjective evaluation data is obtained based on the following method: screening sound samples of in-vehicle electric drive howling noise data under target working conditions, and establishing a noise data sample library based on the screened sound samples; obtaining subjective evaluation scoring results of multiple sound evaluators for the noise data sample library; and eliminating outliers in the subjective evaluation scoring results to obtain subjective evaluation data.

[0008] In the above implementation process, after collecting the electric drive howling noise data in the car, the sound samples are screened to establish a noise data sample library for subjective sound quality evaluation. Multiple sound evaluators will conduct subjective evaluation and scoring of the noise data sample library. After that, all the subjective evaluation and scoring results are analyzed and outliers are eliminated. In this way, the quality of subjective evaluation data is improved.

[0009] Further, in some examples, the objective data affecting parameters include prominence ratio, auditory correlation amount, and pitch.

[0010] In the above implementation process, an optional type of objective data influencing parameter is provided.

[0011] Furthermore, in some examples, the fitting and associating the subjective evaluation data and the objective data influencing parameters includes: fitting and associating the subjective evaluation data and the objective data influencing parameters through a Fourier orthogonal basis fitting algorithm.

[0012] In the above implementation process, the subjective and objective correlation is realized by using the Fourier orthogonal basis fitting algorithm, which can ensure that the subjective and objective correlation model reflects the relationship between the subjective evaluation data and the objective data influencing parameters as accurately as possible.

[0013] Furthermore, in some examples, the subjective evaluation data and the objective data influencing parameters are fitted and associated through a Fourier orthogonal basis fitting algorithm, including: obtaining a sample set based on the subjective evaluation data and the objective data influencing parameters; establishing a Fourier orthogonal basis fitting response surface function of the subjective and objective association model; the input variables of the Fourier orthogonal basis fitting response surface function are the objective data influencing parameters, and the response variables are the subjective evaluation data; iteratively training the subjective and objective association model based on the sample set; during the iterative training process, using an orthogonal basis matrix and a least squares method to solve the unknown coefficients of the Fourier orthogonal basis fitting response surface function.

[0014] In the above implementation process, a specific method of realizing subjective and objective correlation through Fourier orthogonal basis fitting algorithm is provided.

[0015] Furthermore, in some examples, after the iterative training of the subjective-objective association model based on the sample set, it includes: determining whether the subjective-objective association model satisfies a preset robustness condition; if the determination result is no, increasing the number of sample points, and re-iteratively training the subjective-objective association model based on all sample points.

[0016] In the above implementation process, when the subjective and objective correlation model obtained by iterative training does not meet the preset robustness conditions, the number of sample points is increased to increase the amount of data used for iterative training of the subjective and objective correlation model, thereby improving the robustness of the final subjective and objective correlation model.

[0017] Furthermore, in some examples, determining the noise compliance status of the target electric drive system design data based on the subjective-objective correlation model includes: calculating a subjective evaluation score corresponding to the target electric drive system design data based on the subjective-objective correlation model; if the calculated subjective evaluation score is greater than the target evaluation score, determining that the noise level of the target electric drive system design data meets the standard.

[0018] In the above implementation process, the subjective evaluation score corresponding to the target electric drive system design data is calculated according to the subjective and objective correlation model, and compared with the target evaluation score. When the subjective evaluation score corresponding to the target electric drive system design data is greater than the target evaluation score, the design data is judged to be up to standard. In this way, a reference is provided for the NVH performance development target and target achievement judgment in the electric drive system product design, thereby effectively improving development efficiency.

[0019] In a second aspect, the present application provides an electric drive howling noise analysis device, including: an acquisition module, used to acquire subjective evaluation data and objective data influencing parameters based on the in-vehicle electric drive howling noise data under the target working conditions; the objective data influencing parameters are parameters used to analyze the level of the in-vehicle electric drive howling noise; a fitting module, used to fit and associate the subjective evaluation data and the objective data influencing parameters to obtain a subjective and objective correlation model; the subjective and objective correlation model represents the approximate functional relationship between the subjective evaluation data and the objective data influencing parameters; a determination module, used to determine the noise compliance status of the target electric drive system design data based on the subjective and objective correlation model.

[0020] In a third aspect, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on a computer, the computer executes the method as described in any one of the first aspects.

[0022] In a fifth aspect, the present application provides a computer program product, which, when running on a computer, enables the computer to execute the method as described in any one of the first aspects.

[0023] Other features and advantages disclosed in the present application will be described in the following description, or some features and advantages can be inferred or determined without doubt from the description, or can be learned by implementing the above-mentioned technology disclosed in the present application.

[0024] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flow chart of an electric drive howling noise analysis method provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of the workflow of a solution for calculating the subjective and objective correlation of electric drive whistling noise in a pure electric vehicle provided in an embodiment of the present application;

[0028] Figure 3 A block diagram of an electric drive howling noise analysis device provided in an embodiment of the present application;

[0029] Figure 4 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0031] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0032] As described in the background technology, the electric drive whistling noise evaluation method in the related art has the problem that the evaluation results are not accurate enough and easily affect the development efficiency. Based on this, the embodiment of the present application provides an electric drive whistling noise analysis solution to solve the above problem.

[0033] Next, the embodiments of the present application are introduced:

[0034] like Figure 1 As shown, Figure 1 This is a flow chart of an electric drive howling noise analysis method provided in an embodiment of the present application. The method is to intervene in the scheme design during the electric drive system project development stage. The electric drive system can be an electric drive system of a pure electric vehicle. The method can be applied to a server or a terminal.

[0035] The method comprises:

[0036] Step 101: Obtain subjective evaluation data and objective data influencing parameters according to in-vehicle electric drive squeal noise data under target working conditions; the objective data influencing parameters are parameters used to analyze the level of in-vehicle electric drive squeal noise;

[0037] The in-vehicle electric drive whistling noise data mentioned in this step can be noise data collected at the driver's ear for electric vehicles of different models. The target operating condition mentioned in this step can refer to a full-load acceleration and deceleration condition. Optionally, the target operating condition can include a 0.1g vehicle acceleration condition, a 0.2 vehicle acceleration condition, and a -0.1g coasting deceleration condition. After collecting the in-vehicle electric drive whistling noise data, perform subjective evaluation and objective analysis on it, respectively, so as to obtain the subjective evaluation data and objective data influencing parameters.

[0038] In some embodiments, the subjective evaluation data mentioned in this step can be obtained based on the following methods: screening sound samples of the in-vehicle electric drive whistling noise data under the target working condition, and establishing a noise data sample library based on the screened sound samples; obtaining the subjective evaluation and scoring results of multiple sound evaluators for the noise data sample library; eliminating the outliers in the subjective evaluation and scoring results to obtain the subjective evaluation data. In other words, after collecting the in-vehicle electric drive whistling noise data, sound samples can be screened based on time domain OTPA (Operational Transfer Path Analysis), sound engineering and other methods to establish a noise data sample library for subjective sound quality evaluation, and multiple sound evaluators perform subjective evaluation and scoring on the noise data sample library. After the equipment records the scores, data analysis is performed on all the subjective evaluation and scoring results, and outliers, such as scores with large differences, are eliminated to obtain subjective evaluation data. In this way, the quality of the subjective evaluation data is improved.

[0039] The objective data influencing parameters are parameters used to analyze the level of electric drive whistling noise in the car. When implemented, the collected noise data is objectively analyzed, the contribution of each variable parameter to the noise level is analyzed, and the n variable parameters with the highest contribution are taken as objective data influencing parameters. In some embodiments, the objective data influencing parameters mentioned in this step may include prominence ratio, auditory relevance and tone intensity. The prominence ratio here describes the prominence of certain frequency components in the sound signal relative to other frequency components; the auditory relevance (Relative Approach) is a sound signal evaluation parameter, which is based on the principle of psychoacoustics and is used to quantify the influence of interference factors such as echo in the sound signal on the subjective perception of the human ear; tone intensity (Tonality) is an indicator that quantifies the contribution of the tonal components in the sound to the sound perception. These three parameters are key parameters that affect the electric drive whistling noise in the car of pure electric vehicles. Of course, in other embodiments, other variable parameters can be added as objective data influencing parameters according to the needs of specific scenarios.

[0040] Step 102: Fitting and associating the subjective evaluation data and the objective data influencing parameters to obtain a subjective-objective association model; the subjective-objective association model represents an approximate functional relationship between the subjective evaluation data and the objective data influencing parameters;

[0041] In this embodiment, the objective data influencing parameters corresponding to each sound sample and the subjective evaluation database under the target working condition are extracted, and the objective data and the subjective data are fitted and associated to obtain the approximate functional relationship between the independent variable index of the objective parameter of the electric drive system noise and the subjective evaluation score of the dependent variable response, that is, the subjective and objective association model.

[0042] In some embodiments, the fitting association of the subjective evaluation data and the objective data influencing parameters mentioned in this step may include: fitting association of the subjective evaluation data and the objective data influencing parameters through a Fourier orthogonal basis fitting algorithm. The Fourier orthogonal basis fitting algorithm is a fitting algorithm based on the Fourier orthogonal basis, which can be considered as a Fourier transform based on a set of orthogonal functions, and can be used to decompose a function into a combination of orthogonal functions. The subjective and objective association is achieved through the Fourier orthogonal basis fitting algorithm, which can ensure that the subjective and objective association model reflects the relationship between the subjective evaluation data and the objective data influencing parameters as accurately as possible.

[0043] Further, in some embodiments, the aforementioned fitting association of the subjective evaluation data and the objective data influencing parameters through the Fourier orthogonal basis fitting algorithm may include: obtaining a sample set according to the subjective evaluation data and the objective data influencing parameters; establishing a Fourier orthogonal basis fitting response surface function of the subjective-objective association model; the input variable of the Fourier orthogonal basis fitting response surface function is the objective data influencing parameters, and the response variable is the subjective evaluation data; iteratively training the subjective-objective association model based on the sample set; in the iterative training process, using the orthogonal basis matrix and the least squares method to solve the undetermined coefficients of the Fourier orthogonal basis fitting response surface function. That is, when fitting the association, the objective data influencing parameters are used as the input variables X i , taking the subjective evaluation data as the response variable Z, using the Fourier orthogonal basis, the Fourier orthogonal basis fitting response surface function is established as Among them, (a, b 1 ,b 2 ,…,b n ,c 1 ,c 2 ,…,c n ) are 2n+1 undetermined coefficients, i.e., the weights of the Fourier orthogonal basis fitting response surface function, which can be recorded as vector λ; extract multiple sample points from the collected in-vehicle electric drive whistling noise data as sample sets, construct an orthogonal basis matrix based on the sample set and the orthogonal basis function, and then use the least squares method to solve the vector λ, which is the process of correcting the weights of the Fourier orthogonal basis fitting response surface function; repeat the iteration until the iteration conditions are met, such as the relative error of the reliability index is less than the expected error value. In this way, the correlation between the subjective evaluation score of the in-vehicle electric drive whistling noise and the objective data influencing parameters is accurately established.

[0044] Furthermore, in some embodiments, after iteratively training the subjective and objective association model based on the sample set, the following may be included: determining whether the subjective and objective association model meets a preset robustness condition; if the determination result is no, increasing the number of sample points, and re-iteratively training the subjective and objective association model based on all sample points. In other words, when the subjective and objective association model obtained by iterative training does not meet the preset robustness condition, the number of sample points may be increased, and fitting training may be performed again, thereby improving the robustness of the subjective and objective association model. Among them, the preset robustness condition may be a condition set according to the robustness requirements of the model, such as the consistency and correlation between the model calculation results and the actual subjective evaluation results being not less than 85%.

[0045] In addition, in other embodiments, the subjective-objective association model may also be generated based on mathematical programming, genetic algorithm, Monte Carlo method, etc.

[0046] Step 103: Determine the noise compliance status of the target electric drive system design data based on the subjective-objective correlation model.

[0047] The target electric drive system design data mentioned in this step may be the design solution data proposed in the electric drive system project development phase. In this embodiment, the subjective and objective correlation model is used to determine whether the target electric drive system design data meets the electric drive whistling noise standard, thereby guiding the design of the electric drive system solution and improving development efficiency.

[0048] In some embodiments, this step may include: based on the subjective and objective correlation model, calculating the subjective evaluation score corresponding to the target electric drive system design data; if the calculated subjective evaluation score is greater than the target evaluation score, determining that the noise level of the target electric drive system design data meets the standard. That is to say, after constructing the subjective and objective correlation model, a sample can be made according to the target electric drive system design data, and the sample can be tested on a vehicle to obtain the target noise data. Then, in combination with the subjective and objective correlation model, the subjective evaluation score corresponding to the sample is calculated, and the sample is compared with the target evaluation score. When the subjective evaluation score corresponding to the sample is greater than the target evaluation score, the design data is determined to meet the standard and the design data is frozen. When the subjective evaluation score corresponding to the sample is not greater than the target evaluation score, the design data is determined to be substandard and the design data is optimized and iterated. In this way, a reference is provided for the NVH performance development goals and target achievement determination in the electric drive system product design, thereby effectively improving the development efficiency.

[0049] In the embodiment of the present application, subjective evaluation data and objective data influencing parameters are obtained based on the in-vehicle electric drive whistling noise data under the target working conditions, and then the subjective evaluation data and the objective data influencing parameters are fitted and correlated to obtain a subjective-objective correlation model for representing the approximate functional relationship between the subjective evaluation data and the objective data influencing parameters, and then based on the subjective-objective correlation model, it is judged whether the target electric drive system design data meets the standards in terms of electric drive whistling noise, thereby guiding the design of the electric drive system. In this way, by correlating the subjective sound quality evaluation of the in-vehicle electric drive whistling noise with the objective sound quality analysis, the accuracy of the electric drive whistling noise evaluation results is effectively improved, and guidance is provided for the forward design of the electric drive system product, thereby improving development efficiency.

[0050] In order to explain the solution of this application in more detail, a specific embodiment is introduced below:

[0051] This embodiment provides a solution for calculating the subjective and objective correlation of electric drive whistling noise in a pure electric vehicle. The workflow of this solution is as follows: Figure 2 As shown, including:

[0052] S201, collecting electric drive howling noise data in pure electric vehicles;

[0053] Among them, 9 pure electric vehicles were selected, including various models, and the in-vehicle noise data at the driver's ear under full-load acceleration and deceleration conditions were collected; the conditions included 0.1g vehicle acceleration condition, 0.2 vehicle acceleration condition and -0.1g coasting deceleration condition;

[0054] S202, modifying and screening the noise data sample library for subjective sound quality evaluation;

[0055] Among them, for the noise data samples corresponding to the above 9 vehicles, sound samples were screened based on engineering methods such as time domain OTPA and sound engineering. Each vehicle corresponds to the sound data of each working condition, and each sound data sample is 5 seconds long, which is used for the next step of subjective evaluation; 27 sound samples are formed for the three working conditions to establish a noise data sample library; in implementation, a calibrable digital equalizer and high-fidelity headphones are used to screen and statistically analyze the subjective evaluation results;

[0056] S203, obtaining subjective evaluation data;

[0057] Among them, 40 people were organized to conduct subjective evaluation and scoring of the noise data sample library; before the evaluation, these 40 people were trained on the scoring principles, and subjective scoring was carried out based on the company standard 10-point scoring method. In the sound evaluation room, each sound was played back and scored through the headphone playback device, and the scores were recorded; after that, data analysis was carried out on all subjective evaluation scores, and scores with large differences were eliminated to establish the final subjective evaluation data of each model under the three working conditions;

[0058] S204, analyzing objective data influencing parameters;

[0059] Among them, the collected noise data is objectively analyzed to analyze the variable parameters corresponding to the main influencing factors of the data; the variable parameters that affect the electric drive howling noise in pure electric vehicles mainly include protrusion ratio, auditory correlation, tone modulation and other n influencing parameters;

[0060] S205, fitting and correlating the subjective and objective data by using a Fourier orthogonal basis fitting algorithm;

[0061] S206, iterative training to obtain a subjective and objective correlation model;

[0062] Among them, the Fourier orthogonal basis fitting response surface function of the subjective and objective correlation model is expressed as:

[0063]

[0064] Among them, Z is the subjective evaluation score output by the subjective and objective correlation model; X(X 1 ,X 2 ,…,X n ) is the input variable of the subjective and objective correlation model, and is the influencing parameter of objective data; (a, b 1 ,b 2 ,…,b n ,c 1 ,c 2 ,…,c n ) are 2n+1 undetermined coefficients, i.e., the weights of the Fourier orthogonal basis fitting response surface function; the specific training process of the subjective-objective association model can refer to the introduction of the Fourier orthogonal basis fitting algorithm in the relevant technology;

[0065] S207, judging whether the model meets the robustness requirement, if yes, executing S209, otherwise executing S208;

[0066] Among them, the subjective and objective correlation model should meet the following standards: the degree of agreement between the calculation results of the subjective and objective correlation model and the subjective evaluation results reaches more than 85%, and the model robustness d<0.8;

[0067] S208, increase the number of sample points, and return to S205;

[0068] S209, subjecting a sample of the electric drive system design solution data to a vehicle test, and calculating a subjective evaluation score of the sample based on a subjective-objective correlation model;

[0069] S210, determining whether the noise level of the sample meets the standard, if yes, executing S212, otherwise executing S213;

[0070] S211, freeze the electric drive system design data;

[0071] S212, optimizing the electric drive system design data, and then returning to S209.

[0072] This embodiment scheme makes subjective and objective correlations between subjective evaluation and objective noise data, establishes a correlation between subjective evaluation and objective noise data of the whole vehicle NVH, provides guidance for the forward design of electric drive system products, can effectively reduce communication costs, improve development efficiency, and quickly identify the problem points of howling in the vehicle, supporting project development to quickly solve quality problems.

[0073] Corresponding to the embodiments of the aforementioned method, the present application also provides embodiments of an electric drive howling noise analysis device and a terminal using the same:

[0074] like Figure 3 As shown, Figure 3 : is a block diagram of an electric drive howling noise analysis device provided in an embodiment of the present application, the device comprising:

[0075] An acquisition module 31 is used to acquire subjective evaluation data and objective data influencing parameters according to the electric drive whistling noise data in the vehicle under the target working condition; the objective data influencing parameters are parameters used to analyze the level of the electric drive whistling noise in the vehicle;

[0076] A fitting module 32 is used to fit and associate the subjective evaluation data with the objective data influencing parameters to obtain a subjective-objective association model; the subjective-objective association model represents an approximate functional relationship between the subjective evaluation data and the objective data influencing parameters;

[0077] The determination module 33 is used to determine the noise compliance status of the target electric drive system design data based on the subjective and objective correlation model.

[0078] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0079] This application also provides an electronic device, see Figure 4 , Figure 4 A block diagram of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 410, a communication interface 420, a memory 430, and at least one communication bus 440. The communication bus 440 is used to realize direct connection and communication between these components. The communication interface 420 of the electronic device in the embodiment of the present application is used to communicate signaling or data with other node devices. The processor 410 may be an integrated circuit chip with signal processing capabilities.

[0080] The processor 410 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor 410 can also be any conventional processor, etc.

[0081] The memory 430 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electric erasable programmable read-only memory (EEPROM), etc. The memory 430 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 410, the electronic device can execute the above-mentioned Figure 1 The method embodiment involves various steps.

[0082] Optionally, the electronic device may further include a storage controller and an input / output unit.

[0083] The memory 430, storage controller, processor 410, peripheral interface, input and output unit components are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses 440. The processor 410 is used to execute executable modules stored in the memory 430, such as software function modules or computer programs included in the electronic device.

[0084] The input and output unit is used to provide users with the task creation and to create a start optional time period or preset execution time for the task to realize the interaction between the user and the server. The input and output unit can be, but is not limited to, a mouse and a keyboard.

[0085] Understandably, Figure 4 The structure shown is for illustration only, and the electronic device may also include Figure 4 More or fewer components as shown, or with Figure 4 Different configurations shown. Figure 4 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0086] An embodiment of the present application further provides a storage medium having instructions stored thereon. When the instructions are run on a computer, the computer program is executed by a processor to implement the method described in the method embodiment. To avoid repetition, the method will not be described here.

[0087] The present application also provides a computer program product, which, when executed on a computer, enables the computer to execute the method described in the method embodiment.

[0088] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0089] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0090] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0091] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0092] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0093] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for analyzing electric drive howling noise, characterized in that: include: Obtaining subjective evaluation data and objective data influencing parameters according to the in-vehicle electric drive howling noise data under the target working condition; the objective data influencing parameters are parameters used to analyze the level of the in-vehicle electric drive howling noise; Fitting and associating the subjective evaluation data and the objective data influencing parameters to obtain a subjective-objective association model; the subjective-objective association model represents an approximate functional relationship between the subjective evaluation data and the objective data influencing parameters; Based on the subjective and objective correlation model, the noise compliance status of the target electric drive system design data is determined.

2. The method according to claim 1, characterized in that The subjective evaluation data is obtained based on the following method: Screening the sound samples of the electric drive howling noise data in the vehicle under the target working conditions, and establishing a noise data sample library based on the screened sound samples; Obtaining subjective evaluation and scoring results of multiple sound evaluators on the noise data sample library; The outliers in the subjective evaluation scoring results are eliminated to obtain subjective evaluation data.

3. The method according to claim 1, characterized in that The objective data influencing parameters include prominence ratio, auditory correlation amount and pitch.

4. The method according to claim 1, characterized in that: The fitting and associating the subjective evaluation data with the objective data influencing parameters includes: The subjective evaluation data and the objective data influencing parameters are fitted and associated by using a Fourier orthogonal basis fitting algorithm.

5. The method according to claim 4, characterized in that The method of fitting and associating the subjective evaluation data and the objective data influencing parameters by using a Fourier orthogonal basis fitting algorithm includes: Acquire a sample set according to the subjective evaluation data and the objective data influencing parameter; Establishing a Fourier orthogonal basis fitting response surface function of a subjective and objective correlation model; the input variable of the Fourier orthogonal basis fitting response surface function is the objective data influencing parameter, and the response variable is the subjective evaluation data; The subjective-objective correlation model is iteratively trained based on the sample set; during the iterative training process, the undetermined coefficients of the Fourier orthogonal basis fitting response surface function are solved by using the orthogonal basis matrix and the least squares method.

6. The method according to claim 5, characterized in that After the subjective-objective association model is iteratively trained based on the sample set, the method further comprises: Determining whether the subjective and objective correlation model meets a preset robustness condition; If the judgment result is no, the number of sample points is increased, and the subjective-objective association model is iteratively trained again based on all sample points.

7. The method according to claim 1, characterized in that Determining the noise compliance status of the target electric drive system design data based on the subjective and objective correlation model includes: Based on the subjective and objective correlation model, a subjective evaluation score corresponding to the target electric drive system design data is calculated; If the calculated subjective evaluation score is greater than the target evaluation score, it is determined that the noise level of the target electric drive system design data meets the standard.

8. An electric drive howling noise analysis device, characterized in that: include: An acquisition module, used to acquire subjective evaluation data and objective data influencing parameters according to the electric drive whistling noise data in the vehicle under the target working condition; the objective data influencing parameters are parameters used to analyze the level of the electric drive whistling noise in the vehicle; A fitting module, used for fitting and associating the subjective evaluation data with the objective data influencing parameters to obtain a subjective-objective association model; the subjective-objective association model represents an approximate functional relationship between the subjective evaluation data and the objective data influencing parameters; The determination module is used to determine the noise compliance status of the target electric drive system design data based on the subjective and objective correlation model.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

Images