Noise position positioning method and device, electronic equipment and storage medium
By performing frequency domain analysis of the sound signals during refrigerator operation and determining the frequency domain data of different frequency bands, the problem of difficulty in accurately positioning the noise source in the prior art is solved, and more efficient noise positioning and inspection are achieved.
Patent Information
- Application Number
- CN202510134502.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-06
- Publication Date
- 2025-05-13
AI Technical Summary
The prior art is difficult to accurately locate noise sources in refrigerators, resulting in high cost and low efficiency in manual inspections.
By collecting the sound signals during the operation of the equipment, performing frequency domain analysis, determining the frequency domain data of different frequency bands, and positioning the noise position based on these data.
It realizes more accurate identification and positioning of noise sources, improves positioning accuracy, and reduces the cost and time of manual inspections.
Smart Images
Figure CN119986620A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present disclosure relate to the field of data processing technology, and in particular to a method, device, electronic device and computer-readable storage medium for locating a noise position. Background Art
[0002] As a popular household appliance, the acoustic comfort of refrigerators has become an important indicator of concern to consumers. At present, there are two main methods for companies to evaluate the acoustic characteristics of refrigerators: one is manual listening and physical evaluation, but this method is greatly affected by the experience and knowledge level of the reviewers, has high training costs, and is difficult to accurately determine the location of abnormal noises; the other is to analyze the total sound pressure level of the whole machine during operation in a noise room. Although it can determine whether the refrigerator meets the standard, it cannot provide assistance for locating abnormal noises and still requires additional investigation, which increases labor costs. Summary of the invention
[0003] The embodiments of the present disclosure provide a method, device, electronic device and computer-readable storage medium for locating a noise position, aiming to solve at least one of the technical problems in the related art to a certain extent.
[0004] In a first aspect, an embodiment of the present disclosure provides a method for locating a noise position, the method comprising:
[0005] Acquire the sound signals generated by the equipment during operation;
[0006] Based on the sound signal, determining frequency domain data corresponding to different frequency bands;
[0007] Based on the frequency domain data corresponding to each of the frequency bands, the noise position of the device is located.
[0008] In a second aspect, an embodiment of the present disclosure further provides a device for locating a noise position, the device comprising:
[0009] A collection module, used to collect sound signals generated by the device during operation;
[0010] A determination module, used to determine frequency domain data corresponding to different frequency bands based on the sound signal;
[0011] The positioning module is used to locate the noise position of the device based on the frequency domain data corresponding to each of the frequency bands.
[0012] In a third aspect, an embodiment of the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps in the above-mentioned noise location positioning method when executed by the processor.
[0013] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above-mentioned method for locating a noise position are implemented.
[0014] In a fifth aspect, the embodiments of the present disclosure further provide a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. The processor of the computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method provided in various optional implementations of the embodiments of the present disclosure.
[0015] In the disclosed embodiment, the sound signal generated by the device during operation is first collected, and then the frequency domain data corresponding to different frequency bands are determined based on the sound signal. Finally, the noise position of the device is located based on the frequency domain data corresponding to each frequency band. Thus, through frequency domain analysis, the noise energy distribution generated by the device in different frequency bands can be accurately identified, so as to more accurately locate the noise source. Compared with the traditional time domain analysis method, frequency domain analysis can capture more details about the noise characteristics, which helps to improve the accuracy of positioning. Once abnormal noise occurs in the device, the problem can be quickly identified and located through frequency domain data, providing strong support for timely measures.
[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the technical solutions in the present disclosure, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0018] Figure 1 is a flow chart of a method for locating a noise position provided in the first embodiment of the present disclosure;
[0019] Figure 2 is a flow chart of a method for locating a noise position provided in a second embodiment of the present disclosure;
[0020] Figure 3 is a schematic diagram of the structure of a noise location positioning device provided by an embodiment of the present disclosure;
[0021] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION
[0022] Some embodiments of the present disclosure will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. Various changes, modifications and equivalents of the methods, devices and / or systems described herein will become apparent after understanding the present disclosure. For example, the order of operations described herein is merely an example and is not limited to those orders set forth herein, but can be changed as becomes apparent after understanding the present disclosure, except for operations that must be performed in a specific order. In addition, for clarity and brevity, descriptions of features known in the art may be omitted.
[0023] The embodiments described in some embodiments of the present disclosure below do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.
[0024] It should be noted that the execution subject of the noise position positioning method of this embodiment may be a noise position positioning device, and the device may be configured in any type of electronic device, which is not limited here.
[0025] In the embodiments of the present disclosure, the method for locating the noise position will be described by taking the "noise position locating device" as the execution subject, and no limitation is made here. It should be noted that the description order of the following embodiments is not used as a limitation on the priority order of the embodiments.
[0026] Figure 1 It is a flowchart of a method for locating a noise position provided according to the first embodiment of the present disclosure.
[0027] like Figure 1 As shown, the method includes:
[0028] Step 101: Acquire sound signals generated by the device during operation.
[0029] Among them, the collection of sound signals can be used for fault diagnosis, performance monitoring or user experience optimization, which is not limited here.
[0030] The device may be any type of device such as an air conditioner, a refrigerator, a washing machine, etc., and is not limited here.
[0031] Optionally, you can select a suitable sound collection device, such as a microphone, sound sensor, etc., according to the characteristics of the target device and the application scenario.
[0032] Optionally, sound signals generated by the device during operation may be collected based on sensors disposed at various key locations in the device.
[0033] Specifically, sound collection devices can be placed at various key locations of the equipment to ensure that the sound signals generated by the equipment during operation can be fully captured. Key locations may include near the equipment's motor, compressor, fan, transmission system and other components.
[0034] As a possible implementation method, the sampling rate, quantization bit number, number of channels and other parameters of the sound acquisition device can be set according to the acquisition requirements to ensure that the acquisition parameters can meet the requirements of subsequent sound signal analysis and processing.
[0035] It is understandable that sound sensors can be set at key locations of the equipment to convert sound signals into electrical signals for collection. The sensors should have high precision, high sensitivity and good anti-interference ability.
[0036] It is understandable that during the acquisition process, the interference of environmental noise should be controlled as much as possible to ensure that the collected sound signals mainly come from the target device. The influence of environmental noise can be reduced by taking sound insulation measures, collecting in a quiet environment, or using noise suppression algorithms.
[0037] For example, the six-point method in the national standard for noise testing of household and similar electrical appliances (such as the GB / T 4214 series of standards) can be used to arrange sensors in order to comprehensively evaluate the noise level generated by household appliances during operation. This method is commonly used to measure and evaluate the noise performance of washing machines, refrigerators, air conditioners and other equipment. The following are the steps and precautions for arranging sensors according to the six-point method:
[0038] Optionally, the measurement location can be determined based on the type and size of the household appliance. Generally, the measurement should be performed in a non-reflective acoustic environment to ensure the accuracy of the measurement results. The measurement location should be away from walls, floors, and other obstacles that may produce reflections. For example, sound sensors can be placed directly in front of the device, directly behind, on the left, on the right, above, and below (if possible). The location of the sensor should ensure that the noise generated by the device during operation can be accurately captured. For some devices (such as washing machines), the location of the sensor may need to be adjusted to accommodate its specific operation mode and structure.
[0039] Optionally, before starting the measurement, ensure that all sensors are calibrated to the same standard to ensure the accuracy and consistency of the measurement results. The calibration process may include checking the performance indicators of the sensor such as sensitivity, frequency response and linearity.
[0040] Step 102: determining frequency domain data corresponding to different frequency bands based on the sound signal.
[0041] Optionally, the sound signal may be first normalized to obtain an audio signal, and then the audio signal may be short-time Fourier transformed to obtain frequency domain data, and then the frequency domain data may be divided into multiple frequency bands based on the distribution characteristics of each noise type in the frequency domain.
[0042] It is understandable that the purpose of normalization is to adjust the amplitude of the sound signal to a uniform range (usually 0 to 1 or -1 to 1) to facilitate subsequent processing and analysis.
[0043] As a possible implementation method, linear normalization can be performed, or standard normalization (Z-score normalization) can be performed, which converts the signal into a distribution with a mean of 0 and a standard deviation of 1.
[0044] Among them, short-time Fourier transform is used to convert the time domain signal into the representation of time and frequency domain.
[0045] It is understood that the normalized audio signal can be converted into frequency domain data using Fast Fourier Transform (FFT) or Short Time Fourier Transform (STFT). FFT is suitable for analyzing stationary signals, while STFT is more suitable for analyzing non-stationary signals because it can provide frequency change information of the signal on the time axis.
[0046] It should be noted that the distribution characteristics of different noise types in the frequency domain are usually different, so the converted frequency domain data can be divided into multiple frequency bands based on the distribution characteristics of different types of noise in the frequency domain. These frequency bands can be determined based on known physical properties (such as compressor noise is usually in the low frequency band, and fan noise may be distributed in a wider frequency band) or experimental data.
[0047] Here is an example of a possible frequency band division:
[0048] Low frequency band: 0-500Hz (may include compressor noise and low-frequency vibration)
[0049] Mid-frequency band: 500Hz-2kHz (may include fan noise and some electromagnetic noise)
[0050] High frequency band: above 2kHz (may contain refrigerant spray sound, high-frequency electromagnetic noise and detailed information)
[0051] It is understandable that the frequency domain data within each frequency band is analyzed to extract useful feature information, such as energy, peak frequency, spectral density, etc. These features can be used for subsequent noise type identification, fault diagnosis or performance evaluation. It should be noted that if the goal is to identify specific noise types, machine learning or deep learning algorithms can be used to classify the extracted features. This requires the construction of a training data set containing frequency domain feature labels for known noise types.
[0052] As an example, if the noise types include A, B, C and D, and the corresponding frequency bands are 0-500Hz, 500Hz-1kHz, 1kHz-2kHz and above 2kHz, respectively, the current frequency domain data can be divided into 4 frequency bands, namely 0-500Hz, 500Hz-1kHz, 1kHz-2kHz and above 2kHz, which are not limited here. In some cases, some noise types appear in larger frequency bands, such as the frequency band of noise type E is 0-2kHz, then the current frequency domain data can also be divided into 4 frequency bands, namely 0-500Hz, 500Hz-1kHz, 1kHz-2kHz and above 2kHz, which are not limited here.
[0053] Step 103: locate the noise position of the device based on the frequency domain data corresponding to each frequency band.
[0054] The noise location may be a location where the device currently emits noise.
[0055] Specifically, the frequency domain data within each frequency band can be deeply analyzed. For example, the energy distribution within each frequency band can be determined to identify which frequency bands contain the main noise energy. Specific characteristics of the spectrum, such as peak frequency, spectral density, etc., can also be observed to obtain clues about the type of noise.
[0056] As an example, if the noise type is A, the corresponding frequency band is 0-500Hz frequency domain data, and its corresponding peak frequency meets certain conditions, such as exceeding the frequency threshold, then it can be considered that noise type A is currently occurring. It can be understood that each noise type is associated with a certain device location. If noise type A is associated with a compressor, it means that the noise location of the device is the compressor.
[0057] In the disclosed embodiment, the sound signal generated by the device during operation is first collected, and then the frequency domain data corresponding to different frequency bands are determined based on the sound signal. Finally, the noise position of the device is located based on the frequency domain data corresponding to each frequency band. Thus, through frequency domain analysis, the noise energy distribution generated by the device in different frequency bands can be accurately identified, so as to more accurately locate the noise source. Compared with the traditional time domain analysis method, frequency domain analysis can capture more details about the noise characteristics, which helps to improve the accuracy of positioning. Once abnormal noise occurs in the device, the problem can be quickly identified and located through frequency domain data, providing strong support for timely measures.
[0058] Figure 2 It is a flowchart of a method for locating a noise position provided according to the second embodiment of the present disclosure.
[0059] like Figure 2As shown, the method includes:
[0060] Step 201: Acquire sound signals generated by the device during operation.
[0061] Step 202: determining frequency domain data corresponding to different frequency bands based on the sound signal.
[0062] It should be noted that the specific implementation of steps 201 and 202 may refer to the above embodiments and will not be described in detail here.
[0063] Step 203: collecting noise signals of various noise types during the historical operation of the device.
[0064] It should be noted that noise signals of various noise types during the historical operation of the equipment can be collected. By long-term monitoring and recording of noise signals of the equipment under different operating conditions, rich noise data can be accumulated. These data will be used for subsequent analysis to determine the characteristics of different noise types. Optionally, professional sound collection equipment or sensors can be used to regularly or continuously collect noise signals during equipment operation. Ensure that the collected data covers various operating conditions and possible noise types of the equipment.
[0065] For example, operating signals of refrigerators of different models may be collected and a corresponding sample library may be constructed. The operating signals include signals obtained when the refrigerator compressor is operated at different speeds to cover all possible operating conditions that may occur when the user is using the refrigerator.
[0066] Step 204: determine noise characteristic information of the noise signal of each noise type in the corresponding target frequency band.
[0067] It is understandable that the collected noise signals can be analyzed to identify the characteristic performance of different noise types in a specific frequency band. For example, the noise signal can be converted into frequency domain data, and the characteristic information such as energy distribution and frequency components of each noise type in the target frequency band can be determined through methods such as spectrum analysis.
[0068] Specifically, the collected noise signal can be preprocessed first, including denoising, filtering, etc., to improve the signal quality and reduce the impact of interference factors on subsequent analysis. Then, the preprocessed noise signal can be converted from the time domain to the frequency domain. This is usually achieved through short-time fast Fourier transform or other frequency domain conversion methods to obtain the spectrum of the noise signal.
[0069] Furthermore, the spectrum graph can be divided into different target frequency bands based on the device characteristics and noise type. These frequency bands can be determined based on device design parameters, historical noise data, or industry standards. Then, the characteristic information of the noise signal can be extracted within each target frequency band. These characteristics may include: calculating the energy or power spectral density of each frequency band to understand the intensity of the noise in different frequency bands. Then, the main frequency components within the frequency band can be identified, which helps to identify specific noise types, such as mechanical vibration, electromagnetic interference, etc. After that, the time-varying characteristics of the noise signal can be analyzed, such as frequency drift, amplitude modulation, etc., which are not limited here.
[0070] Step 205: Based on the noise characteristic information, configure the warning rule for each noise type in the corresponding target frequency band.
[0071] Specifically, reasonable warning thresholds and conditions can be set based on noise feature information to monitor and warn of potential noise problems in real time when the equipment is running. For example, the warning rules can be configured for each noise type in the corresponding target frequency band based on the noise level during normal operation of the equipment, historical fault data, and industry standards. These rules may include thresholds for noise levels, abnormal changes in frequency components, etc.
[0072] Step 206: determine the target frequency band to which each noise type belongs and the corresponding warning rule.
[0073] It is understandable that the target frequency bands to which different noise types belong may be the same, different, or overlapping, which is not limited here. For example, the target frequency bands to which noise types A, B, C, and D belong are 0-500Hz, 500Hz-1kHz, 1kHz-2kHz, and above 2kHz, respectively, where the target frequency bands to which noise types A, B, C, and D belong do not overlap. The target frequency bands to which noise types E and F belong are 0-1kHz and 500Hz-2kHz. The target frequency band to which noise type E belongs includes the target frequency bands to which A and B belong, and the target frequency band to which noise type F belongs includes the target frequency bands to which C and B belong.
[0074] Among them, the early warning rules can be used to determine whether a noise problem occurs, or to determine whether the noise problem is serious.
[0075] It is understandable that the warning rules corresponding to different noise types can be pre-acquired and configured with reference to the above steps 203-205.
[0076] Step 207: determining the target noise type currently existing in the device based on the frequency domain data of the target frequency band to which each noise type belongs and the corresponding warning rule.
[0077] The target noise type may be a noise type that may currently exist in the equipment, such as intermittent refrigerant spray noise, main control board electromagnetic noise, compressor noise, etc., which are not limited here.
[0078] Optionally, the energy peak can be first determined based on the frequency domain data corresponding to the target frequency band, and then the frequency corresponding to the energy peak can be determined based on the position of the energy peak. Then, when the frequency is in the abnormal frequency range associated with any noise type, any noise type can be determined as the target noise type.
[0079] It is understandable that within the target frequency band, the energy distribution of the frequency domain data can be observed. The energy distribution is usually displayed in the form of a spectrum graph, where the horizontal axis represents frequency and the vertical axis represents energy or power spectrum density. In the spectrum graph, you can find the point with the highest energy, that is, the energy peak. This point represents the frequency at which the noise energy generated by the mechanical equipment is the strongest within the target frequency band. Then, on the spectrum graph, you can read the horizontal axis coordinate corresponding to the energy peak, that is, the frequency value. This frequency value represents the frequency point at which the noise energy of the mechanical equipment is the strongest.
[0080] Among them, the abnormal frequency range can be pre-set, and the abnormal frequency range associated with different noise types can be set according to the characteristics of the mechanical equipment and historical data. For example, mechanical vibration noise may be concentrated in a specific low frequency band, while electromagnetic interference noise may be distributed in a high frequency band.
[0081] Then, the frequency corresponding to the energy peak can be compared with the set abnormal frequency range. If the frequency corresponding to the energy peak falls within the abnormal frequency range of a certain noise type, the noise type can be determined to be the target noise type.
[0082] Assume that within the target frequency band, the energy peak that can be observed corresponds to a frequency of 500Hz, and based on historical data and equipment characteristics, the abnormal frequency range of mechanical vibration noise is set to 450Hz to 550Hz. Since 500Hz falls within this range, it can be determined that the target noise type is mechanical vibration noise.
[0083] Optionally, the sound signal strength corresponding to the target frequency band may be first determined based on the frequency domain data corresponding to the target frequency band, and then any noise type may be determined to be the target noise type when the sound signal strength is greater than a preset threshold associated with any noise type.
[0084] It should be noted that the preset thresholds corresponding to different noise types may be the same or different.
[0085] Taking air conditioning as an example, preset thresholds can be set for different noise types (such as mechanical noise, airflow noise, electromagnetic noise, etc.) based on relevant standards, historical test data and empirical values of air conditioning noise. These preset thresholds represent the maximum or typical sound signal strength that various noise types may reach within the target frequency band. Afterwards, the sound signal strength corresponding to the extracted target frequency band can be compared with the preset thresholds of various noise types. If the sound signal strength corresponding to the target frequency band is greater than the preset threshold of a certain noise type, then this noise type is considered to be the main noise source during the current operation of the air conditioner, that is, the target noise type.
[0086] Assume that when the air conditioner is running, the sound signal intensity corresponding to the average energy of the collected sound signal in the target frequency band (500Hz to 2kHz) is 65dB(A). Among them, the preset threshold set for mechanical noise is 60dB(A), and the preset threshold set for airflow noise is 55dB(A). Since the collected sound signal intensity (65dB(A)) is greater than the preset threshold of mechanical noise (60dB(A)) and greater than the preset threshold of airflow noise (55dB(A)), but according to the usual situation of air conditioning noise, mechanical noise is more common in the frequency band of 500Hz to 2kHz, and this intensity value is closer to the typical value of mechanical noise. Therefore, it can be preliminarily judged that under this test condition, mechanical noise is the main source of noise when the air conditioner is currently running, that is, the target noise type.
[0087] Optionally, the sound signal strength and frequency corresponding to the target frequency band can be first determined based on the frequency domain data corresponding to the target frequency band, and then any noise type can be determined to be the target noise type when the frequency is within the abnormal frequency range associated with any noise type and the sound signal strength is greater than a preset threshold associated with any noise type.
[0088] It can be understood that the sound signal strength and frequency corresponding to the target frequency band can be extracted from the converted frequency domain data. The sound signal strength is usually expressed in decibels (dB), while the frequency is expressed in Hertz (Hz). Then, the preset threshold and abnormal frequency range can be set: according to the relevant standards, historical test data and empirical values of refrigerator noise, preset thresholds and abnormal frequency ranges are set for different noise types (such as compressor noise, refrigerant flow noise, fan noise, etc.). These preset thresholds represent the maximum or typical sound signal strength that various noise types may reach within the target frequency band, and the abnormal frequency range refers to the frequency interval where these noise types usually appear.
[0089] Optionally, the sound signal strength and frequency corresponding to the extracted target frequency band can be compared with preset thresholds and abnormal frequency ranges of various noise types. If a certain frequency is within the abnormal frequency range of a certain noise type and the corresponding sound signal strength is greater than the preset threshold of the noise type, then this noise type is considered to be the main noise source during the current operation of the refrigerator, that is, the target noise type.
[0090] Assume that when the refrigerator is running, the collected sound signal has a significant energy peak at a specific frequency (for example, 450Hz) within the target frequency band (200Hz to 1kHz), and the corresponding sound signal strength is 60dB(A). Among them, the preset threshold set for compressor noise is 55dB(A), and the abnormal frequency range is 400Hz to 500Hz; the preset threshold set for refrigerant flow noise is 50dB(A), and the abnormal frequency range is 100Hz to 200Hz; the preset threshold set for fan noise is 45dB(A), and the abnormal frequency range is 800Hz to 1kHz. Since 450Hz is within the abnormal frequency range of compressor noise, and the corresponding sound signal strength of 60dB(A) is greater than the preset threshold of 55dB(A) for compressor noise, it can be determined that under this test condition, compressor noise is the main noise source when the refrigerator is currently running, that is, the target noise type.
[0091] It should be noted that in actual applications, determining the target noise type may require comprehensive consideration of multiple factors, including the time domain characteristics and frequency domain characteristics of the sound signal, the specific location of the noise source, and the operating status of the refrigerator. In addition, the setting of the preset threshold and abnormal frequency range also needs to be adjusted and optimized according to the specific situation to ensure accuracy and reliability.
[0092] Step 208: Determine the device location associated with the target noise type as the noise location of the device.
[0093] The noise location may be a location where the device currently emits noise.
[0094] For example, if the device location associated with the target noise type is a compressor, it means that the compressor is the location where the device currently emits noise, that is, the noise location. For example, if the device location associated with the target noise type is a motor, it means that the motor is the location where the device currently emits noise, that is, the noise location, which is not limited here.
[0095] For example, if the target noise types are A, B and C, and their associated device positions are a1, b1 and c1 respectively, then a1, b1 and c1 can be used as noise positions, without limitation herein.
[0096] In the disclosed embodiment, the sound signal generated by the device during operation is first collected, and then the frequency domain data corresponding to different frequency bands is determined based on the sound signal, and then the noise signals of various noise types in the historical operation process of the device are collected, and then the noise characteristic information of the noise signal of each noise type in the corresponding target frequency band is determined, and then the warning rules of each noise type in the corresponding target frequency band are configured based on the noise characteristic information, and then the target frequency band to which each noise type belongs and the corresponding warning rules are determined, and then the target noise type currently existing in the device is determined based on the frequency domain data of the target frequency band to which each noise type belongs and the corresponding warning rules, and finally the device position associated with the target noise type is determined as the noise position of the device. Thus, through detailed frequency domain analysis and historical noise data comparison, the components or positions that emit specific noise in the device can be more accurately located. Using the noise characteristic information, different types of noise, such as mechanical friction sound, electrical discharge sound, etc., can be accurately identified, which is helpful for subsequent targeted treatment measures. The configured warning rules can sound an alarm before the noise reaches a dangerous level, so that maintenance personnel can intervene in advance to avoid the occurrence or deterioration of faults. By promptly discovering and dealing with noise problems, the wear and damage caused by long-term abnormal stress on equipment can be reduced, thereby extending the service life of the equipment. Once the location and type of noise are determined, maintenance personnel can take prompt action to reduce downtime and production losses. Based on the analysis of noise data, a more scientific and reasonable maintenance plan can be formulated to avoid unnecessary maintenance operations and reduce maintenance costs.
[0097] In order to better implement the noise location positioning method of the present disclosure, the present disclosure also provides a noise location positioning device based on the above noise location positioning method. The meanings of the terms are the same as those in the above noise location positioning method, and the specific implementation details can refer to the description in the method embodiment.
[0098] See also Figure 3 , Figure 3 : is a schematic diagram of the structure of a noise location positioning device provided in an embodiment of the present disclosure, wherein the noise location positioning device 300 comprises:
[0099] The acquisition module 310 is used to acquire the sound signals generated by the device during operation;
[0100] A determination module 320, configured to determine frequency domain data corresponding to different frequency bands based on the sound signal;
[0101] The positioning module 330 is used to locate the noise position of the device based on the frequency domain data corresponding to each of the frequency bands.
[0102] Optionally, the determining module 320 is specifically configured to:
[0103] Normalizing the sound signal to obtain an audio signal;
[0104] Performing short-time Fourier transform processing on the audio signal to obtain frequency domain data;
[0105] Based on the distribution characteristics of each noise type in the frequency domain, the frequency domain data is divided into a plurality of frequency bands.
[0106] Optionally, the positioning module includes:
[0107] A first determination unit, used to determine the target frequency band to which each noise type belongs and the corresponding warning rule;
[0108] A second determination unit, configured to determine a target noise type currently existing in the device based on frequency domain data of a target frequency band to which each noise type belongs and the corresponding warning rule;
[0109] A third determining unit is configured to determine a device position associated with the target noise type as a noise position of the device.
[0110] Optionally, the device further includes:
[0111] A collection module, used to collect noise signals of various noise types during the historical operation of the device;
[0112] A noise characteristic information determination module, used to determine the noise characteristic information of the noise signal of each noise type in the corresponding target frequency band;
[0113] A configuration module is used to configure the warning rules for each noise type in the corresponding target frequency band based on the noise characteristic information.
[0114] Optionally, the second determining unit is configured to:
[0115] Determining an energy peak value according to frequency domain data corresponding to the target frequency band;
[0116] Determining the frequency corresponding to the energy peak according to the position of the energy peak;
[0117] In the case that the frequency is within the abnormal frequency range associated with any one of the noise types, any one of the noise types is determined to be a target noise type.
[0118] Optionally, the second determining unit is configured to:
[0119] Determining the sound signal strength corresponding to the target frequency band according to the frequency domain data corresponding to the target frequency band;
[0120] In a case where the sound signal strength is greater than a preset threshold associated with any noise type, any noise type is determined to be a target noise type.
[0121] Optionally, the second determining unit is configured to:
[0122] Determining the sound signal strength and frequency corresponding to the target frequency band according to the frequency domain data corresponding to the target frequency band;
[0123] When the frequency is within the abnormal frequency range associated with any noise type and the sound signal strength is greater than a preset threshold associated with any noise type, any noise type is determined to be a target noise type.
[0124] Optionally, the acquisition module 310 is specifically used for:
[0125] Based on the sensors arranged in the key positions of the device, the sound signals generated by the device during operation are collected.
[0126] In the disclosed embodiment, the sound signal generated by the device during operation is first collected, and then the frequency domain data corresponding to different frequency bands are determined based on the sound signal. Finally, the noise position of the device is located based on the frequency domain data corresponding to each frequency band. Thus, through frequency domain analysis, the noise energy distribution generated by the device in different frequency bands can be accurately identified, so as to more accurately locate the noise source. Compared with the traditional time domain analysis method, frequency domain analysis can capture more details about the noise characteristics, which helps to improve the accuracy of positioning. Once abnormal noise occurs in the device, the problem can be quickly identified and located through frequency domain data, providing strong support for timely measures.
[0127] In addition, the present disclosure also provides an electronic device, such as Figure 4 As shown, it shows a schematic diagram of the structure of the electronic device involved in the present disclosure, specifically:
[0128] The electronic device may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a power supply 403, and an input unit 404. Those skilled in the art will appreciate that Figure 4 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.
[0129] The processor 401 is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 402, and calling data stored in the memory 402, the processor 401 performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 401.
[0130] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.
[0131] The electronic device also includes a power supply 403 for supplying power to each component. Preferably, the power supply 403 can be logically connected to the processor 401 through a power management system, so as to manage charging, discharging, and power consumption through the power management system. The power supply 403 can also include one or more DC or AC power supplies, recharging systems, power supply device debugging circuits, power converters or inverters, power status indicators, and other arbitrary components.
[0132] The electronic device may further include an input unit 404, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.
[0133] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 401 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 will run the application programs stored in the memory 402, thereby implementing the steps in any of the noise location positioning methods provided in the embodiments of the present disclosure.
[0134] In the disclosed embodiment, the sound signal generated by the device during operation is first collected, and then the frequency domain data corresponding to different frequency bands are determined based on the sound signal. Finally, the noise position of the device is located based on the frequency domain data corresponding to each frequency band. Thus, through frequency domain analysis, the noise energy distribution generated by the device in different frequency bands can be accurately identified, so as to more accurately locate the noise source. Compared with the traditional time domain analysis method, frequency domain analysis can capture more details about the noise characteristics, which helps to improve the accuracy of positioning. Once abnormal noise occurs in the device, the problem can be quickly identified and located through frequency domain data, providing strong support for timely measures.
[0135] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.
[0136] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.
[0137] To this end, the present disclosure provides a computer-readable storage medium having a computer program stored thereon. The computer program can be loaded by a processor to execute the steps in any one of the noise location locating methods provided in the present disclosure.
[0138] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.
[0139] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.
[0140] Since the instructions stored in the computer-readable storage medium can execute the steps in any one of the noise position locating methods provided in the present disclosure, the beneficial effects that can be achieved by any one of the noise position locating methods provided in the present disclosure can be achieved. Please refer to the previous embodiments for details and will not be repeated here.
[0141] The above is a detailed introduction to a noise location positioning method, device, electronic device and computer-readable storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.
Claims
1. A method for locating a noise position, characterized in that: include: Acquire the sound signals generated by the equipment during operation; Based on the sound signal, determining frequency domain data corresponding to different frequency bands; Based on the frequency domain data corresponding to each of the frequency bands, the noise position of the device is located.
2. The method according to claim 1, characterized in that The determining, based on the sound signal, frequency domain data corresponding to different frequency bands includes: Normalizing the sound signal to obtain an audio signal; Performing short-time Fourier transform processing on the audio signal to obtain frequency domain data; Based on the distribution characteristics of each noise type in the frequency domain, the frequency domain data is divided into a plurality of frequency bands.
3. The method according to claim 1, characterized in that The locating the noise position of the device based on the frequency domain data corresponding to each of the frequency bands includes: Determine the target frequency band for each noise type and the corresponding warning rules; Determine the target noise type currently existing in the device based on the frequency domain data of the target frequency band to which each noise type belongs and the corresponding warning rule; A device location associated with the target noise type is determined as a noise location of the device.
4. The method according to claim 3, characterized in that Also includes: Collecting noise signals of various noise types during historical operation of the device; Determine noise characteristic information of the noise signal of each noise type in the corresponding target frequency band; Based on the noise characteristic information, an early warning rule for each noise type in a corresponding target frequency band is configured.
5. The method according to claim 4, characterized in that in, Based on the frequency domain data of the target frequency band of any noise type and the warning rule corresponding to any noise type, determining whether any noise type is a target noise type includes: Determining an energy peak value according to frequency domain data corresponding to the target frequency band; Determining the frequency corresponding to the energy peak according to the position of the energy peak; In the case that the frequency is within the abnormal frequency range associated with any one of the noise types, any one of the noise types is determined to be a target noise type.
6. The method according to claim 4, characterized in that in, Based on the frequency domain data of the target frequency band of any noise type and the warning rule corresponding to any noise type, determining whether any noise type is a target noise type includes: Determining the sound signal strength corresponding to the target frequency band according to the frequency domain data corresponding to the target frequency band; In a case where the sound signal strength is greater than a preset threshold associated with any noise type, any noise type is determined to be a target noise type.
7. The method according to claim 4, characterized in that in, Based on the frequency domain data of the target frequency band of any noise type and the warning rule corresponding to any noise type, determining whether any noise type is a target noise type includes: Determining the sound signal strength and frequency corresponding to the target frequency band according to the frequency domain data corresponding to the target frequency band; When the frequency is within the abnormal frequency range associated with any noise type and the sound signal strength is greater than a preset threshold associated with any noise type, any noise type is determined to be a target noise type.
8. The method according to claim 1, characterized in that The sound signal generated by the acquisition device during operation includes: Based on the sensors arranged in the key positions of the device, the sound signals generated by the device during operation are collected.
9. A noise location positioning device, characterized in that: include: A collection module, used to collect sound signals generated by the device during operation; A determination module, used to determine frequency domain data corresponding to different frequency bands based on the sound signal; The positioning module is used to locate the noise position of the device based on the frequency domain data corresponding to each of the frequency bands.
10. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps in the method for locating a noise position as claimed in any one of claims 1 to 8.
11. A storage medium, characterized in that: The storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the method for locating a noise position as claimed in any one of claims 1 to 8.