Noise evaluation method and device for power converter

Abstract

This application provides a noise evaluation method and apparatus for a converter. The noise evaluation method includes: acquiring a first noise parameter and a second noise parameter; the first noise parameter includes the average sound pressure level and sound quality parameter of a first converter, and the second noise parameter includes the average sound pressure level and sound quality parameter of a second converter; comparing whether the difference between the average sound pressure level of the first converter and the average sound pressure level of the second converter exceeds a preset difference; if so, determining a noise evaluation result based on the magnitude of the average sound pressure levels of the first converter and the second converter; if not, determining a noise evaluation result based on the sound quality parameters of the first converter and the second converter. This method can achieve a comprehensive and accurate evaluation of the noise level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of converter, in particular to a noise evaluation method and device of a converter. BACKGROUND

[0002] The converter generally refers to a current conversion and transmission unit in a rail transit vehicle, including a traction converter responsible for high-voltage power supply of vehicle traction and an auxiliary converter responsible for low-voltage power supply of lighting, air conditioning and the like on the vehicle, and is a core electrical component in the rail transit vehicle.

[0003] The converter is a complex electrical equipment, which contains various types of vibration noise sources, such as cooling fans, transformers, reactors, filter inductors, capacitors and contactors, and numerous vibration noise sources are mixed together to radiate irregular noise signals outward. With the transition of rail transit vehicles from safety and functionality to comfort and intelligence, the noise indicators of electrical equipment are becoming more and more stringent, and the noise level evaluation and comparison of the converter become an important test content.

[0004] The conventional noise evaluation and comparison of the converter generally obtains a total sound pressure level by testing with a handheld sound level meter, and obtains a weighted average value by multi-point testing, to obtain the noise level of the converter.

[0005] The existing noise level evaluation method cannot achieve comprehensive and accurate evaluation of the noise level. SUMMARY

[0006] The noise evaluation method and device of the converter provided by the embodiments of the present application can achieve comprehensive and accurate evaluation of the noise level.

[0007] To achieve the above-mentioned purpose, the technical scheme is adopted as follows:

[0008] In a first aspect, a noise evaluation method of a converter is provided. The noise evaluation method comprises: obtaining a first noise parameter and a second noise parameter; the first noise parameter comprises an average sound pressure level and a sound quality parameter of a first converter, and the second noise parameter comprises an average sound pressure level and a sound quality parameter of a second converter; comparing whether a difference between the average sound pressure level of the first converter and the average sound pressure level of the second converter exceeds a preset difference value; if yes, determining a noise evaluation result according to the average sound pressure level of the first converter and the average sound pressure level of the second converter; and if no, determining the noise evaluation result according to the sound quality parameter of the first converter and the sound quality parameter of the second converter.

[0009] As described in the first aspect, on the one hand, by comparing the noise parameters of different converters to evaluate the noise level, the noise level can be compared and referenced, thus achieving an accurate evaluation of the noise level. On the other hand, by combining the average sound pressure level and sound quality parameters of the converter to evaluate the noise level, the factors considered in the evaluation of the noise level can be more comprehensive, thus achieving a comprehensive and accurate evaluation of the noise level.

[0010] In one possible design, multiple noise sensors are provided on both the first and second converters. The acquisition of the first noise parameter and the second noise parameter includes: for any one of the first and second converters, acquiring noise signal data collected by the multiple noise sensors of that converter; determining the total sound pressure level of the noise signal data corresponding to each noise sensor; and determining the average sound pressure level of the converter based on the total sound pressure level of the noise signal data corresponding to each noise sensor.

[0011] In this design, since multiple noise sensors are installed on the converter, the total sound pressure level can be determined using the noise signal data from the multiple noise sensors, thereby enabling an effective and accurate determination of the average sound pressure level based on the total sound pressure level.

[0012] In one possible design, determining the total sound pressure level of the noise signal data corresponding to each noise sensor includes: inputting the stable data segment of the noise signal data corresponding to each noise sensor into a preset pulsereflex to obtain the total sound pressure level of the noise signal data corresponding to each noise sensor.

[0013] In this design scheme, since the total sound pressure level is calculated based on the stable data segment in the noise signal data using the preset pulse reflex, the total sound pressure level can be calculated effectively and accurately.

[0014] In one possible design scheme, the noise evaluation method further includes: determining target noise signal data; the total sound pressure level of the target noise signal data being greater than a preset value; and determining the acoustic quality parameters of the converter based on the target noise signal data.

[0015] In this design scheme, since the target noise signal data with a total sound pressure level greater than the preset value is obtained, the sound quality parameters of the converter can be determined based on the target noise signal data, thereby realizing the effective determination of the sound quality parameters.

[0016] In one possible design, determining the acoustic quality parameters of the converter based on the target noise signal data includes: determining the acoustic quality parameters of the converter through an SQ Metrics analysis module based on the target noise signal data.

[0017] In this design scheme, since the SQ Metrics analysis module is used, the acoustic quality parameters of the converter can be determined based on the SQ Metrics analysis module, thereby achieving effective determination of the acoustic quality parameters.

[0018] In one possible design, determining the noise evaluation result based on the magnitudes of the average sound pressure level of the first converter and the average sound pressure level of the second converter includes: if the average sound pressure level of the first converter is greater than the average sound pressure level of the second converter, then the noise of the first converter is better than the noise of the second converter; if the average sound pressure level of the first converter is less than the average sound pressure level of the second converter, then the noise of the second converter is better than the noise of the first converter.

[0019] In this design scheme, by comparing the average sound pressure level of the converter, the noise level of the converter can be judged based on the comparison results, thereby achieving an accurate determination of the noise level of the converter.

[0020] In one possible design, the acoustic quality parameters of both the first converter and the second converter include: loudness, roughness, sharpness, and pure tone level. Determining the noise evaluation result based on the acoustic quality parameters of the first converter and the second converter includes: comparing each acoustic quality parameter of the first converter and the second converter respectively, determining the number of optimal parameters for the first converter and the second converter; and determining the noise evaluation result based on the number of optimal parameters for the first converter and the second converter.

[0021] In this design scheme, by comparing the various acoustic quality parameters of the converter, the noise level of the converter can be judged based on the number of optimal parameters, thereby achieving an accurate determination of the noise level of the converter.

[0022] In a second aspect, a noise evaluation device for a converter is provided. The noise evaluation device includes an acquisition module and a processing module; the noise evaluation device is used to implement the noise evaluation method for the converter described in the first aspect and any possible design scheme of the first aspect.

[0023] The technical effect of the noise evaluation device for the converter described in the second aspect can be referred to the technical effect of the noise evaluation method for the converter described in the first aspect, and will not be repeated here.

[0024] Thirdly, an electronic device is provided. The electronic device includes a processor and a memory; the memory stores a computer program that, when executed by the processor, causes the device to perform the methods described in the first aspect and any possible design of the first aspect.

[0025] In this application, the apparatus described in the third aspect may be a terminal device or a network device, or a chip (system) or other component or assembly disposed in the terminal device or network device, or an apparatus containing the terminal device or network device.

[0026] Furthermore, the technical effects of the device described in the third aspect can be referred to the technical effects of the method described in the first aspect and any possible design scheme of the first aspect, which will not be repeated here.

[0027] Fourthly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, causing the computer to perform the methods described in the first aspect and any possible design of the first aspect.

[0028] Fifthly, a computer program product is provided, comprising a computer program or instructions that, when executed on a computer, cause the computer to perform the methods described in the first aspect and any possible design scheme of the first aspect. Attached Figure Description

[0029] Figure 1 A schematic diagram of the structure of the electronic device provided in the application embodiment;

[0030] Figure 2 A flowchart of a noise evaluation method for a converter provided in the application embodiment;

[0031] Figure 3 A schematic diagram of the noise evaluation device for a converter provided in the application embodiment.

[0032] Icons: 100 - Electronic device; 110 - Processor; 120 - Memory; 130 - Display; 140 - Input / output module; 300 - Noise evaluation device for converter; 310 - Acquisition module; 320 - Processing module. Detailed Implementation

[0033] The technical solution in this application will now be described with reference to the accompanying drawings.

[0034] The technical solutions of this application can be applied to various application scenarios that require noise level evaluation of converters. For example, after obtaining relevant information of multiple converters, the noise levels of these multiple converters can be evaluated using the relevant information; after obtaining the evaluation results, the evaluation results are fed back to the relevant users so that the relevant users can maintain the converters according to the evaluation results, such as repairing or replacing parts.

[0035] This application will present various aspects, embodiments, or features relating to systems that may include multiple devices, components, modules, etc. It should be understood and appreciated that individual systems may include additional devices, components, modules, etc., and / or may not include all the devices, components, modules, etc. discussed in conjunction with the accompanying drawings. Furthermore, combinations of these approaches are also possible.

[0036] Furthermore, in the embodiments of this application, words such as "exemplarily" and "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design scheme described as "exemplary" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. In addition, in the embodiments of this application, the meaning expressed by "and / or" can be both, or it can be either one or the other.

[0037] In the embodiments of this application, sometimes the subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0038] The network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0039] The hardware operating environment corresponding to the noise evaluation method provided in this application embodiment can be an electronic device, which has basic functions such as data processing, data storage, and data transmission. When it is necessary to evaluate the noise level of the converter, the user only needs to transmit the relevant data to the electronic device and initiate the corresponding command, and the electronic device can then evaluate the noise level based on the relevant data.

[0040] Please refer to Figure 1 This is a schematic diagram of the structure of an electronic device 100 provided in an embodiment of this application. The electronic device 100 includes: a processor 110, a memory 120, a display 130, and an input / output module 140. The electronic device 100 can be a terminal device such as a computer, mobile phone, or tablet computer, and is not limited thereto.

[0041] The processor 110 is the control center of the electronic device 100. It can be a single processor or a collective term for multiple processing elements. For example, the processor 110 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0042] Optionally, the processor 110 can perform various functions of the electronic device 100 by running or executing software programs stored in the memory 120 and calling data stored in the memory 120.

[0043] In a specific implementation, as one example, processor 110 may include one or more CPUs.

[0044] In a specific implementation, as one embodiment, the electronic device 100 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0045] The memory 120 is used to store the software program that executes the solution of this application, and the processor 110 controls its execution. The specific implementation method can be referred to in the following method embodiments, which will not be repeated here.

[0046] Optionally, the memory 120 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 120 may be integrated with the processor 110 or exist independently, and may be accessed through the interface circuit of the electronic device 100. Figure 1 (Not shown in the image) is coupled to the processor 110, but this embodiment does not specifically limit this.

[0047] The display 130 can be used to display the processing results determined by the processor 110, such as noise level evaluation results; it can also display intermediate results generated by the processor 110 during processing, such as determined intermediate data. It can also serve as a medium for human-computer interaction, for example, displaying various selectable instructions on the display 130, allowing the user to select and issue commands based on the displayed instructions.

[0048] In some embodiments, the display 130 may be a touch display or a non-touch display, and no limitation is made herein.

[0049] The input / output module 140 can be understood as a tool for users to achieve human-computer interaction. Through the input / output module 140, users can operate the electronic device 100, such as sending commands, uploading data, and downloading data.

[0050] In some embodiments, the input / output module 140 may be a mouse, keyboard, etc., and is not limited thereto.

[0051] It should be noted that, Figure 1 The structure of the electronic device 100 shown does not constitute a limitation on the electronic device 100. The actual electronic device 100 may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0052] Furthermore, the technical effectiveness of electronic device 100 can be referenced by the technical effectiveness of subsequent noise evaluation methods, which will not be elaborated here.

[0053] Based on the aforementioned application scenarios and hardware operating environment, please refer to the following... Figure 2 This is a flowchart illustrating a noise evaluation method for a converter provided in an embodiment of this application. The noise evaluation method includes:

[0054] Step 210: Obtain the first noise parameter and the second noise parameter. The first noise parameter includes the average sound pressure level and sound quality parameter of the first converter. The second noise parameter includes the average sound pressure level and sound quality parameter of the second converter.

[0055] Step 220: Compare whether the difference between the average sound pressure level of the first converter and the average sound pressure level of the second converter exceeds a preset difference.

[0056] Step 230: If so, determine the noise evaluation result based on the magnitude of the first converter and the average sound pressure level and the average sound pressure level of the second converter.

[0057] Step 240: If not, determine the noise evaluation result based on the acoustic quality parameters of the first converter and the second converter.

[0058] This noise assessment method offers several advantages. First, by comparing the noise parameters of different converters to evaluate their noise levels, it provides a comparative reference, leading to an accurate assessment of the noise level. Second, by combining the average sound pressure level and sound quality parameters of the converter to evaluate the noise level, it considers a more comprehensive range of factors, resulting in a more complete and accurate assessment of the noise level.

[0059] The detailed implementation method of this noise assessment method will be described below.

[0060] In this embodiment of the application, the noise evaluation method can be applied to evaluate the noise level of multiple converters, or it can be applied to evaluate the noise level of only one converter. Steps 210-240 above correspond to comparing and evaluating the noise levels of multiple converters.

[0061] In another implementation, in step 210, only the noise parameters of the converter to be evaluated can be obtained. Further, after step 210, it can be determined whether the average sound pressure level of the converter exceeds a preset value. If so, the noise evaluation result of the converter is determined according to the specific value exceeding the preset value; if not, the noise evaluation result of the converter is determined according to the sound quality parameters of the converter.

[0062] In practical applications, it is often necessary to evaluate the noise levels of multiple converters. Therefore, in this embodiment, the implementation of the comparison method in steps 210-240 is described in detail.

[0063] In step 210, the first converter and the second converter can be understood as two converters to be compared and judged. They can be any two converters among multiple converters whose noise level needs to be evaluated, and there is no limitation here.

[0064] For multiple converters whose noise levels need to be evaluated, their corresponding noise parameters should be acquired in advance. After acquisition, the noise parameters can be stored and then uploaded by the user when the noise level needs to be evaluated, or they can be directly acquired locally, etc., without limitation.

[0065] That is, in step 210, noise parameters uploaded by the user can be obtained; or noise parameters stored locally can be obtained.

[0066] The methods for obtaining noise parameters are the same for different converters. The following section will introduce the implementation methods for obtaining noise parameters.

[0067] In this embodiment of the application, the noise parameters of the converter include: average sound pressure level and sound quality parameters.

[0068] In one implementation, multiple noise sensors (microphones) are installed on the converter. The specific installation process may be as follows:

[0069] The converter test bench is hoisted, high-voltage power-on commissioning is completed, and the number of noise sensors to be arranged is determined. For example, one noise sensor is arranged for a surface with a length of less than 2m, and two noise sensors are arranged for a surface with a length of 2 to 5m (with a spacing of more than 1m between the two noise sensors). Therefore, one converter generally corresponds to 4 to 6 sensors. If two noise sensors are arranged on a surface, these two noise sensors can be positioned 1m away from the center of the surface. This saves test resources and ensures relatively accurate and complete noise signal acquisition.

[0070] After the noise sensor is set up, the noise signal collected by the noise sensor can be acquired in real time, and the noise parameters can be determined using the acquired noise signal.

[0071] Furthermore, after the noise sensor is set up, the equipment can be tested to ensure its normal operation. The noise sensor can also be calibrated to ensure the accuracy of the collected parameters. The calibration method for the sensor refers to mature technologies in this field and will not be described in detail here.

[0072] As an optional implementation, the process of obtaining the average sound pressure level includes: acquiring noise signal data collected by multiple noise sensors respectively; determining the total sound pressure level of the noise signal data corresponding to each noise sensor; and determining the average sound pressure level of the converter based on the total sound pressure level of the noise signal data corresponding to each noise sensor.

[0073] Among these, noise signal data can be acquired under various operating conditions. For example, noise signal data of the converter can be acquired under no-load and full-load conditions respectively.

[0074] Based on the noise signal data collected by multiple noise sensors, the noise signal data of each noise sensor is first processed to obtain the total sound pressure level of each sensor channel.

[0075] As an optional implementation, the process of determining the total sound pressure level includes: inputting the stable data segment of the noise signal data corresponding to each noise sensor into a preset pulse reflex to obtain the total sound pressure level of the noise signal data corresponding to each noise sensor.

[0076] In this implementation, pulse reflex is a tool (software) that can automatically calculate the total sound pressure level. Users can pre-configure the tool, and after configuration, they can use it to calculate the total sound pressure level based on input data. The final calculated total sound pressure level is within a certain range (20–20000 Hz).

[0077] The basic principle used in pulse reflex for internal data calculation is the A-weighted algorithm.

[0078] Furthermore, Pulse Reflex has certain formatting requirements for input data. Therefore, data segments must be formatted before being input into Pulse Reflex. Some common formats include: wav, unv, txt, etc.

[0079] In some embodiments, a stable data segment refers to a data segment collected after the converter has been running for a certain period of time (e.g., 5 minutes, which can be set according to the actual application scenario). The time length corresponding to this stable data segment can be 10 seconds, 30 seconds, etc.

[0080] In this embodiment, since the total sound pressure level is calculated based on the stable data segment in the noise signal data using a preset pulse reflex, the total sound pressure level can be calculated effectively and accurately.

[0081] In some embodiments, other calculation tools may also be used to calculate the total sound pressure level, and no limitation is made here.

[0082] After determining the total sound pressure level, the average sound pressure level can be calculated based on the total sound pressure level. As an optional implementation, the average sound pressure level of the converter is obtained by performing a logarithmic average based on the total sound pressure level of the noise signal data corresponding to each noise sensor.

[0083] In this embodiment of the application, since multiple noise sensors are installed on the converter, the total sound pressure level can be determined using the noise signal data of the multiple noise sensors, thereby achieving an effective and accurate determination of the average sound pressure level based on the total sound pressure level.

[0084] Based on the total sound pressure level, not only can the average sound pressure level be determined, but also the sound quality parameters can be determined.

[0085] As an optional implementation, the process of determining the acoustic quality parameters includes: determining target noise signal data; the total sound pressure level of the target noise signal data being greater than a preset value; and determining the acoustic quality parameters of the converter based on the target noise signal data.

[0086] In this implementation, specific noise signal data is selected to calculate the acoustic quality parameters of the converter. The total sound pressure level of the target noise signal data is greater than a preset value, which can be a high total sound pressure level evaluation value; that is, noise signal data with a high total sound pressure level is selected.

[0087] In another alternative implementation, the target noise signal data may also be the noise signal data corresponding to the highest total sound pressure level among all total sound pressure levels.

[0088] As an alternative implementation, the acoustic quality parameters of the converter are determined by the SQ Metrics analysis module based on the target noise signal data.

[0089] The SQ Metrics analysis module is a tool that can analyze sound quality parameters. By inputting target noise signal data into this module, sound quality parameters can be determined.

[0090] In this embodiment of the application, since the SQ Metrics analysis module is used, the acoustic quality parameters of the converter can be determined based on the SQ Metrics analysis module, thereby achieving effective determination of the acoustic quality parameters.

[0091] In the embodiments of this application, the sound quality parameters may include: loudness, roughness, sharpness, and pure tone level. The units of these four parameters are: sone, asper, acum, and dB, respectively.

[0092] Among them: Loudness represents the perceived size of the sound, Roughness represents the degree of fluctuation of the sound, Sharpness represents the harshness of the sound, and Tone Level represents the distinctiveness of the sound. The smaller the value of these four parameters, the better the sound quality, the higher the corresponding noise level, and the more comfortable the human ear feels at the same sound pressure level.

[0093] Based on the above introduction of noise parameters, in step 220, the average sound pressure levels of the first converter and the second converter are compared, specifically the difference between the average sound pressure levels is compared to see if it exceeds a preset difference.

[0094] As an optional implementation, the preset difference can be 1. Of course, in practical applications, other preset difference values ​​can also be selected, and this is not limited here.

[0095] If the difference between the average sound pressure levels of the first converter and the second converter exceeds a preset difference, then in step 230, the noise evaluation result is determined based on the magnitude of the average sound pressure levels of the first converter and the second converter.

[0096] As an optional implementation, step 230 includes: if the average sound pressure level of the first converter is greater than the average sound pressure level of the second converter, then the noise of the first converter is better than the noise of the second converter; if the average sound pressure level of the first converter is less than the average sound pressure level of the second converter, then the noise of the second converter is better than the noise of the first converter.

[0097] Among them, "better noise" refers to a higher noise level.

[0098] In this implementation, since the average sound pressure level of the converter is compared, the noise level of the converter can be determined based on the comparison results, thereby achieving an accurate determination of the noise level of the converter.

[0099] If the difference between the average sound pressure levels of the first converter and the second converter does not exceed the preset difference, then in step 240, the sound quality parameters are compared to determine the noise evaluation result.

[0100] As an optional implementation, step 240 includes: comparing the various acoustic quality parameters of the first converter and the second converter respectively, determining the number of optimal parameters of the first converter and the second converter; and determining the noise evaluation result based on the number of optimal parameters of the first converter and the second converter.

[0101] Here, "better parameter" refers to the parameter with a larger value. Correspondingly, "number of better parameters" refers to the number of parameters whose comparison result is the larger value among all parameters.

[0102] For example, after comparing the four acoustic quality parameters of the first converter and the second converter, if the first converter has three acoustic quality parameters that are larger than the second converter, and the second converter has one acoustic quality parameter that is larger than the first converter, then the number of optimal parameters for the first converter is 3, and the number of optimal parameters for the second converter is 1.

[0103] Based on the number of optimal parameters, if the first converter has more optimal parameters than the second converter, then the first converter has a better noise level. If the second converter has more optimal parameters than the first converter, then the second converter has a better noise level. If the first and second converters have the same number of optimal parameters, then the first and second converters have the same noise level.

[0104] In the embodiments of this application, since the various acoustic quality parameters of the converter are compared, the noise level of the converter can be judged based on the number of optimal parameters, thereby achieving an accurate determination of the noise level of the converter.

[0105] Based on the same inventive concept, please refer to Figure 3 This is a schematic diagram of the structure of the noise evaluation device 300 for a converter provided in this application embodiment, including: an acquisition module 310 and a processing module 320.

[0106] The acquisition module 310 is used to acquire a first noise parameter and a second noise parameter; the first noise parameter includes the average sound pressure level and sound quality parameter of the first converter, and the second noise parameter includes the average sound pressure level and sound quality parameter of the second converter. The processing module 320 is used to compare whether the difference between the average sound pressure level of the first converter and the average sound pressure level of the second converter exceeds a preset difference; if so, a noise evaluation result is determined based on the magnitudes of the average sound pressure levels of the first converter and the second converter; if not, a noise evaluation result is determined based on the sound quality parameters of the first converter and the second converter.

[0107] In this embodiment of the application, the acquisition module 310 is specifically used to: acquire noise signal data collected by multiple noise sensors of any one of the first converter and the second converter; determine the total sound pressure level of the noise signal data corresponding to each noise sensor; and determine the average sound pressure level of the converter based on the total sound pressure level of the noise signal data corresponding to each noise sensor.

[0108] In this embodiment of the application, the processing module 320 is specifically used to: input the stable data segment of the noise signal data corresponding to each noise sensor into a preset pulse reflex to obtain the total sound pressure level of the noise signal data corresponding to each noise sensor.

[0109] In this embodiment of the application, the processing module 320 is further configured to: determine target noise signal data; the total sound pressure level of the target noise signal data is greater than a preset value; and determine the acoustic quality parameters of the converter based on the target noise signal data.

[0110] In this embodiment of the application, the processing module 320 is further configured to: determine the acoustic quality parameters of the converter based on the target noise signal data through the SQMetrics analysis module.

[0111] In this embodiment of the application, the processing module 320 is specifically used to: if the average sound pressure level of the first converter is greater than the average sound pressure level of the second converter, then the noise of the first converter is better than the noise of the second converter; if the average sound pressure level of the first converter is less than the average sound pressure level of the second converter, then the noise of the second converter is better than the noise of the first converter.

[0112] In this embodiment of the application, the processing module 320 is further configured to: compare the various sound quality parameters of the first converter and the second converter respectively, determine the number of optimal parameters of the first converter and the number of optimal parameters of the second converter; and determine the noise evaluation result based on the number of optimal parameters of the first converter and the number of optimal parameters of the second converter.

[0113] The noise evaluation device 300 for the converter corresponds to the aforementioned noise evaluation method. Therefore, the corresponding implementation method is described in the aforementioned embodiments and will not be repeated here.

[0114] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium, including: a computer program or instructions; when the computer program or instructions are run on a computer, the computer causes the computer to execute the aforementioned noise evaluation method for a converter.

[0115] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0116] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0117] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0118] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0119] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0120] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0126] If the aforementioned functions are implemented as software functional units 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the noise of a converter, characterized in that, include: Obtain the first noise parameter and the second noise parameter; The first noise parameter includes the average sound pressure level and sound quality parameter of the first converter, and the second noise parameter includes the average sound pressure level and sound quality parameter of the second converter. Compare whether the difference between the average sound pressure level of the first converter and the average sound pressure level of the second converter exceeds a preset difference. If so, the noise evaluation result is determined based on the magnitudes of the first converter and its average sound pressure level, and the average sound pressure level of the second converter; the determination of the noise evaluation result based on the magnitudes of the first converter and its average sound pressure level, and the average sound pressure level of the second converter, includes: if the average sound pressure level of the first converter is greater than the average sound pressure level of the second converter, then the noise of the first converter is better than the noise of the second converter; if the average sound pressure level of the first converter is less than the average sound pressure level of the second converter, then the noise of the second converter is better than the noise of the first converter. If not, the noise evaluation result is determined based on the acoustic quality parameters of the first converter and the acoustic quality parameters of the second converter. The acoustic quality parameters of both the first converter and the second converter include: loudness, roughness, sharpness, and pure tone level; the step of determining the noise evaluation result based on the acoustic quality parameters of the first converter and the second converter includes: By comparing the various acoustic quality parameters of the first converter and the second converter, the number of optimal parameters of the first converter and the number of optimal parameters of the second converter are determined. The noise evaluation result is determined based on the number of optimal parameters of the first converter and the number of optimal parameters of the second converter.

2. The noise evaluation method according to claim 1, characterized in that, Both the first converter and the second converter are equipped with multiple noise sensors. The acquisition of the first noise parameter and the second noise parameter includes: For any one of the first converter and the second converter, acquire the noise signal data collected by multiple noise sensors of that converter respectively; Determine the total sound pressure level of the noise signal data corresponding to each noise sensor; The average sound pressure level of the converter is determined based on the total sound pressure level of the noise signal data corresponding to each noise sensor.

3. The noise evaluation method according to claim 2, characterized in that, Determining the total sound pressure level of the noise signal data corresponding to each noise sensor includes: The stable data segments of the noise signal data corresponding to each noise sensor are input into the preset pulse reflex to obtain the total sound pressure level of the noise signal data corresponding to each noise sensor.

4. The noise evaluation method according to claim 2, characterized in that, The noise evaluation method further includes: Determine the target noise signal data; the total sound pressure level of the target noise signal data is greater than a preset value; Based on the target noise signal data, the acoustic quality parameters of the converter are determined.

5. The noise evaluation method according to claim 4, characterized in that, The determination of the acoustic quality parameters of the converter based on the target noise signal data includes: Based on the target noise signal data, the acoustic quality parameters of the converter are determined by the SQ Metrics analysis module.

6. A noise evaluation device for a converter, characterized in that, include: The acquisition module is used to acquire the first noise parameter and the second noise parameter; The first noise parameter includes the average sound pressure level and sound quality parameter of the first converter, and the second noise parameter includes the average sound pressure level and sound quality parameter of the second converter. Processing module, used for: Compare whether the difference between the average sound pressure level of the first converter and the average sound pressure level of the second converter exceeds a preset difference. If so, the noise evaluation result is determined based on the magnitude of the average sound pressure level of the first converter and the average sound pressure level of the second converter; If not, the noise evaluation result is determined based on the acoustic quality parameters of the first converter and the acoustic quality parameters of the second converter. The processing module is specifically used to: if the average sound pressure level of the first converter is greater than the average sound pressure level of the second converter, then the noise of the first converter is better than the noise of the second converter; if the average sound pressure level of the first converter is less than the average sound pressure level of the second converter, then the noise of the second converter is better than the noise of the first converter. The acoustic quality parameters of the first converter and the second converter both include loudness, roughness, sharpness, and pure tone level. The processing module is specifically used to: compare each acoustic quality parameter of the first converter and the second converter respectively, determine the number of optimal parameters of the first converter and the second converter, and determine the noise evaluation result based on the number of optimal parameters of the first converter and the second converter.

7. An electronic device, characterized in that, include: Processor, the processor being coupled to memory; The processor is configured to execute a computer program stored in the memory, such that the processor performs the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program or instructions that, when executed on a computer, cause the method as described in any one of claims 1-5 to be performed.

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